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  Subjects -> TRANSPORTATION (Total: 132 journals)
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TRANSPORTATION (84 journals)

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Journal Cover Transportation Research Part C: Emerging Technologies
   [13 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]
  • Introduction to the Special Issue on ‘‘Nature-Inspired
           
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 3
      Author(s): Bin Yu , Dusan Teodorovic , Zhongzhen Yang



      PubDate: 2014-06-18T16:10:35Z
       
  • Transit network design based on travel time reliability
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 3
      Author(s): Baozhen Yao , Ping Hu , Xiaohong Lu , Junjie Gao , Mingheng Zhang
      This paper presents a transit network optimization method, in which travel time reliability on road is considered. A robust optimization model, taking into account the stochastic travel time, is formulated to satisfy the demand of passengers and provide reliable transit service. The optimization model aims to maximize the efficiency of passenger trips in the optimized transit network. Tabu search algorithm is defined and implemented to solve the problem. Then, transit network optimization method proposed in this paper is tested with two numerical examples: a simple route and a medium-size network. The results show the proposed method can effectively improve the reliability of a transit network and reduce the travel time of passengers in general.


      PubDate: 2014-06-18T16:10:35Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 1




      PubDate: 2014-06-18T16:10:35Z
       
  • Prefa Some new technologies used in the transportation field
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 2
      Author(s): Jean-Marc Blosseville



      PubDate: 2014-06-18T16:10:35Z
       
  • Special issue on short-term traffic flow forecasting
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 1
      Author(s): Yunlong Zhang



      PubDate: 2014-06-18T16:10:35Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 3




      PubDate: 2014-06-18T16:10:35Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 2




      PubDate: 2014-06-18T16:10:35Z
       
  • Urban activity pattern classification using topic models from online
           geo-location data
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Samiul Hasan , Satish V. Ukkusuri
      Location-based check-in services in various social media applications have enabled individuals to share their activity-related choices providing a new source of human activity data. Although geo-location data has the potential to infer multi-day patterns of individual activities, appropriate methodological approaches are needed. This paper presents a technique to analyze large-scale geo-location data from social media to infer individual activity patterns. A data-driven modeling approach, based on topic modeling, is proposed to classify patterns in individual activity choices. The model provides an activity generation mechanism which when combined with the data from traditional surveys is potentially a useful component of an activity-travel simulator. Using the model, aggregate patterns of users’ weekly activities are extracted from the data. The model is extended to also find user-specific activity patterns. We extend the model to account for missing activities (a major limitation of social media data) and demonstrate how information from activity-based diaries can be complemented with longitudinal geo-location information. This work provides foundational tools that can be used when geo-location data is available to predict disaggregate activity patterns.


      PubDate: 2014-06-18T16:10:35Z
       
  • A bi-objective maintenance scheduling for power feeding substations in
           electrified railways
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Shao-kuan Chen , Tin-kin Ho , Bao-hua Mao , Yun Bai
      Railway infrastructure is characterized by intensive capital investment, long lifecycle and low return. It is crucial to attain a reasonable system reliability while keeping the recurrent cost manageable throughout the asset lifecycle. Power feeding substations are the key assets on electrified rail lines. As they are subject to various adverse operating conditions, maintenance works of different levels are scheduled to ensure reliability and extend the asset life time if possible. Maintenance scheduling is often regarded as a trade-off between reliability and cost. This study incorporates considerations of different levels of maintenance activities to balance between reliability and cost. In establishing the system reliability model, the contribution of individual component reliability toward the overall system reliability is extracted from the functional relationship among the components. Solution methodologies to this scheduling problem are also proposed here. Evaluation of the scheduling model and the proposed solution is discussed and analyzed through simulation. To cater for the operating conditions in different systems, the impact of weighting factors between reliability and cost on the variations of resulting schedules will be investigated.


