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  Subjects -> TRANSPORTATION (Total: 164 journals)
    - AIR TRANSPORT (7 journals)
    - AUTOMOBILES (21 journals)
    - RAILROADS (5 journals)
    - ROADS AND TRAFFIC (6 journals)
    - SHIPS AND SHIPPING (30 journals)
    - TRANSPORTATION (95 journals)

TRANSPORTATION (95 journals)

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Partially Free   (Followers: 76)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 2)
Archives of Transport     Open Access   (Followers: 16)
Botswana Journal of Technology     Full-text available via subscription  
Case Studies on Transport Policy     Hybrid Journal   (Followers: 8)
Cities in the 21st Century     Open Access   (Followers: 13)
Economics of Transportation     Partially Free   (Followers: 13)
Emission Control Science and Technology     Hybrid Journal   (Followers: 2)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 9)
European Transport Research Review     Open Access   (Followers: 21)
Geosystem Engineering     Hybrid Journal   (Followers: 1)
IATSS Research     Open Access  
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 8)
IET Electrical Systems in Transportation     Hybrid Journal   (Followers: 9)
IET Intelligent Transport Systems     Hybrid Journal   (Followers: 7)
IFAC-PapersOnLine     Open Access  
International Innovation - Transport     Open Access   (Followers: 8)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 7)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 7)
International Journal of e-Navigation and Maritime Economy     Open Access   (Followers: 1)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 8)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 9)
International Journal of Micro-Nano Scale Transport     Full-text available via subscription   (Followers: 2)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 9)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 10)
International Journal of Services Technology and Management     Hybrid Journal  
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 10)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 14)
International Journal of Transportation Science and Technology     Full-text available via subscription   (Followers: 9)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 3)
International Journal of Vehicular Technology     Open Access   (Followers: 4)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 11)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 5)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 6)
Journal of Navigation     Hybrid Journal   (Followers: 173)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 10)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 1)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 5)
Journal of Transport & Health     Hybrid Journal   (Followers: 6)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 10)
Journal of Transport Geography     Hybrid Journal   (Followers: 22)
Journal of Transport History     Full-text available via subscription   (Followers: 15)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 8)
Journal of Transportation Security     Hybrid Journal   (Followers: 3)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 14)
Journal of Transportation Technologies     Open Access   (Followers: 14)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 6)
Les Dossiers du Grihl     Open Access   (Followers: 1)
Logistics & Sustainable Transport     Open Access   (Followers: 2)
Logistique & Management     Full-text available via subscription  
Modern Transportation     Open Access   (Followers: 10)
Nonlinear Dynamics     Hybrid Journal   (Followers: 5)
Open Journal of Safety Science and Technology     Open Access   (Followers: 8)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 2)
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: 12)
Public Transport     Hybrid Journal   (Followers: 18)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 4)
Revista Transporte y Territorio     Open Access   (Followers: 1)
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 2)
Sport, Education and Society     Hybrid Journal   (Followers: 12)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 2)
Streetnotes     Open Access   (Followers: 1)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 2)
Tire Science and Technology     Full-text available via subscription   (Followers: 3)
Transactions on Transport Sciences     Open Access   (Followers: 5)
Transport     Hybrid Journal   (Followers: 13)
Transport and Telecommunication Journal     Open Access   (Followers: 4)
Transport in Porous Media     Hybrid Journal   (Followers: 1)
Transport Problems     Open Access   (Followers: 1)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 9)
Transportation     Hybrid Journal   (Followers: 27)
Transportation Geotechnics     Full-text available via subscription   (Followers: 1)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Transportation Journal     Full-text available via subscription   (Followers: 12)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 3)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 31)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 29)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 20)
Transportation Research Procedia     Open Access   (Followers: 4)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 34)
Transportation Science     Full-text available via subscription   (Followers: 20)
TRANSPORTES     Open Access   (Followers: 5)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 4)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 2)
Travel Behaviour and Society     Full-text available via subscription   (Followers: 5)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
Urban, Planning and Transport Research     Open Access   (Followers: 26)
Vehicular Communications     Full-text available via subscription   (Followers: 3)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 5)
Транспортні системи та технології перевезень     Open Access  
Journal Cover Transportation Research Part C: Emerging Technologies
  [SJR: 2.062]   [H-I: 72]   [20 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [3038 journals]
  • Estimation of trip travel time distribution using a generalized Markov
           chain approach
    • Authors: Zhenliang Ma; Haris N. Koutsopoulos; Luis Ferreira; Mahmoud Mesbah
      Pages: 1 - 21
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Zhenliang Ma, Haris N. Koutsopoulos, Luis Ferreira, Mahmoud Mesbah
      The increasing availability of opportunistic and dedicated sensors is transforming a once data-starved transport field into one of the most data-rich. While link-level travel time information can be derived or inferred from this data, methods for estimation of trip travel times between an origin and a destination pair are still evolving and limited, especially in the context of probability distribution estimation. This paper proposes a generalized Markov chain approach for estimating the probability distribution of trip travel times from link travel time distributions and takes into consideration correlations in time and space. The proposed approach consists of three major components, namely state definition, transition probabilities estimation and probability distribution estimation. A heuristic clustering method, based on Gaussian mixture models, has been developed to cluster link travel time observations with regard to their homogeneity and underlying traffic conditions. A transition probability estimation model is developed as a function of link characteristics and trip conditions using a logit model. By applying a Markov chain procedure, the probability distribution of trip travel times is estimated as the combination of Markov path travel time distributions weighted by their corresponding occurrence probabilities. The link travel time distribution is conditioned on the traffic conditions of the current link that can be estimated from historical observations. A moment generating function based algorithm is used to approximate the Markov path travel time distribution as the sum of correlated link travel time distributions conditional on traffic conditions. The proposed approach is applied in a transit case study using automatic vehicle location data. The results indicate that the method is effective and efficient, especially when correlations and multimodal distributions exist.

      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.11.008
      Issue No: Vol. 74 (2016)
       
  • Crash prediction with behavioral and physiological features for advanced
           vehicle collision avoidance system
    • Authors: Yutao Ba; Wei Zhang; Qinhua Wang; Ronggang Zhou; Changrui Ren
      Pages: 22 - 33
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Yutao Ba, Wei Zhang, Qinhua Wang, Ronggang Zhou, Changrui Ren
      Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.

      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.11.009
      Issue No: Vol. 74 (2016)
       
  • An on-road evaluation of connected motorcycle crash warning interface with
           different motorcycle types
    • Authors: Miao Song; Shane McLaughlin; Zachary Doerzaph
      Pages: 34 - 50
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Miao Song, Shane McLaughlin, Zachary Doerzaph
      Crash warning systems have been deployed in the high-end vehicle market segment for some time and are trickling down to additional motor vehicle industry segments each year. The motorcycle segment, however, has no deployed crash warning system to date. With the active development of next generation crash warning systems based on connected vehicle technologies, this study explored possible interface designs for motorcycle crash warning systems and evaluated their rider acceptance and effectiveness in a connected vehicle context. Four prototype warning interface displays covering three warning mode alternatives (auditory, visual, and haptic) were designed and developed for motorcycles. They were tested on-road with three connected vehicle safety applications - intersection movement assist, forward collision warning, and lane departure warning - which were selected according to the most impactful crash types identified for motorcycles. Combined auditory and haptic displays showed considerable promise for implementation. Auditory display is easily implemented given the adoption rate of in-helmet auditory systems. Its weakness of presenting directional information in this study may be remedied by using simple speech or with the help of haptic design, which performed well at providing such information and was also found to be attractive to riders. The findings revealed both opportunities and challenges of visual displays for motorcycle crash warning systems. More importantly, differences among riders of three major motorcycle types (cruiser, sport, and touring) in terms of rider acceptance of a motorcycle crash warning system were revealed. Based on the results, recommendations were provided for an appropriate crash warning interface design for motorcycles and riders in a connected vehicle environment.

