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  Subjects -> TRANSPORTATION (Total: 134 journals)
    - AIR TRANSPORT (5 journals)
    - AUTOMOBILES (20 journals)
    - RAILROADS (4 journals)
    - ROADS AND TRAFFIC (4 journals)
    - SHIPS AND SHIPPING (15 journals)
    - TRANSPORTATION (86 journals)

TRANSPORTATION (86 journals)

Accident Analysis & Prevention     Partially Free   (Followers: 16)
AI & Society     Hybrid Journal   (Followers: 2)
Archives of Transport     Open Access   (Followers: 4)
Bitácora Urbano-Territorial     Open Access   (Followers: 1)
Cities in the 21st Century     Open Access   (Followers: 9)
Economics of Transportation     Partially Free   (Followers: 11)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 4)
European Transport Research Review     Open Access   (Followers: 10)
Geosystem Engineering     Hybrid Journal   (Followers: 3)
IATSS Research     Open Access  
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 5)
IET Electrical Systems in Transportation     Hybrid Journal   (Followers: 3)
IET Intelligent Transport Systems     Hybrid Journal   (Followers: 4)
International Innovation – Transport     Open Access   (Followers: 1)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 4)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 5)
International Journal of Critical Infrastructure Protection     Hybrid Journal   (Followers: 5)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 3)
International Journal of Electronic Transport     Hybrid Journal   (Followers: 2)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 6)
International Journal of Micro-Nano Scale Transport     Full-text available via subscription   (Followers: 2)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 7)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 6)
International Journal of Services Technology and Management     Hybrid Journal  
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 6)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 13)
International Journal of Transportation Science and Technology     Full-text available via subscription   (Followers: 4)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 4)
International Journal of Vehicular Technology     Open Access   (Followers: 2)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 10)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 1)
Journal of Navigation     Hybrid Journal   (Followers: 18)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 7)
Journal of Sustainable Mobility     Full-text available via subscription  
Journal of the Transportation Research Forum     Open Access   (Followers: 3)
Journal of Transport and Land Use     Open Access   (Followers: 8)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 5)
Journal of Transport Geography     Hybrid Journal   (Followers: 12)
Journal of Transport History     Full-text available via subscription   (Followers: 10)
Journal of Transport Literature     Open Access  
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 6)
Journal of Transportation Security     Hybrid Journal   (Followers: 2)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 12)
Journal of Transportation Technologies     Open Access   (Followers: 8)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 5)
Les Dossiers du Grihl     Open Access  
Logistique & Management     Full-text available via subscription  
Mechatronics, Electrical Power, and Vehicular Technology     Open Access  
Modern Transportation     Open Access   (Followers: 2)
Nonlinear Dynamics     Hybrid Journal   (Followers: 5)
Open Journal of Safety Science and Technology     Open Access   (Followers: 3)
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: 4)
PS: Political Science & Politics     Full-text available via subscription   (Followers: 18)
Public Transport     Hybrid Journal   (Followers: 10)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 3)
Revista Transporte y Territorio     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 1)
Sport, Education and Society     Hybrid Journal   (Followers: 10)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 2)
Streetnotes     Open Access   (Followers: 2)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 2)
Tire Science and Technology     Full-text available via subscription  
Transactions on Transport Sciences     Open Access   (Followers: 3)
Transport     Hybrid Journal   (Followers: 7)
Transport and Telecommunication Journal     Open Access   (Followers: 2)
Transport in Porous Media     Hybrid Journal  
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 5)
Transportation     Hybrid Journal   (Followers: 13)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 3)
Transportation Journal     Full-text available via subscription   (Followers: 4)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 21)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 20)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 12)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 27)
Transportation Science     Full-text available via subscription   (Followers: 15)
TRANSPORTES     Open Access   (Followers: 2)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 1)
Transportmetrica B : Transport Dynamics     Hybrid Journal  
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
Urban, Planning and Transport Research     Open Access   (Followers: 2)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 4)
Транспортні системи та технології перевезень     Open Access  
Journal Cover Transportation Research Part C: Emerging Technologies
   [14 followers]  Follow    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 0968-090X
     Published by Elsevier Homepage  [2563 journals]   [SJR: 1.605]   [H-I: 47]
  • Emerging technologies special issue of ICTIS 2013
    • Abstract: Publication date: Available online 15 August 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Liping Fu , Ming Zhong



      PubDate: 2014-08-16T20:48:09Z
       
  • Treating uncertainty in the estimation of speed from smartphone traffic
           probes
    • Abstract: Publication date: Available online 10 August 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Giuseppe Guido , Vincenzo Gallelli , Frank Saccomanno , Alessandro Vitale , Daniele Rogano , Demetrio Festa
      Before smartphone probes can be used to obtain instantaneous vehicle speeds and other dynamic characteristics, the accuracy of these estimates needs to be established under varying degrees of satellite signal interruption commonly found with varying road and traffic conditions. This paper presents the results of several vehicle tracking tests comparing smartphone speeds to benchmark values obtained for three types of routes. Benchmark values were obtained using a high frequency calibrated V-Box mounted on the test vehicle with four Android OS smartphone units. A relationship is established linking error in smartphone instantaneous speeds to the corresponding signal Circular Error Probable (CEP) range for different road and traffic conditions. This relationship is used to provide speed adjustment factors for the smartphone probe estimates subject to varying satellite signal strength. The CEP test is a reported GPS unit indicator of precision based on a known ground control benchmark. Smartphone speeds (adjusted and unadjusted) are compared to aggregate speed profiles from a stationary radar detector placed at a given location along SS106 in southern Italy. The smartphone devices were found to replicate closely the observed speed profiles obtained from the fixed detector station. Simple t-tests suggest that the means of the smartphone speeds for the unadjusted case differed significantly from the means obtained from the radar detector, when the smartphone estimates were adjusted for uncertainty (CEP range related), however, the difference in mean speeds between the smartphone probes and the radar detector profile was not found to be statistically significant.


