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Transportation Research Part C: Emerging Technologies

  • ISSN: 0968-090X

Editor-In-Chief: Geroliminis

Next planned ship date: April 12, 2024

  • 5 Year impact factor: 10.1
  • Impact factor: 8.3

The focus of Transportation Research: Part C (TR_C) is high-quality, scholarly research that addresses development, applications, and implications, in the field of transportat… Read more

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Next planned ship date:
April 12, 2024

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The focus of Transportation Research: Part C (TR_C) is high-quality, scholarly research that addresses development, applications, and implications, in the field of transportation systems and emerging technologies . The interest is not in the individual technologies per se, but in their ultimate implications for the planning, design, operation, control, maintenance and rehabilitation of transportation systems, services and components. In other words, the intellectual core of the journal is on the transportation side, not on the technology side. The integration of quantitative methods from fields such as operations research, control systems, complex networks, computer science, artificial intelligence are encouraged.

Of particular interest are the impacts of emerging technologies on transportation system performance, in terms of monitoring, efficiency, safety, reliability, resource consumption and the environment. Submissions in the following areas of transportation are welcome: multimodal and intermodal transportation; on-demand transport; intelligent transportation systems; traffic and demand management; real-time operations; connected and autonomous vehicles; logistics; railways; resource and infrastructure management; aviation; pedestrians and soft modes.

Special emphasis is given in open science initiatives and promoting the opening of large-scale datasets for papers published in TR_C that can support transferability and benchmarking of different approaches. The realization of data opportunities that arise from emerging technologies and new sensors in transportation can revolutionize how this data reshape our understanding of congestion mechanisms and can contribute in efficient and sustainable mobility management.