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  Subjects -> TRANSPORTATION (Total: 165 journals)
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    - TRANSPORTATION (94 journals)

TRANSPORTATION (94 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  [3032 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)
       
  • Cooperative GNSS positioning aided by road-features measurements
    • Authors: Luís Conde Bento; Philippe Bonnifait; Urbano J. Nunes
      Pages: 42 - 57
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Luís Conde Bento, Philippe Bonnifait, Urbano J. Nunes
      Cooperation between road users through V2X communication is a way to improve GNSS localization accuracy. When vehicles localization systems involve standalone GNSS receivers, the resulting accuracy can be affected by satellite-specific errors of several meters. This paper studies how road-features like lane marking detected by on-board cameras can be exploited to reduce absolute position errors of cooperative vehicles sharing information in real-time in a network. The algorithms considered in this work are based on a error bounded set membership strategy. In every vehicle, a set membership algorithm computes the absolute position and an estimation of the satellite-specific errors by using raw GNSS pseudoranges, lane boundary measurements and a 2D georeferenced road map which provides absolute geometric constraints. As lane-boundary measurements provide essentially cross-track corrections in the position estimation process, cooperation enables the vehicles to improve their own estimates thanks to the different orientation of the roads. Set-membership methods are very efficient to solve this problem since they do not involve any independence hypothesis of the errors and so, the same information can be used several times in the computation. Such class of algorithm provides a novel approach to improve position accuracy for connected vehicles guaranteeing the integrity of the computed solution which is pivoting for automated automotive systems requiring guaranteed safety-critical solutions. Results from simulations and real experiments show that sharing position corrections reduces significantly satellite-specific GNSS errors effects in both cross-track and along-track components. Moreover, it is shown that lane-boundary measurements help reducing estimation errors for all the networked vehicles even those which are not equipped with an embedded perception system.

      PubDate: 2017-03-27T18:52:11Z
      DOI: 10.1016/j.trc.2017.01.002
      Issue No: Vol. 79 (2017)
       
  • Robust optimization of distance-based tolls in a network considering
           stochastic day to day dynamics
    • Authors: Zhiyuan Liu; Shuaian Wang; Bojian Zhou; Qixiu Cheng
      Pages: 58 - 72
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Zhiyuan Liu, Shuaian Wang, Bojian Zhou, Qixiu Cheng
      This paper investigates the nonlinear distance-based congestion pricing in a network considering stochastic day-to-day dynamics. After an implementation/adjustment of a congestion pricing scheme, the network flows in a certain period of days are not on an equilibrium state, thus it is problematic to take the equilibrium-based indexes as the pricing objective. Therefore, the concept of robust optimization is taken for the congestion toll determination problem, which takes into account the network performance of each day. First, a minimax model which minimizes the maximum regret on each day is proposed. Taking as a constraint of the minimax model, a path-based day to day dynamics model under stochastic user equilibrium (SUE) constraints is discussed in this paper. It is difficult to solve this minimax model by exact algorithms because of the implicity of the flow map function. Hence, a two-phase artificial bee colony algorithm is developed to solve the proposed minimax regret model, of which the first phase solves the minimal expected total travel cost for each day and the second phase handles the minimax robust optimization problem. Finally, a numerical example is conducted to validate the proposed models and methods.

      PubDate: 2017-03-27T18:52:11Z
      DOI: 10.1016/j.trc.2017.03.011
      Issue No: Vol. 79 (2017)
       
  • Rescheduling through stop-skipping in dense railway systems
    • Authors: Estelle Altazin; Stéphane Dauzère-Pérès; François Ramond; Sabine Tréfond
      Pages: 73 - 84
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Estelle Altazin, Stéphane Dauzère-Pérès, François Ramond, Sabine Tréfond
      Based on the analysis of the railway system in the Paris region in France, this paper presents a rescheduling problem in which stops on train lines can be skipped and services are retimed to recover when limited disturbances occur. Indeed, in such mass transit systems, minor disturbances tend to propagate and generate larger delays through the shared use of resources, if no action is quickly taken. An integrated Integer Linear Programming model is presented whose objective function minimizes both the recovery time and the waiting time of passengers. Additional criteria related to the weighted number of train stops that are skipped are included in the objective function. Rolling-stock constraints are also taken into account to propose a feasible plan. Computational experiments on real data are conducted to show the impact of rescheduling decisions depending on key parameters such as the duration of the disturbances and the minimal turning time between trains. The trade-off between the different criteria in the objective function is also illustrated and discussed.