      PubDate: 2014-08-12T20:31:52Z
       
  • From traces to trajectories: How well can we guess activity locations from
           mobile phone traces?
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Cynthia Chen , Ling Bian , Jingtao Ma
      Passively generated mobile phone dataset is emerging as a new data source for research in human mobility patterns. Information on individuals’ trajectories is not directly available from such data; they must be inferred. Many questions remain in terms how well we can capture human mobility patterns from these datasets. Only one study has compared the results from a mobile phone dataset to those from the National Household Travel Survey (NHTS), though the comparison is on two different populations and samples. This study is a very first attempt that develops a procedure to generate a simulated mobile phone dataset containing the ground truth information. This procedure can be used by other researchers and practitioners who are interested in using mobile phone data and want to formally evaluate the effectiveness of an algorithm. To identify activity locations from mobile phone traces, we develop an ensemble of methods: a model-based clustering method to identify clusters, a logistic regression model to distinguish between activity and travel clusters, and a set of behavior-based algorithms to detect types of locations visited. We show that the distribution of the activity locations identified from the simulated mobile phone dataset resembles the ground truth better than the existing studies. For home locations, 70% and 97% of identified homes are within 100 and 1000 m from the truth, respectively. For work places, 65% and 86% of the identified work places are within 100 and 1000 m from the true ones, respectively. These results point to the possibility of using these passively generated mobile phone datasets to supplement or even replace household travel surveys in transportation planning in the future.


      PubDate: 2014-08-06T20:04:45Z
       
  • Traffic volume forecasting based on radial basis function neural network
           with the consideration of traffic flows at the adjacent intersections
    • Abstract: Publication date: Available online 31 July 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Jia Zheng Zhu , Jin Xin Cao , Yuan Zhu
      The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.


      PubDate: 2014-08-02T19:54:04Z
       
  • Real-time estimation of turning movement counts at signalized
           intersections using signal phase information
    • Abstract: Publication date: Available online 31 July 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Amir H. Ghods , Liping Fu
      A variety of sensor technologies, such as loop detectors, traffic cameras, and radar have been developed for real-time traffic monitoring at intersections most of which are limited to providing link traffic information with few being capable of detecting turning movements. Accurate real-time information on turning movement counts at signalized intersections is a critical requirement for applications such as adaptive traffic signal control. Several attempts have been made in the past to develop algorithms for inferring turning movements at intersections from entry and exit counts; however, the estimation quality of these algorithms varies considerably. This paper introduces a method to improve accuracy and robustness of turning movement estimation at signalized intersections. The new algorithm makes use of signal phase status to minimize the underlying estimation ambiguity. A case study was conducted based on turning movement data obtained from a four-leg signalized intersection to evaluate the performance of the proposed method and compare it with two other existing well-known estimation methods. The results show that the algorithm is accurate, robust and fairly straightforward for real world implementation.


      PubDate: 2014-08-02T19:54:04Z
       
  • Hybrid model predictive control for freeway traffic using discrete speed
           limit signals
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): José Ramón D. Frejo , Alfredo Núñez , Bart De Schutter , Eduardo F. Camacho
      In this paper, two hybrid Model Predictive Control (MPC) approaches for freeway traffic control are proposed considering variable speed limits (VSL) as discrete variables as in current real world implementations. These discrete characteristics of the speed limits values and some necessary constraints for the actual operation of VSL are usually underestimated in the literature, so we propose a way to include them using a macroscopic traffic model within an MPC framework. For obtaining discrete signals, the MPC controller has to solve a highly non-linear optimization problem, including mixed-integer variables. Since solving such a problem is complex and difficult to execute in real-time, we propose some methods to obtain reasonable control actions in a limited computation time. The first two methods ( θ -exhaustive and θ -genetic discretization) consist of first relaxing the discrete constraints for the VSL inputs; and then, based on this continuous solution and using a genetic or an exhaustive algorithm, to find discrete solutions within a distance θ of the continuous solution that provide a good performance. The second class of methods split the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits. The speed limits optimization, which is much more time-consuming than the ramp metering one, is solved by a genetic or an exhaustive algorithm in communication with a non-linear solver for the ramp metering. The proposed methods are tested by simulation, showing not only a good performance, but also keeping the computation time reduced.


      PubDate: 2014-08-02T19:54:04Z
       
  • A hierarchical rule-based land use extraction system using geographic and
           remotely sensed data: A case study for residential uses
    • Abstract: Publication date: Available online 30 July 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Seyed Ahad Beykaei , Ming Zhong , Sajad Shiravi , Yun Zhang
      Lack of detailed land use (LU) information and of efficient data gathering methods have made modeling of urban systems difficult. This study aims to develop a hierarchical rule-based LU extraction system using very high resolution (VHR) remotely sensed imagery and geographic vector data. Land cover information extracted from remote sensing and several types of geographic data from the study area, City of Fredericton, Canada, are fused into a comprehensive database, in order to develop a sophisticated LU Extraction Expert System (LUEES). This paper illustrates how the proposed LUEES though a case study for residential uses in the study area. Morphological (individual-based) analysis at the building-level is carried out through a step-wise binary logistic regression model, which differentiates residential and non-residential buildings and results in an overall accuracy of 93.1%. The results derived from morphological analysis are then subject to a post-correction process using a spatial arrangement analysis, in order to further mitigate the misclassification issues arising from the morphological analysis. In this regard, Gabriel Graph connectivity examines the spatial structure and arrangements of urban features concerning different LU types. It is found that the spatial arrangement analysis further enhances the residential LU classification accuracy, which gives rise to an overall accuracy of 97.4%. It is believed that, equipped with such a powerful LU data collection tool and resulting detailed/accurate LU data, urban planners/modelers should be able to more reliably and precisely represent/predict economic interactions, activity locations, space and housing developments, business expansion, and trip patterns.


      PubDate: 2014-08-02T19:54:04Z
       
  • Development of an enhanced route choice model based on cumulative prospect
           theory
    • Abstract: Publication date: Available online 30 July 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Jufen Yang , Guiyan Jiang
      Route choice models have important theoretical value and practical significance for the systematic analysis of urban transportation. The method utilized to determine the reference point of travel time by considering the restrictions on reserved travel time of travelers is improved in this study. On this basis, a route choice model based on cumulative prospect theory (CPT) is proposed. A stochastic user equilibrium model based on CPT that integrates the Wardrop equilibrium principle is also developed. The data analysis results indicate that CPT provides a better description of risk attitudes than expected utility theory (EUT). The improved reference point of travel time can capture the demand of reliable travel time from different travelers. Furthermore, the numerical results of the stochastic user equilibrium model based on CPT are in agreement with the route choice behavior observed in an actual road network.