      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.11.005
      Issue No: Vol. 74 (2016)
       
  • Optimizing train operational plan in an urban rail corridor based on the
           maximum headway function
    • Authors: Feng Shi; Shuo Zhao; Zhao Zhou; Pu Wang; Michael G.H. Bell
      Pages: 51 - 80
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Feng Shi, Shuo Zhao, Zhao Zhou, Pu Wang, Michael G.H. Bell
      The train operational plan (TOP) plays a crucial role in the efficient and effective operation of an urban rail system. We optimize the train operational plan in a special network layout, an urban rail corridor with one terminal yard, by decomposing it into two sub-problems, i.e., the train departure profile optimization and the rolling stock circulation optimization. The first sub-problem synthetically optimizes frequency setting, timetabling and the rolling stock circulation at the terminal without a yard. The maximum headway function is generated to ensure the service of the train operational plan without considering travel demand, then we present a model to minimize the number of train trips, and design a heuristic algorithm to maximize the train headway. On the basis of a given timetable, the rolling stock circulation optimization only involves the terminal with a yard. We propose a model to minimize the number of trains and yard–station runs, and an algorithm to find the optimal assignment of train-trip pair connections is designed. The computational complexities of the two algorithms are both linear. Finally, a real case study shows that the train operational plan developed by our approach enables a better match of train headway and travel demand, and reduces the operational cost while satisfying the requirement of the level of service.

      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.11.007
      Issue No: Vol. 74 (2016)
       
  • Bus arrival time calculation model based on smart card data
    • Authors: Yuyang Zhou; Lin Yao; Yanyan Chen; Yi Gong; Jianhui Lai
      Pages: 81 - 96
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Yuyang Zhou, Lin Yao, Yanyan Chen, Yi Gong, Jianhui Lai
      Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.

      PubDate: 2016-11-26T01:34:51Z
      DOI: 10.1016/j.trc.2016.11.014
      Issue No: Vol. 74 (2016)
       
  • Editorial for the virtual special issue on “Advances in alternative fuel
           vehicle transportation systems”
    • Authors: Eric Yongxi Huang; Michael Kuby; Joseph Y.J. Chow
      Pages: 97 - 98
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Eric Yongxi Huang, Michael Kuby, Joseph Y.J. Chow


      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.11.012
      Issue No: Vol. 74 (2016)
       
  • Airline crew pairing with fatigue: Modeling and analysis
    • Authors: Burak C. Yildiz; Fatma Gzara; Samir Elhedhli
      Pages: 99 - 112
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Burak C. Yildiz, Fatma Gzara, Samir Elhedhli
      Crew fatigue is one of the main causes of airline accidents. Regulatory authorities such as the Federal Aviation Agency constantly introduce new fatigue regulations, often in the form of hard constraints on the length of duty and rest periods. The complex nature of travel-related fatigue, however, makes it difficult to account for it indirectly through such constraints. Recent studies show that fatigue depends on human factors such as the homeostatic process and the circadian body clock as well as time-zone differences. In this work, we explicitly account for fatigue in crew pairing optimization through the Three Process Model of Alertness, one of the most comprehensive fatigue models available in the literature. We provide a mathematical model for the crew pairing problem that incorporates fatigue and solve it using a column generation approach. Numerical analysis on two real data sets reveals that the proposed approach is able to reduce the crew fatigue levels substantially with minimal impact on cost. In particular, it is shown that hard constraints on fatigue may still lead to high fatigue levels and that jet-lag and time-zone differences have a major impact. The results of the tests also show that some of the rules and regulations in practice may be omitted if the fatigue is accounted for directly.

      PubDate: 2016-11-26T01:34:51Z
      DOI: 10.1016/j.trc.2016.11.002
      Issue No: Vol. 74 (2016)
       
  • Estimation of driving style in naturalistic highway traffic using maneuver
           transition probabilities
    • Authors: Guofa Li; Shengbo Eben Li; Bo Cheng; Paul Green
      Pages: 113 - 125
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Guofa Li, Shengbo Eben Li, Bo Cheng, Paul Green
      Accurately estimating driving styles is crucial to designing useful driver assistance systems and vehicle control systems for autonomous driving that match how people drive. This paper presents a novel way to identify driving style not in terms of the durations or frequencies of individual maneuver states, but rather the transition patterns between them to see how they are interrelated. Driving behavior in highway traffic was categorized into 12 maneuver states, based on which 144 (12×12) maneuver transition probabilities were obtained. A conditional likelihood maximization method was employed to extract typical maneuver transition patterns that could represent driving style strategies, from the 144 probabilities. Random forest algorithm was adopted to classify driving styles using the selected features. Results showed that transitions concerning five maneuver states – free driving, approaching, near following, constrained left and right lane changes – could be used to classify driving style reliably. Comparisons with traditional methods were presented and discussed in detail to show that transition probabilities between maneuvers were better at predicting driving style than traditional maneuver frequencies in behavioral analysis.
      Graphical abstract image

      PubDate: 2016-11-26T01:34:51Z
      DOI: 10.1016/j.trc.2016.11.011
      Issue No: Vol. 74 (2016)
       
  • Strategic assessment of capacity consumption in railway networks:
           Framework and model
    • Authors: Lars Wittrup Jensen; Alex Landex; Otto Anker Nielsen; Leo G. Kroon; Marie Schmidt
      Pages: 126 - 149
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Lars Wittrup Jensen, Alex Landex, Otto Anker Nielsen, Leo G. Kroon, Marie Schmidt
      In this paper, we develop a new framework for strategic planning purposes to calculate railway infrastructure occupation and capacity consumption in networks, independent of a timetable. Furthermore, a model implementing the framework is presented. In this model different train sequences are generated and assessed to obtain timetable independence. A stochastic simulation of delays is used to obtain the capacity consumption. The model is tested on a case network where four different infrastructure scenarios are considered. Both infrastructure occupation and capacity consumption results are obtained efficiently with little input. The case illustrates the model’s ability to quantify the capacity gain from infrastructure scenario to infrastructure scenario which can be used to increase the number of trains or improve the robustness of the system.

