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  Subjects -> TRANSPORTATION (Total: 154 journals)
    - AIR TRANSPORT (7 journals)
    - AUTOMOBILES (20 journals)
    - RAILROADS (4 journals)
    - ROADS AND TRAFFIC (4 journals)
    - SHIPS AND SHIPPING (26 journals)
    - TRANSPORTATION (93 journals)

TRANSPORTATION (93 journals)

Accident Analysis & Prevention     Partially Free   (Followers: 34)
AI & Society     Hybrid Journal   (Followers: 2)
Archives of Transport     Open Access   (Followers: 5)
Bitácora Urbano-Territorial     Open Access   (Followers: 2)
Botswana Journal of Technology     Full-text available via subscription  
Cities in the 21st Century     Open Access   (Followers: 11)
Economics of Transportation     Partially Free   (Followers: 10)
Emission Control Science and Technology     Hybrid Journal  
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 4)
European Transport Research Review     Open Access   (Followers: 11)
Geosystem Engineering     Hybrid Journal   (Followers: 3)
IATSS Research     Open Access  
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 5)
IET Electrical Systems in Transportation     Hybrid Journal   (Followers: 6)
IET Intelligent Transport Systems     Hybrid Journal   (Followers: 5)
International Innovation – Transport     Open Access   (Followers: 4)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 5)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 4)
International Journal of Critical Infrastructure Protection     Hybrid Journal   (Followers: 5)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 5)
International Journal of Electronic Transport     Hybrid Journal   (Followers: 2)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 5)
International Journal of Micro-Nano Scale Transport     Full-text available via subscription   (Followers: 2)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 7)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 8)
International Journal of Services Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 6)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 15)
International Journal of Transportation Science and Technology     Full-text available via subscription   (Followers: 5)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 4)
International Journal of Vehicular Technology     Open Access   (Followers: 2)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 11)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 1)
Journal of Navigation     Hybrid Journal   (Followers: 86)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 9)
Journal of Sustainable Mobility     Full-text available via subscription  
Journal of the Transportation Research Forum     Open Access   (Followers: 4)
Journal of Transport & Health     Hybrid Journal   (Followers: 3)
Journal of Transport and Land Use     Open Access   (Followers: 13)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 6)
Journal of Transport Geography     Hybrid Journal   (Followers: 17)
Journal of Transport History     Full-text available via subscription   (Followers: 10)
Journal of Transport Literature     Open Access  
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 8)
Journal of Transportation Security     Hybrid Journal   (Followers: 2)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 14)
Journal of Transportation Technologies     Open Access   (Followers: 11)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 7)
Les Dossiers du Grihl     Open Access  
Logistique & Management     Full-text available via subscription  
Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 1)
Mobility in History     Full-text available via subscription  
Modern Transportation     Open Access   (Followers: 2)
Nonlinear Dynamics     Hybrid Journal   (Followers: 5)
Open Journal of Safety Science and Technology     Open Access   (Followers: 5)
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 9)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 6)
PS: Political Science & Politics     Full-text available via subscription   (Followers: 22)
Public Transport     Hybrid Journal   (Followers: 10)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 2)
Revista Transporte y Territorio     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 1)
Sport, Education and Society     Hybrid Journal   (Followers: 11)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 2)
Streetnotes     Open Access   (Followers: 3)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 2)
Tire Science and Technology     Full-text available via subscription  
Transactions on Transport Sciences     Open Access   (Followers: 4)
Transport     Hybrid Journal   (Followers: 7)
Transport and Telecommunication Journal     Open Access   (Followers: 2)
Transport in Porous Media     Hybrid Journal   (Followers: 1)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 7)
Transportation     Hybrid Journal   (Followers: 19)
Transportation Geotechnics     Full-text available via subscription  
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 6)
Transportation Journal     Full-text available via subscription   (Followers: 5)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 26)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 24)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 16)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 32)
Transportation Science     Full-text available via subscription   (Followers: 14)
TRANSPORTES     Open Access   (Followers: 2)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 1)
Transportmetrica B : Transport Dynamics     Hybrid Journal  
Travel Behaviour and Society     Full-text available via subscription  
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
Urban, Planning and Transport Research     Open Access   (Followers: 3)
Vehicular Communications     Full-text available via subscription  
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 3)
Транспортні системи та технології перевезень     Open Access  
Journal Cover   Transportation Research Part C: Emerging Technologies
  [SJR: 1.943]   [H-I: 55]   [18 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [2586 journals]
  • Automated classification based on video data at intersections with heavy
           pedestrian and bicycle traffic: Methodology and application
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Sohail Zangenehpour , Luis F. Miranda-Moreno , Nicolas Saunier
      Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.


      PubDate: 2015-04-24T18:38:57Z
       
  • Highway voting system: Embracing a possible paradigm shift in traffic data
           acquisition
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Wei-Hua Lin , Hong K. Lo
      The integration of internet and mobile phones has opened the door to a new wave of utilizing private vehicles as probes not only for performance evaluation but for traffic control as well, gradually replacing the role of traffic surveillance systems as the dominant source of traffic data. To prepare for such a paradigm shift, one needs to overcome some key institutional barriers, in particular, the privacy issue. A Highway Voting System (HVS) is proposed to address this issue in which drivers provide link- and/or path-based vehicle data to the traffic management system in the form of “votes” in order to receive favorable service from traffic control. The proposed HVS offers a platform that links data from individual vehicles directly with traffic control. In the system, traffic control responds to voting vehicles in a way similar to the current system responding to prioritized vehicles and providing the requested services accordingly. We show in the paper that the proposed “voting” system can effectively resolve the privacy issue which often hampers traffic engineers from getting detailed data from drivers. Strategies to entice drivers into “voting” so as to increase the market penetration level under all traffic conditions are discussed. Though the focus of the paper is on addressing the institutional issues associated with data acquisition from individual vehicles, other research topics associated with the proposed system are identified. Two examples are given to demonstrate the impact of the proposed system on algorithm development and traffic control.


      PubDate: 2015-04-24T18:38:57Z
       
  • Location privacy preferences: A survey-based analysis of consumer
           awareness, trade-off and decision-making
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Caitlin D. Cottrill , Piyushimita “Vonu” Thakuriah
      With the advent and rapid dissemination of location-sensing information technology, the issue of location information privacy is receiving growing attention. Perhaps of greatest concern is ensuring that potential users of mobile Information and Communications Technologies (e.g., Location-Based Services and Intelligent Transportation Systems) are comfortable with the levels of privacy protection afforded them, as well as with the benefits they will receive in return for providing private location information. This paper explores the concepts of privacy risks, benefits, willingness to trade, and compensation in relationship to mobile and locational technologies using a stated preference survey to ascertain areas of interest in determining the trade-offs that consumers will be willing to make in return for mobility enhancements. Analysis of the survey leads to findings that while respondents believe that sharing data in the mobile environment may pose privacy risks, they do not generally take steps necessary to address these risks; that privacy preferences are impacted by a range of factors, including both personal and contextual considerations (such as factors arising from their specific situation at the time of information seeking); and that willingness to trade private location data is dependent upon a number of factors related to context, personal characteristics, expected benefits and degree of trust in the collecting organization.


