Journal of Intelligent Transportation Systems

Papers
(The H4-Index of Journal of Intelligent Transportation Systems is 16. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-09-01 to 2025-09-01.)
ArticleCitations
Analysis on autonomous vehicle detection performance according to various road geometry settings39
Simulation analysis of urban network performance under link disruptions: Impacts of information provisions in different street configurations35
Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne30
Massively parallelizable approach for evaluating signalized arterial performance using probe-based data30
ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage30
A simulation-based testing framework for autonomous driving: ensuring realism and priority of test scenarios27
Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles25
Robust real-time traffic light detector on small-form platform for autonomous vehicles20
Handling inevitable collision states by Advanced Driver Assistance Systems functions: software-in-the-loop performance assessment of an injury risk-based logic in a “lane departure” scenario20
Trajectory optimization for connected and automated vehicles in a drop-off area of the departure curbside20
Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods19
Adaptive bidirectional spatial-temporal prediction model for traffic speed in large-scale road networks18
A self-enforced optimal framework for inter-platoon transfer in connected vehicles17
The mathematical algorithms for maintaining vehicle platoons in unpredictable situations17
Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices16
Inferring the number of vehicles between trajectory-observed vehicles16
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