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  Subjects -> TRANSPORTATION (Total: 166 journals)
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TRANSPORTATION (95 journals)

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Journal Cover Transportation Research Part C: Emerging Technologies
  [SJR: 2.062]   [H-I: 72]   [20 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [3039 journals]
  • Naturalistic driving data collection to investigate into the effects of
           road geometrics on track behaviour
    • Authors: G. Cerni; M. Bassani
      Pages: 1 - 15
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): G. Cerni, M. Bassani
      Road designers assume that drivers will follow the road alignment with trajectories centred in the lane, and move at the design speed parallel to the road centreline (i.e., the horizontal alignment). Therefore, they assume that if the horizontal alignment indicates the “designed trajectory”, the driving path indicates the “operating trajectory”. However, at present, they do not have the necessary tools to measure the relationship between the designed alignment and possible vehicle trajectories. The paper has two objectives: (a) to develop an understanding of the root causes of differences between road alignment and vehicle trajectories; and (b) to define and calibrate a model that estimates the local curvature of trajectories on the basis of the designed horizontal alignment. The two objectives were pursued by carrying out a naturalistic survey using vehicles equipped with high precision GPS in real-time kinematics (RTK) mode driven by test drivers on road sections of known geometric characteristics. The results provide an insight into the effects of road geometrics on driver behaviour, thus anticipating possible driving errors or unexpected/undesired behaviours, information which can then be used to correct possible inconsistencies when making decisions at the design stage.

      PubDate: 2017-01-26T07:39:07Z
      DOI: 10.1016/j.trc.2017.01.012
      Issue No: Vol. 77 (2017)
       
  • Comparing decision tree algorithms to estimate intercity trip distribution
    • Authors: Cira Souza Pitombo; Andreza Dornelas de Souza; Anabele Lindner
      Pages: 16 - 32
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Cira Souza Pitombo, Andreza Dornelas de Souza, Anabele Lindner
      Traditional trip distribution models usually ignore the fact that destination choices are made individually in addition to aggregated factors, such as employment and average travel costs. This paper proposes a disaggregated analysis of destination choices for intercity trips, taking into account aggregated characteristics of the origin city, an impedance measurement and disaggregated variables related to the individual, by applying nonparametric Decision Tree (DT) algorithms. Furthermore, each algorithm’s performance is compared with traditional gravity models estimated from a stepwise procedure (1) and a doubly constrained procedure (2). The analysis was based on a dataset from the 2012 Origin-Destination Survey carried out in Bahia, Brazil. The final selected variables to describe the destination choices were population of the origin city, GDP of the origin city and travel distances at an aggregated level, as well as the variables: age, occupation, level of education, income (monthly), number of cars per household and gender at a disaggregated one. The comparison of the DT models with gravity models demonstrated that the former models provided better accuracy when predicting the destination choices (trip length distribution, goodness-of-fit measures and qualitative perspective). The main conclusion is that Decision Tree algorithms can be applied to distribution modeling to improve traditional trip distribution approaches by assimilating the effect of disaggregated variables.

      PubDate: 2017-01-26T07:39:07Z
      DOI: 10.1016/j.trc.2017.01.009
      Issue No: Vol. 77 (2017)
       
  • Fuzzy ontology-based sentiment analysis of transportation and city feature
           reviews for safe traveling
    • Authors: Farman Ali; Daehan Kwak; Pervez Khan; S.M. Riazul Islam; Kye Hyun Kim; K.S. Kwak
      Pages: 33 - 48
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Farman Ali, Daehan Kwak, Pervez Khan, S.M. Riazul Islam, Kye Hyun Kim, K.S. Kwak
      Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.

      PubDate: 2017-02-01T07:39:16Z
      DOI: 10.1016/j.trc.2017.01.014
      Issue No: Vol. 77 (2017)
       
  • Discovering themes and trends in transportation research using topic
           modeling
    • Authors: Lijun Sun; Yafeng Yin
      Pages: 49 - 66
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Lijun Sun, Yafeng Yin
      Transportation research is a key area in both science and engineering. In this paper, we present an empirical analysis of 17,163 articles published in 22 leading transportation journals from 1990 to 2015. We apply a latent Dirichlet allocation (LDA) model on article abstracts to infer 50 key topics. We show that those characterized topics are both representative and meaningful, mostly corresponding to established sub-fields in transportation research. These identified fields reveal a research landscape for transportation. Based on the results of LDA, we quantify the similarity of journals and countries/regions in terms of their aggregated topic distributions. By measuring the variation of topic distributions over time, we find some general research trends, such as topics on sustainability, travel behavior and non-motorized mobility are becoming increasingly popular over time. We also carry out this temporal analysis for each journal, observing a high degree of consistency for most journals. However, some interesting anomaly, such as special issues on particular topics, are detected from temporal variation as well. By quantifying the temporal trends at the country/region level, we find that countries/regions display clearly distinguishable patterns, suggesting that research communities in different regions tend to focus on different sub-fields. Our results could benefit different parties in the academic community—including researchers, journal editors and funding agencies—in terms of identifying promising research topics/projects, seeking for candidate journals for a submission, and realigning focus for journal development.

      PubDate: 2017-02-01T07:39:16Z
      DOI: 10.1016/j.trc.2017.01.013
      Issue No: Vol. 77 (2017)
       
  • Evaluation of spatial heterogeneity in the sensitivity of on-street
           parking occupancy to price change
    • Authors: Ziyuan Pu; Zhibin Li; John Ash; Wenbo Zhu; Yinhai Wang
      Pages: 67 - 79
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Ziyuan Pu, Zhibin Li, John Ash, Wenbo Zhu, Yinhai Wang
      Adjustment of parking price has long been considered an effective way to control parking demand and demand has often been shown to be affected by spatial factors. The primary objective of this study is to investigate the spatial heterogeneity in the sensitivity of parking occupancy to price change using data obtained in downtown San Francisco between 2011 and 2014. The performance-based pricing implemented in the study area allows parking rate to increase, decrease or remain unchanged in neighborhoods with parking occupancy levels higher than, lower than, or within a desired range. As such, the relationship between change in occupancy and change in parking rate is explored. The geographically weighted regression (GWR) method was used to capture the spatial heterogeneity in sensitivity in different blocks and modeling results showed that there is a significant negative correlation between occupancy change and parking rate change. Thus, sensitivity of on-street parking occupancy to price change has an obvious trend of spatial variation. By capturing the spatial heterogeneity in the dataset, the GWR model achieved higher prediction accuracy than a global model. Variables including time of day, block-level features, and socio-demographic characteristics were also found to be correlated with occupancy change. Based on the GWR outputs, a generalized linear model was estimated to further identify how various factors affect sensitivity in different block areas. Findings of this study can be used to help parking authorities with tasks such as identifying which blocks are suitable for balancing parking demand and supply by adjusting price and designing optimal parking rate schemes to achieve desired on-street parking occupancy levels.

      PubDate: 2017-02-01T07:39:16Z
      DOI: 10.1016/j.trc.2017.01.008
      Issue No: Vol. 77 (2017)
       
  • Distributed MPC for cooperative highway driving and energy-economy
           validation via microscopic simulations
    • Authors: Peng Liu; Umit Ozguner; Yeqing Zhang
      Pages: 80 - 95
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Peng Liu, Umit Ozguner, Yeqing Zhang
      Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control scheme that takes into account macroscopic traffic management and microscopic vehicle dynamics to achieve efficiently cooperative highway driving. Critical traffic information beyond the scope of human perception is obtained from connected vehicles downstream to establish necessary traffic management mitigating congestion. With backpropagating traffic management advice, a connected vehicle having an adjustment intention exchanges control-oriented information with immediately connected neighbors to establish potential cooperation consensus, and to generate cooperative control actions. To achieve this goal, a distributed model predictive control (DMPC) scheme is developed accounting for driving safety and efficiency. By coupling the states of collaborators in the optimization index, connected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The performance of the distributed control scheme and the energy-saving potential of conducting such cooperation are tested in a mixed highway traffic environment by the means of microscopic simulations.