      PubDate: 2014-08-02T19:54:04Z
       
  • Development of a new microscopic passing maneuver model for two-lane rural
           roads
    • Abstract: Publication date: Available online 4 July 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Carlos Llorca , Ana Tsui Moreno , Annique Lenorzer , Jordi Casas , Alfredo Garcia
      Microsimulation is a useful tool to analyze traffic operation. On two-lane highways, the complexity of passing and the interaction with oncoming traffic requires specific models. This study focused on the development of a passing desire, decision and execution model. Results of the observation of 1752 maneuvers on 10 rural roads in Spain were used for this development. The model incorporated the effect of new factors such as available sight distance, delay and remaining travel time until the end of the highway segment. Outputs of the model were compared to observed data: firstly, individual passing maneuvers; secondly, traffic flow, percent followers and number of passing maneuvers in four single passing zones with two different traffic levels. The model was validated in four alternative passing zones.


      PubDate: 2014-07-28T19:34:45Z
       
  • Estimation of Annual Average Daily Traffic from one-week traffic counts. A
           combined ANN-Fuzzy approach
    • Abstract: Publication date: Available online 12 July 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Massimiliano Gastaldi , Gregorio Gecchele , Riccardo Rossi
      This paper presents an approach to estimation of the Annual Average Daily Traffic (AADT) from a one-week seasonal traffic count (STC) of a road section. The proposed method uses fuzzy set theory to represent the fuzzy boundaries of road groups and neural networks to assign a road segment to one or more predefined road groups. The approach was tested with data obtained in the Province of Venice, Italy, for the period of the year in which STCs are taken. The method produced accurate results, which may be of interest for proper planning of monitoring and minimizing traffic count costs.


      PubDate: 2014-07-28T19:34:45Z
       
  • Improving the accessibility of urban transportation networks for people
           with disabilities
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Laura Ferrari , Michele Berlingerio , Francesco Calabrese , Jon Reades
      What is the most effective way to enhance the accessibility of our oldest and largest public transportation systems for people with reduced mobility? The intersection of limits to government support with the growing mobility needs of the elderly and of people with disabilities calls for the development of tools that enable us to better prioritise investment in those areas that would deliver the greatest benefits to travellers. In principle and, to a lesser extent, in practice, many trains and buses are already accessible to nearly all users, leaving the stations and interchanges as the single largest and most expensive challenge facing operators trying to improve overall access to the network. Focussing on travel time and interchange differences, we present a method that uses network science and spatio-temporal analysis to rank stations in a way that minimises the divergence between accessible and non-accessible routes. Taking London as case study, we show that 50% of the most frequently followed journeys become 50% longer when wheelchair accessibility becomes a constraint. Prioritising accessibility upgrades using our network approach yields a total travel time that is more than 8 times better than a solution based on random choice, and 30% more effective than a solution that seeks solely to minimise the number of interchanges facing those with mobility constraints. These results highlight the potential for the analysis of ‘smart card’ data to enable network operators to obtain maximum value from their infrastructure investments in support of expanded access to all users.


      PubDate: 2014-07-28T19:34:45Z
       
  • An integrated traffic-driving simulation framework: Design,
           implementation, and validation
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Yunfei Hou , Yunjie Zhao , Kevin F. Hulme , Shan Huang , Yaqin Yang , Adel W. Sadek , Chunming Qiao
      This paper first describes the process of integrating two distinct transportation simulation platforms, Traffic Simulation models and Driving Simulators, so as to broaden the range of applications for which either type of simulator is applicable. To integrate the two distinct simulation platforms, several technical challenges needed to be overcome including reconciling differences in update frequency, coordinate systems, and the fidelity levels of the vehicle dynamics models and graphical rendering requirements of the two simulators. Following the successful integration, the integrated simulator was validated by having several human subjects drive a 2.5mile long segment of a signalized arterial in both the virtual environment of the integrated simulator, and in the real-world during the evening “rush hour”. Several aspects of driving behavior were then compared between the human subjects’ driving in the “virtual” and the real world. The comparisons revealed generally similar behavior, in terms of average corridor-level travel time, deceleration/acceleration patterns, lane-changing behavior, as well as energy consumption and emissions production. The paper concludes by suggesting possible extensions of the developed prototype which the researchers are currently pursuing, including integration with a computer networking simulator, to facilitate Connected Vehicle (CV) and Vehicle Ad-hoc Network (VANET) related studies, and a multiple participant component that allows several human drivers to interact simultaneously within the integrated simulator.


      PubDate: 2014-07-28T19:34:45Z
       
  • Improving rail network velocity: A machine learning approach to predictive
           maintenance
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Hongfei Li , Dhaivat Parikh , Qing He , Buyue Qian , Zhiguo Li , Dongping Fang , Arun Hampapur
      Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad. Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.


      PubDate: 2014-07-28T19:34:45Z
       
  • Temporal and weather related variation patterns of urban travel time:
           Considerations and caveats for value of travel time, value of variability,
           and mode choice studies
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Camille Kamga , M. Anıl Yazıcı
      By merging a large data set containing GPS records of taxi trips and historical weather records for New York City (NYC), the descriptive statistics of travel time (e.g. average travel time (ATT), standard deviation (SDTT), and coefficient of variation (CoV)) are calculated for each hourly period throughout the week and various weather conditions. Then, a Classification and Regression Trees methodology is used to determine the temporal patterns of ADTT, SDTT, and CoV, again for all time periods and weather conditions. Finally, the identified temporal patterns are discussed with respect to the findings and assumptions of value of time (VOT), value of reliability (VOR), and mode choice studies in the literature. The analysis shows that traditional peak hours are not necessarily the most congested periods and that the peak periods also exhibit inter-period heterogeneity in terms of ATT and SDTT. As opposed to ATT and SDTT, the coefficient of variation was shown to exhibit more consistent patterns among the days. In this respect, caution is advised for VOT–VOR studies regarding the temporal discrepancies in ATT and SDTT patterns; and CoV is suggested to be considered in VOT studies as a more robust measure. In terms of weather impacts, inclement weather is shown to have the potential to decrease SDTT and CoV at certain time periods, resulting in higher travel time reliability. This counter-intuitive finding is discussed with regards to traveler perceptions and possible implications on route and mode choice.