      PubDate: 2016-11-26T01:34:51Z
      DOI: 10.1016/j.trc.2016.10.013
      Issue No: Vol. 74 (2016)
       
  • A new methodology for vehicle trajectory reconstruction based on wavelet
           analysis
    • Authors: Mehdi Rafati Fard; Afshin Shariat Mohaymany; Matin Shahri
      Pages: 150 - 167
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Mehdi Rafati Fard, Afshin Shariat Mohaymany, Matin Shahri
      Vehicle trajectories with high spatial and temporal resolution are known as the most ideal source of data for developing innovative microscopic traffic models. Aside from the method applied for collecting the vehicle trajectories, such data are more or less error-infected. The ever-increasing noise amplitude during the process of deriving the data (such as speed and acceleration) required for developing models, might change or even hide the structure of data and lead to useful information being overlooked. This highlights the importance of presenting the efficient methods which are adequate to remove noise and enhance the quality of vehicle trajectory data. Accordingly, in this paper a simple two-step technique based on wavelet analysis has been recommended for filtering errors and reconstructing trajectory data. Primarily, by using wavelet transform a special treatment was employed to identify and modify the outliers. Next, the noise in trajectory data was eliminated by applying the wavelet-based filter. The results of applying the proposed method to the synthetic noise-infected trajectory and the NGSIM dataset reveal how appropriate its performance is compared with other methodologies in terms of quantitative criteria.

      PubDate: 2016-11-26T01:34:51Z
      DOI: 10.1016/j.trc.2016.11.010
      Issue No: Vol. 74 (2016)
       
  • Short-term traffic flow prediction using time-varying Vasicek model
    • Authors: Yalda Rajabzadeh; Amir Hossein Rezaie; Hamidreza Amindavar
      Pages: 168 - 181
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Yalda Rajabzadeh, Amir Hossein Rezaie, Hamidreza Amindavar
      This paper provides a two-step approach based on the stochastic differential equations (SDEs) to improve short-term prediction. In the first step of this framework, a Hull-White (HW) model is applied to obtain a baseline prediction model from previous days. Then, the extended Vasicek model (EV) is employed for modeling the difference between observations and baseline predictions (residuals) during an individual day. The parameters of this time-varying model are estimated at each sample using the residuals in a short duration of time before the time point of prediction; so it provides a real time prediction. The extracted model recovers the valuable local variation information during each day. The performance of our method in comparison with other methods improves significantly in terms of root mean squared error (RMSE), mean absolute error (MAE) and mean relative error (MRE) for real data from Tehran’s highways and the open-access PeMS database. We also demonstrate that the proposed model is appropriate for imputing the missing data in traffic dataset and it is more efficient than the probabilistic principal component analysis (PPCA) and k-Nearest neighbors (k-NN) methods.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.001
      Issue No: Vol. 74 (2016)
       
  • Integrated planning of park-and-ride facilities and transit service
    • Authors: Ziqi Song; Yi He; Lihui Zhang
      Pages: 182 - 195
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Ziqi Song, Yi He, Lihui Zhang
      This paper proposes an integrated planning framework to locate park-and-ride (P&R) facilities and optimize their capacities as well as transit service frequencies simultaneously. P&R users’ route choice behavior is explicitly considered, and a link-based multimodal user equilibrium model is established. The optimal location and capacity of P&R facilities and transit service design problem is formulated as a mathematical program with complementarity constraints (MPCC), and a solution algorithm based on the active-set approach is developed to solve the optimal design problem effectively. A numerical example is employed to demonstrate that the optimal design shifts commuters from the automobile mode to transit and P&R modes and, hence improves the net social benefit dramatically.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.017
      Issue No: Vol. 74 (2016)
       
  • Recent success stories on integrated optimization of railway systems
    • Authors: Ralf Borndörfer; Torsten Klug; Leonardo Lamorgese; Carlo Mannino; Markus Reuther; Thomas Schlechte
      Pages: 196 - 211
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Ralf Borndörfer, Torsten Klug, Leonardo Lamorgese, Carlo Mannino, Markus Reuther, Thomas Schlechte
      Planning and operating railway transportation systems is an extremely hard task due to the combinatorial complexity of the underlying discrete optimization problems, the technical intricacies, and the immense size of the problem instances. Because of that, however, mathematical models and optimization techniques can result in large gains for both railway customers and operators, e.g., in terms of cost reductions or service quality improvements. In the last years a large and growing group of researchers in the OR community have devoted their attention to this domain developing mathematical models and optimization approaches to tackle many of the relevant problems in the railway planning process. However, there is still a gap to bridge between theory and practice (e.g. Cacchiani et al., 2014; Borndörfer et al., 2010), with a few notable exceptions. In this paper we address three individual success stories, namely, long-term freight train routing (part I), mid-term rolling stock rotation planning (part II), and real-time train dispatching (part III). In each case, we describe real-life, successful implementations. We will discuss the individual problem setting, survey the optimization literature, and focus on particular aspects addressed by the mathematical models. We demonstrate on concrete applications how mathematical optimization can support railway planning and operations. This gives proof that mathematical optimization can support the planning of railway resources. Thus, mathematical models and optimization can lead to a greater efficiency of railway operations and will serve as a powerful and innovative tool to meet recent challenges of the railway industry.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.015
      Issue No: Vol. 74 (2016)
       
  • Designing an integrated distribution system for catering services for
           high-speed railways: A three-echelon location routing model with tight
           time windows and time deadlines
    • Authors: Xin Wu; Lei Nie; Meng Xu
      Pages: 212 - 244
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Xin Wu, Lei Nie, Meng Xu
      An emerging task in catering services for high-speed railways (CSHR) is to design a distribution system for the delivery of high-quality perishable food products to trains in need. This paper proposes a novel model for integrating location decision making with daily rail catering operations, which are affected by various aspects of rail planning, to meet time-sensitive passenger demands. A three-echelon location routing problem with time windows and time budget constraints (3E-LRPTWTBC) is thus proposed toward formulating this integrated distribution system design problem. This model attempts to determine the capacities/locations of distribution centers and to optimize the number of meals delivered to stations. The model also attempts to generate a schedule for refrigerated cars traveling from distribution centers to rail stations for train loading whereby meals can be catered to trains within tight time windows and sold before a specified time deadline. By relaxing the time-window constraints, a relaxation model that can be solved using an off-the-shelf mixed integer programming (MIP) solver is obtained to provide a lower bound on the 3E-LRPTWTBC. A hybrid cross entropy algorithm (HCEA) is proposed to solve the 3E-LRPTWTBC. A small-scale case study is implemented, which reveals a 9.3% gap between the solution obtained using the HCEA and that obtained using the relaxation model (RM). A comparative analysis of the HCEA and an exhaustive enumeration algorithm indicates that the HCEA shows good performance in terms of computation time. Finally, a case study considering 156 trains on the Beijing-Shanghai high-speed corridor and a large-scale case study considering 1130 trains on the Chinese railway network are addressed in a comprehensive study to demonstrate the applicability of the proposed models and algorithm.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.006
      Issue No: Vol. 74 (2016)
       