      PubDate: 2014-06-18T16:10:35Z
       
  • A study on smart parking guidance algorithm
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Jong-Ho Shin , Hong-Bae Jun
      Parking problem becomes one of major issues in the city transportation management since the spatial resource of a city is limited and the parking cost is expensive. Lots of cars on the road should spend unnecessary time and consume energy during searching for parking due to limited parking space. To cope with these limitations and give more intelligent solutions to drivers in the selection of parking facility, this study proposes a smart parking guidance algorithm. The proposed algorithm supports drivers to find the most appropriate parking facility considering real-time status of parking facilities in a city. To suggest the most suitable parking facility, several factors such as driving distance to the guided parking facility, walking distance from the guided parking facility to destination, expected parking cost, and traffic congestion due to parking guidance, are considered in the proposed algorithm. To evaluate the effectiveness of the proposed algorithm, simulation tests have been carried out. The proposed algorithm helps to maximize the utilization of space resources of a city, and reduce unnecessary energy consumption and CO2 emission of wandering cars since it is designed to control the utilization of parking facility efficiently and reduce traffic congestion due to parking space search.


      PubDate: 2014-06-18T16:10:35Z
       
  • High-resolution numerical relaxation approximations to second-order
           macroscopic traffic flow models
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): A.I. Delis , I.K. Nikolos , M. Papageorgiou
      A novel numerical approach for the approximation of several, widely applied, macroscopic traffic flow models is presented. A relaxation-type approximation of second-order non-equilibrium models, written in conservation or balance law form, is considered. Using the relaxation approximation, the nonlinear equations are transformed to a semi-linear diagonilizable problem with linear characteristic variables and stiff source terms. To discretize the resulting relaxation system, low- and high-resolution reconstructions in space and implicit–explicit Runge–Kutta time integration schemes are considered. The family of spatial discretizations includes a second-order MUSCL scheme and a fifth-order WENO scheme, and a detailed formulation of the scheme is presented. Emphasis is given on the WENO scheme and its performance for solving the different traffic models. To demonstrate the effectiveness of the proposed approach, extensive numerical tests are performed for the different models. The computations reported here demonstrate the simplicity and versatility of relaxation schemes as solvers for macroscopic traffic flow models.


      PubDate: 2014-06-18T16:10:35Z
       
  • Brake light cellular automaton model with advanced randomization for
           traffic breakdown
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Jun-fang Tian , Ning Jia , Ning Zhu , Bin Jia , Zhen-zhou Yuan
      Traffic breakdown is one of the most important empirical phenomena in traffic flow theory. Unfortunately, it cannot be simulated by many traffic flow models. In order to clarify its mechanism, the new brake light cellular automaton model has been proposed. Comparing with previous brake light models, three different aspects have been considered: (i) drivers tend to take large decelerations if the time gap is smaller than the safe time gap and the leading vehicle’s brake light is on; (ii) the brake light rule is set according to the reality; (iii) the randomization rule is put forward before the acceleration rule to weaken the impact of brake light on driving behaviors. Analyses show that the new model can explain the mechanism of traffic breakdown and the failures of other brake light models. Simulations confirm that all empirical features of traffic breakdown are successfully reproduced. At last, brake light models are calibrated and validated by the I-80 empirical data provided by NGSIM. Results show that the performance of the new model is the best and models in the three-phase theory are not necessarily better than models in the fundamental diagram approach and vice versa, at least for the brake light models.


      PubDate: 2014-06-18T16:10:35Z
       
  • A novel permit scheme for managing parking competition and bottleneck
           congestion
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Wei Liu , Hai Yang , Yafeng Yin , Fangni Zhang
      Morning commuters may have to depart from home earlier to secure a parking space when parking supply in the city center is insufficient. Recent studies show that parking reservations can reduce highway congestion and deadweight loss of parking competition simultaneously. This study develops a novel tradable parking permit scheme to realize or implement parking reservations when commuters are either homogeneous or heterogeneous in their values of time. It is found that an expirable parking permit scheme with an infinite number of steps, i.e., the ideal-scheme, is superior to a time-varying pricing scheme in the sense that designing a permit scheme does not require commuters’ value of time information and the performance of the scheme is robust to the variation of commuters’ value of time. Although it is impractical to implement the ideal-scheme with an infinite number of steps, the efficiency loss of a permit scheme with finite steps can be bounded in both cases of homogeneous and heterogeneous commuters. Moreover, considering the permit scheme may lead to an undesirable benefit distribution among commuters, we propose an equal cost-reduction distribution of parking permits where auto commuters with higher value of time will receive fewer permits.