      PubDate: 2014-07-28T19:34:45Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45




      PubDate: 2014-07-28T19:34:45Z
       
  • Advances in computing and communications technologies and their impact on
           transportation science and applications
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Adel W. Sadek , Chunming Qiao



      PubDate: 2014-07-28T19:34:45Z
       
  • Estimating vehicle speed with embedded inertial sensors
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Eyal Levenberg
      Pavements were instrumented with inertial sensors, and the possibility of estimating the speed of a passing vehicle was investigated numerically and experimentally from the measurements of two embedded accelerometers. The sensors were spaced apart in the travel direction, and subsequently the speed was directly related to the time delay between the received signals. No assumption was made regarding the vehicle and pavement properties. Model accelerations were presented, studied, and contrasted against field measurements; the latter were shown to be dominated by random vibration sources. Two calculation techniques were offered and applied to handle the noisy data. The first was based on time-centroids, and the second was based on cross-correlation with kernel presmoothing. The overall concept is deemed promising not only for inferring speeds but also for extracting additional traffic characteristics such as axle spacing and relative axle load distributions.


      PubDate: 2014-07-28T19:34:45Z
       
  • Demand-driven timetable design for metro services
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Lijun Sun , Jian Gang Jin , Der-Horng Lee , Kay W. Axhausen , Alexander Erath
      Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers.


      PubDate: 2014-07-28T19:34:45Z
       
  • Scheduling of airport runway operations using stochastic branch and bound
           methods
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Gustaf Sölveling , John-Paul Clarke
      In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochastic branch and bound algorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochastic branch and bound algorithm have, on average, 5–7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1min of computation time.


      PubDate: 2014-07-28T19:34:45Z
       
  • Within day rescheduling microsimulation combined with macrosimulated
           traffic
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Luk Knapen , Tom Bellemans , Muhammad Usman , Davy Janssens , Geert Wets
      The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation.


      PubDate: 2014-07-28T19:34:45Z
       
  • Multi-agent simulation of individual mobility behavior in carpooling
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Stéphane Galland , Luk Knapen , Ansar-Ul-Haque Yasar , Nicolas Gaud , Davy Janssens , Olivier Lamotte , Abderrafiaa Koukam , Geert Wets
      Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely; (i) create a motive to carpool, (ii) communicate this motive with other agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans, and (v) provide a feedback to all concerned agents. In this paper, we present a conceptual design of an agent-based model (ABM) for the carpooling a that serves as a proof of concept. Our model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our carpooling application, we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm, and a utility function to trigger the negotiation process between agents. We developed a prototype of our agent-based carpooling application based on the work presented in this paper and carried out a validation study of our results with real data collected in Flanders, Belgium.


      PubDate: 2014-07-28T19:34:45Z
       
  • Robust sampled-data cruise control scheduling of high speed train
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Shukai Li , Lixing Yang , Keping Li , Ziyou Gao
      This paper investigates the robust cruise control scheduling of high speed train based on sampled-data. The dynamics model of a high speed train is modeled by a cascade of cars which are connected by flexible couplers, and is subject to rolling mechanical resistance, aerodynamic drag and wind gust. The robust cruise controller is designed for high speed train based on sampled-data. By using the method of converting the sampling period into a bounded time-varying delay, the addressed problem is transformed to the problem of stability analysis of time-varying delays system. Based on Lyapunov stability theory, sufficient conditions for the existence of robust sampled-data cruise control scheduling are given in terms of linear matrix inequality (LMI), under which the high speed train can track the desired speed, the relative spring displacement between the two neighbouring cars is robustly stable at the equilibrium state, and a prescribed H ∞ disturbance attenuation level with respect to the wind gust is guaranteed, which ensures the safety and comfort of the operating of high speed train. Numerical examples are given to illustrate the effectiveness of the proposed methods.


      PubDate: 2014-07-28T19:34:45Z
       
  • Optimal placement of omnidirectional sensors in a transportation network
           for effective emergency response and crash characterization
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Tejswaroop Geetla , Rajan Batta , Alan Blatt , Marie Flanigan , Kevin Majka
      Rapid motor vehicle crash detection and characterization is possible through the use of Intelligent Transportation Systems (ITS) and sensors are an integral part of any ITS system. The major focus of this paper is on developing optimal placement of accident detecting omnidirectional sensors to maximize incident detection capabilities and provide ample opportunities for data fusion and crash characterization. Both omnidirectional sensors (placed in suitable infrastructure locations) and mobile sensors are part of our analysis. The surrogates used are acoustic sensors (omnidirectional) and Advanced Automated Crash Notification (AACN) sensors (mobile). This data fusion rich placement is achieved through a hybrid optimization model comprising of an explicit–implicit coverage model followed by an evaluation and local search optimization using simulation. The compound explicit–implicit model delivers good initial solutions and improves the detection and data fusion capabilities compared to the explicit model alone. The results of the studies conducted quantify the use of a data fusion capable environment in crash detection scenarios, and the simulation tool developed helps a decision maker evaluate sensor placement strategy.


      PubDate: 2014-07-28T19:34:45Z
       
  • A Hybrid Queue-based Bayesian Network framework for passenger facilitation
           modelling
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Paul Pao-Yen Wu , Jegar Pitchforth , Kerrie Mengersen
      This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R 2 goodness of fit of 0.9994 and 0.9982 respectively over a 10h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.


      PubDate: 2014-07-28T19:34:45Z
       
  • Ensemble based traffic light control for city zones using a reduced number
           of sensors
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Dan Pescaru , Daniel-Ioan Curiac
      Rapid advances in computing, sensing and telecommunication technology offer unprecedented opportunities for artificial intelligence concepts to expand their applications in the field of traffic management and control. Our methodology gravitates around a powerful decision-making method: ensemble-based systems. This technique is used to accurately classify the near future traffic conditions and to make efficient decisions for adapting the traffic lights sequences within an urban area to optimize the traffic flows. The proposed approach requires only measurements provided by traffic sensors located along the principal roads entering the zone. This reduced number of sensors are considered to be enough relevant for classifying the near future state of the traffic and moreover, their measurements can be validated through analytical/hardware redundancy. Our methodology is meant to be implemented within the framework of a wireless sensor and actuator network and is confirmed by computer simulation, including normal or abnormal traffic conditions, for the central part of the city of Timisoara-Romania.


      PubDate: 2014-07-28T19:34:45Z
       
  • Hybrid powertrain optimization with trajectory prediction based on
           inter-vehicle-communication and vehicle-infrastructure-integration
    • Abstract: Publication date: August 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 45
      Author(s): Mohd Azrin Mohd Zulkefli , Jianfeng Zheng , Zongxuan Sun , Henry X. Liu
      Recent advances in Inter-Vehicle Communications (IVC) and Vehicle-Infrastructure Integration (VII) paved ways to real-time information sharing among vehicles, which are beneficial for vehicle energy management strategies (EMS). This is especially valuable for power-split hybrid electrical vehicles (HEV) in order to determine the optimal power-split between two different power sources at any particular time. Certainly, researches in this area have been done, but tradeoffs between optimality, driving-cycle sensitivity, speed of calculation and charge-sustaining (CS) conditions have not been cohesively addressed before. In light of this, a combined approach of a time-efficient powertrain optimization strategy, utilizing trajectory prediction based on IVC and VII is proposed. First, Gipps’ car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model (CTM) for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin’s Minimum Principle and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG’s improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case.