  • A systemic modelling of ground handling services using the functional
           resonance analysis method
    • Authors: Milena Studic; Arnab Majumdar; Wolfgang Schuster; Washington Y. Ochieng
      Pages: 245 - 260
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Milena Studic, Arnab Majumdar, Wolfgang Schuster, Washington Y. Ochieng
      In contrast to air transport safety, safety in ground handling is not concerned only with aircraft accidents but also the Occupational Health and Safety of the employees who work at airport aprons. Ground handling safety costs the aviation industry tens of billions USD every year which raises the questions about the effectiveness of linear safety risk management of Ground Handling Services (GHS). This paper uses the state-of-the-art safety theory to justify and highlight the need for a systemic approach to safety risk management of GHS on the apron. A hybrid Total Apron Safety Management (TASM) framework, based on the combination of Functional Resonance Analysis Method (FRAM), Grounded Theory, Template Analysis and Goals-Means Task Analysis (GMTA) was developed to support systemic safety modelling of GHS. The data that underpins the TASM framework includes extensive literature review, 15 observations, 43 interviews and expert judgement across five international airports. While the TASM framework can be applied in retrospective, prospective and system design analysis to improve both the safety management and the efficiency of apron operations, this paper showcases only one of its application on a case study of a historical safety occurrence. The results of the investigation carried out in this paper clearly demonstrate the benefits of the systemic as opposed to the existing linear approaches to retrospective safety analyses and the suitability of the TASM framework for occurrence analysis and prevention.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.004
      Issue No: Vol. 74 (2016)
       
  • Optimization of horizontal alignment geometry in road design and
           reconstruction
    • Authors: Gerardo Casal; Duarte Santamarina; Miguel E. Vázquez-Méndez
      Pages: 261 - 274
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Gerardo Casal, Duarte Santamarina, Miguel E. Vázquez-Méndez
      This paper presents a general formulation for optimization of horizontal road alignment, composed of tangential segments and circular curves suitably connected with transition curves (clothoids). It consists of a constrained optimization problem where the objective function is given by a line integral along the layout. The integrand is a function representing the cost of the road going through each point and, by considering different costs, a wide range of problems can be included in this formulation. To show it, we apply this methodology to three different situations. The two first cases are related with the design of a new road layout and used to solve a pair of academic examples. The third problem deals with the improvement of a road adapting the old path to current legislation, and it is solved taking as case study the reconstruction project for a regional road (NA-601) in the north of Spain.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.019
      Issue No: Vol. 74 (2016)
       
  • Analytical and simulation approaches to understand combined effects of
           transit signal priority and road-space priority measures
    • Authors: Long Tien Truong; Graham Currie; Majid Sarvi
      Pages: 275 - 294
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Long Tien Truong, Graham Currie, Majid Sarvi
      Transit signal priority (TSP) may be combined with road-space priority (RSP) measures to increase its effectiveness. Previous studies have investigated the combination of TSP and RSP measures, such as TSP with dedicated bus lanes (DBLs) and TSP with queue jump lanes (QJLs). However, in these studies, combined effects are usually not compared with separate effects of each measure. In addition, there is no comprehensive study dedicated to understanding combined effects of TSP and RSP measures. It remains unclear whether combining TSP and RSP measures creates an additive effect where the combined effect of TSP and RSP measures is equal to the sum of their separate effects. The existence of such an additive effect would suggest considerable benefits from combining TSP and RSP measures. This paper explores combined effects of TSP and RSP measures, including TSP with DBLs and TSP with QJLs. Analytical results based on time-space diagrams indicate that at an intersection level, the combined effect on bus delay savings is smaller than the additive effect if there is no nearside bus stop and the traffic condition in the base case is under-saturated or near-saturated. With a near-side bus stop, the combined effect on bus delay savings at an intersection level can be better than the additive effect (or over-additive effect), depending on dwell time, distance from the bus stop to the stop line, traffic demand, and cycle length. In addition, analytical results suggest that at an arterial level, the combined effect on bus delay savings can be the over-additive effect with suitable signal offsets. These results are confirmed by a micro-simulation case study. Combined effects on arterial and side-street traffic delays are also discussed.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.020
      Issue No: Vol. 74 (2016)
       
  • Assessing the impact of reduced visibility on traffic crash risk using
           microscopic data and surrogate safety measures
    • Authors: Yichuan Peng; Mohamed Abdel-Aty; Qi Shi; Rongjie Yu
      Pages: 295 - 305
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Yichuan Peng, Mohamed Abdel-Aty, Qi Shi, Rongjie Yu
      Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.022
      Issue No: Vol. 74 (2016)
       
  • Development of signal optimization models for asymmetric two-leg
           continuous flow intersections
    • Authors: Xianfeng Yang; Yao Cheng
      Pages: 306 - 326
      Abstract: Publication date: January 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 74
      Author(s): Xianfeng Yang, Yao Cheng
      Despite extensive studies have been reported to address the operational issues of full Continuous Flow Intersection (CFI) in the literature, the asymmetric two-leg CFI, which is more applicable in practice, has not received adequate attentions yet. To satisfy such need, this study develops two signal optimization models for asymmetric CFI based on its unique geometric features. The first proposed model, following a two-step procedure, determines the cycle length, phase design and sequence, and green split in the first step and optimizes intersection offset in the second step. To benefit both intersections’ capacity maximization and signal progression design by optimizing phase plan and sequence, the second proposed model takes the Mixed-Integer-Linear-Programming (MILP) technique to concurrently optimize all signal control variables. With extensive case studies on a field site in Maryland, the simulation results prove that the proposed models can effectively provide signal progression to critical path-flows and prevent the potential queue spillover on the short turning bays/links. Further comparisons between the two proposed models reveal that the second model is more flexible in designing phase plan but the first model performs better in reducing link queue length.

      PubDate: 2016-12-03T08:41:05Z
      DOI: 10.1016/j.trc.2016.11.021
      Issue No: Vol. 74 (2016)
       
  • Algorithms to find shortest and alternative paths in free flow and
           congested traffic regimes
    • Authors: Alberto Faro; Daniela Giordano
      Pages: 1 - 29
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Alberto Faro, Daniela Giordano
      Location-based systems can be very helpful to mobile users if they are able to suggest shortest paths to destination taking into account the actual traffic conditions. This would allow to inform the drivers not only about the current shortest paths to destination but also about alternative, timely computed paths to avoid being trapped in the traffic jams signaled by cyber-physical-social systems. To this aim, the paper proposes a set of algorithms that solve very fast the All Pair Shortest Paths problem in both the free flow and congested traffic regimes, for road networks of medium-large size, thus enabling location-based systems to deal with emergencies and critical traffic conditions in city and metropolitan areas, whose transport networks typically range from some hundreds to many thousands of nodes, respectively. The paths to avoid being trapped in the traffic jams are computed by using a simulation of the shockwave propagation, instead of historical data. A parallel version of the algorithms is also proposed to solve the All Pair Shortest Paths problem for metropolitan areas with very large road networks. A time performance analysis of the proposed algorithms for transport networks of various size is carried out.