      PubDate: 2014-06-18T16:10:35Z
       
  • Data-based speed-limit-respecting eco-driving system
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Evgenia Suzdaleva , Ivan Nagy
      The paper describes application of data-based Bayesian approach to model identification and control problems in the field of fuel consumption optimization for conventional vehicles. The main contributions of the presented approach are: (i) analysis of data measured on a driven vehicle; (ii) data-based model construction, its real-time estimation and adaptation; (iii) control criterion using simultaneously setpoints for fuel consumption and speed; and (iv) universal recursive Bayesian algorithms of estimation and control implemented as semi-automatic eco-driving system. Experiments with real data report reduction in fuel consumption.


      PubDate: 2014-06-18T16:10:35Z
       
  • Real-time merging traffic control for throughput maximization at motorway
           work zones
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): A. Tympakianaki , A. Spiliopoulou , A. Kouvelas , I. Papamichail , M. Papageorgiou , Y. Wang
      Work zones on motorways necessitate the drop of one or more lanes which may lead to significant reduction of traffic flow capacity and efficiency, traffic flow disruptions, congestion creation, and increased accident risk. Real-time traffic control by use of green–red traffic signals at the motorway mainstream is proposed in order to achieve safer merging of vehicles entering the work zone and, at the same time, maximize throughput and reduce travel delays. A significant issue that had been neglected in previous research is the investigation of the impact of distance between the merge area and the traffic lights so as to achieve, in combination with the employed real-time traffic control strategy, the most efficient merging of vehicles. The control strategy applied for real-time signal operation is based on an ALINEA-like proportional–integral (PI-type) feedback regulator. In order to achieve maximum performance of the control strategy, some calibration of the regulator’s parameters may be necessary. The calibration is first conducted manually, via a typical trial-and-error procedure. In an additional investigation, the recently proposed learning/adaptive fine-tuning (AFT) algorithm is employed in order to automatically fine-tune the regulator parameters. Experiments conducted with a microscopic simulator for a hypothetical work zone infrastructure, demonstrate the potential high benefits of the control scheme.


      PubDate: 2014-06-18T16:10:35Z
       
  • Characterization and prediction of air traffic delays
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Juan Jose Rebollo , Hamsa Balakrishnan
      This paper presents a new class of models for predicting air traffic delays. The proposed models consider both temporal and spatial (that is, network) delay states as explanatory variables, and use Random Forest algorithms to predict departure delays 2–24h in the future. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and links (origin–destination pairs) in the network, new network delay variables that characterize the global delay state of the entire National Airspace System at the time of prediction are proposed. The paper analyzes the performance of the proposed prediction models in both classifying delays as above or below a certain threshold, as well as predicting delay values. The models are trained and validated on operational data from 2007 and 2008, and are evaluated using the 100 most-delayed links in the system. The results show that for a 2-h forecast horizon, the average test error over these 100 links is 19% when classifying delays as above or below 60min. Similarly, the average over these 100 links of the median test error is found to be 21min when predicting departure delays for a 2-h forecast horizon. The effects of changes in the classification threshold and forecast horizon on prediction performance are studied.


      PubDate: 2014-06-18T16:10:35Z
       
  • Energy-aware control for automated container terminals using integrated
           flow shop scheduling and optimal control
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Jianbin Xin , Rudy R. Negenborn , Gabriël Lodewijks
      The performance of container terminals needs to be improved to handle the growth of transported containers and maintain port sustainability. This paper provides a methodology for improving the handling capacity of an automated container terminal in an energy-efficient way. The behavior of a container terminal is considered as consisting of a higher level and a lower level represented by discrete-event dynamics and continuous-time dynamics, respectively. These dynamics represent the behavior of a large number of terminal equipment. The dynamics need to be controlled. For controlling the higher level dynamics, a minimal makespan problem is solved. For this, the minimal time required by equipment for performing an operation at the lower level is needed. The minimal time for performing an operation at the lower level is obtained using Pontryagin’s Minimum Principle. The actual operation time allowed by the higher level for processing an operation at the lower level is subsequently determined by a scheduling algorithm at the higher level. Given an actual operation time, the lower level dynamics are controlled using optimal control to achieve minimal energy consumption while respecting the time constraint. Simulation studies illustrate how energy-efficient management of equipment for the minimal makespan could be obtained using the proposed methodology.