      PubDate: 2017-02-08T12:08:46Z
      DOI: 10.1016/j.trc.2016.12.016
      Issue No: Vol. 77 (2017)
       
  • Real-time trip purpose prediction using online location-based search and
           discovery services
    • Authors: Alireza Ermagun; Yingling Fan; Julian Wolfson; Gediminas Adomavicius; Kirti Das
      Pages: 96 - 112
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Alireza Ermagun, Yingling Fan, Julian Wolfson, Gediminas Adomavicius, Kirti Das
      The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.

      PubDate: 2017-02-08T12:08:46Z
      DOI: 10.1016/j.trc.2017.01.020
      Issue No: Vol. 77 (2017)
       
  • Multi-scenario optimization approach for assessing the impacts of advanced
           traffic information under realistic stochastic capacity distributions
    • Authors: Mingxin Li; Nagui M. Rouphail; Monirehalsadat Mahmoudi; Jiangtao Liu; Xuesong Zhou
      Pages: 113 - 133
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Mingxin Li, Nagui M. Rouphail, Monirehalsadat Mahmoudi, Jiangtao Liu, Xuesong Zhou
      In this study, to incorporate realistic discrete stochastic capacity distribution over a large number of sampling days or scenarios (say 30–100days), we propose a multi-scenario based optimization model with different types of traveler knowledge in an advanced traveler information provision environment. The proposed method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions (and related reliability measure) across a large number of different sampling days. Using a gap function framework or describing the mixed user equilibrium under different information availability over a long-term steady state, a nonlinear programming model is formulated to describe the route choice behavior of the perfect information (PI) and expected travel time (ETT) user classes under stochastic day-dependent travel time. Driven by a computationally efficient algorithm suitable for large-scale networks, the model was implemented in a standard optimization solver and an open-source simulation package and further applied to medium-scale networks to examine the effectiveness of dynamic traveler information under realistic stochastic capacity conditions.

      PubDate: 2017-02-08T12:08:46Z
      DOI: 10.1016/j.trc.2017.01.019
      Issue No: Vol. 77 (2017)
       
  • Integrating robust timetabling in line plan optimization for railway
           systems
    • Authors: Sofie Burggraeve; Simon Henry Bull; Pieter Vansteenwegen; Richard Martin Lusby
      Pages: 134 - 160
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Sofie Burggraeve, Simon Henry Bull, Pieter Vansteenwegen, Richard Martin Lusby
      We propose a heuristic algorithm to build a railway line plan from scratch that minimizes passenger travel time and operator cost and for which a feasible and robust timetable exists. A line planning module and a timetabling module work iteratively and interactively. The line planning module creates an initial line plan. The timetabling module evaluates the line plan and identifies a critical line based on minimum buffer times between train pairs. The line planning module proposes a new line plan in which the time length of the critical line is modified in order to provide more flexibility in the schedule. This flexibility is used during timetabling to improve the robustness of the railway system. The algorithm is validated on the DSB S-tog network of Copenhagen, which is a high frequency railway system, where overtakings are not allowed. This network has a rather simple structure, but is constrained by limited shunt capacity. While the operator and passenger cost remain close to those of the initially and (for these costs) optimally built line plan, the timetable corresponding to the finally developed robust line plan significantly improves the minimum buffer time, and thus the robustness, in eight out of ten studied cases.

      PubDate: 2017-02-08T12:08:46Z
      DOI: 10.1016/j.trc.2017.01.015
      Issue No: Vol. 77 (2017)
       
  • Experimental Economics and choice in transportation: Incentives and
           context
    • Authors: Vinayak V. Dixit; Andreas Ortmann; E. Elisabet Rutström; Satish V. Ukkusuri
      Pages: 161 - 184
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Vinayak V. Dixit, Andreas Ortmann, E. Elisabet Rutström, Satish V. Ukkusuri
      This paper reviews the preconditions for successful applications of Experimental Economics methods to research on transportation problems, as new transportation and research technologies emerge. We argue that the application of properly designed incentives, the hallmark of Experimental Economics, provides a high degree of experimental control, leading to internal validity and incentive compatibility. Both of these are essential for ensuring that findings generalize to contexts outside the immediate application. New technologies, such as virtual reality simulators, can generate external validity for the experiments by providing realistic contexts. GPS and other tracking technologies, as well as smart phones, smart cards and connected vehicle technologies can allow detailed observations on actions and real-time interactions with drivers in field experiments. Proper application of these new technologies in research requires an understanding of how to maintain a high level of internal validity and incentive compatibility as external validity is increased. In this review of past applications of Experimental Economics to transportation we focus on their success in achieving external and internal validity.

      PubDate: 2017-02-08T12:08:46Z
      DOI: 10.1016/j.trc.2017.01.011
      Issue No: Vol. 77 (2017)
       
  • Deployment of stationary and dynamic charging infrastructure for electric
           vehicles along traffic corridors
    • Authors: Zhibin Chen; Wei Liu; Yafeng Yin
      Pages: 185 - 206
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Zhibin Chen, Wei Liu, Yafeng Yin
      As charging-while-driving (CWD) technology advances, charging lanes can be deployed in the near future to charge electric vehicles (EVs) while in motion. Since charging lanes will be costly to deploy, this paper investigates the deployment of two types of charging facilities, namely charging lanes and charging stations, along a long traffic corridor to explore the competitiveness of charging lanes. Given the charging infrastructure supply, i.e., the number of charging stations, the number of chargers installed at each station, the length of charging lanes, and the charging prices at charging stations and lanes, we analyze the charging-facility-choice equilibrium of EVs. We then discuss the optimal deployment of charging infrastructure considering either the public or private provision. In the former, a government agency builds and operates both charging lanes and stations to minimize social cost, while in the latter, charging lanes and stations are assumed to be built and operated by two competing private companies to maximize their own profits. Numerical experiments based on currently available empirical data suggest that charging lanes are competitive in both cases for attracting drivers and generating revenue.

      PubDate: 2017-02-08T12:08:46Z
      DOI: 10.1016/j.trc.2017.01.021
      Issue No: Vol. 77 (2017)
       
  • Modeling the impact of parking price policy on free-floating carsharing:
           Case study for Zurich, Switzerland
    • Authors: Milos Balac; Francesco Ciari; Kay W. Axhausen
      Pages: 207 - 225
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Milos Balac, Francesco Ciari, Kay W. Axhausen
      The research on carsharing has already shown that a non-negligible part of carsharing members give up a vehicle after joining a carsharing program, or avoid a vehicle purchase. This arguably reduces overall parking space needed. This might well be one of the most important impacts of a carsharing program on the transportation system, but also one of the least researched. The rapid diffusion of free-floating carsharing, which for its very nature might have a stronger impact on parking, makes the relationship between carsharing and parking an appealing topic for new research. This work presents a method for the investigation of this relationship using an agent-based simulation and explores the impacts of different parking prices on the demand for free-floating carsharing in the city of Zurich, Switzerland. Three levels of free-floating fleet-size in the city of Zurich coupled with three levels of parking prices were simulated. The obtained results show that free-floating vehicles are able to use parking spaces more efficiently than private vehicles. Moreover, the average parking occupancy tends to be more homogeneous with higher fleet-size of free-floating carsharing and with the increase of parking prices, thus avoiding spatial parking pressure peaks.

