Transportation Research Part C: Emerging Technologies

Aims & Scope

The focus of Transportation Research: Part C is high-quality, scholarly research that addresses development, applications, and implications, in the field of transportation, of emerging technologies from such fields as operations research, computer science, electronics, control systems, artificial intelligence, and telecommunications, among others.

The interest is not in the individual technologies or methodologies per se, but in their ultimate implications for the planning, design, operation, control, management, maintenance and rehabilitation of transportation systems, services and components.

Of particular interest are the impacts of emerging technologies on transportation system performance, in terms of level of service, capacity, safety, reliability, resource consumption and the environment, economics and finance, privacy, standards, and liability.

Submissions in the following areas of transportation are encouraged by Part C: the impact of emerging technologies for all modes and for intermodal transportation; intelligent transportation systems; real-time operations; logistics; resource management; consumer/traveler adoption, acceptance and usage of new technologies; infrastructure applications of emerging technologies.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	7.9

SJR (SCImago Journal Rank)

Year	Value
2024	2.734

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	196

Journal Rank

Year	Value
2024	838

Journal Citation Indicator

Year	Value
2024	11457

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Decision Sciences, Engineering and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

  Citation: 1790

  Authors: Xiaolei, Zhimin, Yinhai, Haiyang, Yunpeng

• The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery

  Citation: 1257

  Authors: Chase C., Amanda G.

• Influence of connected and autonomous vehicles on traffic flow stability and throughput

  Citation: 1126

  Authors: Alireza, Hani S.

• Short-term traffic forecasting: Where we are and where we’re going

  Citation: 976

  Authors: Eleni I., Matthew G., John C.

• The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios

  Citation: 957

  Authors: Daniel J., Kara M.

• Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

  Citation: 930

  Authors: Vicente, Steven E.

• Assessing public opinions of and interest in new vehicle technologies: An Austin perspective

  Citation: 858

  Authors: Prateek, Kara M., Amit

• Deep learning for short-term traffic flow prediction

  Citation: 848

  Authors: Nicholas G., Vadim O.

• Comparison of parametric and nonparametric models for traffic flow forecasting

  Citation: 840

  Authors: Brian L, Billy M, R

• User preferences regarding autonomous vehicles

  Citation: 806

  Authors: Chana J., Robert, Yoram

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