      PubDate: 2014-07-28T19:34:45Z
       
  • An optimal variable speed limits system to ameliorate traffic safety risk
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Rongjie Yu , Mohamed Abdel-Aty
      Active Traffic Management (ATM) systems have been emerging in recent years in the US and Europe. They provide control strategies to improve traffic flow and reduce congestion on freeways. This study investigates the feasibility of utilizing a Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm is proposed. First, an extension of the METANET (METANET: A macroscopic simulation program for motorway networks) traffic flow model is employed to analyze VSL’s impact on traffic flow. Then, a real-time crash risk evaluation model is estimated for the purpose of quantifying crash risk. Finally, optimal VSL control strategies are achieved by employing an optimization technique to minimize the total crash risk along the VSL implementation corridor. Constraints are setup to limit the increase of average travel time and the differences of the posted speed limits temporarily and spatially. This novel VSL control algorithm can proactively reduce crash risk and therefore improve traffic safety. The proposed VSL control algorithm is implemented and tested for a mountainous freeway bottleneck area through the micro-simulation software VISSIM. Safety impacts of the VSL system are quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels are modeled in VISSIM to monitor the sensitivity of VSL effects on driver compliance. Conclusions demonstrated that the proposed VSL system could improve traffic safety by decreasing crash risk and enhancing speed homogeneity under both the high and moderate compliance levels; while the VSL system fails to significantly enhance traffic safety under the low compliance scenario. Finally, future implementation suggestions of the VSL control strategies and related research topics are also discussed.


      PubDate: 2014-07-28T19:34:45Z
       
  • Behavioural data mining of transit smart card data: A data fusion approach
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Takahiko Kusakabe , Yasuo Asakura
      The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers’ behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset.


      PubDate: 2014-07-28T19:34:45Z
       
  • Improved vehicle classification from dual-loop detectors in congested
           traffic
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Lan Wu , Benjamin Coifman
      Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion. To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10mph and then grow exponentially as speeds drop further. Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class.


      PubDate: 2014-07-28T19:34:45Z
       
  • Use of infrared thermography for assessing HMA paving and compaction
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Christina Plati , Panos Georgiou , Andreas Loizos
      The assessment of paving and compaction temperatures effect on Hot Mix Asphalt (HMA) properties has been the subject of various researches. The present study aims to build upon these researches, by investigating the effectiveness and practicality of infrared thermography (IRT) as an emerging technology to assess HMA paving and compaction. For this purpose a field experiment was performed using a thermographic system to investigate the impact of the temperature in a paving project where two different types of HMA were used. The recorded mat surface temperatures are used effectively for the identification of temperature differentials, as well the detection of pavement defects. In addition, density-growth curves are developed for the specific mixtures and compaction pattern being used. IRT data is further analyzed for the development of simple HMA cooling models, providing a quick and efficient means to estimate the compaction time. More details and discussion are outlined in the paper.


      PubDate: 2014-07-28T19:34:45Z
       
  • Integrated feedback ramp metering and mainstream traffic flow control on
           motorways using variable speed limits
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Rodrigo Castelan Carlson , Ioannis Papamichail , Markos Papageorgiou
      Ramp metering (RM) is the most direct and efficient tool for the motorway traffic flow management. However, because of the usually short length of the on-ramps, RM is typically deactivated to avoid interference of the created ramp queue with adjacent street traffic. By the integration of local RM with mainstream traffic flow control (MTFC) enabled via variable speed limits (VSL), control operation upstream of active bottlenecks could be continued even if the on-ramp is full or if the RM lower bound has been reached. Such integration is proposed via the extension of an existing local cascade feedback controller for MTFC-VSL by use of a split-range-like scheme that allows different control periods for RM and MTFC-VSL. The new integrated controller remains simple yet efficient and suitable for field implementation. The controller is evaluated in simulation for a real motorway infrastructure (a ring-road) fed with real (measured) demands and compared to stand-alone RM or MTFC-VSL, both with feedback and optimal control results. The controller’s performance is shown to meet the specifications and to approach the optimal control results for the investigated scenario.


      PubDate: 2014-07-28T19:34:45Z
       
  • Short-term forecasting of high-speed rail demand: A hybrid approach
           combining ensemble empirical mode decomposition and gray support vector
           machine with real-world applications in China
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Xiushan Jiang , Lei Zhang , Xiqun (Michael) Chen
      Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.


      PubDate: 2014-06-18T16:10:35Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44




      PubDate: 2014-06-18T16:10:35Z
       
  • Local online kernel ridge regression for forecasting of urban travel times
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): James Haworth , John Shawe-Taylor , Tao Cheng , Jiaqiu Wang
      Accurate and reliable forecasting of traffic variables is one of the primary functions of Intelligent Transportation Systems. Reliable systems that are able to forecast traffic conditions accurately, multiple time steps into the future, are required for advanced traveller information systems. However, traffic forecasting is a difficult task because of the nonlinear and nonstationary properties of traffic series. Traditional linear models are incapable of modelling such properties, and typically perform poorly, particularly when conditions differ from the norm. Machine learning approaches such as artificial neural networks, nonparametric regression and kernel methods (KMs) have often been shown to outperform linear models in the literature. A bottleneck of the latter approach is that the information pertaining to all previous traffic states must be contained within the kernel, but the computational complexity of KMs usually scales cubically with the number of data points in the kernel. In this paper, a novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model. Exploiting the observation that traffic data exhibits strong cyclic patterns characterised by rush hour traffic, LOKRR makes use of local kernels with varying parameters that are defined around each time point. This approach has 3 advantages over the standard single kernel approach: (1) It allows parameters to vary by time of day, capturing the time varying distribution of traffic data; (2) It allows smaller kernels to be defined that contain only the relevant traffic patterns, and; (3) It is online, allowing new traffic data to be incorporated as it arrives. The model is applied to the forecasting of travel times on London’s road network, and is found to outperform three benchmark models in forecasting up to 1h ahead.