      PubDate: 2017-02-15T12:35:03Z
      DOI: 10.1016/j.trc.2017.01.022
      Issue No: Vol. 77 (2017)
       
  • Dynamic pricing and reservation for intelligent urban parking management
    • Authors: Chao Lei; Yanfeng Ouyang
      Pages: 226 - 244
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Chao Lei, Yanfeng Ouyang
      Despite rapid advances of information technologies for intelligent parking systems, it remains a challenge to optimally manage limited parking resources in busy urban neighborhoods. In this paper, we use dynamic location-dependent parking pricing and reservation to improve system-wide performance of an intelligent parking system. With this system, the parking agency is able to decide the spatial and temporal distribution of parking prices to achieve a variety of objectives, while drivers with different origins and destinations compete for limited parking spaces via online reservation. We develop a multi-period non-cooperative bi-level model to capture the complex interactions among the parking agency and multiple drivers, as well as a non-myopic approximate dynamic programming (ADP) approach to solve the model. It is shown with numerical examples that the ADP-based pricing policy consistently outperforms alternative policies in achieving greater performance of the parking system, and shows reliability in handling the spatial and temporal variations in parking demand.

      PubDate: 2017-02-15T12:35:03Z
      DOI: 10.1016/j.trc.2017.01.016
      Issue No: Vol. 77 (2017)
       
  • Travel analytics: Understanding how destination choice and business
           clusters are connected based on social media data
    • Authors: Arthur Huang; Luciano Gallegos; Kristina Lerman
      Pages: 245 - 256
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Arthur Huang, Luciano Gallegos, Kristina Lerman
      Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.

      PubDate: 2017-02-15T12:35:03Z
      DOI: 10.1016/j.trc.2016.12.019
      Issue No: Vol. 77 (2017)
       
  • Effects of low speed limits on freeway traffic flow
    • Authors: Francesc Soriguera; Irene Martínez; Marcel Sala; Mónica Menéndez
      Pages: 257 - 274
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Francesc Soriguera, Irene Martínez, Marcel Sala, Mónica Menéndez
      Recent years have seen a renewed interest in Variable Speed Limit (VSL) strategies. New opportunities for VSL as a freeway metering mechanism or a homogenization scheme to reduce speed differences and lane changing maneuvers are being explored. This paper examines both the macroscopic and microscopic effects of different speed limits on a traffic stream, especially when adopting low speed limits. To that end, data from a VSL experiment carried out on a freeway in Spain are used. Data include vehicle counts, speeds and occupancy per lane, as well as lane changing rates for three days, each with a different fixed speed limit (80km/h, 60km/h, and 40km/h). Results reveal some of the mechanisms through which VSL affects traffic performance, specifically the flow and speed distribution across lanes, as well as the ensuing lane changing maneuvers. It is confirmed that the lower the speed limit, the higher the occupancy to achieve a given flow. This result has been observed even for relatively high flows and low speed limits. For instance, a stable flow of 1942veh/h/lane has been measured with the 40km/h speed limit in force. The corresponding occupancy was 33%, doubling the typical occupancy for this flow in the absence of speed limits. This means that VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here, where speed limits have a reduced ability to limit flows. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Additionally, results show that lower speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rate. This means that VSL strategies aiming to homogenize traffic and reduce lane changing activity might not be successful when adopting such low speed limits. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane is avoided. These findings are useful for the development of better traffic models that are able to emulate these effects. Moreover, they are crucial for the implementation and assessment of VSL strategies and other traffic control algorithms.

      PubDate: 2017-02-15T12:35:03Z
      DOI: 10.1016/j.trc.2017.01.024
      Issue No: Vol. 77 (2017)
       
  • Automating a framework to extract and analyse transport related social
           media content: The potential and the challenges
    • Authors: Tsvi Kuflik; Einat Minkov; Silvio Nocera; Susan Grant-Muller; Ayelet Gal-Tzur; Itay Shoor
      Pages: 275 - 291
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Tsvi Kuflik, Einat Minkov, Silvio Nocera, Susan Grant-Muller, Ayelet Gal-Tzur, Itay Shoor
      Harnessing the potential of new generation transport data and increasing public participation are high on the agenda for transport stakeholders and the broader community. The initial phase in the program of research reported here proposed a framework for mining transport-related information from social media, demonstrated and evaluated it using transport-related tweets associated with three football matches as case studies. The goal of this paper is to extend and complement the previous published studies. It reports an extended analysis of the research results, highlighting and elaborating the challenges that need to be addressed before a large-scale application of the framework can take place. The focus is specifically on the automatic harvesting of relevant, valuable information from Twitter. The results from automatically mining transport related messages in two scenarios are presented i.e. with a small-scale labelled dataset and with a large-scale dataset of 3.7m tweets. Tweets authored by individuals that mention a need for transport, express an opinion about transport services or report an event, with respect to different transport modes, were mined. The challenges faced in automatically analysing Twitter messages, written in Twitter’s specific language, are illustrated. The results presented show a strong degree of success in the identification of transport related tweets, with similar success in identifying tweets that expressed an opinion about transport services. The identification of tweets that expressed a need for transport services or reported an event was more challenging, a finding mirrored during the human based message annotation process. Overall, the results demonstrate the potential of automatic extraction of valuable information from tweets while pointing to areas where challenges were encountered and additional research is needed. The impact of a successful solution to these challenges (thereby creating efficient harvesting systems) would be to enable travellers to participate more effectively in the improvement of transport services.
      Graphical abstract

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.003
      Issue No: Vol. 77 (2017)
       
  • Platoons of connected vehicles can double throughput in urban roads
    • Authors: Jennie Lioris; Ramtin Pedarsani; Fatma Yildiz Tascikaraoglu; Pravin Varaiya
      Pages: 292 - 305
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Jennie Lioris, Ramtin Pedarsani, Fatma Yildiz Tascikaraoglu, Pravin Varaiya
      Intersections are the bottlenecks of the urban road system because an intersection’s capacity is only a fraction of the maximum flows that the roads connecting to the intersection can carry. This capacity can be increased if vehicles cross the intersections in platoons rather than one by one as they do today. Platoon formation is enabled by connected vehicle technology. This paper assesses the potential mobility benefits of platooning. It argues that saturation flow rates, and hence intersection capacity, can be doubled or tripled by platooning. The argument is supported by the analysis of three queuing models and by the simulation of a road network with 16 intersections and 73 links. The queuing analysis and the simulations reveal that a signalized network with fixed time control will support an increase in demand by a factor of (say) two or three if all saturation flows are increased by the same factor, with no change in the control. Furthermore, despite the increased demand vehicles will experience the same delay and travel time. The same scaling improvement is achieved when the fixed time control is replaced by the max pressure adaptive control. Part of the capacity increase can alternatively be used to reduce queue lengths and the associated queuing delay by decreasing the cycle time. Impediments to the control of connected vehicles to achieve platooning at intersections appear to be small.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.01.023
      Issue No: Vol. 77 (2017)
       
  • Forecasting short-term subway passenger flow under special events
           scenarios using multiscale radial basis function networks
    • Authors: Yang Li; Xudong Wang; Shuo Sun; Xiaolei Ma; Guangquan Lu
      Pages: 306 - 328
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Yang Li, Xudong Wang, Shuo Sun, Xiaolei Ma, Guangquan Lu
      Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.005
      Issue No: Vol. 77 (2017)
       