      PubDate: 2017-03-27T18:52:11Z
      DOI: 10.1016/j.trc.2017.03.012
      Issue No: Vol. 79 (2017)
       
  • Integrated vehicle and powertrain optimization for passenger vehicles with
           vehicle-infrastructure communication
    • Authors: Jia Hu; Yunli Shao; Zongxuan Sun; Joe Bared
      Pages: 85 - 102
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Jia Hu, Yunli Shao, Zongxuan Sun, Joe Bared
      This research proposes an optimal controller to improve fuel efficiency for a vehicle equipped with automatic transmission traveling on rolling terrain without the presence of a close preceding vehicle. Vehicle acceleration and transmission gear position are optimized simultaneously to achieve a better fuel efficiency. This research leverages the emerging Connected Vehicle technology and utilizes present and future information—such as real-time dynamic speed limit, vehicle speed, location and road topography—as optimization input. The optimal control is obtained using the Relaxed Pontryagin’s Minimum Principle. The benefit of the proposed optimal controller is significant compared to the regular cruise control and other eco-drive systems. It varies with the hill length, grade, and the number of available gear positions. It ranges from an increased fuel saving of 18–28% for vehicles with four-speed transmission and 25–45% for vehicles with six-speed transmission. The computational time for the optimization is 1.0–2.1s for the four-speed vehicle and 1.8–3.9s for the six-speed vehicle, given a 50s optimization time horizon and 0.1s time step. The proposed controller can potentially be used in real-time.

      PubDate: 2017-03-27T18:52:11Z
      DOI: 10.1016/j.trc.2017.03.010
      Issue No: Vol. 79 (2017)
       
  • Mining factors affecting taxi drivers’ incomes using GPS
           trajectories
    • Authors: Guoyang Qin; Tienan Li; Bin Yu; Yunpeng Wang; Zhenhua Huang; Jian Sun
      Pages: 103 - 118
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Guoyang Qin, Tienan Li, Bin Yu, Yunpeng Wang, Zhenhua Huang, Jian Sun
      Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Mining underlying factors affecting the income level will not only benefit the newcomers and low-income drivers but also assist in developing effective optimization algorithms for taxi operations. This paper intends to disclose the factors affecting incomes along with their quantitative influence by mining over 167 million GPS records from nearly 8000 taxis in Shanghai. We first identify a marked difference in drivers’ incomes and categorize drivers into three income levels accordingly. We next investigate the overall search-delivery process, thereby defining several factors that may affect the income level. We then develop a generalized multi-level ordered logit (GMOL) model to find the significant factors that influence incomes. Finally, we compute the elasticity for those significant factors and present their contributions, as well as challenge some preconceived ideas regarding how to earn high incomes.

      PubDate: 2017-03-27T18:52:11Z
      DOI: 10.1016/j.trc.2017.03.013
      Issue No: Vol. 79 (2017)
       