      PubDate: 2014-06-18T16:10:35Z
       
  • A trade-off analysis between penetration rate and sampling frequency of
           mobile sensors in traffic state estimation
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Christopher Bucknell , Juan C. Herrera
      The rapid-growth of smartphones with embedded navigation systems such as GPS modules provides new ways of monitoring traffic. These devices can register and send a great amount of traffic related data, which can be used for traffic state estimation. In such a case, the amount of data collected depends on two variables: the penetration rate of devices in traffic flow (P) and their data sampling frequency (z). Referring to data composition as the way certain number of observations is collected, in terms of P and z, we need to understand the relation between the amount and composition of data collected, and the accuracy achieved in traffic state estimation. This was accomplished through an in-depth analysis of two datasets of vehicle trajectories on freeways. The first dataset consists of trajectories over a real freeway, while the second dataset is obtained through microsimulation. Hypothetical scenarios of data sent by equipped vehicles were created, based on the composition of data collected. Different values of P and z were used, and each unique combination defined a specific scenario. Traffic states were estimated through two simple methods, and a more advanced one that incorporates traffic flow theory. A measure to quantify data to be collected was proposed, based on travel time, number of vehicles, penetration rate and sampling frequency. The error was below 6% for every scenario in each dataset. Also, increasing data reduced variability in data count estimation. The performance of the different estimation methods varied through each dataset and scenario. Since the same number of observations can be gathered with different combinations of P and z, the effect of data composition was analyzed (a trade-off between penetration rate and sampling frequency). Different situations were found. In some, an increase in penetration rate is more effective to reduce estimation error than an increase in sampling frequency, considering an equal increase in observations. In other areas, the opposite relationship was found. Between these areas, an indifference curve was found. In fact, this curve is the solution to the optimization problem of minimizing the error given any fixed number of observations. As a general result, increasing sampling frequency (penetration rate) is more beneficial when the current sampling frequency (penetration rate) is low, independent of the penetration rate (sampling frequency).


      PubDate: 2014-06-18T16:10:35Z
       
  • Using connected vehicle technology to improve the efficiency of
           intersections
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): S. Ilgin Guler , Monica Menendez , Linus Meier
      Information from connected vehicles, such as the position and speed of individual vehicles, can be used to optimize traffic operations at an intersection. This paper proposes such an algorithm for two one-way-streets assuming that only a certain percentage of cars are equipped with this technology. The algorithm enumerates different sequences of cars discharging from the intersection to minimize the objective function. Benefits of platooning (multiple cars consecutively discharging from a queue) and signal flexibility (adaptability to demand) are also considered. The goal is to gain insights about the value (in terms of delay savings) of using connected vehicle technology for intersection control. Simulations are conducted for different total demand values and demand ratios to understand the effects of changing the minimum green time at the signal and the penetration rate of connected cars. Using autonomous vehicle control systems, the signal could rapidly change the direction of priority without relying on the reaction of drivers. However, without this technology a minimum green time is necessary. The results of the simulations show that a minimum green time increases the delay only for the low and balanced demand scenarios. Therefore, the value of using cars with autonomous vehicle control can only be seen at intersections with this kind of demand patterns, and could result in up to 7% decrease in delay. On the other hand, using information from connected vehicles to better adapt the traffic signal has proven to be indeed very valuable. Increases in the penetration rate from 0% up to 60% can significantly reduce the average delay (in low demand scenarios a decrease in delay of up to 60% can be observed). That being said, after a penetration rate of 60%, while the delays continue to decrease, the rate of reduction decreases and the marginal value of information from communication technologies diminishes. Overall, it is observed that connected vehicle technology could significantly improve the operation of traffic at signalized intersections, at least under the proposed algorithm.


      PubDate: 2014-06-18T16:10:35Z
       
  • Experimental evaluation of CAM and DENM messaging services in vehicular
           communications
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): José Santa , Fernando Pereñíguez , Antonio Moragón , Antonio F. Skarmeta
      The Cooperative Awareness Basic Service and Decentralized Environmental Notification Basic Service have been standardized by the European Telecommunications Standards Institute (ETSI) to support vehicular safety and traffic efficiency applications needing continuous status information about surrounding vehicles and asynchronous notification of events, respectively. These standard specifications detail not only the packet formats for both the Cooperative Awareness Message (CAM) and Decentralized Environmental Notification Message (DENM), but also the general message dissemination rules. These basic services, also known as facilities, have been developed as part of a set of standards in which both ISO and ETSI describe the Reference Communication Architecture for future Intelligent Transportation Systems (ITS). By using a communications stack that instantiates this reference architecture, this paper puts in practice the usage of both facilities in a real vehicular scenario. This research work details implementation decisions and evaluates the performance of CAM and DENM facilities through a experimental testbed deployed in a semi-urban environment that uses IEEE 802.11p (ETSI G5-compliant), which is a WiFi-like communication technology conceived for vehicular communications. On the one hand, this validation considers the development of two ITS applications using CAM and DENM functionalities for tracking vehicles and disseminating traffic incidences. In this case, CAM and DENM have demonstrated to be able to offer all the necessary functionality for the study case. On the other hand, both facilities have been also validated in a extensive testing campaign in order to analyze the influence in CAM and DENM performance of aspects such as vehicle speed, signal quality or message dissemination rules. In these tests, the line of sight, equipment installation point and hardware capabilities, have been found as key variables in the network performance, while the vehicle speed has implied a slight impact.


      PubDate: 2014-06-18T16:10:35Z
       
  • Multi-modal traffic signal control with priority, signal actuation and
           coordination
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Qing He , K. Larry Head , Jun Ding
      Both coordinated-actuated signal control systems and signal priority control systems have been widely deployed for the last few decades. However, these two control systems are often conflicting with each due to different control objectives. This paper aims to address the conflicting issues between actuated-coordination and multi-modal priority control. Enabled by vehicle-to-infrastructure (v2i) communication in Connected Vehicle Systems, priority eligible vehicles, such as emergency vehicles, transit buses, commercial trucks, and pedestrians are able to send request for priority messages to a traffic signal controller when approaching a signalized intersection. It is likely that multiple vehicles and pedestrians will send requests such that there may be multiple active requests at the same time. A request-based mixed-integer linear program (MILP) is formulated that explicitly accommodate multiple priority requests from different modes of vehicles and pedestrians while simultaneously considering coordination and vehicle actuation. Signal coordination is achieved by integrating virtual coordination requests for priority in the formulation. A penalty is added to the objective function when the signal coordination is not fulfilled. This “soft” signal coordination allows the signal plan to adjust itself to serve multiple priority requests that may be from different modes. The priority-optimal signal timing is responsive to real-time actuations of non-priority demand by allowing phases to extend and gap out using traditional vehicle actuation logic. The proposed control method is compared with state-of-practice transit signal priority (TSP) both under the optimized signal timing plans using microscopic traffic simulation. The simulation experiments show that the proposed control model is able to reduce average bus delay, average pedestrian delay, and average passenger car delay, especially for highly congested condition with a high frequency of transit vehicle priority requests.