  • Area-wide urban traffic control: A Bee Colony Optimization approach
    • Authors: Aleksandar Jovanović; Miloš Nikolić; Dušan Teodorović
      Pages: 329 - 350
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Aleksandar Jovanović, Miloš Nikolić, Dušan Teodorović
      The paper describes a new method of optimizing traffic signal settings. The area-wide urban traffic control system developed in the paper is based on the Bee Colony Optimization (BCO) technique. The BCO method is based on the principles of the collective intelligence applied by the honeybees during the nectar collecting process. The optimal (or near-optimal) values of cycle length, offsets, and splits are discovered by minimizing the total travel time of all network users travelling through signalized intersections. The set of numerical experiments is performed on well-known traffic benchmark network. The results obtained by the BCO approach are compared with the results found by Simulated Annealing (SA). It has been shown that the suggested BCO approach outperformed the SA.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.006
      Issue No: Vol. 77 (2017)
       
  • Modelling driver distraction effects due to mobile phone use on reaction
           time
    • Authors: Pushpa Choudhary; Nagendra R. Velaga
      Pages: 351 - 365
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Pushpa Choudhary, Nagendra R. Velaga
      Phone use during driving causes decrease in situation awareness and delays response to the events happening in driving environment which may lead to accidents. Reaction time is one of the most suitable parameters to measure the effect of distraction on event detection performance. Therefore, this paper reports the results of a simulator study which analysed and modelled the effects of mobile phone distraction upon reaction time of the Indian drivers belonging to three different age groups. Two different types of hazardous events: (1) pedestrian crossing event and (2) road crossing event by parked vehicles were included for measuring drivers’ reaction times. Four types of mobile phone distraction tasks: simple conversation, complex conversation, simple texting and complex texting were included in the experiment. Two Weibull AFT (Accelerated Failure Time) models were developed for the reaction times against both the events separately, by taking all the phone use conditions and various other factors (such as age, gender, and phone use habits during driving) as explanatory variables. The developed models showed that in case of pedestrian crossing event, the phone use tasks: simple conversation, complex conversation, simple texting and complex texting caused 40%, 95%, 137% and 204% increment in the reaction times and in case of road crossing event by parked vehicles, the tasks caused 48%, 65%, 121% and 171% increment in reaction times respectively. Thus all the phone use conditions proved to be the most significant factors in degrading the driving performance.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.007
      Issue No: Vol. 77 (2017)
       
  • Performance analysis of the phase swap sorting strategy for an isolated
           intersection
    • Authors: Qiaoli Yang; Zhongke Shi
      Pages: 366 - 388
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Qiaoli Yang, Zhongke Shi
      A separate left-turn phase wastes the capacity of intersection, because all the lanes on the approach are not fully utilized during either the left-turn or through green phase. Under the phase swap sorting strategy (Xuan, 2011), different types of movements can be reorganized by a pre-signal so that all the lanes in the sorting area can be used to discharge vehicles during their green phases. Thus the capacity is improved significantly. In fact, when a pre-signal is installed upstream of the intersection signal (also named main signal), the two signals will have a great impact on not only the capacity, but other traffic performances, such as delays, queue formations, maximum queue length, residual queue, and spillback, etc., which are very important performance factors for the design and application of the phase swap sorting strategy. In order to more fully quantify and characterize the performance of the phase swap sorting strategy, a three-dimensional Markov queueing model is presented. Two levels of performance evaluation indices are formulated using the matrix analytic techniques. All these indices can be used to establish a more comprehensive analytical framework to evaluate the use of the phase swap sorting strategy. Model validation shows that the proposed model can provide a reliable performance analysis for the phase swap sorting strategy under various different conditions. In addition, in order to intuitively illustrate the effects of various factors on the performance of the phase swap sorting strategy, a series of numerical experiments is conducted.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.01.018
      Issue No: Vol. 77 (2017)
       
  • Container liner fleet deployment: A systematic overview
    • Authors: Shuaian Wang; Qiang Meng
      Pages: 389 - 404
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Shuaian Wang, Qiang Meng
      Container liner fleet deployment (CLFD) is the assignment of containerships to port rotations (ship routes) for efficient transport of containers. As liner shipping services have fixed schedules, the ship-related operating cost is determined at the CLFD stage. This paper provides a critical review of existing mathematical models developed for the CLFD problems. It first gives a systematic overview of the fundamental assumptions used by the existing CLFD models. The operating characteristics dealt with in existing studies are then examined, including container transshipment and routing, uncertain demand, empty container repositioning, ship sailing speed optimization and ship repositioning. Finally, this paper points out four important future research opportunities: fleet deployment considering ship surveys and inspections, service dependent demand, pollutant emissions, and CLFD for shipping alliances.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.010
      Issue No: Vol. 77 (2017)
       
  • Resolving freeway jam waves by discrete first-order model-based predictive
           control of variable speed limits
    • Authors: Yu Han; Andreas Hegyi; Yufei Yuan; Serge Hoogendoorn; Markos Papageorgiou; Claudio Roncoli
      Pages: 405 - 420
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Yu Han, Andreas Hegyi, Yufei Yuan, Serge Hoogendoorn, Markos Papageorgiou, Claudio Roncoli
      In this paper we develop a fast model predictive control (MPC) approach for variable speed limit coordination to resolve freeway jam waves. Existing MPC approaches that are based on the second-order traffic flow models suffer from high computation load due to the non-linear and non-convex optimization formulation. In recent years, simplified MPC approaches which are based on discrete first-order traffic flow models have attracted more and more attention because they are beneficial for real-time applications. In literature, the type of traffic jam resolved by these approaches is limited to the standing queue in which the jam head is fixed at the bottleneck. Another type of traffic jam known as the jam wave, has been neglected by the discrete first-order model-based MPC approaches. To fill this gap, we develop a fast MPC approach based on a more accurate discrete first-order model. The model keeps the linear property of the classical discrete first-order model, meanwhile takes traffic flow features of jam waves propagation into consideration. A classical non-linear MPC and a recently proposed linear MPC are compared with the proposed MPC in terms of computation speed and jam wave resolution by a benchmark problem. Simulation results show that the proposed MPC resolves the jam wave with a real-time feasible computation speed.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.009
      Issue No: Vol. 77 (2017)
       
  • Tweeting Transit: An examination of social media strategies for transport
           information management during a large event
    • Authors: Caitlin Cottrill; Paul Gault; Godwin Yeboah; John D. Nelson; Jillian Anable; Thomas Budd
      Pages: 421 - 432
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Caitlin Cottrill, Paul Gault, Godwin Yeboah, John D. Nelson, Jillian Anable, Thomas Budd
      Social media platforms are seeing increasing adoption by public transport agencies, as they provide a cost-effective, reliable, and timely mechanism for sharing information with passengers and other travellers. In this paper, we use a case study of the @GamesTravel2014 Twitter account to evaluate how this social media platform was used over the course of the 2014 Commonwealth Games in Glasgow, Scotland to provide and share transport-related information and respond to information requests. The case study provides an exemplar for the public co-ordination of information from multiple partners in a complex environment during a time of transport disruption. We evaluate both the structure and intent of the @GamesTravel2014 social media strategy via interviews with involved parties and an analysis of Tweets related to the account. Findings indicate the potential for future applications of social media by transport operators and authorities in producing a more effective network of communication with passengers.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.008
      Issue No: Vol. 77 (2017)
       