  • A three-stage evacuation decision-making and behavior model for the onset
           of an attack
    • Authors: Shuying Li; Jun Zhuang; Shifei Shen
      Pages: 119 - 135
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Shuying Li, Jun Zhuang, Shifei Shen
      Pedestrian behavior models have successfully reproduced human movement in many situations. However, few studies focus on modeling human behavior in the context of terrorist attacks. Terrorist attacks commonly occur in crowded public areas and result in a large number of casualties. This paper proposes a three-stage model to reproduce a series of complex behaviors and decision-making processes at the onset of an attack, when pedestrians generally do not have clear targets and have to deal with fuzzy information from the attack. The first stage of the model builds a Bayesian belief network to represent the pedestrians’ initial judgment of the threat and their evacuation decisions. The second stage focuses on pedestrians’ global assessment of the situation through an analogy with diffusion processes. The third stage uses a cost function to reproduce the trade-offs of distance, safety, and emotional impact when considering a path to take. The model is validated using a video from the November 2015 Paris attack. The behavioral characteristics and trajectories of three pedestrians extracted from the video are reproduced by the simulation results based on the model. The research can be used to set rules when performing risk analysis and strategic defensive resource allocation of terrorist attacks using agent-based simulation methods.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.008
      Issue No: Vol. 79 (2017)
       
  • An adaptive hawkes process formulation for estimating time-of-day zonal
           trip arrivals with location-based social networking check-in data
    • Authors: Wangsu Hu; Peter J. Jin
      Pages: 136 - 155
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Wangsu Hu, Peter J. Jin
      Location-Based Social Networking (LBSN) services, such as Foursquare, Facebook check-ins, and Geo-tagged Twitter tweets, have emerged as new secondary data sources for studying individual travel mobility patterns at a fine-grained level. However, the differences between human social behavioral and travel patterns can cause significant sampling bias for travel demand estimation. This paper presents a dynamic model to estimate time-of-day zonal trip arrival patterns. In the proposed model, the state propagation is formulated by the Hawkes process; the observation model implements LBSN sampling. The proposed model is applied to Foursquare check-in data collected from Austin, Texas in 2010 and calibrated with Origin-Destination (OD) data and time of day factor from Capital Area Metropolitan Planning Organization (CAMPO). The proposed model is compared with a simple aggregation model of trip purposes and time of day based on a prior daily OD estimation model using the LBSN data. The results illustrate the promising benefits of applying stochastic point process models and state-space modeling in time-of-day zonal arrival pattern estimation with the LBSN data. The proposed model can significantly reduce the number of parameters to calibrate in order to reduce the sampling bias of LBSN data for trip arrival estimation.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.02.002
      Issue No: Vol. 79 (2017)
       
  • Pricing scheme design of ridesharing program in morning commute problem
    • Authors: Yang Liu; Yuanyuan Li
      Pages: 156 - 177
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Yang Liu, Yuanyuan Li
      This paper examines the dynamic user equilibrium of the morning commute problem in the presence of ridesharing program. Commuters simultaneously choose departure time from home and commute mode among three roles: solo driver, ridesharing driver, and ridesharing rider. Considering the congestion evolution over time, we propose a time-varying compensation scheme to maintain a positive ridesharing ridership at user equilibrium. To match the demand and the supply of ridesharing service over time, the compensation scheme should be set according to the inconvenience cost functions and the out-of-pocket cost functions. When the price charged per time unit is higher than the inconvenience cost per time unit perceived by the ridesharing drivers, the ridesharing participants will travel at the center of peak hours and solo drivers will commute at the two tails. Within the feasible region with positive ridership, the ridesharing program can reduce the congestion and all the commuters will be better off. To support system optimum (SO), we derive a time-varying toll combined with a flat ridesharing price from eliminating queuing delay. Under SO toll, the ridesharing program can attract more participants and have an enlarged feasible region. This reveals that the commuters are more tolerant to the inconvenience caused by sharing a ride at SO because of the lower travel time. Compared with no-toll equilibrium, both overall congestion and individual travel cost are further reduced at SO.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.02.020
      Issue No: Vol. 79 (2017)
       