      PubDate: 2015-04-24T18:38:57Z
       
  • Orchestration of driving simulator scenarios based on dynamic actor
           preparation and automated action planning
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Zhitao Xiong , Johan Olstam
      In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving.


      PubDate: 2015-04-24T18:38:57Z
       
  • Effect of speed limits in degradable transport networks
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Chen-Yang Yan , Rui Jiang , Zi-You Gao , Hu Shao
      This paper studies how link-specific speed limits influence the performance of degradable transport networks, in which the capacity of each link is a degradable random variable. The distribution and cumulative distribution of link travel time have been presented with the effect of speed limits taken into account. The mean and variance of link and route travel time are formulated. Three link states have been classified, and their physical meanings have been discussed. The relationship between critical capacity, travel time and speed limit has been elaborated. We have proposed a Speed Limit- and Reliability-based User Equilibrium (SLRUE), adopting travel time budget as the principle of travelers’ route choice. A heuristic method employing the method of successive averages is developed to solve the SLRUE in degradable networks. Through numerical studies, we find that for some networks both the mean and standard deviation of the total travel time could be reduced simultaneously by imposing some speed limits. The speed limit design problem has been studied, and it is found that imposing speed limits cannot always reduce the total travel time budget of a network.


      PubDate: 2015-04-24T18:38:57Z
       
  • Vehicle detection grammars with partial occlusion handling for traffic
           surveillance
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Bin Tian , Ming Tang , Fei-Yue Wang
      Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehicle detection method by designing vehicle detection grammars and handling partial occlusion. The grammar model is implemented by novel detection grammars, including structure, deformation and pairwise SVM grammars. First, the vehicle is divided into its constitute parts, called semantic parts, which can represent the vehicle effectively. To increase the robustness of part detection, the semantic parts are represented by their detection score maps. The semantic parts are further divided into sub-parts automatically. The two-layer division of the vehicle is modeled into a grammar model. Then, the grammar model is trained by a designed training procedure to get ideal grammar parameters, including appearance models and grammar productions. After that, vehicle detection is executed by a designed detection procedure with respect to the grammar model. Finally, the issue of vehicle occlusion is handled by designing and training specific grammars. The strategy adopted by our method is first to divide the vehicle into the semantic parts and sub-parts, then to train the grammar productions for semantic parts and sub-parts by introducing novel pairwise SVM grammars and finally to detect the vehicle by applying the trained grammars. Experiments in practical urban scenarios are carried out for complex traffic surveillance. It can be shown that our method adapts to partial occlusion and various challenging cases.


      PubDate: 2015-04-24T18:38:57Z
       
  • A modified Density-Based Scanning Algorithm with Noise for spatial travel
           pattern analysis from Smart Card AFC data
    • Abstract: Publication date: Available online 6 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Le-Minh Kieu , Ashish Bhaskar , Edward Chung
      Smart Card Automated Fare Collection (AFC) data has been extensively exploited to understand passenger behavior, passenger segment, trip purpose and improve transit planning through spatial travel pattern analysis. The literature has been evolving from simple to more sophisticated methods such as from aggregated to individual travel pattern analysis, and from stop-to-stop to flexible stop aggregation. However, the issue of high computing complexity has limited these methods in practical applications. This paper proposes a new algorithm named Weighted Stop Density Based Scanning Algorithm with Noise (WS-DBSCAN) based on the classical Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to detect and update the daily changes in travel pattern. WS-DBSCAN converts the classical quadratic computation complexity DBSCAN to a problem of sub-quadratic complexity. The numerical experiment using the real AFC data in South East Queensland, Australia shows that the algorithm costs only 0.45% in computation time compared to the classical DBSCAN, but provides the same clustering results.


      PubDate: 2015-04-24T18:38:57Z
       
  • Traffic signal control and route choice: A new assignment and control
           model which designs signal timings
    • Abstract: Publication date: Available online 8 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Mike Smith
      This paper outlines a dynamical assignment and control model which calculates stage green-times for a signal-controlled network. In the dynamical model route flows, bottleneck delays and stage green-times all change simultaneously over iterations. It is shown that if the initial problem is feasible and the P 0 policy is utilised to move stage green-times then convergence is certain in the vertical queueing case. The model makes some reasonable systematic allowance for travellers’ route choices by encouraging congestion-reducing route choices in the future; the model (in generating signal timings) maximises network capacity (taking account of route choices) when queueing is vertical. Blocking back however appears difficult to deal with (while retaining the convergence guarantee) and represents an area for further study. The model may be used to design fixed-time or time of day signal timings; it may also be used to pre-prepare timings for rapid implementation in case of a predictable incident; finally if computation speeds are high enough the method may possibly be used responsively, in real-time.


      PubDate: 2015-04-24T18:38:57Z
       
  • Robust causal dependence mining in big data network and its application to
           traffic flow predictions
    • Abstract: Publication date: Available online 8 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Li Li , Xiaonan Su , Yanwei Wang , Yuetong Lin , Zhiheng Li , Yuebiao Li
      In this paper, we focus on a special problem in transportation studies that concerns the so called “Big Data” challenge, which is: how to build concise yet accurate traffic flow prediction models based on the massive data collected by different sensors? The size of the data, the hidden causal dependence and the complexity of traffic time series are some of the obstacles that affect making reliable forecast at a reasonable cost, both time-wise and computation-wise. To better prepare the data for traffic modeling, we introduce a multiple-step strategy to process the raw “Big Data” into compact time series that are better suited for regression and causality analysis. First, we use the Granger causality to define and determine the potential dependence among data, and produce a much condensed set of times series who are also highly dependent. Next, we deploy a decomposition algorithm to separate daily-similar trend and nonstationary bursts components from the traffic flow time series yielded by the Granger test. The decomposition results are then treated by two rounds of Lasso regression: the standard Lasso method is first used to quickly filter out most of the irrelevant data, followed by a robust Lasso method to further remove the disturbance caused by bursts components and recover the strongest dependence among the remaining data. Test results show that the proposed method significantly reduces the costs of building prediction models. Moreover, the obtained causal dependence graph reveals the relationship between the structure of road networks and the correlations among traffic time series. All these findings are useful for building better traffic flow prediction models.