      PubDate: 2016-10-21T18:03:15Z
      DOI: 10.1016/j.trc.2016.09.009
      Issue No: Vol. 73 (2016)
       
  • Information-traffic coupled cell transmission model for information
           spreading dynamics over vehicular ad hoc network on road segments
    • Authors: Lili Du; Siyuan Gong; Lu Wang; Xiang-Yang Li
      Pages: 30 - 48
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Lili Du, Siyuan Gong, Lu Wang, Xiang-Yang Li
      Vehicular Ad Hoc Network (VANET) makes real-time traffic information accessible to vehicles en routes, thus possesses a great potential to improve traffic safety and mobility in the near future. Existing literature shows that we are still lack of approaches to track information spreading dynamics via VANET, which will prevent the potential applications from success. Motivated by this view, this research develops an information-traffic coupled cell transmission model (IT-CTM) to capture information spreading dynamics via VANET. More exactly, this study considers information spreading over a road segment forms a wave with a front and tail, each of which goes through the road segment following an intermittent transmission pattern due to traffic flow dynamics. The approach of IT-CTM discretizes a road segment into a number of cells. Each cell covers several intermittent transmissions. Mathematical methods are developed to capture the inner-cell and inter-cell movements of information front and tail, which enable us to track the information spreading dynamics along cells. Numerical experiments based on simulation and field data indicate that the IT-CTM can closely track the dynamic movements of information front and tail as well as the dynamic information coverage as a single or multiple piece(s) of information propagating via VANET on a one-way or two-way road segment. The mean absolute error (MAE) for tracking dynamic information coverage is <5% across all experiments in this study.

      PubDate: 2016-10-21T18:03:15Z
      DOI: 10.1016/j.trc.2016.10.007
      Issue No: Vol. 73 (2016)
       
  • A delay root cause discovery and timetable adjustment model for enhancing
           the punctuality of railway services
    • Authors: Wei-Hsun Lee; Li-Hsien Yen; Chien-Ming Chou
      Pages: 49 - 64
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Wei-Hsun Lee, Li-Hsien Yen, Chien-Ming Chou
      Knock-on delay, which is the key factor in punctuality of railway service, is mainly related to two factors including the quality of timetable in the planning phase and disturbances which may result in unscheduled trains’ waiting or meeting in operation phase. If the delay root cause and the interactions among the factors responsible for these can be clearly clarified, then the punctuality of railway operations can be enhanced by taking reactions such as timetable adjustment, rescheduling or rerouting of railway traffic in case of disturbances. These delay reasons can be used to predict the lengths of railway disruptions and effective reactions can be applied in disruption management. In this work, a delay root cause discovery model is proposed, which integrates heterogeneous railway operation data sources to reconstruct the details of the railway operations. A supervised decision tree method following the machine learning and data mining techniques is designed to estimate the key factors in knock-on delays. It discovers the root cause delay factor by logically analyzing the scheduled or un-scheduled trains meetings and overtaking behaviors, and the subsequent delay propagations. Experiment results show that the proposed decision tree can predict the delay reason with the accuracy of 83%, and it can be further enhance to 90% if the delay cause is only considered “prolonged passengers boarding” and “meeting or overtaking” factors. The delay root cause can be discovered by the proposed model, verified by frequency filtering in operation records, and resolved by the adjustment of timetable which is an important reference for the next timetable rescheduling. The results of this study can be applied to railway operation decision support and disruption management, especially with regard to timetable rescheduling, trains resequencing or rerouting, system reliability analysis, and service quality improvements.

      PubDate: 2016-10-28T21:44:40Z
      DOI: 10.1016/j.trc.2016.10.009
      Issue No: Vol. 73 (2016)
       
  • Real-time traffic network state estimation and prediction with decision
           support capabilities: Application to integrated corridor management
    • Authors: Hossein Hashemi; Khaled F. Abdelghany
      Pages: 128 - 146
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Hossein Hashemi, Khaled F. Abdelghany
      This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.

      PubDate: 2016-11-11T22:05:59Z
      DOI: 10.1016/j.trc.2016.10.012
      Issue No: Vol. 73 (2016)
       
  • Prediction of aircraft performances based on data collected by air traffic
           control centers
    • Authors: Marko Hrastovec; Franc Solina
      Pages: 167 - 182
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Marko Hrastovec, Franc Solina
      Accurate prediction of aircraft position is becoming more and more important for the future of air traffic. Currently, the lack of information about flights prevents us to fulfill future demands for the needed accuracy in 4D trajectory prediction. Until we get the necessary information from aircraft and until new more accurate methods are implemented and used, we propose an alternative method for predicting aircraft performances using machine learning from historical data about past flights collected in a multidimensional database. In that way, we can improve existing applications by providing them better inputs for their trajectory calculations. Our method uses flight plan data to predict performance values, which are suited individually for each flight. The results show that based on recorded past aircraft performances and related flight data we can effectively predict performances for future flights based on how similar flights behaved in the past.

      PubDate: 2016-11-11T22:05:59Z
      DOI: 10.1016/j.trc.2016.10.018
      Issue No: Vol. 73 (2016)
       
  • Short-term speed predictions exploiting big data on large urban road
           networks
    • Authors: Gaetano Fusco; Chiara Colombaroni; Natalia Isaenko
      Pages: 183 - 201
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Gaetano Fusco, Chiara Colombaroni, Natalia Isaenko
      Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.

      PubDate: 2016-11-11T22:05:59Z
      DOI: 10.1016/j.trc.2016.10.019
      Issue No: Vol. 73 (2016)
       
  • Study on the framework of hybrid collision warning system using loop
           detectors and vehicle information
    • Authors: Sehyun Tak; Soomin Woo; Hwasoo Yeo
      Pages: 202 - 218
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Sehyun Tak, Soomin Woo, Hwasoo Yeo
      Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.

      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.10.014
      Issue No: Vol. 73 (2016)
       
  • Integrated signal optimization and non-traditional lane assignment for
           urban freeway off-ramp congestion mitigation
    • Authors: Jing Zhao; Yue Liu
      Pages: 219 - 238
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Jing Zhao, Yue Liu
      Exiting flow from urban freeway off-ramps coupled with limited capacity and traffic weaving at the downstream intersections creates major bottlenecks in urban road network. This paper presents an integrated design model for non-traditional lane assignment and signal optimization at the off-ramp, its downstream intersection, and their connecting segment with the objective to mitigate or eliminate traffic weaving and to maximize the section’s overall capacity. A mixed-integer non-linear program model is formulated to capture real-world operational constraints regarding the non-traditional lane assignment, special phasing treatment and signal timing. The mathematical model is linearized and solved by the standard branch-and-bound technique. Extensive numerical analysis and case study results validate the effectiveness of the proposed integrated model and demonstrate its promising application at locations where the upstream freeway off-ramp is located at the middle of the road cross section and the space between the stop line and off-ramp is limited.

      PubDate: 2016-11-18T22:15:22Z
      DOI: 10.1016/j.trc.2016.11.003
      Issue No: Vol. 73 (2016)
       
  • Estimating potential increases in travel with autonomous vehicles for the
           non-driving, elderly and people with travel-restrictive medical conditions
           
    • Authors: Corey D. Harper; Chris T. Hendrickson; Sonia Mangones; Constantine Samaras
      Pages: 1 - 9
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Corey D. Harper, Chris T. Hendrickson, Sonia Mangones, Constantine Samaras
      Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.