  • Tradable network permits: A new scheme for the most efficient use of
           network capacity
    • Authors: Takashi Akamatsu; Kentaro Wada
      Pages: 178 - 195
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Takashi Akamatsu, Kentaro Wada
      Akamatsu et al. (2006) proposed a new transportation demand management scheme called “tradable bottleneck permits” (TBP), and proved its efficiency properties for a single bottleneck model. This paper explores the properties of a TBP system for general networks. An equilibrium model is first constructed to describe the states under the TBP system with a single OD pair. It is proved that equilibrium resource allocation is efficient in the sense that the total transportation cost in a network is minimized. It is also shown that the “self-financing principle” holds for the TBP system. Furthermore, theoretical relationships between TBP and congestion pricing (CP) are discussed. It is demonstrated that TBP has definite advantages over CP when demand information is not perfect, whereas both TBP and CP are equivalent for the perfect information case. Finally, it is shown that the efficiency result also holds for more general demand conditions.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.009
      Issue No: Vol. 79 (2017)
       
  • Driving analytics using smartphones: Algorithms, comparisons and
           challenges
    • Authors: Eleni I. Vlahogianni; Emmanouil N. Barmpounakis
      Pages: 196 - 206
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Eleni I. Vlahogianni, Emmanouil N. Barmpounakis
      The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and “free-driving” conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.014
      Issue No: Vol. 79 (2017)
       
  • A discrete choice framework for modeling and forecasting the adoption and
           diffusion of new transportation services
    • Authors: Feras El Zarwi; Akshay Vij; Joan L. Walker
      Pages: 207 - 223
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Feras El Zarwi, Akshay Vij, Joan L. Walker
      Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc.). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.004
      Issue No: Vol. 79 (2017)
       
  • Real-time airport surface movement planning: Minimizing aircraft emissions
    • Authors: C. Evertse; H.G. Visser
      Pages: 224 - 241
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): C. Evertse, H.G. Visser
      This paper presents a study towards the development of a real-time taxi movement planning system that seeks to optimize the timed taxiing routes of all aircraft on an airport surface, by minimizing the emissions that result from taxiing aircraft operations. To resolve this online planning problem, one of the most commonly employed operations research methods for large-scale problems has been successfully used, viz., mixed-integer linear programming (MILP). The MILP formulation implemented herein permits the planning system to update the total taxi planning every 15 s, allowing to respond to unforeseen disturbances in the traffic flow. Extensive numerical experiments involving a realistic (hub) airport environment bear out that an estimated environmental benefit of 1–3 percent per emission product can be obtained. This research effort clearly demonstrates that a surface movement planning system capable of minimizing the emissions in conjunction with the total taxiing time can be beneficial for airports that face dense surface traffic and stringent environmental requirements.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.018
      Issue No: Vol. 79 (2017)
       
  • How can the taxi industry survive the tide of ridesourcing? Evidence
           from Shenzhen, China
    • Authors: Yu (Marco) Nie
      Pages: 242 - 256
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Yu (Marco) Nie
      This paper aims to examine the impact of ridesourcing on the taxi industry and explore where, when and how taxis can compete more effectively. To this end a large taxi GPS trajectory data set collected in Shenzhen, China is mined and more than 2,700 taxis (or about 18% of all registered in the city) are tracked in a period of three years, from January 2013 to November 2015, when both e-hailing and ridesourcing were rapidly spreading in the city. The long sequence of GPS data points is first broken into separate “trips”, each corresponding to a unique passenger state, an origin/destination zone, and a starting/ending time. By examining the trip statistics, we found that: (1) the taxi industry in Shenzhen has experienced a significant loss in its ridership that can be indisputably credited to the competition from ridesourcing. Yet, the evidence is also strong that the shock was relatively short and that the loss of the taxi industry had begun to stabilize since the second half of 2015; (2) taxis are found to compete more effectively with ridesourcing in peak period (6–10 AM, 5–8 PM) and in areas with high population density. (3) e-hailing helps lift the capacity utilization rate of taxis. Yet, the gains are generally modest except for the off-peak period, and excessive competition can lead to severely under-utilized capacities; and (4) ridesourcing worsens congestion for taxis in the city, but the impact was relatively mild. We conclude that a dedicated service fleet with exclusive street-hailing access will continue to co-exist with ridesourcing and that regulations are needed to ensure this market operate properly.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.017
      Issue No: Vol. 79 (2017)
       