      PubDate: 2014-06-18T16:10:35Z
       
  • A finite mixture model of vehicle-to-vehicle and day-to-day variability of
           traffic network travel times
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Jiwon Kim , Hani S. Mahmassani
      This study proposes an approach to modeling the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma–Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of Gamma–Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation–Maximization (EM) algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-to-day variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component Gamma–Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma–Gamma model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability—vehicle-to-vehicle and day-to-day variability—but also the unobserved heterogeneity within these variability types, thereby allowing the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way.


      PubDate: 2014-06-18T16:10:35Z
       
  • Transit network design by genetic algorithm with elitism
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Muhammad Ali Nayeem , Md. Khaledur Rahman , M. Sohel Rahman
      The transit network design problem is concerned with the finding of a set of routes with corresponding schedules for a public transport system. This problem belongs to the class of NP-Hard problem because of the vast search space and multiple constraints whose optimal solution is really difficult to find out. The paper develops a Population based model for the transit network design problem. While designing the transit network, we give preference to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Genetic Algorithm (GA) optimization. The Genetic Algorithm is similar to evolution strategy which iterates through fitness assessment, selection and breeding, and population reassembly. In this paper, we will show two different experimental results performed on known benchmark problems. We clearly show that results obtained by Genetic Algorithm with increasing population is better than so far best technique which is really difficult for future researchers to beat.


      PubDate: 2014-06-18T16:10:35Z
       
  • Dynamics of connected vehicle systems with delayed acceleration feedback
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Jin I. Ge , Gábor Orosz
      In this paper, acceleration-based connected cruise control (CCC) is proposed to increase roadway traffic mobility. CCC is designed to be able to use acceleration signals received from multiple vehicles ahead through wireless vehicle-to-vehicle (V2V) communication. We consider various connectivity structures in heterogeneous platoons comprised of human-driven and CCC vehicles. We show that inserting a few CCC vehicles with appropriately designed gains and delays into the flow, one can stabilize otherwise string unstable vehicle platoons. Exploiting the flexibility of ad-hoc connectivity, CCC can be applied in a large variety of traffic scenarios. Moreover, using acceleration feedback in a selective manner, CCC provides robust performance and remains scalable for large systems of connected vehicles. Our conclusions are verified by simulations at the nonlinear level.
      Graphical abstract image

      PubDate: 2014-06-18T16:10:35Z
       
  • Optimization of nonlinear control strategy for anti-lock braking system
           with improvement of vehicle directional stability on split-μ roads
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Hossein Mirzaeinejad , Mehdi Mirzaei
      In a hard braking on a split-μ road, the achievement of shorter stopping distance while maintaining the vehicle in the straight line are of great importance. In this paper, to achieve these conflicting aims, an optimal nonlinear algorithm based on the prediction of vehicle responses is presented to distribute the wheel braking forces properly. The base of this algorithm is reducing the maximum achievable braking forces of one side wheels, as low as possible, so that the minimum stabilizing yaw moment is produced. The optimal property of the proposed control method makes it possible to get a trade-off between the shorter stopping distance and the less deviation of the vehicle heading from the straight line. The special case of this algorithm leads to the conventional anti-lock braking system (ABS) which generates the maximum braking forces for all wheels to attain the minimum stopping distance. However, the ABS cannot control the vehicle directional stability directly. The simulation results carried out using a nonlinear 8-DOF vehicle model demonstrate that the designed control system has a suitable performance to attain the desired purposes compared with the convectional ABS.


      PubDate: 2014-06-18T16:10:35Z
       
  • The time slot allocation problem under uncertain capacity
    • Abstract: Publication date: September 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 46
      Author(s): Luca Corolli , Guglielmo Lulli , Lewis Ntaimo
      This paper presents two stochastic programming models for the allocation of time slots over a network of airports. The proposed models address three key issues. First, they provide an optimization tool to allocate time slots, which takes several operational aspects and airline preferences into account; second, they execute the process on a network of airports; and third they explicitly include uncertainty. To the best of our knowledge, these are the first models for time slot allocation to consider both the stochastic nature of capacity reductions and the problem’s network structure. From a practical viewpoint, the proposed models provide important insights for the allocation of time slots. Specifically, they highlight the tradeoff between the schedule/request discrepancies, i.e., the time difference between allocated time slots and airline requests, and operational delays. Increasing schedule/request discrepancies enables a reduction in operational delays. Moreover, the models are computationally viable. A set of realistic test instances that consider the scheduling of four calendar days on different European airport networks has been solved within reasonable – for the application’s context – computation times. In one of our test instances, we were able to reduce the sum of schedule/request discrepancies and operational delays by up to 58%. This work provides slot coordinators with a valuable decision making tool, and it indicates that the proposed approach is very promising and may lead to relevant monetary savings for airlines and aircraft operators.


      PubDate: 2014-06-18T16:10:35Z
       
  • Multi-objective optimization of train routing problem combined with train
           scheduling on a high-speed railway network
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Yahua Sun , Chengxuan Cao , Chao Wu
      Based on train scheduling, this paper puts forward a multi-objective optimization model for train routing on high-speed railway network, which can offer an important reference for train plan to provide a better service. The model does not only consider the average travel time of trains, but also take the energy consumption and the user satisfaction into account. Based on this model, an improved GA is designed to solve the train routing problem. The simulation results demonstrate that the accurate algorithm is suitable for a small-scale network, while the improved genetic algorithm based on train control (GATC) applies to a large-scale network. Finally, a sensitivity analysis of the parameters is performed to obtain the ideal parameters; a perturbation analysis shows that the proposed method can quickly handle the train disturbance.