  • Nominal flight time optimization for arrival time scheduling through
           estimation/resolution of delay accumulation
    • Authors: Noboru Takeichi
      Pages: 433 - 443
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Noboru Takeichi
      In the arrival air traffic management, aircraft are provided with each one’s scheduled time of arrival to satisfy the required time interval from the preceding one at a specific point, e.g. runway threshold, metering and merging points, etc. In general, the interval between aircraft in a traffic stream is controlled by delaying the following one, which inevitably results in delay accumulation and operational inefficiency. Because the scheduled time of arrival also depends on the nominal flight time toward specific points, it is considered possible to optimize the nominal flight time to minimize the operational cost of whole traffic stream. In this study, the nominal flight time optimization strategies through the estimation/resolution of the delay accumulation are proposed, and its feasibility is discussed. Through mathematical delay analyses, it is proven possible to estimate the delay accumulation by using the statistics of the traffic arriving at the initial point of the arrival scheduling. The feasibility to optimize the nominal flight time is then clearly demonstrated through the numerical traffic simulation. When the nominal flight time is longer than the minimum one, it is possible to reduce the flight time. When the traffic control with the flight time reduction is applied, it becomes possible to resolve the delay accumulation. It is further clarified that the optimum scheduled time of arrival can be directly estimated from the cost function of a single aircraft in the case of such a traffic free of delay accumulation. In addition, the maximum runway use and the equality of the operation performance are simultaneously achieved.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.01.025
      Issue No: Vol. 77 (2017)
       
  • Connecting e-hailing to mass transit platform: Analysis of relative
           spatial position
    • Authors: Peng (Will) Chen; Yu (Marco) Nie
      Pages: 444 - 461
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Peng (Will) Chen, Yu (Marco) Nie
      This paper analyzes and compares two different relative spatial position (RSP) designs in an integrated e-hailing/fixed-route transit system: a zone-based design that operates e-hailing vehicles within a zone, and a line-based design that operates e-hailing vehicles along a fixed-route transit line and with a stable headway. To conduct a meaningful comparison, the optimal design problems for both systems are formulated using a same analytical framework based on the continuous approximation approach. A comprehensive numerical experiment is performed to compare various cost components corresponding to the optimal designs, and a discrete-event simulation model is developed to validate the analysis. The analytical and simulation results agree with each other well, with a discrepancy in the total system cost less than 5% in most test scenarios. These results also suggest that the line-based system consistently outperforms the zone-based system in terms of both agency and user costs, for all scenarios tested. Compared to the zone-based design, the line-based design features a sparser fixed-route network (resulting in larger stop spacing) but a higher dispatching frequency. It is concluded that the higher efficiency of the line-based design is likely derived from the strategy of operating e-hailing vehicles with a more regular route/headway structure and allowing ride-sharing.

      PubDate: 2017-02-21T12:42:44Z
      DOI: 10.1016/j.trc.2017.02.013
      Issue No: Vol. 77 (2017)
       
  • On the tactical and operational train routing selection problem
    • Authors: Marcella Samà; Paola Pellegrini; Andrea D’Ariano; Joaquin Rodriguez; Dario Pacciarelli
      Pages: 1 - 15
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Marcella Samà, Paola Pellegrini, Andrea D’Ariano, Joaquin Rodriguez, Dario Pacciarelli
      In the real-time railway traffic management problem, the number of alternative routings available to each train strongly affects the size of the problem and the time required to optimally solve it. The train routing selection problem identifies a suitable subset of alternative routings to be used for each train in the real-time railway traffic management. This paper analyzes the impact of solving the train routing selection problem at different levels. The problem can be solved at tactical level right after the timetabling process, using historical traffic data and with abundant computation time. In this case the problem constitutes an integration step between the timetabling and the real-time traffic management. Alternatively, the problem can be solved at operational level right before the real-time railway traffic management problem solution, using up to date traffic perturbation and a real-time time limit of computation. Experiments are performed on two French test cases, the line around Rouen and the Lille station area, for several disturbed and disrupted scenarios. The results show that the best approach depends on the type of traffic disturbance tackled.

      PubDate: 2017-01-11T08:55:14Z
      DOI: 10.1016/j.trc.2016.12.010
      Issue No: Vol. 76 (2017)
       
  • Modelling contact mode and frequency of interactions with social network
           members using the multiple discrete–continuous extreme value model
    • Authors: Chiara Calastri; Stephane Hess; Andrew Daly; Michael Maness; Matthias Kowald; Kay Axhausen
      Pages: 16 - 34
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Chiara Calastri, Stephane Hess, Andrew Daly, Michael Maness, Matthias Kowald, Kay Axhausen
      Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat, 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data.

      PubDate: 2017-01-11T08:55:14Z
      DOI: 10.1016/j.trc.2016.12.012
      Issue No: Vol. 76 (2017)
       
  • Are you in the loop? Using gaze dispersion to understand driver visual
           attention during vehicle automation
    • Authors: Tyron Louw; Natasha Merat
      Pages: 35 - 50
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Tyron Louw, Natasha Merat
      This driving simulator study, conducted as part of the EC-funded AdaptIVe project, assessed drivers’ visual attention distribution during automation and on approach to a critical event, and examined whether such attention changes following repeated exposure to an impending collision. Measures of drivers’ horizontal and vertical gaze dispersion during both conventional and automated (SAE Level 2) driving were compared on approach to such critical events. Using a between-participant design, 60 drivers (15 in each group) experienced automation with one of four screen manipulations: (1) no manipulation, (2) manipulation by light fog, (3) manipulation by heavy fog, and (4) manipulation by heavy fog with a secondary task, which were used to induce varying levels of engagement with the driving task. Results showed that, during automation, drivers’ horizontal gaze was generally more dispersed than that observed during manual driving. Drivers clearly looked around more when their view of the driving scene was completely blocked by an opaque screen in the heavy fog condition. By contrast, horizontal gaze dispersion was (unsurprisingly) more concentrated when drivers performed a visual secondary task, which was overlaid on the opaque screen. However, once the manipulations ceased and an uncertainty alert captured drivers’ attention towards an impending incident, a similar gaze pattern was found for all drivers, with no carry-over effects observed after the screen manipulations. Results showed that drivers’ understanding of the automated system increased as time progressed, and that scenarios that encourage driver gaze towards the road centre are more likely to increase situation awareness during high levels of automation.

      PubDate: 2017-01-11T08:55:14Z
      DOI: 10.1016/j.trc.2017.01.001
      Issue No: Vol. 76 (2017)
       
  • Understanding ridesplitting behavior of on-demand ride services: An
           ensemble learning approach
    • Authors: Xiqun (Michael) Chen; Majid Zahiri; Shuaichao Zhang
      Pages: 51 - 70
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Xiqun (Michael) Chen, Majid Zahiri, Shuaichao Zhang
      In this paper, we present an ensemble learning approach for better understanding ridesplitting behavior of passengers of ridesourcing companies who provide prearranged and on-demand transportation services. An ensemble learning model is a weighted combination of multiple classification models or week classifiers to form a strong classification model. The goal of ensemble learning is to combine decisions or predictions of several base classifiers to improve prediction, generalizability, and robustness over a single classifier. This paper employs the Boosting ensemble by growing individual decision trees sequentially and then assembling these trees to produce a powerful classification model. To improve the prediction accuracy of ridesplitting choices, we explored real-world individual level data extracted from the on-demand ride service platform of DiDi in Hangzhou, China. Over one million trips of the four service types, i.e., Taxi Hailing Service, Express, Private Car Service, and Hitch, are explored with descriptive statistics. A variety of features that may impact ridesplitting behavior are ranked and selected by using the ReliefF algorithm, such as trip travel time, trip costs, trip length, waiting time fee, travel time reliability of origins/destinations and so on. The Boosting ensemble trees with full features and selected features are trained and validated using two independent datasets. This paper also verifies that ensemble learning is particularly useful and powerful in the ridesplitting analysis and outperforms three other widely used classifiers. This paper is one of the first quantitative studies that empirically reveal the real-world demand and supply pattern by exploring the city-wide data of an on-demand ride service platform.

