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Transportmetrica A: Transport Science is a peer-reviewed journal published by Taylor & Francis, covering Transportation Planning and Optimization, Traffic control and management, Urban Transport and Accessibility. Over its lifetime it has published 1,012 papers, which have received 18,237 citations.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Transportmetrica A: Transport Science 12 (3): 45–58.
Formats any DOI in Transportmetrica A: Transport Science style. No sign-up. |
| Publishes research in | Transportation Planning and Optimization Traffic control and management Urban Transport and Accessibility Traffic Prediction and Management Techniques Transportation and Mobility Innovations |
| ISSN | 2324-9935 |
| Citation impact (2-yr) | 2.36 |
| h-index | 57 |
| i10-index | 512 |
| Top institutions publishing here | Beijing Jiaotong University |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
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 traffic data. This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework. In the proposed framework, the…
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…
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…
Knowing passengers’ behavioural intentions to use transit service can be a useful support for transit managers and marketers who can define the most convenient strategies to satisfy existing passengers and attract new ones. We retain that analysing passengers’ intentions to continue to use transit services in the future together with relevant concepts such as service…
Service quality and customer satisfaction as perceived by 1235 passengers on high-speed rail (HSR) services in Taiwan were examined using structural equation modeling (SEM) to explain customer loyalty. The relationships among the constructs in the SEM model were tested, namely: service quality, customer satisfaction and customer loyalty. Results indicated that the five service quality attributes…
