Taylor & Francis
About: Transportmetrica is an academic journal. The journal publishes majorly in the area(s): Poison control & Traffic flow. It has an ISSN identifier of 1812-8602. Over the lifetime, 685 publications have been published receiving 11975 citations.
Papers published on a yearly basis
TL;DR: In this paper, the authors developed, implemented and applied a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns.
Abstract: In this article we develop, implement and apply a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns. Our study has two main contributions. The first is the feasibility of the Bayesian estimator we derive. Whereas classical estimation of HCMs is fairly complex, we show that the Bayesian approach for HCMs is methodologically easier to implement than simulated maximum likelihood because the inclusion of latent variables translates into adding independent ordinary regressions; we also find that, using the Bayesian estimates, forecasting and deriving confidence intervals for willingness to pay measures is straightforward. The second is the capacity of HCMs to adapt to practical situations. Our empirical results coincide with a priori expectations, namely that environmentally-conscious consumers are willing to pay more for low-emission vehicles. The model outperforms standard discr...
TL;DR: A global view of the literature on the modelling of travel time is presented, introducing essential concepts and giving a thorough classification of the existing techniques, which will focus on travel time estimation and travel time prediction.
Abstract: Due to the increase in vehicle transit and congestion in road networks, providing information about the state of the traffic to commuters has become a critical issue for Advanced Traveller Information Systems. These systems should assist users in making pre-trip and en-route decisions and, for this purpose, delivering travel time information is very useful because it is very intuitive and easily understood by all travellers. The aim of this paper is to present a global view of the literature on the modelling of travel time, introducing essential concepts and giving a thorough classification of the existing techniques. Most of the attention will focus on travel time estimation and travel time prediction, which are two of the most relevant challenges in travel time modelling. The definition and goals of these two modelling tasks along with the methodologies used to carry them out will be further explored and categorised.
TL;DR: The tolerance-based DUO principle is introduced and its solution existence and uniqueness is discussed, a solution heuristic is developed, and its properties are demonstrated through numerical examples.
Abstract: Dynamic Traffic Assignment (DTA) is long recognized as a key component for network planning and transport policy evaluations as well as for real-time traffic operation and management. How traffic is encapsulated in a DTA model has important implications on the accuracy and fidelity of the model results. This study compares and contrasts the properties of DTA modelled with point queues versus those with physical queues, and discusses their implications. One important finding is that with the more accurate physical queue paradigm, under certain congested conditions, solutions for the commonly adopted dynamic user optimal (DUO) route choice principle just do not exist. To provide some initial thinking to accommodate this finding, this study introduces the tolerance-based DUO principle. This paper also discusses its solution existence and uniqueness, develops a solution heuristic, and demonstrates its properties through numerical examples. Finally, we conclude by presenting some prospective future research di...
TL;DR: This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework that outperforms baseline models in terms of accuracy.
Abstract: Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of tr...
TL;DR: Numerical simulations show that the proposed new continuum model is able to explain some of the observed traffic phenomena such as platoon dispersion that challenge old homogeneous models presented in the literature.
Abstract: In this paper, we propose a new continuum model and study some qualitative properties. The new model contains an additional speed gradient term (anisotropic term) in comparison to Berg's model (Ber...
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