      PubDate: 2016-09-24T11:26:00Z
      DOI: 10.1016/j.trc.2016.09.003
      Issue No: Vol. 72 (2016)
       
  • Emergence of cooperation in congested road networks using ICT and future
           and emerging technologies: A game-based review
    • Authors: Ido Klein; Eran Ben-Elia
      Pages: 10 - 28
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Ido Klein, Eran Ben-Elia
      Information and communications technologies (ICT) and future and emerging technologies (FET) are expected to revolutionize transportation in the next generation. Travelers’ behavioral adaptation is a key to their success. We discuss the notion of managing traffic congestion by enhancing cooperation in road networks enabled with ICT and FET. Cooperation is an emergent social state related to the dynamics and complexity of road traffic and reinforced learning. Game theory and research in behavioral economics show that cooperation can be leveraged to efficiently solve social dilemmas similar to traffic congestion. We review the applicability of behavioral economics and game theory concepts to route, mode and departure time choice problems. Beyond advancing theory, research on cooperation in the context of transportation is still in its infancy. We discuss state-of-the-art methodologies and their weaknesses and review the unexplored opportunities inherent in game-based methodologies. A behavioral-technological research agenda for FET is also discussed.

      PubDate: 2016-09-24T11:26:00Z
      DOI: 10.1016/j.trc.2016.09.005
      Issue No: Vol. 72 (2016)
       
  • A model and optimization-based heuristic for the operational aircraft
           maintenance routing problem
    • Authors: Nayla Ahmad Al-Thani; Mohamed Ben Ahmed; Mohamed Haouari
      Pages: 29 - 44
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Nayla Ahmad Al-Thani, Mohamed Ben Ahmed, Mohamed Haouari
      This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.

      PubDate: 2016-09-24T11:26:00Z
      DOI: 10.1016/j.trc.2016.09.004
      Issue No: Vol. 72 (2016)
       
  • Headway-based bus bunching prediction using transit smart card data
    • Authors: Haiyang Yu; Dongwei Chen; Zhihai Wu; Xiaolei Ma; Yunpeng Wang
      Pages: 45 - 59
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Haiyang Yu, Dongwei Chen, Zhihai Wu, Xiaolei Ma, Yunpeng Wang
      Bus bunching severely deteriorates the quality of transit service with poor on-time performance and excessive waiting time. To mitigate bus bunching, this paper presents a predictive framework to capture the stop-level headway irregularity based on transit smart card data. Historical headway, passenger demands, and travel time are utilized to model the headway fluctuation at the following stops. A Least Squares Support Vector Machine regression is established to detect bus bunching with the predicted headway pattern. An empirical experiment with two bus routes in Beijing is conducted to demonstrate the effectiveness of the proposed approach. The predictive method can successfully identify more than 95% of bus bunching occurrences in comparison with other well-established prediction algorithms. Moreover, the detection accuracy does not significantly deteriorate as the prediction lead time increases. Instead of regularizing the headways at all costs by adopting certain correction actions, the proposed framework can provide timely and accurate information for potential bus bunching prevention and inform passengers when the next bus will arrive. This feature will greatly increase transit ridership and reduce operating costs for transit authorities.

      PubDate: 2016-09-30T14:34:26Z
      DOI: 10.1016/j.trc.2016.09.007
      Issue No: Vol. 72 (2016)
       
  • Vehicular ad-hoc network simulations of overtaking maneuvers on two-lane
           rural highways
    • Authors: Michael Motro; Alice Chu; Junil Choi; Patricia S. Lavieri; Abdul Rawoof Pinjari; Chandra R. Bhat; Joydeep Ghosh; Robert W. Heath
      Pages: 60 - 76
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Michael Motro, Alice Chu, Junil Choi, Patricia S. Lavieri, Abdul Rawoof Pinjari, Chandra R. Bhat, Joydeep Ghosh, Robert W. Heath
      The objective of this paper is to evaluate the effectiveness of a dedicated short-range communication (DSRC)-based wireless vehicle-to-vehicle (V2V) communication system, called the overtaking assistant, devised for improving safety during overtaking (also referred to as passing) maneuvers on two-lane rural highways. Specifically, the paper examines the influence of vehicular kinematics (vehicle speeds, accelerations and distances), driver behavior (drivers’ perception/reaction time and overtaking rate), and DSRC characteristics (power settings, communication range, packet errors, sensor errors, and estimation inaccuracy) on the effectiveness of DSRC systems in predicting unsafe overtaking maneuvers. To this end, the paper utilizes a microscopic traffic simulator called VEhicles In Network Simulation (VEINS) that supports the simulation of wireless communication protocols in Vehicular Ad-hoc NEtworks (VANETs). 18,000 overtaking maneuvers – with roughly 10,000 collision maneuvers – were simulated to consider heterogeneity in vehicular kinematics, driver behavior, and DSRC performance. The overtaking assistant predicts whether a collision will occur and warns the driver before the maneuver begins. A descriptive analysis followed by a multivariate analysis (using binary discrete outcome models) of the simulated data reveals that the majority of collisions that could not be detected were due to the vehicles being out of communication range for the communication power settings used in the simulation. Packet errors, or failed communications, at a rate of up to 50% did not have a significant influence on the ability to detect collisions. These results suggest that the most important step in paving the way toward advanced driver assistance systems for rural highway overtaking maneuvers is to broaden the communication range of DSRC devices.

      PubDate: 2016-09-30T14:34:26Z
      DOI: 10.1016/j.trc.2016.09.006
      Issue No: Vol. 72 (2016)
       
  • Probabilistic analysis of the release of liquefied natural gas (LNG)
           tenders due to freight-train derailments
    • Authors: Xiang Liu; Bryan W. Schlake
      Pages: 77 - 92
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Xiang Liu, Bryan W. Schlake
      Liquefied natural gas (LNG) has emerged as a possible alternative fuel for freight railroads in the United States, due to the availability of cheap domestic natural gas and continued pursuit of environmental and energy sustainability. A safety concern regarding the deployment of LNG-powered trains is the risk of breaching the LNG tender car (a special type of hazardous materials car that stores fuel for adjacent locomotives) in a train accident. When a train is derailed, an LNG tender car might be derailed or damaged, causing a release and possible fire. This paper describes the first study that focuses on modeling the probability of an LNG tender car release incident due to a freight train derailment on a mainline. The model accounts for a number of factors such as FRA track class, method of operation, annual traffic density level, train length, the point of derailment, accident speed, the position(s) of the LNG tender(s) in a train, and LNG tender car design. The model can be applied to any specified route or network with LNG-fueled trains. The implementation of the model can be undertaken by the railroad industry to develop proactive risk management solutions when using LNG as an alternative railroad fuel.

      PubDate: 2016-09-30T14:34:26Z
      DOI: 10.1016/j.trc.2016.08.017
      Issue No: Vol. 72 (2016)
       
  • Road network inference through multiple track alignment
    • Authors: Xingzhe Xie; Kevin Bing-Yung Wong; Hamid Aghajan; Peter Veelaert; Wilfried Philips
      Pages: 93 - 108
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Xingzhe Xie, Kevin Bing-Yung Wong, Hamid Aghajan, Peter Veelaert, Wilfried Philips
      Road networks are a critical aspect of both path optimization and route planning. This paper proposes to generate the road network automatically from GPS traces through jointly aligning tracks for each road segment. First, intersections are clustered from turning points where the road users’ moving directions change. GPS traces are partitioned into small tracks for individual road segments by directly-connected intersections. The tracks for each road segment are aligned using a greedy method based on successor classification. A “forward-track” procedure is proposed to locate a warp path through jointly traversing all tracks in a way which keeps the points associated by the path element spatially close to each other. This involves an iterative procedure to cluster successor points on the tracks. The warp path produced during the alignment is used to average the tracks as the geometric representation of the road segment, and to analyze the velocity variation along the road segment. Experimental results show our method outperforms other existing methods in producing no spurious road edges and more accurate geometric road representation.

