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  Subjects -> TRANSPORTATION (Total: 151 journals)
    - AIR TRANSPORT (6 journals)
    - AUTOMOBILES (20 journals)
    - RAILROADS (4 journals)
    - ROADS AND TRAFFIC (4 journals)
    - SHIPS AND SHIPPING (25 journals)
    - TRANSPORTATION (92 journals)

TRANSPORTATION (92 journals)

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Journal Cover   Transportation Research Part C: Emerging Technologies
  [SJR: 1.605]   [H-I: 47]   [17 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [2589 journals]
  • Introducing heterogeneous users and vehicles into models and algorithms
           for the dial-a-ride problem
    • Abstract: Publication date: August 2011
      Source:Transportation Research Part C: Emerging Technologies, Volume 19, Issue 5
      Author(s): Sophie N. Parragh
      Dial-a-ride problems deal with the transportation of people between pickup and delivery locations. Given the fact that people are subject to transportation, constraints related to quality of service are usually present, such as time windows and maximum user ride time limits. In many real world applications, different types of users exist. In the field of patient and disabled people transportation, up to four different transportation modes can be distinguished. In this article we consider staff seats, patient seats, stretchers and wheelchair places. Furthermore, most companies involved in the transportation of the disabled or ill dispose of different types of vehicles. We introduce both aspects into state-of-the-art formulations and branch-and-cut algorithms for the standard dial-a-ride problem. Also a recent metaheuristic method is adapted to this new problem. In addition, a further service quality related issue is analyzed: vehicle waiting time with passengers aboard. Instances with up to 40 requests are solved to optimality. High quality solutions are obtained with the heuristic method.


      PubDate: 2015-02-24T15:05:39Z
       
  • Data management and applications in a world-leading bus fleet
    • Abstract: Publication date: June 2012
      Source:Transportation Research Part C: Emerging Technologies, Volume 22
      Author(s): N.B. Hounsell , B.P. Shrestha , A. Wong
      Automatic Vehicle Location (AVL) Systems are being introduced increasingly in many major cities around the world to improve the efficiency of our road-based passenger transport systems. Satellite-based location and communication systems, particularly the Global Positioning System (GPS) have been the platform for AVL systems which are now supporting real-time passenger information (RTPI), fleet management and operations (FMOs) and public transport priorities (PTPs), to name three key applications. The process of real-time on-board bus location can result in a substantial database where the progress of the bus is stored typically on a second-by-second basis. This is necessary for the primary real-time applications such as those listed above (e.g. RTPI, FMO and PTP). In addition, it is clear that such data could have an array of ‘secondary’ purposes, including use off-line for improving scheduling efficiency and for automatic performance monitoring, thus reducing or removing the need for manual on-street surveys. This paper looks at these and other innovative uses of AVL data for public transport, taking the recent iBus system in London as a current example of a modern AVL/GPS application in a capital city. It describes the data architecture and management in iBus and then illustrates two further examples of secondary data use – dwell time estimation and bus performance analysis. The paper concludes with a discussion of some key data management issues, including data quantity and quality, before drawing conclusions.
      Highlights ► We review data management systems for a large scale application – London’s iBus. ► With 8000 buses, we illustrate the range of applications to improve bus operations. ► From bus priority to dynamic bus fleet management, we show the potential of iBus. ► We show how bus dwell times at stops can be obtained automatically from iBus data.

      PubDate: 2015-02-24T15:05:39Z
       
  • Exploratory visualisation of congestion evolutions on urban transport
           networks
    • Abstract: Publication date: November 2013
      Source:Transportation Research Part C: Emerging Technologies, Volume 36
      Author(s): Tao Cheng , Garavig Tanaksaranond , Chris Brunsdon , James Haworth
      Visualisation is an effective tool for studying traffic congestion using massive traffic datasets collected from traffic sensors. Existing techniques can reveal where/when congested areas are formed, developed, and moved on one or several highway roads, but it is still challenging to visualise the evolution of traffic congestion on the whole road network, especially on dense urban networks. To address this challenge, this paper proposes three 3D exploratory visualisation techniques: the isosurface, the constrained isosurface, and the wall map. These three techniques have different advantages and should be combined to leverage their respective strong points. We present our visualisation techniques with the case of link travel time data from Automatic Number Plate Recognition (ANPR) in London.