      PubDate: 2015-04-24T18:38:57Z
       
  • Optimal number and location of Bluetooth sensors considering stochastic
           travel time prediction
    • Abstract: Publication date: Available online 8 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Hyoshin Park , Ali Haghani
      Determining the optimal number and location of sensors is essential to effectively manage traffic on highways. Optimal solutions dealing with dynamic traffic patterns and relocation of sensors have received little attention. In this study, existing fixed sensors are used to estimate travel time prediction errors at candidate locations where we deploy portable sensors. Potential sampling error of each candidate location is also counted in selecting optimal locations. A two-stage stochastic formulation considers uncertainty of traffic conditions based on scenarios generated by principal component analysis and clustering analysis to uncover the underlying spatial correlations and temporal patterns. The first stage decision, determining the optimal number of sensors, is made before the deployment. The second stage, evaluating the expected travel time prediction errors, specifies sensor arrangements in each scenario. A dynamic model has predefined rearrangement stages. At each stage, sensor locations are modified as the pattern of travel time error changes over time, considering sensor acquisition and relocation expenses. The deterministic and stochastic solutions serve as a lower bound and an upper bound for the dynamic solution. Higher relocation expense leads to more sensors being used, while higher sensor costs leads to fewer sensors being used with more frequent relocations.


      PubDate: 2015-04-24T18:38:57Z
       
  • Network Signal Setting Design: Meta-heuristic optimisation methods
    • Abstract: Publication date: Available online 9 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Giulio E. Cantarella , Stefano de Luca , Roberta Di Pace , Silvio Memoli
      This paper aims to investigate the application of meta-heuristic optimisation methods to Network Signal Setting Design. The adopted approaches are (i) three step optimisation, in which first the stage matrix (stage composition and sequence), the green timings at each single junction are optimised, then the node offsets are computed in three successive steps; (ii) two step optimisation, in which the stage matrix is defined at a first step, then the green timings and the node offsets are computed at a second step. In both approaches the stage matrix optimisation is carried out through explicit complete enumeration. In the first approach multi-criteria optimisation is followed for single junction signal setting design (green timings), whilst the coordination (node offsets) is approached through mono-criterion optimisation, as well as for the synchronisation (green timings and offsets) in the second approach. A new traffic flow model mixing CTM and PDM has been applied. This model allows to explicitly represent horizontal queuing phenomena as well as dispersion along a link. Some meta-heuristic algorithms (i.e. Genetic Algorithms, Hill Climbing and Simulated Annealing) are investigated in order to solve the two problems. The proposed strategies are applied to two different layouts (a two junction arterial vs. a four junction network) and their effectiveness is evaluated by comparing the obtained results with those from benchmark approaches implementing mono-criterion optimisation only.


      PubDate: 2015-04-24T18:38:57Z
       
  • An integrated solution for lane level irregular driving detection on
           highways
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Rui Sun , Washington Yotto Ochieng , Shaojun Feng
      Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.


      PubDate: 2015-04-24T18:38:57Z
       
  • Electric vehicles in multi-vehicle households
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Michael A. Tamor , Miloš Milačić
      The suitability of an electric vehicle of a given range to serve in place of a given conventional vehicle is not limited by the daily travel over distances within that that range, but rather by the occasional inconvenience of finding alternative transport for longer trips. While the frequency of this inconvenience can be computed from usage data, the willingness of individual users to accept that replacement depends on details of available transportation alternatives and their willingness to use them. The latter can be difficult to assess. Fortunately, 65% of US households have access to the most convenient alternative possible: a second car. In this paper we describe an analysis of prospective EV acceptance and travel electrification in two-car households in the Puget Sound region. We find that EVs with 60miles of useful range could be acceptable (i.e. incur inconvenience no more than three days each year) to nearly 90% of two-car households and electrify nearly 55% of travel in those households (32% of all travel). This compares to 120miles range required to achieve the same fraction of electrified travel via one-for-one replacement of individual vehicles. Even though only one third of personal vehicles in the US may be replaced in this paradigm, the ‘EV as a second-car’ concept is attractive in that a significant fraction of travel can be electrified by vehicles with modest electric range and virtually no dependence on public charging infrastructure.


      PubDate: 2015-04-24T18:38:57Z
       
  • A simulation-based approach in determining permitted left-turn capacities
    • Abstract: Publication date: Available online 9 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Nikiforos Stamatiadis , Adam Hedges , Adam Kirk
      A fundamental objective of traffic signal operations is the development of phasing plans that reduce delays while maintaining a high level of safety. One issue of concern is the treatment of left-turn phasing, which can operate as a protected movement, a permitted movement yielding to conflicting traffic, a combination protected–permitted movement or as a split-phase intersection. While protected-only movements can improve safety for the turning movement, they can also increase delays and congestion at the intersection. Most states maintain independent guidance for determining left-turn phasing; however, the most common identified guidance for protected left-turn phases is using a threshold based on the cross product of the left-turn volume and opposing through movements. The use of the cross product has been questioned recently as an indicator for determining phase selection. Based on simulation analysis within this research, the cross product is shown to be a poor indicator of left-turn capacity and congestion at the intersection. This research proposes a simplified single variable exponential model to determine left-turn capacity based on opposing volume and percent green time to determine left-turn capacity thresholds for protected left-turn phasing. The model is developed based on observed capacity from 450 VISSIM microsimulation scenarios which evaluated varying opposing volume, opposing number of lanes, cycle lengths and green time splits. Validation of the model based on complex Highway Capacity Manual procedures, indicates that the proposed model provides similar correlation to observed capacities. Finally, a nomograph is developed which presents the model in a simple form for interpretation and application by practicing traffic engineers, when required to determine left-turn phasing options. This procedure allows simple determination based on minimum input data needs similar to the cross product determination, without the need for complex hand calculations or computing requirements of the Highway Capacity Manual.


      PubDate: 2015-04-24T18:38:57Z
       
  • Dealing with uncertainty in detailed calibration of traffic simulation
           models for safety assessment
    • Abstract: Publication date: Available online 11 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Carlos Lima Azevedo , Biagio Ciuffo , João Lourenço Cardoso , Moshe E. Ben-Akiva
      With the increasing level of detail of traffic simulation models, the need for a consistent understanding of simulators’ performance and the adequate calibration and validation procedures to control uncertainty is crucial, particularly in applications focusing on complex driving behaviour and detailed outputs, such as road safety analysis. In this work the calibration of traffic microscopic simulation models for safety analysis is analyzed considering four different key uncertainty sources: the input data, the calibration methodology, the model structure and its parameters, and the output data. The use of a multi-step sensitivity analysis (SA) framework is proposed and applied to the simulation of an urban motorway scenario, using a complex traffic simulation model with more than one hundred parameters. A three-level analysis is presented: (1) different advanced SA and calibration methods are described, compared and integrated in a multi-step global SA framework; (2) the proposed method is tested using both vehicle trajectory and aggregated traffic data to assess the impact of model parameters uncertainty and different types of input data on relevant outputs; and (3) accident and non-accident scenario-specific calibrations are performed to test the capacity of the simulator in replicating changes in detailed traffic and safety related measurements. Different techniques are adopted in each phase of the global SA and calibration method, attending to the problem complexity, the dimensionality of the experiment, and minimizing the necessary number of model evaluations. The proposed method successfully identified the role played by all parameters and by the model stochasticity on different safety outputs. The final model calibration, carried out by explicitly considering the presence of uncertainty at different levels, confirmed the potential of advanced microscopic traffic models to adequately replicate detailed traffic and safety measurements, shedding light on different aspects of the interaction between road safety and traffic dynamics.


