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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  [3041 journals]
  • Deep learning for short-term traffic flow prediction
    • Authors: Nicholas G. Polson; Vadim O. Sokolov
      Pages: 1 - 17
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Nicholas G. Polson, Vadim O. Sokolov
      We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using ℓ 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.

      PubDate: 2017-03-21T03:04:27Z
      DOI: 10.1016/j.trc.2017.02.024
      Issue No: Vol. 79 (2017)
       
  • An integrated framework for assessing service efficiency and stability of
           rail transit systems
    • Authors: Yung-Cheng Lai; Chi-Sang Ip
      Pages: 18 - 41
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Yung-Cheng Lai, Chi-Sang Ip
      A well-designed service plan efficiently utilizes its infrastructure and ensures an acceptable level of service stability with consideration of potential incidents that disturb or disrupt the rail transit services. To perform service evaluation, an integrated process combining capacity, resource usage, and system reliability is required to quantify service efficiency and stability in a consistent way. This study adopts capacity-based indices, “capacity utilization” and “expected recovery time”, as the attributes for service efficiency and stability, and develops a comprehensive evaluation framework with three corresponding modules to incorporate capacity, service plan, and system reliability and maintainability simultaneously. The capacity analysis module computes the rail transit capacities under normal and degraded operations. The reliability module classifies and fits the proper reliability and maintainability distributions to the historical interruption data. The service efficiency and stability module analyzes the results of the previous two modules and evaluates the service efficiency and stability of rail transit service plans. Empirical results show that the established evaluation framework can not only evaluate the service efficiency and stability but also identify critical sections and time slots. This tool can help rail transit operators rapidly assess their operational changes and investment strategies related to efficiency and stability so as to provide efficient and stable services to their customers.

      PubDate: 2017-03-21T03:04:27Z
      DOI: 10.1016/j.trc.2017.03.006
      Issue No: Vol. 79 (2017)
       
  • The Hybrid Vehicle Routing Problem
    • Authors: Simona Mancini
      Pages: 1 - 12
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Simona Mancini
      In this paper the Hybrid Vehicle Routing Problem (HVRP) is introduced and formalized. This problem is an extension of the classical VRP in which vehicles can work both electrically and with traditional fuel. The vehicle may change propulsion mode at any point of time. The unitary travel cost is much lower for distances covered in the electric mode. An electric battery has a limited capacity and may be recharged at a recharging station (RS). A limited number of RS are available. Once a battery has been completely discharged, the vehicle automatically shifts to traditional fuel propulsion mode. Furthermore, a maximum route duration is imposed according to contracts regulations established with the driver. In this paper, a Mixed Integer Linear Programming formulation is presented and a Large Neighborhood Search based Matheuristic is proposed. The algorithm starts from a feasible solution and consists into destroying, at each iteration, a small number of routes, letting unvaried the other ones, and reconstructing a new feasible solution running the model on only the subset of customers involved in the destroyed routes. This procedure allows to completely explore a large neighborhood within very short computational time. Computational tests that show the performance of the matheuristic are presented. The method has also been tested on a simplified version of the HVRP already presented in the literature, the Green Vehicle Routing Problem (GVRP), and competitive results have been obtained.

      PubDate: 2017-03-07T12:31:06Z
      DOI: 10.1016/j.trc.2017.02.004
      Issue No: Vol. 78 (2017)
       