      PubDate: 2014-04-29T19:00:00Z
       
  • Comparison of emerging ground propulsion systems for electrified aircraft
           taxi operations
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Rui Guo , Yu Zhang , Qing Wang
      Aviation is a mode with high fuel consumption per passenger mile and has significant environmental impacts. It is important to seek ways to reduce fuel consumption by the aviation sector, but it is difficult to improve fuel efficiency during the en-route cruise phase of flight because of technology barriers, safety requirements, and the mode of operations of air transportation. Recent efforts have emphasized the development of innovative Aircraft Ground Propulsion Systems (AGPS) for electrified aircraft taxi operations. These new technologies are expected to significantly reduce aircraft ground-movement-related fuel burn and emissions. This study compares various emerging AGPS systems and presents a comprehensive review on the merits and demerits of each system, followed with the local environmental impacts assessment of these systems. Using operational data for the 10 busiest U.S. airports, a comparison of environmental impacts is performed for four kinds of AGPS: conventional, single engine-on, external, and on-board systems. The results show that there are tradeoffs in fuel and emissions among these emerging technologies. On-board system shows the best performance in the emission reduction, while external system shows the least fuel burn. Compared to single-engine scenario, external AGPS shows the reduction of HC and CO emissions but the increase of NO x emission. When a general indicator is considered, on-board AGPS shows the best potential of reducing local environmental impacts. The benefit-cost analysis shows that both external and on-board systems are worth being implemented and the on-board system appeals to be more beneficial.


      PubDate: 2014-04-29T19:00:00Z
       
  • An analysis of airport–airline vertical relationships with risk
           sharing contracts under asymmetric information structures
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Katsuya Hihara
      We analyze the double moral hazard problem at the joint venture type airport–airline vertical relationship, where two parties both contribute efforts to the joint venture but neither of them can see the other’s efforts. With the continuous-time stochastic dynamic programming model, we show that by the de-centralized utility maximizations of two parties under very strict conditions, i.e., optimal efforts’ cost being negligible and their risk averse parameters both asymptotically approaching to zero, the vertical contract could be agreed as the optimal sharing rule, which is the linear function of the final state with the slope being the product of their productivity difference and uncertainty (diffusion rate) level index. If both parties’ productivities are same, or the diffusion rate of the underlying process is unity, optimal linear sharing rule do not depend on the final state. If their conditions not dependent on final state are symmetric as well, then risk sharing disappears completely. In numerical examples, we illustrate the complex impact of uncertainty increase and end-of-period load factor improvement on the optimal sharing rule, and the relatively simple impact on total utility levels.


      PubDate: 2014-04-29T19:00:00Z
       
  • Validating travel behavior estimated from smartcard data
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Marcela Munizaga , Flavio Devillaine , Claudio Navarrete , Diego Silva
      In this paper, we present a validation of public transport origin–destination (OD) matrices obtained from smartcard and GPS data. These matrices are very valuable for management and planning but have not been validated until now. In this work, we verify the assumptions and results of the method using three sources of information: the same database used to make the estimations, a Metro OD survey in which the card numbers are registered for a group of users, and a sample of volunteers. The results are very positive, as the percentages of correct estimation are approximately 90% in all cases.


      PubDate: 2014-04-29T19:00:00Z
       
  • Ship speed optimization: Concepts, models and combined speed-routing
           scenarios
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Harilaos N. Psaraftis , Christos A. Kontovas
      The purpose of this paper is to clarify some important issues as regards ship speed optimization at the operational level and develop models that optimize ship speed for a spectrum of routing scenarios in a single ship setting. The paper’s main contribution is the incorporation of those fundamental parameters and other considerations that weigh heavily in a ship owner’s or charterer’s speed decision and in his routing decision, wherever relevant. Various examples are given so as to illustrate the properties of the optimal solution and the various trade-offs that are involved.


      PubDate: 2014-04-29T19:00:00Z
       
  • Multicomponent decomposition of a time-varying acoustic Doppler signal
           generated by a passing railway vehicle using Complex Shifted Morlet
           Wavelets
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Yiakopoulos Christos , Maczak Jedrzej , Rodopoulos Konstantinos , Antoniadis Ioannis
      Complex Shifted Morlet Wavelets (CSMW) present a number of advantages, since the concept of shifting the Morlet wavelet in the frequency domain allow the simultaneous optimal selection of both the wavelet center frequency and the wavelet bandwidth. According to the proposed method, a cluster of CSMW wavelets is used, covering appropriate ranges in the frequency domain. Then, instead of directly processing the instantaneous frequency of each CSMW, an invariance approach is used to indirectly recover the individual harmonic components of the signal. This invariance approach is based actually on the same rotational approach, using the same matrix properties, which consists the core of the well known ESPRIT algorithm. Moreover, the DESFRI ( DE tection of S ource F requencies via R otational I nvariance) approach is introduced to support the proposed CSMW method to semi-automated selection of the center frequency of the applied Morlet window. This approach is based on the singular values that are extracted as an intermediate product of the proposed decomposition process. By the application of the method in a multi-component synthetic signal a way to select the critical parameters of the Morlet wavelet, is investigated. The method is further tested on a time-varying acoustic Doppler signal generated by a passing railway vehicle, indicating promising results for the estimation of the variable instantaneous frequency and the multi-component decomposition of it.


      PubDate: 2014-04-29T19:00:00Z
       
  • Microscopic modeling of pedestrian movement behavior: Interacting with
           visual attractors in the environment
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): W.L. Wang , S.M. Lo , S.B. Liu , H. Kuang
      Goal-directed pedestrian movement behavior is extensively studied by researchers from varied fields, but pedestrian’s movement actions such as ‘impulse stops’ resulting from exploratory movement behavior receive little attention. To understand this, an effective tool that can reveal the attractive interactions between pedestrians and attractors in the environment is needed. This study introduces an agent-based microscopic pedestrian simulation model—CityFlow-U. To determine whether a pedestrian would stop for visual attractors, factors of attractor’s attractiveness, distance to the attractor as well as the visibility of the attractor from current location of the agent are considered. By analyzing the parameters in this model, we have successfully revealed different pedestrian movement modes, attractor preferences and movement trajectories in a notional setting. The reliability of the model is then demonstrated with a simulation scenario targeting at a circulation region of a shopping mall in Hong Kong. Observational data is used for model input and the number changes of attracted pedestrians in front of a major attractor are compared between simulation results and empirical video data. Results from the parameter analysis and simulation scenario show that the model is flexible and can benefit in real applications such as shop arrangement as well as street furniture placement.


      PubDate: 2014-04-29T19:00:00Z
       
 
 
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