      PubDate: 2015-04-24T18:38:57Z
       
  • Interface design of eco-driving support systems – Truck
           drivers’ preferences and behavioural compliance
    • Abstract: Publication date: Available online 13 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Carina Fors , Katja Kircher , Christer Ahlström
      The aim of the study was to investigate the perceived usefulness of various types of in-vehicle feedback and advice on fuel efficient driving. Twenty-four professional truck drivers participated in a driving simulator study. Two eco-driving support systems were included in the experiment: one that provided continuous information and one that provided intermittent information. After the simulator session, the participants were interviewed about their experiences of the various constituents of the systems. In general, the participants had a positive attitude towards eco-driving support systems and behavioural data indicated that they tended to comply with the advice given. However, different drivers had very different preferences with respect to what type of information they found useful. The majority of the participants preferred simple and clear information. The eco-driving constituents that were rated as most useful were advice on gas pedal pressure, speed guidance, feedback on manoeuvres, fuel consumption information and simple statistics. It is concluded that customisable user interfaces are recommended for eco-driving support systems for trucks.


      PubDate: 2015-04-24T18:38:57Z
       
  • Freeway traffic incident reconstruction – A bi-parameter approach
    • Abstract: Publication date: Available online 13 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): A. Dabiri , B. Kulcsár
      The paper suggests a novel alternative to generalized traffic incident descriptions within the macroscopic traffic model framework. The contribution of the paper is twofold. First, by extending already existing second order macroscopic conservation laws to characterize off-nominal traffic conditions, we define two main incident parameters such as direct and indirect ones. Physical interpretations of this incident parametrization is provided. These incident indicators are relative in view of the nominal traffic flow model parameters and carries physically meaningful macroscopic content. Second, the paper proposes to use a constrained and nonlinear, joint traffic state- and incident parameter reconstruction method and validates the suggested modeling idea via real traffic measurements fitting. Evaluation of the numerical results demonstrate the effectiveness of the methodology.


      PubDate: 2015-04-24T18:38:57Z
       
  • Computationally efficient model predictive control of freeway networks
    • Abstract: Publication date: Available online 15 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Ajith Muralidharan , Roberto Horowitz
      A computationally efficient model predictive controller for congestion control in freeway networks is presented in this paper. The controller utilizes a modified Link-Node Cell Transmission Model (LN-CTM) to simulate traffic state trajectories under the effect of ramp metering, variable speed limit control and compute performance objectives. The modified LN-CTM simulates freeway traffic dynamics in the presence of capacity drop and ramp weaving effects. The objective of the controller can be chosen to represent commonly used congestion performance measures like total congestion delay measured in units of vehicle hours. The optimal control formulation based on this modified model is non-convex making it inefficient for direct use within a model predictive framework. Heuristic restrictions and relaxations are presented which allow the computation of the solution using optimal solutions of a sequence of derived linear programs. Mainly, the freeway is cleverly divided into regions, and limited restrictions are placed on solution trajectories to allow us to derive computationally efficient control actions. In the absence of capacity drop, this solution strategy provides optimal solutions to the original optimal control problem by solving a single linear program. The properties of the solution are discussed along with the role of variable speed limits when capacity drop is present/absent. Examples are provided to showcase the computational efficiency of the solution strategy, and scenarios simulated using the modified LN-CTM are analyzed to investigate the role of variable speed limits as a congestion control strategy.


      PubDate: 2015-04-24T18:38:57Z
       
  • Closing the loop in real-time railway control: Framework design and
           impacts on operations
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): F. Corman , E. Quaglietta
      Railway traffic is heavily affected by disturbances and/or disruptions, which are often cause of delays and low performance of train services. The impact and the propagation of such delays can be mitigated by relying on automatic tools for rescheduling traffic in real-time. These tools predict future track conflict based on current train information and provide suitable control measures (e.g. reordering, retiming and/or rerouting) by using advanced mathematical models. A growing literature is available on these tools, but their effects on real operations are blurry and not yet well known, due to the very scarce implementation of such systems in practice. In this paper we widen the knowledge on how automatic real-time rescheduling tools can influence train performance when interfaced with railway operations. To this purpose we build up a novel traffic control framework that couples the state-of-the art automatic rescheduling tool ROMA, with the realistic railway traffic simulation environment EGTRAIN, used as a surrogate of the real field. At regular times ROMA is fed with current traffic information measured from the field (i.e. EGTRAIN) in order to predict possible conflicts and compute (sub) optimal control measures that minimize the max consecutive delay on the network. We test the impact of the traffic control framework based on different types of interaction (i.e. open loop, multiple open loop, closed loop) between the rescheduling tool and the simulation environment as well as different combinations of parameter values (such as the rescheduling interval and prediction horizon). The influence of different traffic prediction models (assuming e.g. aggressive versus conservative driving behaviour) is also investigated together with the effects on traffic due to control delays of the dispatcher in implementing the control measures computed by the rescheduling tool. Results obtained for the Dutch railway corridor Utrecht–Den Bosch show that a closed loop interaction outperforms both the multiple open loop and the open loop approaches, especially with large control delays and limited information on train entrance delays and dwell times. A slow rescheduling frequency and a large prediction horizon improve the quality of the control measure. A limited control delay and a conservative prediction of train speed help filtering out uncertain traffic dynamics thereby increasing the effectiveness of the implemented measures.


      PubDate: 2015-04-24T18:38:57Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54




      PubDate: 2015-04-24T18:38:57Z
       
  • Overview of missing physical commodity trade data and its imputation using
           data augmentation
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): J. Farhan
      The physical aspects of commodity trade are becoming increasingly important on a global scale for transportation planning, demand management for transportation facilities and services, energy use, and environmental concerns. Such aspects (for example, weight and volume) of commodities are vital for logistics industry to allow for medium-to-long term planning at the strategic level and identify commodity flow trends. However, incomplete physical commodity trade databases impede proper analysis of trade flow between various countries. The missing physical values could be due to many reasons such as, (1) non-compliance of reporter countries with the prescribed regulations by World Customs Organization (WCO) (2) confidentiality issues, (3) delays in processing of data, or (4) erroneous reporting. The traditional missing data imputation methods, such as the substitution by mean, substitution by linear interpolation/extrapolation using adjacent points, the substitution by regression, and the substitution by stochastic regression, have been proposed in the context of estimating physical aspects of commodity trade data. However, a major demerit of these single imputation methods is their failure to incorporate uncertainty associated with missing data. The use of computationally complex stochastic methods to improve the accuracy of imputed data has recently become possible with the advancement of computer technology. Therefore, this study proposes a sophisticated data augmentation algorithm in order to impute missing physical commodity trade data. The key advantage of the proposed approach lies in the fact that instead of using a point estimate as the imputed value, it simulates a distribution of missing data through multiple imputations to reflect uncertainty and to maintain variability in the data. This approach also provides the flexibility to include fundamental distributional property of the variables, such as physical quantity, monetary value, price elasticity of demand, price variation, and product differentiation, and their correlations to generate reasonable average estimates of statistical inferences. An overview and limitations of most commonly used data imputation approaches is presented, followed by the theoretical basis and imputation procedure of the proposed approach. Lastly, a case study is presented to demonstrate the merits of the proposed approach in comparison to traditional imputation methods.