  • Highway traffic state estimation with mixed connected and conventional
           vehicles: Microscopic simulation-based testing
    • Authors: Markos Fountoulakis; Nikolaos Bekiaris-Liberis; Claudio Roncoli; Ioannis Papamichail; Markos Papageorgiou
      Pages: 13 - 33
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Markos Fountoulakis, Nikolaos Bekiaris-Liberis, Claudio Roncoli, Ioannis Papamichail, Markos Papageorgiou
      This paper presents a thorough microscopic simulation investigation of a recently proposed methodology for highway traffic estimation with mixed traffic, i.e., traffic comprising both connected and conventional vehicles, which employs only speed measurements stemming from connected vehicles and a limited number (sufficient to guarantee observability) of flow measurements from spot sensors. The estimation scheme is tested using the commercial traffic simulator Aimsun under various penetration rates of connected vehicles, employing a traffic scenario that features congested as well as free-flow conditions. The case of mixed traffic comprising conventional and connected vehicles equipped with adaptive cruise control, which feature a systematically different car-following behavior than regular vehicles, is also considered. In both cases, it is demonstrated that the estimation results are satisfactory, even for low penetration rates.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.015
      Issue No: Vol. 78 (2017)
       
  • Editors’ notes: Special issue on connected and autonomous vehicles
    • Authors: Yuanchang Xie; Nathan H. Gartner; Mashrur Chowdhury
      Pages: 34 - 36
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Yuanchang Xie, Nathan H. Gartner, Mashrur Chowdhury


      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.017
      Issue No: Vol. 78 (2017)
       
  • User preferences regarding autonomous vehicles
    • Authors: Chana J. Haboucha; Robert Ishaq; Yoram Shiftan
      Pages: 37 - 49
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Chana J. Haboucha, Robert Ishaq, Yoram Shiftan
      This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated: (1) Continue to commute using a regular car that you have in your possession. (2) Buy and shift to commuting using a privately-owned autonomous vehicle (PAV). (3) Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute. A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects. Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles. Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.01.010
      Issue No: Vol. 78 (2017)
       
  • Hardware-in-the-loop testbed for evaluating connected vehicle applications
    • Authors: Mohd Azrin Mohd Zulkefli; Pratik Mukherjee; Zongxuan Sun; Jianfeng Zheng; Henry X. Liu; Peter Huang
      Pages: 50 - 62
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Mohd Azrin Mohd Zulkefli, Pratik Mukherjee, Zongxuan Sun, Jianfeng Zheng, Henry X. Liu, Peter Huang
      Connected vehicle environment provides the groundwork of future road transportation. Researches in this area are gaining a lot of attention to improve not only traffic mobility and safety, but also vehicles’ fuel consumption and emissions. Energy optimization methods that combine traffic information are proposed, but actual testing in the field proves to be rather challenging largely due to safety and technical issues. In light of this, a Hardware-in-the-Loop-System (HiLS) testbed to evaluate the performance of connected vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a vehicle, is connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target vehicle information. Test results show fast data transfer at every 200 ms and good tracking of the optimized engine operating points and the desired vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.019
      Issue No: Vol. 78 (2017)
       
  • An improved cellular automaton with axis information for microscopic
           traffic simulation
    • Authors: Xin Ruan; Junyong Zhou; Huizhao Tu; Zeren Jin; Xuefei Shi
      Pages: 63 - 77
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Xin Ruan, Junyong Zhou, Huizhao Tu, Zeren Jin, Xuefei Shi
      Cellular Automaton (CA), an efficient dynamic modeling method that is widely used in traffic engineering, is newly introduced for traffic load modeling. This modeling method significantly addresses the modest traffic loads for long-span bridges. It does, however, require improvement to calculate precise load effects. This paper proposed an improved cellular automaton with axis information, defined as the Multi-axle Single-cell Cellular Automaton (MSCA), for the precise micro-simulation of random traffic loads on bridges. Four main ingredients of lattice, cells’ states, neighborhoods and transition rules are redefined in MSCA to generate microscopic vehicle sequences with detailed vehicle axle positions, user-defined cell sizes and time steps. The simulation methodology of MSCA is then proposed. Finally, MSCA is carefully calibrated and validated using site-specific WIM data. The results indicate: (1) the relative errors (REs) for the traffic parameters, such as volumes, speeds, weights, and headways, from MSCA are basically no more than ±10% of those of WIM data; (2) the load effects of three typical influence lines (ILs) with varied lengths of 50, 200 and 1000m are also confidently comparable, both of which validate the rationality and precision of MSCA. Furthermore, the accurate vehicle parameters and gaps generated from MSCA can be applied not only for precise traffic loading on infrastructures but also for the accurate estimation of vehicle dynamics and safety. Hence, wide application of MSCA can potentially be expected.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.023
      Issue No: Vol. 78 (2017)
       