      PubDate: 2016-09-30T14:34:26Z
      DOI: 10.1016/j.trc.2016.09.010
      Issue No: Vol. 72 (2016)
       
  • Isolated intersection control for various levels of vehicle technology:
           Conventional, connected, and automated vehicles
    • Authors: Kaidi Yang; S. Ilgin Guler; Monica Menendez
      Pages: 109 - 129
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Kaidi Yang, S. Ilgin Guler, Monica Menendez
      Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection. Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.

      PubDate: 2016-09-30T14:34:26Z
      DOI: 10.1016/j.trc.2016.08.009
      Issue No: Vol. 72 (2016)
       
  • A Spatial Hazard-Based analysis for modelling vehicle selection in
           station-based carsharing systems
    • Authors: Sisi Jian; Taha Hossein Rashidi; Kasun P. Wijayaratna; Vinayak V. Dixit
      Pages: 130 - 142
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Sisi Jian, Taha Hossein Rashidi, Kasun P. Wijayaratna, Vinayak V. Dixit
      Carsharing, as an alternative to private vehicle ownership, has spread worldwide in recent years due to its potential of reducing congestion, improving auto utilization rate and limiting the environmental impact of emissions release. To determine the most efficient allocation of resources within a carsharing program, it is critical to understand what factors affect the users’ behavior when selecting vehicles. This study attempts to investigate the importance of users’ attributes and fleet characteristics on choice set formation behavior in selecting vehicles using a Spatial Hazard Based Model (SHBM). In the SHBM model, “distance to a vehicle” is considered as the prospective decision criteria that carsharing users follow when evaluating the set of alternative vehicles. This variable is analogous to the duration in a conventional hazard-based model. In addition, user socio-demographic attributes, vehicle characteristics, land use type of the trip origin, etc., collected from the Australian carsharing company GoGet are utilized to parameterize the shape/scale/location parameter of the hazard function. A number of forms of parametric SHBMs are tested to determine the best fit to the data. The accelerated failure time model with a Log-logistic distribution was found to provide the best fit. The estimation results of the coefficients of the parameters can provide a starting point for carsharing organizations to optimize their pod locations and types of cars available at different pods to maximize usage.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.008
      Issue No: Vol. 72 (2016)
       
  • Optimal deployment of autonomous vehicle lanes with endogenous market
           penetration
    • Authors: Zhibin Chen; Fang He; Lihui Zhang; Yafeng Yin
      Pages: 143 - 156
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Zhibin Chen, Fang He, Lihui Zhang, Yafeng Yin
      This paper develops a mathematical approach to optimize a time-dependent deployment plan of autonomous vehicle (AV) lanes on a transportation network with heterogeneous traffic stream consisting of both conventional vehicles (CVs) and AVs, so as to minimize the social cost and promote the adoption of AVs. Specifically, AV lanes are exclusive lanes that can only be utilized by AVs, and the deployment plan specifies when, where, and how many AV lanes to be deployed. We first present a multi-class network equilibrium model to describe the flow distributions of both CVs and AVs, given the presence of AV lanes in the network. Considering that the net benefit (e.g., reduced travel cost) derived from the deployment of AV lanes will further promote the AV adoption, we proceed to apply a diffusion model to forecast the evolution of AV market penetration. With the equilibrium model and diffusion model, a time-dependent deployment model is then formulated, which can be solved by an efficient solution algorithm. Lastly, numerical examples based on the south Florida network are presented to demonstrate the proposed models.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.013
      Issue No: Vol. 72 (2016)
       
  • A low-cost alternative for higher capacities at four-way signalized
           intersections
    • Authors: Peter Kozey; Yiguang Xuan; Michael J. Cassidy
      Pages: 157 - 167
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Peter Kozey, Yiguang Xuan, Michael J. Cassidy
      Protecting left-turn movements on all four approaches to a signalized intersection conventionally requires a minimum of two extra phases per cycle. Losses in capacity often result. Various intersection designs have been proposed to combat those losses. Perhaps the best known of these designs is the continuous flow intersection. It features specially-configured approach lanes and mid-block pre-signals. These enable opposing left-turn and through-moving vehicles to proceed through the intersection free of conflicts, and without need for additional protected-turn phases. The present paper offers an alternative design for four-way intersections, which to our knowledge has not previously been proposed. The design furnishes lower capacities than do continuous flow intersections, but spares the expense of having to reconfigure approach lanes. Pre-signals store queues and route traffic through the intersection much as in a continuous flow design. The distinguishing feature of the alternative is that it enables all four turn movements to be served during a single protected phase. Only one additional phase is therefore required per cycle. Numerical analysis shows that the plan regularly achieves higher intersection capacities than do conventional designs. Capacity gains as high as 80% are predicted. The proposed design is rather mentally taxing to drivers. Hence, opportunities for deploying the design in real settings are discussed with an eye toward the more connected and automated driving expected in the future.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.012
      Issue No: Vol. 72 (2016)
       
  • An efficient realization of deep learning for traffic data imputation
    • Authors: Yanjie Duan; Yisheng Lv; Yu-Liang Liu; Fei-Yue Wang
      Pages: 168 - 181
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Yanjie Duan, Yisheng Lv, Yu-Liang Liu, Fei-Yue Wang
      Traffic data provide the basis for both research and applications in transportation control, management, and evaluation, but real-world traffic data collected from loop detectors or other sensors often contain corrupted or missing data points which need to be imputed for traffic analysis. For this end, here we propose a deep learning model named denoising stacked autoencoders for traffic data imputation. We tested and evaluated the model performance with consideration of both temporal and spatial factors. Through these experiments and evaluation results, we developed an algorithm for efficient realization of deep learning for traffic data imputation by training the model hierarchically using the full set of data from all vehicle detector stations. Using data provided by Caltrans PeMS, we have shown that the mean absolute error of the proposed realization is under 10veh/5-min, a better performance compared with other popular models: the history model, ARIMA model and BP neural network model. We further investigated why the deep leaning model works well for traffic data imputation by visualizing the features extracted by the first hidden layer. Clearly, this work has demonstrated the effectiveness as well as efficiency of deep learning in the field of traffic data imputation and analysis.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.015
      Issue No: Vol. 72 (2016)
       