      PubDate: 2015-04-24T18:38:57Z
       
  • Analyzing passenger train arrival delays with support vector regression
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Nikola Marković , Sanjin Milinković , Konstantin S. Tikhonov , Paul Schonfeld
      We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.


      PubDate: 2015-04-24T18:38:57Z
       
  • Categorizing bicycling environments using GPS-based public bicycle speed
           data
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Shinhye Joo , Cheol Oh , Eunbi Jeong , Gunwoo Lee
      A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.


      PubDate: 2015-04-24T18:38:57Z
       
  • Marginal cost congestion pricing based on the network fundamental diagram
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): M.D. Simoni , A.J. Pel , R.A. Waraich , S.P. Hoogendoorn
      Congestion pricing schemes have been traditionally derived based on analytical representations of travel demand and traffic flows, such as in bottleneck models. A major limitation of these models, especially when applied to urban networks, is the inconsistency with traffic dynamics and related phenomena such as hysteresis and the capacity drop. In this study we propose a new method to derive time-varying tolling schemes using the concept of the Network Fundamental Diagram (NFD). The adopted method is based on marginal cost pricing, while it also enables to account realistically for the dynamics of large and heterogeneous traffic networks. We derive two alternative cordon tolls using network-aggregated traffic flow conditions: a step toll that neglects the spatial distribution of traffic by simply associating the marginal costs of any decrease in production within the NFD to the surplus of traffic; and a step toll that explicitly accounts for how network performance is also influenced by the spatial variance in a 3D-NFD. This pricing framework is implemented in the agent-based simulation model MATSim and applied to a case study of the city of Zurich. The tolling schemes are compared with a uniform toll, and they highlight how the inhomogeneous distribution of traffic may compromise the effectiveness of cordon tolls.


      PubDate: 2015-04-24T18:38:57Z
       
  • Real-time estimation of lane-based queue lengths at isolated signalized
           junctions
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Seunghyeon Lee , S.C. Wong , Y.C. Li
      In this study, we develop a real-time estimation approach for lane-based queue lengths. Our aim is to determine the numbers of queued vehicles in each lane, based on detector information at isolated signalized junctions. The challenges involved in this task are to identify whether there is a residual queue at the start time of each cycle and to determine the proportions of lane-to-lane traffic volumes in each lane. Discriminant models are developed based on time occupancy rates and impulse memories, as calculated by the detector and signal information from a set of upstream and downstream detectors. To determine the proportions of total traffic volume in each lane, the downstream arrivals for each cycle are estimated by using the Kalman filter, which is based on upstream arrivals and downstream discharges collected during the previous cycle. Both the computer simulations and the case study of real-world traffic show that the proposed method is robust and accurate for the estimation of lane-based queue lengths in real time under a wide range of traffic conditions. Calibrated discriminant models play a significant role in determining whether there are residual queued vehicles in each lane at the start time of each cycle. In addition, downstream arrivals estimated by the Kalman filter enhance the accuracy of the estimates by minimizing any error terms caused by lane-changing behavior.


      PubDate: 2015-04-24T18:38:57Z
       
  • Does it pay to reveal safety information? The effect of safety
           information on flight choice
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Aliza Fleischer , Anat Tchetchik , Tomer Toledo
      The case of flights safety lends itself as a natural case study for choice under of information asymmetry that involves dread risk and emotional factors. Specifically it allows one to experiment how the releasing of information will affect consumer choice. Previous studies, which followed the deregulation of commercial aviation, raised concerns about the corresponding potential for a marked deterioration in airline safety. Measures to prevent that decline were subsequently proposed. Specifically, it was argued that the public sector should establish and release flight safety indicators in addition to accidents’ statistics, which are currently available. It was argued that such safety indicators will also enable airlines to diversify their safety offerings. Underlying this argument are the assumptions that consumers’ flight safety preferences vary and that, provided with safety information, consumers will use it when making decisions. The present work, however, refutes the first assumption and sheds light on the second. It further investigates whether and how consumers react to and interpret safety information when choosing a flight, while accounting explicitly for a psychological trait. Employing an advanced experimental design and econometric approach, we find that: 1. When formal flight safety ratings are supplied, individuals abandoned their priors and rely on the information provided. 2. When it comes to “bad death” probabilities, people are not sensitive to the different shades of safety, and instead, they simply discern flights as either safe or unsafe. 3. Under a certain conditions disclosed information can alleviated fear and change the decision making of airline passengers.


      PubDate: 2015-04-24T18:38:57Z
       
  • Vehicular Ad-Hoc Networks sampling protocols for traffic monitoring and
           incident detection in Intelligent Transportation Systems
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Andrea Baiocchi , Francesca Cuomo , Mario De Felice , Gaetano Fusco
      Vehicular Ad-Hoc Networks (VANETs) are an emerging technology soon to be brought to everyday life. Many Intelligent Transport Systems (ITS) services that are nowadays performed with expensive infrastructure, like reliable traffic monitoring and car accident detection, can be enhanced and even entirely provided through this technology. In this paper, we propose and assess how to use VANETs for collecting vehicular traffic measurements. We provide two VANET sampling protocols, named SAME and TOME, and we design and implement an application for one of them, to perform real time incident detection. The proposed framework is validated through simulations of both vehicular micro-mobility and communications on the 68km highway that surrounds Rome, Italy. Vehicular traffic is generated based on a large real GPS traces set measured on the same highway, involving about ten thousand vehicles over many days. We show that the sampling monitoring protocol, SAME, collects data in few seconds with relative errors less than 10%, whereas the exhaustive protocol TOME allows almost fully accurate estimates within few tens of seconds. We also investigate the effect of a limited deployment of the VANET technology on board of vehicles. Both traffic monitoring and incident detection are shown to still be feasible with just 50% of equipped vehicles.