  • The multi-objective railway timetable rescheduling problem
    • Authors: Stefan Binder; Yousef Maknoon; Michel Bierlaire
      Pages: 78 - 94
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Stefan Binder, Yousef Maknoon, Michel Bierlaire
      Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes ε -constraints for the two other ones. By solving the problem for different values of ε , the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.001
      Issue No: Vol. 78 (2017)
       
  • Comparing traffic state estimators for mixed human and automated traffic
           flows
    • Authors: Ren Wang; Yanning Li; Daniel B. Work
      Pages: 95 - 110
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Ren Wang, Yanning Li, Daniel B. Work
      This article addresses the problem of modeling and estimating traffic streams with mixed human operated and automated vehicles. A connection between the generalized Aw Rascle Zhang model and two class traffic flow motivates the choice to model mixed traffic streams with a second order traffic flow model. The traffic state is estimated via a fully nonlinear particle filtering approach, and results are compared to estimates obtained from a particle filter applied to a scalar conservation law. Numerical studies are conducted using the Aimsun micro simulation software to generate the true state to be estimated. The experiments indicate that when the penetration rate of automated vehicles in the traffic stream is variable, the second order model based estimator offers improved accuracy compared to a scalar modeling abstraction. When the variability of the penetration rate decreases, the first order model based filters offer similar performance.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.011
      Issue No: Vol. 78 (2017)
       
  • Logistics impacts of student online shopping – Evaluating delivery
           consolidation to halls of residence
    • Authors: Tom Cherrett; Janet Dickinson; Fraser McLeod; Jason Sit; Gavin Bailey; Gary Whittle
      Pages: 111 - 128
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Tom Cherrett, Janet Dickinson, Fraser McLeod, Jason Sit, Gavin Bailey, Gary Whittle
      Growth in online shopping has led to increased numbers of small delivery vehicles in urban areas leading to a range of negative externalities. Young people are significant generators of home deliveries and, when clustered in university halls of residence, can generate considerable freight traffic to one location. This paper explores the potential to consolidate these deliveries using an urban consolidation centre. Based on the case of Southampton, UK, data were compiled from three linked sources: a delivery audit of four halls of residence at the University of Southampton housing 5050 residents; annual package receipt records from Southampton Solent University halls (2294 residents); and an online shopping survey distributed to Southampton University students (486 responses). The results suggest that in cities with multiple higher education institutions (HEIs), where in excess of 8000 students live in halls, over 13,000 courier trips could be generated annually, delivering over 4000m3 of packages. These could be consolidated onto fewer than 300 vehicles for an annual service cost of approximately £18 per student, reducing congestion, parking infringements and improving air quality. Analysis indicated student acceptance of a consolidated parcel service but operational challenges would include enforcement, performance risk, finance and delivery speed.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.02.021
      Issue No: Vol. 78 (2017)
       