      PubDate: 2014-06-18T16:10:35Z
       
  • Camera calibration and vehicle tracking: Highway traffic video analytics
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Yiwen Wan , Yan Huang , Bill Buckles
      We describe a real-time highway surveillance system (RHSS), which operates autonomously to collect statistics (speed and volume) and generates incident alerts (e.g., stopped vehicles). The system is designed to optimize long-term real-time performance accuracy. It also provides convenient integration to an existing surveillance infrastructure with different levels of service. Innovations include a novel 3-D Hungarian algorithm which is utilized for object tracking and a practical, hands-off mechanism for camera calibration. Speed is estimated based on trajectories after mapping/alignment with respect to dominant paths learned based on an evolutionary dynamics model. The system, RHSS, is intensively evaluated under different scenarios such as rain, low-contrast and high-contrast lightings. Performance is presented in comparison to a current commercial product. The contribution is innovation of new technologies that enable hands-off calibration (i.e., automatic detection of vanishing points) and improved accuracy (i.e., illumination balancing, tracking via a new 3-D Hungarian algorithm, and re-initialization of background detection on-the-fly). Results indicate the capability and applicability of the proposed system in real-time and real-world settings.


      PubDate: 2014-06-18T16:10:35Z
       
  • Expirable parking reservations for managing morning commute with parking
           space constraints
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Wei Liu , Hai Yang , Yafeng Yin
      When total parking supply in an urban downtown area is insufficient, morning commuters would choose their departure times not only to trade off bottleneck congestion and schedule delays, but also to secure a parking space. Recent studies found that an appropriate combination of reserved and unreserved parking spaces can spread the departures of those morning commuters and hence reduce their total travel cost. To further mitigate both traffic congestion and social cost from competition for parking, this study considers a parking reservation scheme with expiration times, where commuters with a parking reservation have to arrive at parking spaces for the reservation before a predetermined expiration time. We first show that if all parking reservations have the same expiration time, it is socially preferable to set the reservations to be non-expirable, i.e., without expiration time. However, if differentiated expiration times are properly designed, the total travel cost can be further reduced as compared with the reservation scheme without expiration time, since the peak will be further smoothed out. We explore socially desirable equilibrium flow patterns under the parking reservation scheme with differentiated expiration times. Finally, efficiencies of the reservation schemes are examined.


      PubDate: 2014-06-18T16:10:35Z
       
  • An efficient algorithm for smoothing airspace congestion by fine-tuning
           take-off times
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Jenaro Nosedal , Miquel A. Piera , Sergio Ruiz , Alvaro Nosedal
      Current technological advances in communications and navigation have improved air traffic management (ATM) with new decision support tools to balance airspace capacity with user demands. Despite agreements achieved in flying reference business trajectories (RBTs) among different stakeholders, tight spatio-temporal connectivity between trajectories in dense sectors can cause perturbations that might introduce time or space deviations into the original RBTs, thus potentially affecting other 4D trajectories. In this paper, several challenging results are presented by properly tuning the Calculated Take-Off Times (CTOTs) as a tool for mitigating the propagation of perturbations between trajectories that can readily appear in dense sectors. Based on the identification of “collective microregions”, a tool for predicting potential spatio-temporal concurrence events between trajectories over the European airspace was developed, together with a CTOT algorithm to sequence the departures that preserve the scheduled slots while relaxing tight trajectory interactions. The algorithm was tested by considering a realistic scenario (designed and analyzed in the STREAM project (Stream, 2013)) to evaluate relevant ATM KPIs that provide aggregated information about the sensitivity of the system to trajectory interactions, taking into account the system dynamics at a network level. The proposed approach contributes to enhancing the ATM capacity of airports to mitigate network perturbations.