      PubDate: 2015-04-24T18:38:57Z
       
  • Modeling dry-port-based freight distribution planning
    • Abstract: Publication date: Available online 18 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Teodor Gabriel Crainic , Paolo Dell’Olmo , Nicoletta Ricciardi , Antonino Sgalambro
      In this paper we review the dry port concept and its outfalls in terms of optimal design and management of freight distribution. Some optimization challenges arising from the presence of dry ports in intermodal freight transport systems are presented and discussed. Then we consider the tactical planning problem of defining the optimal routes and schedules for the fleet of vehicles providing transportation services between the terminals of a dry-port-based intermodal system. An original service network design model based on a mixed integer programming mathematical formulation is proposed to solve the considered problem. An experimental framework built upon realistic instances inspired by regional cases is described and the computational results of the model are presented and discussed.


      PubDate: 2015-04-24T18:38:57Z
       
  • Quantifying transit travel experiences from the users’ perspective
           with high-resolution smartphone and vehicle location data: Methodologies,
           validation, and example analyses
    • Abstract: Publication date: Available online 16 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Andre Carrel , Peter S.C. Lau , Rabi G. Mishalani , Raja Sengupta , Joan L. Walker
      While transit agencies have increasingly adopted systems for collecting data on passengers and vehicles, the ability to derive high-resolution passenger trajectories and directly associate them with transit vehicles in a general and transferable manner remains a challenge. In this paper, a system of integrated methods is presented to reconstruct and track travelers usage of transit at a detailed level by matching location data from smartphones to automatic transit vehicle location (AVL) data and by identifying all out-of-vehicle and in-vehicle portions of the passengers trips. High-resolution travel times and their relationships with the timetable are then derived. Approaches are presented for processing relatively sparse smartphone location data in dense transit networks with many overlapping bus routes, distinguishing waits and transfers from non-travel related activities, and tracking underground travel in a Metro network. The derived information enables a range of analyses and applications, including the development of user-centric performance measures. Results are presented from an implementation and deployment of the system on San Francisco’s Muni network. Based on 103 ground-truth passenger trips, the detection accuracy is found to be approximately 93%. A set of example applications and findings presented in this paper underscore the value of the previously unattainable high-resolution traveler-vehicle coupled movements on a large-scale basis.


      PubDate: 2015-04-24T18:38:57Z
       
  • Development of control models for the planning of sustainable
           transportation systems
    • Abstract: Publication date: Available online 20 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Pankaj Maheshwari , Pushkin Kachroo , Alexander Paz , Romesh Khaddar
      Planning of sustainable transportation systems requires integration of multiple systems while considering a holistic approach. A limited amount of research has been conducted that simultaneously considers all the transportation, economic activity, environmental and social effects. The proposed research envisages incorporating considerations related to sustainability and providing solutions to stakeholders in policy making. In this paper, a dynamic model for planning and development of sustainable transportation systems is presented. This is given by a system of three nonlinear differential equations representing the dynamics of the three independent states, namely, transportation, activity, and environmental systems. A policy scenario considering investment in energy efficient technologies and its effects on the states is discussed to assist making investment decisions. Optimal control techniques are used to design the controls. The results show that it is possible to formulate an optimal control to achieve the desired target. Numerical results, based on actual parameters, are presented to illustrate the long-term trends of the states. The methodology discussed in this paper will be helpful to decision makers in making optimal decisions. The contribution of this research work is the introduction of a systems and controls methodology to develop optimal policies for the design of sustainable systems.


      PubDate: 2015-04-24T18:38:57Z
       
  • Axis of travel: Modeling non-work destination choice with GPS data
    • Abstract: Publication date: Available online 20 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Arthur Huang , David Levinson
      The advent of GIS and GPS has revolutionized how we monitor and model destination choice. New methodologies in building choice sets can be developed and new insights on travel behavior can be gained given the real-time GPS travel data. To this end, this research investigates how land use, road network structure, and axis of travel influence home-based, non-work destination choice based on in-vehicle GPS travel data in the Minneapolis-St. Paul Metropolitan Area in 2008. We propose a novel choice set formation approach combining survival analysis and random selection and a new approach to deciding choice set size. Mixed-effects logit models are used to model our data with repeated observations for each participant. Our findings identify the following factors that influence non-work destination choice: (1) Walkable opportunities and diversity of services at destinations, (2) Route-specific factors such as turn index and speed discontinuity, and (3) Axis of travel measured by relative travel time to work, home, and downtown. A destination closer to the axis of home and work, all else equal, is more likely to be selected. A destination far away from downtown is more attractive to auto users. This research contributes to methodologies in building choice sets for modeling non-work destination choice. The results enhance our understanding of non-work destination choice and have implications for transportation and land use planning.


      PubDate: 2015-04-24T18:38:57Z
       
  • Agent-based en-route diversion: Dynamic behavioral responses and network
           performance represented by Macroscopic Fundamental Diagrams
    • Abstract: Publication date: Available online 22 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Chenfeng Xiong , Xiqun Chen , Xiang He , Xi Lin , Lei Zhang
      This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications.


      PubDate: 2015-04-24T18:38:57Z
       
  • Eco-driving training of professional bus drivers – Does it work?
    • Abstract: Publication date: Available online 22 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Mark J.M. Sullman , Lisa Dorn , Pirita Niemi
      The drive to reduce fuel consumption and greenhouse gas emissions is one shared by both businesses and governments. Although many businesses in the European Union undertake interventions, such as driver training, there is relatively little research which has tested the efficacy of this approach and that which does exist has methodological limitations. One emerging technology employed to deliver eco-driving training is driver training using a simulator. The present study investigated whether bus drivers trained in eco-driving techniques were able to implement this learning in a simulator and whether this training would also transfer into the workplace. A total of 29 bus drivers attended an all-day eco-driving course and their driving was tested using a simulator both before and after the course. A further 18 bus drivers comprised the control group, and they attended first aid courses as well as completing the same simulator drives (before-after training). The bus drivers who were given the eco-driving training significantly improved fuel economy figures in the simulator, while there was no change in fuel economy for the control group. Actual fuel economy figures were also provided by the bus companies immediately before the training, immediately after the training and six months after the training. As expected there were no significant changes in fuel economy for the control group. However, fuel economy for the treatment group improved significantly immediately after the eco-driving training (11.6%) and this improvement was even larger six months after the training (16.9%). This study shows that simulator-based training in eco-driving techniques has the potential to significantly reduce fuel consumption and greenhouse gas emissions in the road transport sector.