  • Traffic evacuation simulation based on multi-level driving decision model
    • Authors: Shengcheng Yuan; Soon Ae Chun; Bruno Spinelli; Yi Liu; Hui Zhang; Nabil R. Adam
      Pages: 129 - 149
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Shengcheng Yuan, Soon Ae Chun, Bruno Spinelli, Yi Liu, Hui Zhang, Nabil R. Adam
      Traffic evacuation is a critical task in disaster management. Planning its evacuation in advance requires taking many factors into consideration such as the destination shelter locations and numbers, the number of vehicles to clear, the traffic congestions as well as traffic road configurations. A traffic evacuation simulation tool can provide the emergency managers with the flexibility of exploring various scenarios for identifying more accurate model to plan their evacuation. This paper presents a traffic evacuation simulation system based on integrated multi-level driving-decision models which generate agents’ behavior in a unified framework. In this framework, each agent undergoes a Strategic, Cognitive, Tactical and Operational (SCTO) decision process, in order to make a driving decision. An agent’s actions are determined by a combination, on each process level, of various existing behavior models widely used in different driving simulation models. A wide spectrum of variability in each agent’s decision and driving behaviors, such as in pre-evacuation activities, in choice of route, and in the following or overtaking the car ahead, are represented in the SCTO decision process models to simulate various scenarios. We present the formal model for the agent and the multi-level decision models. A prototype simulation system that reflects the multi-level driving-decision process modeling is developed and implemented. Our SCTO framework is validated by comparing with MATSim tool, and the experimental results of evacuation simulation models are compared with the existing evacuation plan for densely populated Beijing, China in terms of various performance metrics. Our simulation system shows promising results to support emergency managers in designing and evaluating more realistic traffic evacuation plans with multi-level agent’s decision models that reflect different levels of individual variability of handling stress situations. The flexible combination of existing behavior and decision models can help generating the best evacuation plan to manage each crisis with unique characteristics, rather than resorting to a fixed evacuation plan.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.03.001
      Issue No: Vol. 78 (2017)
       
  • Are consumers willing to pay to let cars drive for them? Analyzing
           response to autonomous vehicles
    • Authors: Ricardo A. Daziano; Mauricio Sarrias; Benjamin Leard
      Pages: 150 - 164
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Ricardo A. Daziano, Mauricio Sarrias, Benjamin Leard
      Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.

      PubDate: 2017-03-15T07:43:22Z
      DOI: 10.1016/j.trc.2017.03.003
      Issue No: Vol. 78 (2017)
       
  • Turning meter transactions data into occupancy and payment behavioral
           information for on-street parking
    • Authors: Shuguan Yang; Zhen (Sean) Qian
      Pages: 165 - 182
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Shuguan Yang, Zhen (Sean) Qian
      Over 95% of on-street paid parking stalls are managed by parking meters or kiosks. By analyzing meter transactions data, this paper provides a methodology to estimate on-street time-varying parking occupancy and understand payment behavior in an effective and inexpensive way. We propose a probabilistic payment model to simulate individual payment and parking behavior for each parker. Aggregating the payment/parking of all transactions leads to time-varying occupancy estimation. Two data sets are used to evaluate the methodology, parking spaces near Carnegie Mellon University (CMU) campus, and near the Civic Center in San Francisco. The proposed model generally provides reliable estimations of occupancies at a low error rate and substantially outperforms other naive models in the literature. From the results of the experiments we find that people generally tend to slightly underpay in CMU area, whereas for Civic Center area, payment behavior varies by time of day and day of week. For Fridays, people generally tend to overpay and stay longer in the mornings, compared to underpaying and parking for shorter durations in the late afternoons. Parkers’ payment behavior, in general, is more variable and noisier around Civic Center than around CMU. Moreover, we explore the effective granularity, defined as the highest spatial resolution for this model to perform reliably. For CMU areas, the effective granularity is around 10–20 spaces for each block of streets, while it is 150–200 spaces for the Civic Center area due to more random parking behavior.

      PubDate: 2017-03-21T03:04:27Z
      DOI: 10.1016/j.trc.2017.02.022
      Issue No: Vol. 78 (2017)
       