      PubDate: 2014-06-18T16:10:35Z
       
  • A Bayesian approach to detect pedestrian destination-sequences from WiFi
           signatures
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Antonin Danalet , Bilal Farooq , Michel Bierlaire
      In this paper, we propose a methodology to use the communication network infrastructure, in particular WiFi traces, to detect the sequence of activity episodes visited by pedestrians. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers activity-episode locations based on WiFi traces and calculates the likelihood of observing these traces in the pedestrian network, taking into account prior knowledge. The output of the method consists of candidates of activity-episodes sequences associated with the likelihood to be the true one. The methodology is validated on traces generated by a known sequence of activities, while the performance being evaluated on a set of anonymous users. Results show that it is possible to predict the number of episodes and the activity-episodes locations and durations, by merging information about the activity locations on the map, WiFi measurements and prior information about schedules and the attractivity in pedestrian infrastructure. The ambiguity of each activity episode in the sequence is explicitly measured.


      PubDate: 2014-06-18T16:10:35Z
       
  • Predictability impacts of airport surface automation
    • Abstract: Publication date: July 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 44
      Author(s): Yi Liu , Mark Hansen , Gautam Gupta , Waqar Malik , Yoon Jung
      Past evaluations of airport surface operations automation technologies have focused on capacity utilization, delay mitigation and fuel efficiency impacts. Predictability, while recognized as an important operational performance goal, has received little attention. One reason could be that applicable predictability metrics have not been developed in the context of airport surface operations management. This research fills the gap by proposing metrics for predictability performance evaluation. Using results from a SARDA human-in-the-loop simulation conducted at NASA Ames’ Future Flight Central, we present a comprehensive assessment of the predictability impacts of airport surface automation. A wide range of the impacts is considered, which includes variability in taxi-out time, predictability of take-off time and take-off sequence, entropy of the airfield state, and perceived predictability from users.


      PubDate: 2014-06-18T16:10:35Z
       
  • 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
       
  • A space–time diurnal method for short-term freeway travel time
           prediction
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 1
      Author(s): Yajie Zou , Xinxin Zhu , Yunlong Zhang , Xiaosi Zeng
      A number of short-term travel time prediction approaches have been developed in the past decade. However, few studies take into account spatial and temporal travel time information simultaneously in the prediction approach. In this study, we proposed a space–time diurnal (ST-D) method, which merges the spatial and temporal travel time information to obtain accurate short-term travel time prediction for freeway corridors under different traffic conditions. The proposed approach can take into account important characteristics of travel times: spatial and temporal correlation, diurnal pattern, and the nonnegativity of the travel time. We use two distributions to model the 5-min average travel time: a truncated normal distribution and a lognormal distribution. Contrary to the most existing methods that yield a point prediction of short-term travel time, this probabilistic modeling approach can overcome the drawbacks of the point prediction by fitting a probability distribution to describe the uncertainty of the future travel times, and from which prediction intervals can be calculated. We use minimum continuous ranked probability score (CRPS) estimation to numerically estimate the parameters in the prediction models. The ST-D method is examined using the travel time data collected on a segment along the US-290 in Houston, Texas. The proposed method provides prediction of travel time over 5-min intervals for up to 1h in advance. It was found that travel time data from neighboring links along the freeway corridors can be efficiently used to obtain reliable short-term prediction of travel time. The study results suggest the ST-D method is more robust than the traditional vector autoregressive models.


      PubDate: 2014-06-18T16:10:35Z
       
  • Using cell phone data to measure quality of service and passenger flows of
           Paris transit system
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 2
      Author(s): Vincent Aguiléra , Sylvain Allio , Vincent Benezech , François Combes , Chloé Milion
      This paper shows that the particular conditions under which a cellular phone network is operated underground can make it possible to measure passenger flows in an underground transit system. With the help of the mobile network operator Orange, some experiments have been conducted in Paris underground transit system to assess the potential of this new kind of data for transportation studies. The results show that good estimates of dynamic quantities, such as travel times, train occupancy levels and origin–destination flows can be derived from cellular data. The travel times, train occupancy levels and origin–destination flows inferred from cellular data have been compared to direct field observations and Automatic Fare Collection data provided by the STIF (the public transport authority in the Paris metropolitan area). The quantities inferred from cellular data are shown to be consistent with those inferred from the other data sources.