      PubDate: 2015-04-24T18:38:57Z
       
  • Large-scale automated proactive road safety analysis using video data
    • Abstract: Publication date: Available online 22 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Paul St-Aubin , Nicolas Saunier , Luis Miranda-Moreno
      Due to the complexity and pervasiveness of transportation in daily life, the use and combination of larger data sets and data streams promises smarter roads and a better understanding of our transportation needs and environment. For this purpose, ITS systems are steadily being rolled out, providing a wealth of information, and transitionary technologies, such as computer vision applied to low-cost surveillance or consumer cameras, are already leading the way. This paper presents, in detail, a practical framework for implementation of an automated, high-resolution, video-based traffic-analysis system, particularly geared towards researchers for behavioural studies and road safety analysis, or practitioners for traffic flow model validation. This system collects large amounts of microscopic traffic flow data from ordinary traffic using CCTV and consumer-grade video cameras and provides the tools for conducting basic traffic flow analyses as well as more advanced, pro-active safety and behaviour studies. This paper demonstrates the process step-by-step, illustrated with examples, and applies the methodology to a case study of a large and detailed study of roundabouts (nearly 80,000 motor vehicles tracked up to 30 times per second driving through a roundabout). In addition to providing a rich set of behavioural data about Time-to-Collision and gap times at nearly 40 roundabout weaving zones, some data validation is performed using the standard Measure of Tracking Accuracy with results in the 85–95% range.


      PubDate: 2015-04-24T18:38:57Z
       
  • Matthew memorial special issue (optimization) – Preface
    • Abstract: Publication date: Available online 23 April 2015
      Source:Transportation Research Part C: Emerging Technologies




      PubDate: 2015-04-24T18:38:57Z
       
  • Corrigendum to “Methodology for safety improvement programming using
           constrained network-level optimization” [Transportation Research
           Part C: Emerging Technologies 50 (2015) 106–116]
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Jackeline Murillo-Hoyos , Nathee Athigakunagorn , Samuel Labi



      PubDate: 2015-04-24T18:38:57Z
       
  • Integration of a cell transmission model and macroscopic fundamental
           diagram: Network aggregation for dynamic traffic models
    • Abstract: Publication date: Available online 23 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Zhao Zhang , Brian Wolshon , Vinayak V. Dixit
      Network size can have a significant impact on the computational performance of traffic simulation models. Due to this, methods to reduce network size can be valuable when analyzing large networks. In this research, a novel model integrating a Cell Transmission Model (CTM) with the Macroscopic Fundamental Diagram (MFD) for urban networks is proposed and its effects analyzed. The concept that underlies this work is that a road network can be classified into two types of networks: the first includes roads that are modeled using CTM, and the second are components of the network that can be aggregated into large self-contained cells that also maintain properties of the MFD. To test the proposed model and its computational efficiency, a case study involving an evacuation is introduced. The network and its demand, built from the Southeast Louisiana Hurricane Katrina evacuation event, were modeled using a combination of CTM and MFD. The spatio-temporal profiles of volume and speeds on key routes and destinations from the proposed model were compared to observed data from the event. The results suggest that the model was able to realistically capture the observed shock wave phenomena, and reproduce the spatio-temporal characteristics of the evacuation traffic. This simple methodology has considerable potential to improve computational efficiency in dynamic traffic assignment models, particularly for those large-scale networks and processes, while ensuring that the traffic dynamics are realistically modeled.


      PubDate: 2015-04-24T18:38:57Z
       
  • Long short-term memory neural network for traffic speed prediction using
           remote microwave sensor data
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Xiaolei Ma , Zhimin Tao , Yinhai Wang , Haiyang Yu , Yunpeng Wang
      Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.


      PubDate: 2015-04-24T18:38:57Z
       
  • A dynamic Bayesian network model for real-time crash prediction using
           traffic speed conditions data
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Jie Sun , Jian Sun
      Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.


      PubDate: 2015-04-24T18:38:57Z
       
  • A fuzzy reasoning system for evaluating the efficiency of cabin baggage
           screening at airports
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Jacek Skorupski , Piotr Uchroński
      The growing threat of unlawful interference and terrorist acts has led to widespread implementation of screening systems for checking people and baggage at airports. Introducing limits regarding objects permitted to be transported and screening procedures themselves have decreased the comfort of travelling and reduced the capacity of terminals. It is therefore important to examine the efficiency of screening, whether carried out under regular circumstances or in a situation where threat level is high. The purpose of this study is to develop an effective method and calculation tool making it possible to quickly and exactly determine the effectiveness of cabin baggage screening, depending on the equipment available, the choice of screening staff, and the organisational solutions applied. What is more, the human factor is of great significance as far as cabin baggage screening is concerned. It introduces a certain amount of subjectivity, imprecision, and incompleteness of description. Due to this, fuzzy reasoning solutions have been employed. The results indicate that it is possible for the efficiency of cabin baggage screening to vary significantly at various screening checkpoints (SC), even within one airport. It is also demonstrated that it is possible to actually manage the level of screening efficiency, also in a situation where the risk of an attack is greater than usual. One should avoid taking global decisions and, instead, focus on assessing screening at particular SCs and take steps on the basis of the results of such an assessment. Results obtained with the use of a computer tool under the name of COBAFAS demonstrate that it is then possible to improve the efficiency of screening without hindering the capacity of the airport at the same time.


      PubDate: 2015-04-24T18:38:57Z
       
  • An optimization model of energy and transportation systems: Assessing the
           high-speed rail impacts in the United States
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Venkat Krishnan , Eirini Kastrouni , V. Dimitra Pyrialakou , Konstantina Gkritza , James D. McCalley
      This paper presents a long-term investment planning model that co-optimizes infrastructure investments and operations across transportation and electric infrastructure systems for meeting the energy and transportation needs in the United States. The developed passenger transportation model is integrated within the modeling framework of a National Long-term Energy and Transportation Planning (NETPLAN) software, and the model is applied to investigate the impact of high-speed rail (HSR) investments on interstate passenger transportation portfolio, fuel and electricity consumption, and 40-year cost and carbon dioxide (CO2) emissions. The results show that there are feasible scenarios under which significant HSR penetration can be achieved, leading to reasonable decrease in national long-term CO2 emissions and costs. At higher HSR penetration of approximately 30% relative to no HSR in the portfolio promises a 40-year cost savings of up to $0.63T, gasoline and jet fuel consumption reduction of up to 34% for interstate passenger trips, CO2 emissions reduction by about 0.8 billion short tons, and increased resilience against petroleum price shocks. Additionally, sensitivity studies with respect to light-duty vehicle mode share reveal that in order to realize such long-term cost and emission benefits, a change in the passenger mode choice is essential to ensure higher ridership for HSR.