  • Optimizing signals for arterials experiencing heavy mixed scooter-vehicle
           flows
    • Authors: Chien-Lun Lan; Gang-Len Chang
      Pages: 182 - 201
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Chien-Lun Lan, Gang-Len Chang
      Recognizing the increasing popularity of scooters among urban commuters in developing countries and the significant impacts of their dynamic maneuverability on the progression of mixed traffic, this study presents a simulation-based signal optimization model for arterials experiencing heavy scooter-vehicle flows. The proposed model consists of a macroscopic simulation and a signal optimization module, where the former functions to capture the interactions between scooter and passenger-car flows over the process of discharging, propagation, and formation of intersection queues. The latter offers a specially-designed algorithm to search for the optimal signal plan and arterial offsets, based on the complex departure and arrival patterns of mixed flows estimated with the simulation module. To account for scooters’ unique parallel moving and queue patterns in a travel lane, the proposed signal module has adopted the sub-lane concept in estimating the mixed-flow queue distribution across lanes and their discharging flow rates. The results of extensive experimental analyses with various mixed-flow scenarios confirm that the proposed model offers the potential for signal design for arterials plagued by heavy scooter-vehicle mixed flows.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.011
      Issue No: Vol. 72 (2016)
       
  • Adaptive traffic parameter prediction: Effect of number of states and
           transferability of models
    • Authors: Gurcan Comert; Anton Bezuglov; Mecit Cetin
      Pages: 202 - 224
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Gurcan Comert, Anton Bezuglov, Mecit Cetin
      Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.014
      Issue No: Vol. 72 (2016)
       
  • A gradient boosting logit model to investigate driver’s stop-or-run
           behavior at signalized intersections using high-resolution traffic data
    • Authors: Chuan Ding; Xinkai Wu; Guizhen Yu; Yunpeng Wang
      Pages: 225 - 238
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Chuan Ding, Xinkai Wu, Guizhen Yu, Yunpeng Wang
      Driver’s stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers’ stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver’s SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers’ behaviors using thousands drivers’ decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply ‘black-box’ procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver’s stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.

      PubDate: 2016-10-08T19:18:02Z
      DOI: 10.1016/j.trc.2016.09.016
      Issue No: Vol. 72 (2016)
       
  • Meeting points in ridesharing: A privacy-preserving approach
    • Authors: Ulrich Matchi Aïvodji; Sébastien Gambs; Marie-José Huguet; Marc-Olivier Killijian
      Pages: 239 - 253
      Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Ulrich Matchi Aïvodji, Sébastien Gambs, Marie-José Huguet, Marc-Olivier Killijian
      Nowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pick-up and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current’s ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data. In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory.

      PubDate: 2016-10-15T02:57:37Z
      DOI: 10.1016/j.trc.2016.09.017
      Issue No: Vol. 72 (2016)
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73


      PubDate: 2016-11-26T01:34:51Z
       
  • Co-utile P2P ridesharing via decentralization and reputation management
    • Authors: David Sergio; Josep Domingo-Ferrer
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): David Sánchez, Sergio Martínez, Josep Domingo-Ferrer
      Ridesharing has the potential to bring a wealth of benefits both to the actors directly involved in the shared trip (e.g., shared travel costs or access to high-occupancy vehicle facilities) and also to the society in general (e.g., reduced traffic congestion and CO 2 emissions). However, even though ridesharing is based on a win-win collaboration and modern mobile communication technologies have significantly eased discovering and managing ride matches, the adoption of ridesharing has paradoxically decreased during the last years. In this respect, recent studies have highlighted how privacy concerns and the lack of trust among peers are crucial issues that hamper the success of ridesharing. In this paper, we tackle both of these issues by means of (i) a fully decentralized P2P ridesharing management network that avoids centralized ride-matching agencies (and hence private data compilation by such agencies); and (ii) an also decentralized reputation management protocol that brings trust among peers, even when they have not previously interacted. Our proposal rests on the recently proposed notion of co-utility (essentially, self-enforcing and mutually beneficial collaboration), which ensures that rational (even purely selfish) peers will find no incentives to deviate from the prescribed protocols. We have tested our system by using data gathered from real mobility traces of cabs in the San Francisco Bay area, and according to several metrics that quantify the degree of adoption of ridesharing and the ensuing individual and societal benefits.

      PubDate: 2016-11-04T21:53:27Z
       
  • Mining and correlating traffic events from human sensor observations with
           official transport data using self-organizing-maps
    • Authors: Enrico Steiger; Bernd Resch Porto Albuquerque Alexander Zipf
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Enrico Steiger, Bernd Resch, João Porto de Albuquerque, Alexander Zipf
      Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r =0.73), traffic incidents (r =0.59) and hazard disruptions (r =0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.

      PubDate: 2016-11-04T21:53:27Z
       
  • Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost
           truck detection using a single presence detection sensor
    • Authors: Ahmad Haj; Mosa Kyandoghere Kyamakya Ralf Junghans Mouhannad Ali Fadi
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Ahmad Haj Mosa, Kyandoghere Kyamakya, Ralf Junghans, Mouhannad Ali, Fadi Al Machot, Markus Gutmann
      This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.

      PubDate: 2016-11-04T21:53:27Z
       
  • Railroad caller districting with reliability, contiguity, balance, and
           compactness considerations
    • Authors: Siyang Xie; Yanfeng Ouyang
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Siyang Xie, Yanfeng Ouyang
      Railroad companies rely on good call centers to reliably handle incoming crew/resource call demands so as to maintain efficient operations and customer services in their networks. This paper formulates a reliable caller districting problem which aims at partitioning an undirected network into a fixed number of districts. The demand of each district is assigned to crew caller desks (one primary desk, multiple backups) under possible desk disruption scenarios. We simultaneously take into account several operational criteria, such as district contiguity and compactness, workload balance, and caller desk service reliability. The resulted districting problem is modeled in the form of a challenging mixed-integer program, and we develop a customized heuristic algorithm (based on constructive heuristic and neighborhood search) to provide near-optimum solutions in a reasonable amount of time. Hypothetical and empirical numerical examples are presented to demonstrate the performance and effectiveness of our methodology for different network sizes and parameter settings. Managerial insights are also drawn.

      PubDate: 2016-11-04T21:53:27Z
       
  • A robust approach for road users classification using the motion cues
    • Authors: Haider Talib; Karim Ismail Ali Kassim
      Abstract: Publication date: December 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 73
      Author(s): Haider Talib, Karim Ismail, Ali Kassim
      Video monitoring of traffic is a common practice in major cities. The data generated by video monitoring has practical uses such as traffic analysis for city planning. However, the usefulness of video monitoring of traffic is limited unless there is also a reliable way to automatically classify road users. This paper presents an automated method of road users’ classification into vehicles, cyclists, and pedestrians by using their motion cues. In this method, the movement of road users was captured on sequences of video frames. The videos were analysed using a feature-based tracking system, which has returned the tracks of road users. The separate pieces of information gained from these tracks are hereafter called Classifiers. There are nineteen classifiers included in this method. The classifiers’ values were assessed and integrated into a fuzzy membership framework, which in turn required prior configurations to be available. This led to the final classification of road users. The performance of this method demonstrated promising results. An important contribution of this paper is the creation of a robust approach that can integrate different classifiers using fuzzy membership framework. The developed method also uses parametric classifiers, which do not depend on the specific geometry or traffic operation of the intersection. This is a key advantage because it enables transferability and improves the practicality and usefulness of the method.

      PubDate: 2016-11-04T21:53:27Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72


      PubDate: 2016-10-28T21:44:40Z
       
 
 
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