  • Mobile mapping systems and spatial data collection strategies assessment
           in the identification of horizontal alignment of highways
    • Authors: G. Marinelli; M. Bassani; M. Piras; A.M. Lingua
      Pages: 257 - 273
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): G. Marinelli, M. Bassani, M. Piras, A.M. Lingua
      The horizontal alignment of existing highways may be identified by using several terrestrial or aerial geomatics technologies. Such technologies involve different levels of precision and accuracy; hence, different results can be expected. At present, there are no comparisons available between the solutions resulting from the use of different technologies and data sources for the same road alignment. In this investigation, a number of terrestrial mobile mapping techniques and data collection strategies were evaluated. The centerline of a 3.6km section of a highway was used to estimate radii, centers of curvature and orientation of tangents. Two statistical fitting methods were used to back-calculate these parameters, and the results were then compared with as-built alignment data. Terrestrial images from a mobile mapping vehicle were used to determine the centerline, which was also estimated as the average line of the carriageway and pavement edges, and as the average line of the two driving trajectories. Positions were surveyed using low-cost sensors (an integrated GPS-IMU platform, HD webcam). For comparison purposes, aerial orthophotos and a GNSS (high-cost) receiver were used simultaneously. Although the GPS-IMU data and estimated trajectories provided results comparable to those of the GNSS receiver, the use of georeferenced images proved less accurate. The results and comments in the paper should be of use to survey practitioners when they need to select an acquisition methodology appropriate to the desired level of accuracy and in line with budget constraints.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.03.020
      Issue No: Vol. 79 (2017)
       
  • Analyzing year-to-year changes in public transport passenger behaviour
           using smart card data
    • Authors: Anne-Sarah Briand; Etienne Côme; Martin Trépanier; Latifa Oukhellou
      Pages: 274 - 289
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Anne-Sarah Briand, Etienne Côme, Martin Trépanier, Latifa Oukhellou
      In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage.

      PubDate: 2017-04-11T06:27:20Z
      DOI: 10.1016/j.trc.2017.03.021
      Issue No: Vol. 79 (2017)
       
  • Eco approaching at an isolated signalized intersection under partially
           connected and automated vehicles environment
    • Authors: Huifu Jiang; Jia Hu; Shi An; Meng Wang; Byungkyu Brian Park
      Pages: 290 - 307
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Huifu Jiang, Jia Hu, Shi An, Meng Wang, Byungkyu Brian Park
      This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin’s Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO2 emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV’s market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.

      PubDate: 2017-04-18T02:01:34Z
      DOI: 10.1016/j.trc.2017.04.001
      Issue No: Vol. 79 (2017)
       
  • Bus travel time prediction using a time-space discretization approach
    • Authors: B. Anil Kumar; Lelitha Vanajakshi; Shankar C. Subramanian
      Pages: 308 - 332
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): B. Anil Kumar, Lelitha Vanajakshi, Shankar C. Subramanian
      The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction.

      PubDate: 2017-04-18T02:01:34Z
      DOI: 10.1016/j.trc.2017.04.002
      Issue No: Vol. 79 (2017)
       
  • Empirically quantifying city-scale transportation system resilience to
           extreme events
    • Authors: Brian Donovan; Daniel B. Work
      Pages: 333 - 346
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Brian Donovan, Daniel B. Work
      This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from probe vehicles such as taxis. The granularity of the GPS data necessary for the method is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. Periods of time containing extreme deviations are identified as events. The method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the city transportation infrastructure resilience to Hurricane Sandy. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation announcements coincided with only minor disruptions, but significant delays were encountered during the post-disaster response period. Since the implementation of this method is very efficient, it could potentially be used as an online monitoring tool, representing a first step toward quantifying city scale resilience with coarse GPS data.

      PubDate: 2017-04-18T02:01:34Z
      DOI: 10.1016/j.trc.2017.03.002
      Issue No: Vol. 79 (2017)
       
  • Estimating traffic volumes for signalized intersections using connected
           vehicle data
    • Authors: Jianfeng Zheng; Henry X. Liu
      Pages: 347 - 362
      Abstract: Publication date: June 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 79
      Author(s): Jianfeng Zheng, Henry X. Liu
      Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.