  • Locating charging infrastructure for electric buses in Stockholm
    • Authors: Maria Xylia; Sylvain Leduc; Piera Patrizio; Florian Kraxner; Semida Silveira
      Pages: 183 - 200
      Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78
      Author(s): Maria Xylia, Sylvain Leduc, Piera Patrizio, Florian Kraxner, Semida Silveira
      Charging infrastructure requirements are being largely debated in the context of urban energy planning for transport electrification. As electric vehicles are gaining momentum, the issue of locating and securing the availability, efficiency and effectiveness of charging infrastructure becomes a complex question that needs to be addressed. This paper presents the structure and application of a model developed for optimizing the distribution of charging infrastructure for electric buses in the urban context, and tests the model for the bus network of Stockholm. The major public bus transport hubs connecting to the train and subway system show the highest concentration of locations chosen by the model for charging station installation. The costs estimated are within an expected range when comparing to the annual bus public transport costs in Stockholm. The model could be adapted for various urban contexts to promptly assist in the transition to fossil-free bus transport. The total costs for the operation of a partially electrified bus system in both optimization cases considered (cost and energy) differ only marginally from the costs for a 100% biodiesel system. This indicates that lower fuel costs for electric buses can balance the high investment costs incurred in building charging infrastructure, while achieving a reduction of up to 51% in emissions and up to 34% in energy use in the bus fleet.

      PubDate: 2017-03-21T03:04:27Z
      DOI: 10.1016/j.trc.2017.03.005
      Issue No: Vol. 78 (2017)
       
  • 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)
       
  • A data-driven optimization-based approach for siting and sizing of
           electric taxi charging stations
    • Authors: Jie Yang; Jing Dong; Liang Hu
      Pages: 462 - 477
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Jie Yang, Jing Dong, Liang Hu
      This paper presents a data-driven optimization-based approach to allocate chargers for battery electric vehicle (BEV) taxis throughout a city with the objective of minimizing the infrastructure investment. To account for charging congestion, an M / M / x / s queueing model is adopted to estimate the probability of BEV taxis being charged at their dwell places. By means of regression and logarithmic transformation, the charger allocation problem is formulated as an integer linear program (ILP), which can be solved efficiently using Gurobi solver. The proposed method is applied using large-scale GPS trajectory data collected from the taxi fleet of Changsha, China. The key findings from the results include the following: (1) the dwell pattern of the taxi fleet determines the siting of charging stations; (2) by providing waiting spots, in addition to charging spots, the utilization of chargers increases and the number of required chargers at each site decreases; and (3) the tradeoff between installing more chargers versus providing more waiting spaces can be quantified by the cost ratio of chargers and parking spots.

      PubDate: 2017-03-01T12:26:23Z
      DOI: 10.1016/j.trc.2017.02.014
      Issue No: Vol. 77 (2017)
       
  • Optimal road design through ecologically sensitive areas considering
           animal migration dynamics
    • Authors: Nicholas Davey; Simon Dunstall; Saman Halgamuge
      Pages: 478 - 494
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Nicholas Davey, Simon Dunstall, Saman Halgamuge
      With increasing land transportation requirements in both urban and rural areas, roads are encroaching ever more on animal habitats, where collisions with vehicles are a leading contributor to wildlife mortality. While road designers recognise the importance of accounting for such impacts at the design level, existing approaches simply either ignore viable habitat or avoid such regions entirely. Respectively, this can result in road alignments that are overly damaging to vulnerable species or prohibitively expensive to build and operate. The research presented in this paper investigates the effects of explicitly accounting for animal mortality on the design of a road through an ecologically sensitive area. The model presented achieves this by incorporating a spatially-explicit animal migration and road mortality model into an accepted optimal road alignment algorithm to propose low-cost roads that maintain the animal population above a minimum threshold by the end of a specified design horizon. The new method was applied to an example scenario to demonstrate the effect of setting a minimum required animal population on the road design. This model was able to consistently produce a road that met a minimum required species conservation benefit. This reflected a major improvement over the model that ignored animal habitats while only requiring a minor increase in construction and operating costs compared to the model that avoids habitat.