      PubDate: 2017-01-11T08:55:14Z
      DOI: 10.1016/j.trc.2016.12.018
      Issue No: Vol. 76 (2017)
       
  • Flexing service schedules: Assessing the potential for demand-adaptive
           hybrid transit via a stated preference approach
    • Authors: Charlotte Frei; Michael Hyland; Hani S. Mahmassani
      Pages: 71 - 89
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Charlotte Frei, Michael Hyland, Hani S. Mahmassani
      This paper assesses the demand for a flexible, demand-adaptive transit service, using the Chicago region as an example. We designed and implemented a stated-preference survey in order to (1) identify potential users of flexible transit, and (2) inform the service design of the flexible transit mode. Multinomial logit, mixed-logit, and panel mixed-logit choice models were estimated using the data obtained from the survey. The survey instrument employed a dp-efficient design and the Google Maps API to capture precise origins and destinations in order to create realistic choice scenarios. The stated-preference experiments offered respondents a choice between traditional transit, car, and a hypothetical flexible transit mode. Wait time, access time, travel time, service frequency, cost, and number of transfers varied across the choice scenarios. The choice model results indicate mode-specific values of in-vehicle travel time ranging between $16.3 per hour (car) and $21.1 per hour (flexible transit). The estimated value of walking time to transit is $25.9 per hour. The estimated value of waiting time at one’s point of origin for a flexible transit vehicle is $11.3 per hour; this value is significantly lower than the disutility typically associated with waiting at a transit stop/station indicating that the ‘at-home’ pick-up option of flexible transit is a highly desirable feature. The choice model results also indicate that respondents who use active-transport modes or public transit for their current commute trip, or are bikeshare members, were significantly more likely to choose flexible and traditional transit than car commuters in the choice experiments. The implications of these and other relevant model results for the design and delivery of flexible, technology-enabled services are discussed.

      PubDate: 2017-01-11T08:55:14Z
      DOI: 10.1016/j.trc.2016.12.017
      Issue No: Vol. 76 (2017)
       
  • Route design for last-in, first-out deliveries with backhauling
    • Authors: Sifeng Lin; Jonathan F. Bard; Ahmad I. Jarrah; Xinhui Zhang; Luis J. Novoa
      Pages: 90 - 117
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Sifeng Lin, Jonathan F. Bard, Ahmad I. Jarrah, Xinhui Zhang, Luis J. Novoa
      The retail route design problem extends the capacitated vehicle routing problem with time windows by introducing several operational constraints, including order loading and delivery restrictions (last-in, first-out), order-dependent vehicle capacity, material handling limits at the warehouse, backhauling, and driving time bounds. In this paper, the problem is modeled on a directed network for an application associated with a major grocery chain. Because the corresponding mixed-integer program proved too difficult to solve with commercial software for real instances, we developed a greedy randomized adaptive search procedure (GRASP) augmented with tabu search to provide solutions. Testing was done using data sets provided Kroger, the largest grocery chain in the US, and benchmarked against a previously developed column generation algorithm. The results showed that cost reductions of $4887 per day or 5.58% per day on average, compared to Kroger’s corresponding solutions.

      PubDate: 2017-01-19T07:35:15Z
      DOI: 10.1016/j.trc.2017.01.005
      Issue No: Vol. 76 (2017)
       
  • Controlling algorithmic blocking: Calibration and Cost Redistribution
           approach to real-world operational railroad application
    • Authors: Erick D. Wikum
      Pages: 118 - 131
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Erick D. Wikum
      While algorithmic blocking has been shown to have significant advantages over a traditional, tag table approach for the operational implementation of a freight railroad’s blocking plan, railroads have been reluctant to pursue that approach, partly due to the perception that algorithmic blocking is difficult to control. Control refers to configuring the data underlying algorithmic blocking so that block paths returned for rail car movements are those desired by railroad management. This paper presents three complementary control strategies—Every Day, Calibration and Block Cost Redistribution—which together provide a practical way to achieve control with algorithmic blocking. The paper also includes a comprehensive description of an implementation of algorithmic blocking upon which these control strategies are based.

      PubDate: 2017-01-19T07:35:15Z
      DOI: 10.1016/j.trc.2017.01.006
      Issue No: Vol. 76 (2017)
       
  • Fine-tuning ADAS algorithm parameters for optimizing traffic safety and
           mobility in connected vehicle environment
    • Authors: Hao Liu; Heng Wei; Ting Zuo; Zhixia Li; Y. Jeffrey Yang
      Pages: 132 - 149
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Hao Liu, Heng Wei, Ting Zuo, Zhixia Li, Y. Jeffrey Yang
      Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers’ behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).

      PubDate: 2017-01-19T07:35:15Z
      DOI: 10.1016/j.trc.2017.01.003
      Issue No: Vol. 76 (2017)
       
  • A generalized partite-graph method for transportation data association
    • Authors: Paul Anderson; Bilal Farooq
      Pages: 150 - 169
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Paul Anderson, Bilal Farooq
      There are many problems in transportation which involve reconstructing the associations between different entities. For example, data points related to a vehicle from different sensors could be matched to reconstruct the trajectories of vehicles. Or, in population synthesis for microsimulation, lists of persons, dwellings, and vehicles could be generated individually from source data and then matched into synthetic households. There are numerous other examples. The unifying theme is a desire to construct realistic unit-level associations from aggregate or anonymized data. The problem demands a method that is behaviorally consistent and operationally efficient to handle large datasets. We adapt concepts from graph theory to formulate this class of problems as a k-partite graph. This approach is generic and can incorporate expectations of behavior in the form of edge weights. A Dijkstra algorithm based solution is proposed for a subset of k-partite graphs which permits a direct comparison with pair-wise matching and applied to a case study of bicycle tracklets. We then propose an iterative improvement algorithm as a generic method and apply it to a complete k-partite graph in a population synthesis case study. The first case study shows that the k-partite algorithm outperforms the previously used pair-wise matching algorithms. The second case study demonstrates the generality of the proposed algorithm to all k-partite graphs and shows that the generic method is fast and scalable to large problems. As a whole, this paper aims to show that k-partite methods are behaviorally consistent, efficient, and potentially applicable to a wide variety of transportation data association problems.

      PubDate: 2017-01-19T07:35:15Z
      DOI: 10.1016/j.trc.2017.01.004
      Issue No: Vol. 76 (2017)
       
  • Vehicle headway modeling and its inferences in macroscopic/microscopic
           traffic flow theory: A survey
    • Authors: Li Li; Xiqun (Michael) Chen
      Pages: 170 - 188
      Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76
      Author(s): Li Li, Xiqun (Michael) Chen
      Vehicle headway distribution models are widely used in traffic engineering fields, since they reflect the fundamental uncertainty in drivers' car-following maneuvers and meanwhile provide a concise way to describe the stochastic feature of traffic flows. This paper presents a systematic review of vehicle headway distribution studies in the last few decades. Since it is impossible to enumerate the merits and drawbacks of all of existing distribution models, we emphasize four advances of headway distribution modeling in this paper. First, we highlight the chronicle of key assumptions on the existing distribution models and explain why this evolution occurs. Second, we show that departure headways measured for interrupted flows on urban streets and headways measured for uninterrupted flows on freeways have common features and can be simulated by a unified microscopic car-following model. The interesting finding helps gather two kinds of headway distribution models under one umbrella. Third, we review different approaches that aim to link microscopic car-following models and mesoscopic vehicle headway distribution models. Fourth, we show that both the point scattering on the density-flow plot and the shape of traffic flow breakdown curve implicitly depend on the vehicular headway distribution. These findings reveal pervasive connections between macroscopic traffic flow models and mesoscopic headway distribution. All these new insights bring new vigor into vehicle headway studies and open research frontiers in this field.