      PubDate: 2015-04-24T18:38:57Z
       
  • A distributed VNS algorithm for optimizing dial-a-ride problems in
           large-scale scenarios
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Santiago Muelas , Antonio LaTorre , José-María Peña
      These days, transportation and logistic problems in large cities are demanding smarter transportation services that provide flexibility and adaptability. A possible solution to this arising problem is to compute the best routes for each new scenario. In this problem, known in the literature as the dial-a-ride problem, a number of passengers are transported between pickup and delivery locations trying to minimize the routing costs while respecting a set of prespecified constraints. This problem has been solved in the literature with several approaches from small to medium sized problems. However, few efforts have dealt with large scale problems very common in massive scenarios (big cities or highly-populated regions). In this study, a new distributed algorithm based on the partition of the requests space and the combination of the routes is presented and tested on a set of 24 different scenarios of a large-scale problem (up to 16,000 requests or 32,000 locations) in the city of San Francisco. The results show that, not only the distributed algorithm is able to solve large problem instances that the corresponding sequential algorithm is unable to solve in a reasonable time, but also to have an average improvement of 9% in the smaller problems. The results have been validated by means of statistical procedures proving that the distributed algorithm can be an effective way to solve high dimensional dial-a-ride problems.


      PubDate: 2015-04-24T18:38:57Z
       
  • The flying sidekick traveling salesman problem: Optimization of
           drone-assisted parcel delivery
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Chase C. Murray , Amanda G. Chu
      Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.


      PubDate: 2015-04-24T18:38:57Z
       
  • Co-design of traffic network topology and control measures
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Zhe Cong , Bart De Schutter , Robert Babuška
      The two main directions to improve traffic flows in networks involve changing the network topology and introducing new traffic control measures. In this paper, we consider a co-design approach to apply these two methods jointly to improve the interaction between different methods and to get a better overall performance. We aim at finding the optimal network topology and the optimal parameters of traffic control laws at the same time by solving a co-optimization problem. However, such an optimization problem is usually highly non-linear and non-convex, and it possibly involves a mixed-integer form. Therefore, we discuss four different solution frameworks that can be used for solving the co-optimization problem, according to different requirements on the computational complexity and speed. A simulation-based study is implemented on the Singapore freeway network to illustrate the co-design approach and to compare the four different solution frameworks.


      PubDate: 2015-04-24T18:38:57Z
       
  • Competing risk mixture model and text analysis for sequential incident
           duration prediction
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Ruimin Li , Francisco C. Pereira , Moshe E. Ben-Akiva
      Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a text analysis technique, is used to process the textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model.


      PubDate: 2015-04-24T18:38:57Z
       
  • A modified reinforcement learning algorithm for solving coordinated
           signalized networks
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Cenk Ozan , Ozgur Baskan , Soner Haldenbilen , Halim Ceylan
      This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased.


      PubDate: 2015-04-24T18:38:57Z
       
  • Empirical analysis of free-floating carsharing usage: The Munich and
           Berlin case
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Stefan Schmöller , Simone Weikl , Johannes Müller , Klaus Bogenberger
      Carsharing has become an important addition to existing mobility services over the last years. Today, several different systems are operating in many big cities. For an efficient and economic operation of any carsharing system, the identification of customer demand is essential. This demand is investigated within the presented research by analyzing booking data of a German free-floating carsharing system. The objectives of this paper are to describe carsharing usage and to identify factors that have an influence on the demand for carsharing. Therefore, the booking data are analyzed for temporal aspects, showing recurring patterns of varying lengths. The spatial distribution of bookings is investigated using a geographic information system and indicates a relationship between city structure and areas with high demand for carsharing. The temporal and spatial facets are then combined by applying a cluster analysis to identify groups of days with similar spatial booking patterns and show asymmetries in the spatiotemporal distribution of vehicle supply and demand. Influences on demand can be either short-term or long-term. The paper shows that changes in the weather conditions are a short-term influence as users of free-floating carsharing react to those. Furthermore, the application of a linear regression analysis reveals that socio-demographic data are suitable for making long-term demand predictions since booking numbers show quite a strong correlation with socio-demography, even in a simple model.


      PubDate: 2015-04-24T18:38:57Z
       
  • A continuous-flow-intersection-lite design and traffic control for
           oversaturated bottleneck intersections
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Weili Sun , Xinkai Wu , Yunpeng Wang , Guizhen Yu
      Oversaturation has become a severe problem for urban intersections, especially the bottleneck intersections that cause queue spillover and network gridlock. Further improvement of oversaturated arterial traffic using traditional mitigation strategies, which aim to improve intersection capacity by merely adjusting signal control parameters, becomes challenging since exiting strategies may (or already) have reached their “theoretical” limits of optimum. Under such circumstance, several novel unconventional intersection designs, including the well-recognized continuous flow intersection (CFI) design, are originated to improve the capacity at bottleneck intersections. However, the requirement of installing extra sub-intersections in a CFI design would increase vehicular stops and, more critically, is unacceptable in tight urban areas with closed spaced intersections. To address these issues, this research proposes a simplified continuous flow intersection (called CFI-Lite) design that is ideal for arterials with short links. It benefits from the CFI concept to enable simultaneous move of left-turn and through traffic at bottleneck intersections, but does not need installation of sub-intersections. Instead, the upstream intersection is utilized to allocate left-turn traffic to the displaced left-turn lane. It is found that the CFI-Lite design performs superiorly to the conventional design and regular CFI design in terms of bottleneck capacity. Pareto capacity improvement for every traffic stream in an arterial system can be achieved under effortless conditions. Case study using data collected at Foothill Blvd in Los Angeles, CA, shows that the new design is beneficial in more than 90% of the 408 studied cycles. The testing also shows that the average improvements of green bandwidths for the synchronized phases are significant.


      PubDate: 2015-04-24T18:38:57Z
       
  • A heuristic model of bounded route choice in urban areas
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): E.J. Manley , S.W. Orr , T. Cheng
      There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, ‘good enough’ decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks.


      PubDate: 2015-04-19T12:07:04Z
       
  • Calibration of traffic flow models using a memetic algorithm
    • Abstract: Publication date: Available online 7 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Alexander Paz , Victor Molano , Ember Martinez , Carlos Gaviria , Cristian Arteaga
      A Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models is proposed in this study. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of the search space and identifies a zone where a possible global solution could be located. After this zone has been found, the simulated annealing algorithm refines the search and locates an optimal set of parameters within that zone. The design and implementation of this methodology seeks to enable the generalized calibration of microscopic traffic flow models. Two different Corridor Simulation (CORSIM) vehicular traffic systems were calibrated for this study. All parameters after the calibration were within reasonable boundaries. The calibration methodology was developed independently of the characteristics of the traffic flow models. Hence, it is easily used for the calibration of any other model. The proposed methodology has the capability to calibrate all model parameters, considering multiple performance measures and time periods simultaneously. A comparison between the proposed MA and the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm was provided; results were similar between the two. However, the effort required to fine-tune the MA was considerably smaller when compared to the SPSA. The running time of the MA-based calibration was larger when it was compared to the SPSA running time. The MA still required some knowledge of the model in order to set adequate optimization parameters. The perturbation of the parameters during the mutation process must have been large enough to create a measurable change in the objective function, but not too large to avoid noisy measurements.


      PubDate: 2015-04-08T16:00:13Z
       
 
 
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