      PubDate: 2017-03-01T12:26:23Z
      DOI: 10.1016/j.trc.2017.02.016
      Issue No: Vol. 77 (2017)
       
  • Coordinated distributed adaptive perimeter control for large-scale urban
           road networks
    • Authors: Jack Haddad; Boris Mirkin
      Pages: 495 - 515
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Jack Haddad, Boris Mirkin
      Perimeter traffic control for large-scale urban road networks has been studied by several researchers during the last decade. Recently, the initial steps towards taking into account model’s uncertainties under control synthesis were made in Haddad and Shraiber (2014) and Haddad (2015), where by considering one- and multi- region control problem, respectively, a robust perimeter control has been designed to systematically take into account uncertainties in MFD-based dynamics, e.g. the MFD scatter. The robust control design can provide a fixed controller with constant gains to compensate all uncertainties, following the worst case scenario concept. In this paper, an adaptive control scheme is developed. Similarly to robust control, the developed adaptive control scheme postulates one controller structure, however, the controllers’ gains vary with time to adapt themselves against the model parameter uncertainties. In this paper, in order to accommodate uncertainties and take into consideration the restrictions on the available information, we deal with the adaptive perimeter control problem for multi-region MFD systems, which have an interconnected structure composing several homogeneous regions. Unlike previous works that assume centralized approach, where feedback informations are needed from all urban regions, in this paper we follow a coordinated distributed control approach, where regional control laws are developed depending on (i) real on-line local information of the region, i.e. regional accumulation and its perimeter control input only, and (ii) reference signal information forwarded to all distributed perimeter controllers by a high level coordinator controller.

      PubDate: 2017-03-07T12:31:06Z
      DOI: 10.1016/j.trc.2016.12.002
      Issue No: Vol. 77 (2017)
       
  • Transport habits of travelers using new energy type modes: A random
           parameters hazard-based approach of travel distance
    • Authors: Panagiotis Ch. Anastasopoulos; Grigorios Fountas; Md Tawfiq Sarwar; Matthew G. Karlaftis; Adel W. Sadek
      Pages: 516 - 528
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Panagiotis Ch. Anastasopoulos, Grigorios Fountas, Md Tawfiq Sarwar, Matthew G. Karlaftis, Adel W. Sadek
      With environmental concerns on the rise, identifying the factors that affect travel demand for energy efficient and environmentally friendly means of transportation becomes critical. It is, thus, beneficial to identify the characteristics of new energy type – low CO2 emissions – transport habits. Travel time is one such characteristic that has been extensively studied within the context of activity-based travel demand modeling. However, when travelers choose a new energy type with low CO2 emissions transport mode, it is likely that travel time may be playing a secondary role in their mode choice criteria as opposed to travel distance, especially in highly congested urban areas. Within this context, the origin-destination distance of a trip has the potential to reveal interesting traveler preferences and trip patterns. This paper presents an exploratory empirical analysis of origin-destination distance for such new energy type mode transport habits, in the Metropolitan area of Athens, Greece. Using hazard-based econometric modeling and random parameters to account for the longitudinal nature of trip distance and unobserved heterogeneity, respectively, trip distance is found to be affected by a number of influential factors, such as traveler socio-economic and demographic characteristics, trip purpose, trip time, trip frequency, and time of day for the trip.

      PubDate: 2017-03-07T12:31:06Z
      DOI: 10.1016/j.trc.2017.01.017
      Issue No: Vol. 77 (2017)
       
  • Corrigendum to “Transit network design by genetic algorithm with
           elitism” [Transport. Res. Part C: Emerg. Technol. 46 (2014) 30–45]
    • Authors: Muhammad Ali Nayeem; Md. Khaledur Rahman; M. Sohel Rahman
      Pages: 529 - 530
      Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77
      Author(s): Muhammad Ali Nayeem, Md. Khaledur Rahman, M. Sohel Rahman


      PubDate: 2017-03-21T03:04:27Z
      DOI: 10.1016/j.trc.2017.02.012
      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)
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77


      PubDate: 2017-03-21T03:04:27Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: March 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 76


      PubDate: 2017-02-08T12:08:46Z
       
 
 
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