      PubDate: 2017-01-26T07:39:07Z
      DOI: 10.1016/j.trc.2017.01.007
      Issue No: Vol. 76 (2017)
       
  • A flexible traffic stream model and its three representations of traffic
           flow
    • Authors: Liang Zheng; Zhengbing He; Tian He
      Pages: 136 - 167
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Liang Zheng, Zhengbing He, Tian He
      To connect microscopic driving behaviors with the macro-correspondence (i.e., the fundamental diagram), this study proposes a flexible traffic stream model, which is derived from a novel car-following model under steady-state conditions. Its four driving behavior-related parameters, i.e., reaction time, calmness parameter, speed- and spacing-related sensitivities, have an apparent effect in shaping the fundamental diagram. Its boundary conditions and homogenous case are also analyzed in detail and compared with other two models (i.e., Longitudinal Control Model and Intelligent Driver Model). Especially, these model formulations and properties under Lagrangian coordinates provide a new perspective to revisit the traffic flow and complement with those under Eulerian coordinate. One calibration methodology that incorporates the monkey algorithm with dynamic adaptation is employed to calibrate this model, based on real-field data from a wide range of locations. Results show that this model exhibits the well flexibility to fit these traffic data and performs better than other nine models. Finally, a concrete example of transportation application is designed, in which the impact of three critical parameters on vehicle trajectories and shock waves with three representations (i.e., respectively defined in x-t, n-t and x-n coordinates) is tested, and macro- and micro-solutions on shock waves well agree with each other. In summary, this traffic stream model with the advantages of flexibility and efficiency has the good potential in level of service analysis and transportation planning.

      PubDate: 2017-01-01T02:50:05Z
      DOI: 10.1016/j.trc.2016.12.006
      Issue No: Vol. 75 (2017)
       
  • An integrated metro operation optimization to minimize energy consumption
    • Authors: Ning Zhao; Clive Roberts; Stuart Hillmansen; Zhongbei Tian; Paul Weston; Lei Chen
      Pages: 168 - 182
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Ning Zhao, Clive Roberts, Stuart Hillmansen, Zhongbei Tian, Paul Weston, Lei Chen
      Energy efficient techniques are receiving increasing attention because of rising energy prices and environmental concerns. Railways, along with other transport modes, are facing increasing pressure to provide more intelligent and efficient power management strategies. This paper presents an integrated optimization method for metro operation to minimize whole day substation energy consumption by calculating the most appropriate train trajectory (driving speed profile) and timetable configuration. A train trajectory optimization algorithm and timetable optimization algorithm are developed specifically for the study. The train operation performance is affected by a number of different systems that are closely interlinked. Therefore, an integrated optimization process is introduced to obtain the optimal results accurately and efficiently. The results show that, by using the optimal train trajectory and timetable, the substation energy consumption and load can be significantly reduced, thereby improving the system performance and stability. This also has the effect of reducing substation investment costs for new metros.

      PubDate: 2017-01-01T02:50:05Z
      DOI: 10.1016/j.trc.2016.12.013
      Issue No: Vol. 75 (2017)
       
  • Bike route choice modeling using GPS data without choice sets of paths
    • Authors: Maëlle Zimmermann; Tien Mai; Emma Frejinger
      Pages: 183 - 196
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Maëlle Zimmermann, Tien Mai, Emma Frejinger
      Concerned by the nuisances of motorized travel on urban life, policy makers are faced with the challenge of making cycling a more attractive alternative for everyday transportation. Route choice models can help achieve this objective by gaining insights into the trade-offs cyclists make when choosing their routes and by allowing the effect of infrastructure improvements to be analyzed. We estimate a link-based bike route choice model from a sample of GPS observations in the city of Eugene on a network comprising over 40,000 links. The so-called recursive logit (RL) model (Fosgerau et al., 2013) does not require to sample any choice set of paths. We show the advantages of this approach in the context of prediction by focusing on two applications of the model: link flows and accessibility measures. Compared to the path-based approach which requires to generate choice sets, the RL model proves to make significant gains in computational time and to avoid paradoxical accessibility measure results discussed in previous works, e.g. Nassir et al. (2014).

      PubDate: 2017-01-01T02:50:05Z
      DOI: 10.1016/j.trc.2016.12.009
      Issue No: Vol. 75 (2017)
       
  • Exploring the capacity of social media data for modelling travel
           behaviour: Opportunities and challenges
    • Authors: Taha H. Rashidi; Alireza Abbasi; Mojtaba Maghrebi; Samiul Hasan; Travis S. Waller
      Pages: 197 - 211
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Taha H. Rashidi, Alireza Abbasi, Mojtaba Maghrebi, Samiul Hasan, Travis S. Waller
      In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.

      PubDate: 2017-01-01T02:50:05Z
      DOI: 10.1016/j.trc.2016.12.008
      Issue No: Vol. 75 (2017)
       
  • Robust identification of air traffic flow patterns in Metroplex terminal
           areas under demand uncertainty
    • Authors: Stavros Sidiropoulos; Ke Han; Arnab Majumdar; Washington Y. Ochieng
      Pages: 212 - 227
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Stavros Sidiropoulos, Ke Han, Arnab Majumdar, Washington Y. Ochieng
      Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs).

      PubDate: 2017-01-01T02:50:05Z
      DOI: 10.1016/j.trc.2016.12.011
      Issue No: Vol. 75 (2017)
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76


      PubDate: 2017-02-08T12:08:46Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75


      PubDate: 2017-01-19T07:35:15Z
       
  • Large-scale dynamic transportation network simulation: A space-time-event
           parallel computing approach
    • Authors: Yunchao Qu; Xuesong Zhou
      Pages: 1 - 16
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Yunchao Qu, Xuesong Zhou
      This paper describes a computationally efficient parallel-computing framework for mesoscopic transportation simulation on large-scale networks. By introducing an overall data structure for mesoscopic dynamic transportation simulation, we discuss a set of implementation issues for enabling flexible parallel computing on a multi-core shared memory architecture. First, we embed an event-based simulation logic to implement a simplified kinematic wave model and reduce simulation overhead. Second, we present a space-time-event computing framework to decompose simulation steps to reduce communication overhead in parallel execution and an OpenMP-based space-time-processor implementation method that is used to automate task partition tasks. According to the spatial and temporal attributes, various types of simulation events are mapped to independent logical processes that can concurrently execute their procedures while maintaining good load balance. We propose a synchronous space-parallel simulation strategy to dynamically assign the logical processes to different threads. The proposed method is then applied to simulate large-scale, real-world networks to examine the computational efficiency under different numbers of CPU threads. Numerical experiments demonstrate that the implemented parallel computing algorithm can significantly improve the computational efficiency and it can reach up to a speedup of 10 on a workstation with 32 computing threads.
      Graphical abstract image

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.12.003
      Issue No: Vol. 75 (2016)
       