      PubDate: 2014-06-18T16:10:35Z
       
  • Application specific instance generator and a memetic algorithm for
           capacitated arc routing problems
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 3
      Author(s): Min Liu , Hemant Kumar Singh , Tapabrata Ray
      Capacitated arc routing problem (CARP) is a well known combinatorial problem that requires identifying minimum total distance traveled by a fleet of vehicles in order to serve a set of roads without violating the vehicles’ capacity constraints. A number of optimization algorithms have been proposed over the years to solve basic CARPs and their performance have been analyzed using selected benchmark suites available in literature. From an application point of view, there is a need to assess the performance of algorithms on specific class of instances that resemble realistic applications, e.g., inspection of electric power lines, garbage collection, winter gritting etc. In this paper we introduce a benchmark generator that controls the size and complexity of the underlying road network resembling a target application. It allows generation of road networks with multiple lanes, one-way/two-way roads and varying degree of connectedness. Furthermore, an algorithm capable of solving real life CARP instances efficiently within a fixed computational budget of evaluations is introduced. The proposed algorithm, referred to as MA-CARP, is a memetic algorithm embedded with a similarity based parent selection scheme inspired by multiple sequence alignment, hybrid crossovers and a modified neighborhood search to improve its rate of convergence. The mechanism of test instance generation is presented for three typical scenarios, namely, inspection of electric power lines, garbage collection and winter gritting. The code for the generator is available from http://seit.unsw.adfa.edu.au/research/sites/mdo/Research-Data/InstanceGenerator.rar. The performance of the algorithm is compared with a state-of-the-art algorithm for three generated benchmarks. The results obtained using the proposed algorithm are better for all the above instances clearly highlighting its potential for solving CARP problems.


      PubDate: 2014-06-18T16:10:35Z
       
  • Toward accurate localization in guided transport: Combining GNSS data and
           imaging information
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 2
      Author(s): Juliette Marais , Cyril Meurie , Dhouha Attia , Yassine Ruichek , Amaury Flancquart
      Global Navigation Satellite Systems (GNSS) are widely spread (with Global Positioning System – GPS) in intelligent transport systems and offer a low cost, continuous and global solution for positioning. Unfortunately, urban users are often the most demanding of accurate localization but receive a degraded service because of signal propagation conditions. Several mitigation solutions can be developed. We propose, within CAPLOC project (2010–2013) to deal with inaccuracy by associating image processing techniques and signal propagation knowledge. In this paper, we focus on the contribution of image processing in more accurate position estimation. Thus, we use a laboratory vehicle, which is equipped with a fisheye camera and two GNSS receivers. The camera is located on the roof and oriented upwards to capture images of the sky. The GNSS receivers are used to obtain raw data, the position of the vehicle and the reference trajectory. The proposed approach consists in determining where satellites are located in the fisheye image, and then excluding those located in non-sky regions when calculating the position. For that, the strategy is based on an image simplification step coupled with a pixels classification. The image-based exclusion procedure is compared with the classical one based on the application of a threshold on carrier-to-noise (CN0) ratio to separate LOS and NLOS signals. Accuracy improvement is satisfying with the CN0-based method and show an improvement from 13m to 4.5m. Image-based detection shows mixed improvements but promising: good in a static area and too harsh in another configuration of the scenario.


      PubDate: 2014-06-18T16:10:35Z
       
  • Short-term traffic forecasting: Where we are and where we’re going
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 1
      Author(s): Eleni I. Vlahogianni , Matthew G. Karlaftis , John C. Golias
      Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work.