      PubDate: 2017-04-18T02:01:34Z
      DOI: 10.1016/j.trc.2017.03.007
      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)
       
  • Dynamic charging-while-driving systems for freight delivery services with
           electric vehicles: Traffic and energy modelling
    • Abstract: Publication date: Available online 24 April 2017
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Francesco Deflorio, Luca Castello
      This paper presents a research on traffic modelling developed for assessing traffic and energy performance of electric systems installed along roads for dynamic charging-while-driving (CWD) of fully electric vehicles (FEVs). The logic adopted by the developed traffic model is derived from a particular simulation scenario of electric charging: a freight distribution service operated using medium-sized vans. In this case, the CWD service is used to recover the state of charge of the FEV batteries to shortly start with further activities after arrival at the depot. The CWD system is assumed to be implemented in a multilane ring road with several intermediate on-ramp entrances, where the slowest lane is reserved for the dynamic charging of authorized electric vehicles. A specific traffic model is developed and implemented based on a mesoscopic approach, where energy requirements and charging opportunities affect driving and traffic behaviours. Overtaking manoeuvres as well as new entries in the CWD lane of vehicles that need to charge are modelled according to a cooperative driving system, which manages adequate time gaps between consecutive vehicles. Finally, a speed control strategy is simulated at a defined node to create an empty time-space slot in the CWD lane, by delaying the arriving vehicles. This simulated control, implemented to allow maintenance operations for CWD that may require clearing a charging zone for a short time slot, could also be applied to facilitate on-ramp merging manoeuvres.

      PubDate: 2017-04-25T03:41:03Z
       
  • Solving the gate assignment problem through the Fuzzy Bee Colony
           Optimization
    • Authors: Mauro Dell'Orco; Mario Marinelli; Maria Giovanna Altieri
      Abstract: Publication date: Available online 5 April 2017
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Mauro Dell'Orco, Mario Marinelli, Maria Giovanna Altieri
      In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.

      PubDate: 2017-04-11T06:27:20Z
      DOI: 10.1016/j.trc.2017.03.019
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: May 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 78


      PubDate: 2017-04-04T12:47:12Z
       
  • An investigation of timed transfer coordination using event-based multi
           agent simulation
    • Authors: Le Minh Kieu; Ashish Bhaskar; Mario Cools; Edward Chung
      Abstract: Publication date: Available online 3 April 2017
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Le Minh Kieu, Ashish Bhaskar, Mario Cools, Edward Chung
      While transfers extend transit service coverage by omnidirectional connections, poorly coordinated transfers significantly increase passenger waiting time, especially in case of missed connections. This paper proposes a simulation approach to investigate the feasibilities of different timed transfer strategies in both schedule planning and operational control. In particular, an Event-Based Multi-Agent Simulation (EMAS) model is proposed, that captures the interactions between the transit vehicles, its passengers and the (urban) environment by considering the transit vehicles and passengers as separate classes of agents which interact in a dynamic system. The model is validated by using observed Automatic Vehicle Location (AVL) and Automatic Fare Collection (AFC) data from two routes with transfers in South East Queensland, Australia. EMAS is then used to evaluate different timed transfer strategies for both schedule planning and operational control. The analysis on timed transfers in schedule planning provides valuable insights on the probability of missing a transfer and extra waiting time for transfer. Six different strategies for timed transfers in operational control are thoroughly tested, including an elaborate sensitivity analysis of the effectiveness of the strategies for different levels of transferring demand and schedule headways. This paper assists transit operators to exploit observed AVL and AFC data to augment the transfer coordination quality.

      PubDate: 2017-04-04T12:47:12Z
      DOI: 10.1016/j.trc.2017.02.018
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: April 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 77


      PubDate: 2017-03-21T03:04:27Z
       
 
 
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