  • Impacts of weather on public transport ridership: Results from mining data
           from different sources
    • Authors: Meng Zhou; Donggen Wang; Qingquan Li; Yang Yue; Wei Tu; Rui Cao
      Pages: 17 - 29
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Meng Zhou, Donggen Wang, Qingquan Li, Yang Yue, Wei Tu, Rui Cao
      The existing studies concerning the influence of weather on public transport have mainly focused on the impacts of average weather conditions on the aggregate ridership of public transit. Not much research has examined these impacts at disaggregate levels. This study aims to fill this gap by accounting for intra-day variations in weather as well as public transport ridership and investigating the effect of weather on the travel behavior of individual public transit users. We have collected smart card data for public transit and meteorological records from Shenzhen, China for the entire month of September 2014. The data allow us to establish association between the system-wide public transit ridership and weather condition on not only daily, but also hourly basis and for each metro station. In addition, with the detailed trip records of individual card holders, the travel pattern by public transit are constructed for card holders and this pattern is linked to the weather conditions he/she has experienced. Multivariate modeling approach is applied to analyze the influence of weather on public transit ridership and the travel behavior of regular transit users. Results show that some weather elements have more influence than others on public transportation. Metro stations located in urban areas are more vulnerable to outdoor weather in regard to ridership. Regular transit users are found to be rather resilient to changes in weather conditions. Findings contribute to a more in-depth understanding of the relationship between everyday weather and public transit travels and also provide valuable information for short-term scheduling in transit management.

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.12.001
      Issue No: Vol. 75 (2016)
       
  • Predicting travel time reliability using mobile phone GPS data
    • Authors: Dawn Woodard; Galina Nogin; Paul Koch; David Racz; Moises Goldszmidt; Eric Horvitz
      Pages: 30 - 44
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Dawn Woodard, Galina Nogin, Paul Koch, David Racz, Moises Goldszmidt, Eric Horvitz
      Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.10.011
      Issue No: Vol. 75 (2016)
       
  • A mesoscopic integrated urban traffic flow-emission model
    • Authors: Anahita Jamshidnejad; Ioannis Papamichail; Markos Papageorgiou; Bart De Schutter
      Pages: 45 - 83
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Anahita Jamshidnejad, Ioannis Papamichail, Markos Papageorgiou, Bart De Schutter
      Due to the noticeable environmental and economical problems caused by traffic congestion and by the emissions produced by traffic, analysis and control of traffic is essential. One of the various traffic analysis approaches is the model-based approach, where a mathematical model of the traffic system is developed/used based on the governing physical rules of the system. In this paper, we propose a framework to interface and integrate macroscopic flow models and microscopic emission models. As a result, a new mesoscopic integrated flow-emission model is obtained that provides a balanced trade-off between high accuracy and low computation time. The proposed approach considers an aggregated behavior for different groups of vehicles (mesoscopic) instead of considering the behavior of individual vehicles (microscopic) or the entire group of vehicles (macroscopic). A case study is done to evaluate the proposed framework, considering the performance of the resulting mesoscopic integrated flow-emission model. The traffic simulation software SUMO combined with the microscopic emission model VT-micro is used as the comparison platform. The results of the case study prove that the proposed approach provides excellent results with high accuracy levels. In addition, the mesoscopic nature of the integrated flow-emission model guarantees a low CPU time, which makes the proposed framework suitable for real-time model-based applications.

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.11.024
      Issue No: Vol. 75 (2016)
       
  • Evolution of public transit modes in a commuter corridor
    • Authors: Yanshuo Sun; Qianwen Guo; Paul Schonfeld; Zhongfei Li
      Pages: 84 - 102
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Yanshuo Sun, Qianwen Guo, Paul Schonfeld, Zhongfei Li
      This paper explores how the selection of public transit modes can be optimized over a planning horizon. This conceptual analysis sacrifices geographic detail in order to better highlight the relations among important factors. First, a set of static models is proposed to identify which type of service, e.g., bus only, rail only, or bus and rail, is the most cost-effective in terms of the average trip cost for given demand. After analyzing essential factors in a long-term planning process, e.g., economies of scale in rail extension and future cost discounting, a dynamic model incorporating such considerations is formulated to optimize the decision over a planning horizon. While analytical solutions can be obtained for some decision variables, the final model is solved with a graphical method by exploring the tradeoffs between the initial and recurring costs. Major findings from this study include: (a) there exists a minimum economic length for a rail line, which can be determined numerically; (b) economies of scale favor large extensions and excess supplied capacity; (c) the rail-only service is largely dominated by the feeder-trunk service, even in the long run.

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.12.007
      Issue No: Vol. 75 (2016)
       
  • A two-dimensional simulation model for modelling turning vehicles at
           mixed-flow intersections
    • Authors: Zian Ma; Jian Sun; Yunpeng Wang
      Pages: 103 - 119
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Zian Ma, Jian Sun, Yunpeng Wang
      The turning behavior is one of the most challenging driving maneuvers under non-protected phase at mixed-flow intersections. Currently, one-dimensional simulation models focus on car-following and gap-acceptance behaviors in pre-defined lanes with few lane-changing behaviors, and they cannot model the lateral and longitudinal behaviors simultaneously, which has limitation in representing the realistic turning behavior. This paper proposes a three-layered “plan-decision-action” (PDA) framework to obtain acceleration and angular velocity in the turning process. The plan layer firstly calculates the two-dimensional optimal path and dynamically adjusts the trajectories according to interacting objects. The decision layer then uses the decision tree method to select a suitable behavior in three alternatives: car-following, turning and yielding. Finally, in the action layer, a set of corresponding operational models specify the decided behavior into control parameters. The proposed model is tested by reproducing 210 trajectories of left-turn vehicles at a two-phase mixed-flow intersection in Shanghai. As a result, the simulation reproduces the variation of trajectories, while the coverage rate of the trajectories is 88.8%. Meanwhile, both the travel time and post-encroachment time of simulation and empirical turning vehicles are similar and do not show statistically significant difference.

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.12.005
      Issue No: Vol. 75 (2016)
       
  • System energy optimisation strategies for metros with regeneration
    • Authors: Zhongbei Tian; Paul Weston; Ning Zhao; Stuart Hillmansen; Clive Roberts; Lei Chen
      Pages: 120 - 135
      Abstract: Publication date: February 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 75
      Author(s): Zhongbei Tian, Paul Weston, Ning Zhao, Stuart Hillmansen, Clive Roberts, Lei Chen
      Energy and environmental sustainability in transportation are becoming ever more important. In Europe, the transportation sector is responsible for about 30% of the final end use of energy. Electrified railway systems play an important role in contributing to the reduction of energy usage and CO2 emissions compared with other transport modes. For metro-transit systems with frequently motoring and braking trains, the effective use of regenerated braking energy is a significant way to reduce the net energy consumption. Although eco-driving strategies have been studied for some time, a comprehensive understanding of how regeneration affects the overall system energy consumption has not been developed. This paper proposes a multi-train traction power network modelling method to determine the system energy flow of the railway system with regenerating braking trains. The initial results show that minimising traction energy use is not the same as minimising the system energy usage in a metro system. An integrated optimisation method is proposed to solve the system energy-saving problem, which takes train movement and electrical power flow into consideration. The results of a study of the Beijing Yizhuang metro line indicate that optimised operation could reduce the energy consumption at the substations by nearly 38.6% compared to that used with the existing ATO operation.

      PubDate: 2016-12-25T02:49:54Z
      DOI: 10.1016/j.trc.2016.12.004
      Issue No: Vol. 75 (2016)
       
 
 
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