      PubDate: 2014-06-18T16:10:35Z
       
  • A spatio-temporal approach for identifying the sample size for transport
           mode detection from GPS-based travel surveys: A case study of
           London’s road network
    • Abstract: Publication date: June 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 43, Part 2
      Author(s): Adel Bolbol , Tao Cheng , Ioannis Tsapakis
      Compared with conventional household one/two days travel survey, GPS-based travel surveys hold many attractive features for travel behaviour studies. Different machine learning-based techniques have been developed to infer the transportation mode based upon GPS data from such surveys. However, nearly none of these studies calculate the sample size required for validating these techniques. Since different surveys target different study areas for different temporal periods and different travel modes, identifying sample sizes for all transport modes at different spatio-temporal granularities is of imperative urgency given the high time and financial costs of GPS-based travel surveys. Here we use road network journey time data of London to calculate appropriate sample sizes for travel surveys designed either for a specific period-of-the-day, day-of-the-week or month-of-the-year. We also use different transportation analysis zones (central, inner and outer London) to demonstrate the spatial variability of the data over these different survey durations. Then we finally calculate and analyse the range of required sample sizes for different travel modes within these spatio-temporal granularities. This case study provides a good reference of sample size design for GPS-based travel survey in big cities.


      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
       
  • Flow rate and time mean speed predictions for the urban freeway network
           using state space models
    • Abstract: Publication date: Available online 21 March 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Chunjiao Dong , Chunfu Shao , Stephen H. Richards , Lee D. Han
      Short-term predictions of traffic parameters such as flow rate and time mean speed is a crucial element of current ITS structures, yet complicated to formulate mathematically. Classifying states of traffic condition as congestion and non-congestion, the present paper is focused on developing flexible and explicitly multivariate state space models for network flow rate and time mean speed predictions. Based on the spatial–temporal patterns of the congested and non-congested traffic, the NSS model and CSS model are developed by solving the macroscopic traffic flow models, conservation equation and Payne–Whitham model for flow rate and time mean speed prediction, respectively. The feeding data of the proposed models are from historical time series and neighboring detector measurements to improve the prediction accuracy and robustness. Using 2-min measurements from urban freeway network in Beijing, we provide some practical guidance on selecting the most appropriate models for congested and non-congested conditions. The result demonstrates that the proposed models are superior to ARIMA models, which ignores the spatial component of the spatial–temporal patterns. Compared to the ARIMA models, the benefit from spatial contribution is much more evident in the proposed models for all cases, and the accuracy can be improved by 5.62% on average. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Our findings suggest that the NSS model is a better alternative for flow rate prediction under non-congestion conditions, and the CSS model is a better alternative for time mean speed prediction under congestion conditions.


      PubDate: 2014-04-29T19:00:00Z
       
  • Erratum to “Supporting the design of railway systems by means of a
           Sobol variance-based sensitivity analysis” [Transp. Res. –
           Part C 34 (2013) 38–54]
    • Abstract: Publication date: Available online 24 April 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Egidio Quaglietta , Vincenzo Punzo



      PubDate: 2014-04-29T19:00:00Z
       
  • Trade-offs between mobility and equity maximization under environmental
           
    • Abstract: Publication date: Available online 28 April 2014
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Tao Feng , Harry J.P. Timmermans
      This paper investigates the performance of a policy decision tool proposed for multi-objective decision under different policy interventions. This tool deals with the trade-off between mobility and equity maximization under environmental capacity constraints. Two system objectives, maximization of mobility and equity, are formulated in terms of the sum of total car ownership and number of trips, and the differences in accessibility between zones. Environmental capacities are based on production efficiency theory in which the frontier emission under maximum system efficiency is taken as environmental capacity. To examine the performance of the proposed model, three types of hypothetical policies (network improvement, population increase and urban sprawl) are formulated. Effects are simulated using data pertaining to Dalian City, China. Results show that the proposed model is capable of representing the trade-offs between mobility and equity based on different policy interventions. Compared with two extreme cases with the single objective of mobility maximization or equity maximization, the Pareto-optimal solutions provide more interesting practical options for decision makers. Taking the solution based on the maximum equity as an example, the policy of urban sprawl yields the most significant improvement in both emission and accessibility of the three scenarios.


      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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