      PubDate: 2015-02-24T15:05:39Z
       
  • A hybrid Bayesian Network approach to detect driver cognitive distraction
    • Abstract: Publication date: January 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 38
      Author(s): Yulan Liang , John D. Lee
      Driver cognitive distraction (e.g., hand-free cell phone conversation) can lead to unapparent, but detrimental, impairment to driving safety. Detecting cognitive distraction represents an important function for driver distraction mitigation systems. We developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering—to detect cognitive distraction using eye movement and driving performance measures. In this study, the algorithm was trained and tested with the data collected in a simulator-based study, where drivers drove either with or without an auditory secondary task. We calculated 19 distraction indicators and defined cognitive distraction using the experimental condition (i.e., “distraction” as in the drives with the secondary task, and “no distraction” as in the drives without the secondary task). We compared the layered algorithm with previously developed DBN and Support Vector Machine (SVM) algorithms. The results showed that the layered algorithm achieved comparable prediction performance as the two alternatives. Nonetheless, the layered algorithm shortened training and prediction time compared to the original DBN because supervised clustering improved computational efficiency by reducing the number of inputs for DBNs. Moreover, the supervised clustering of the layered algorithm revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that the layered algorithm can capitalize on the best attributes of component data mining methods and can identify human cognitive state efficiently. The study also shows the value in considering the supervised clustering method as an approach to feature reduction in data mining applications.


      PubDate: 2015-02-24T15:05:39Z
       
  • 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: 2015-02-24T15:05:39Z
       
  • Spatio-temporal clustering for non-recurrent traffic congestion detection
           on urban road networks
    • Abstract: Publication date: November 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 48
      Author(s): Berk Anbaroglu , Benjamin Heydecker , Tao Cheng
      Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on urban road networks. In this paper we propose an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined as those LJTs that are greater than a threshold. The threshold is calculated by multiplying the expected LJTs with a congestion factor. To evaluate the effectiveness of the proposed NRC detection method, we propose two novel criteria. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be associated with incidents. The proposed NRC detection methodology is tested for London’s urban road network. The optimum value of the congestion factor is determined by sensitivity analysis by using a Weighted Product Model (WPM). It is found out those LJTs that are at least 40% higher than their expected values should belong to an NRC; as such NRCs are found to maintain the best balance between the proposed evaluation criteria.


      PubDate: 2015-02-24T15:05:39Z
       
  • Evaluating a concept design of a crowd-sourced ‘mashup’
           providing ease-of-access information for people with limited mobility
    • Abstract: Publication date: December 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 49
      Author(s): Andrew May , Christopher J. Parker , Neil Taylor , Tracy Ross
      This study investigates the impact of using a concept map-based ‘mashup’ (www.accessadvisr.net) to provide volunteered (i.e. user contributed) ease of access information to travellers with limited mobility. A scenario-based user trial, centred around journey planning, was undertaken with 20 participants, divided equally between (1) those who have physical restrictions on their mobility, due to disability, illness or injury, and (2) those with practical mobility constraints due to being parents with young children who have to use a child’s pushchair when using public transport. Both user groups found the concept useful, but its potential impact was less for the pushchair user group. There were mixed views in relation to the ability of the mashup to convey the trustworthiness, credibility and reliability of information necessary for journey planning. The study identified a number of key information-related user requirements which help enable effective design of user contributed web-based resources for travellers with mobility-related issues.


      PubDate: 2015-02-24T15:05:39Z
       
  • Real time detection of driver attention: Emerging solutions based on
           robust iconic classifiers and dictionary of poses
    • Abstract: Publication date: December 2014
      Source:Transportation Research Part C: Emerging Technologies, Volume 49
      Author(s): G.L. Masala , E. Grosso
      Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages. In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user. On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.


      PubDate: 2015-02-24T15:05:39Z
       
 
 
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