Journal of Intelligent Transportation Systems

Papers
(The H4-Index of Journal of Intelligent Transportation Systems is 15. 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 2021-11-01 to 2025-11-01.)
ArticleCitations
Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne34
Simulation analysis of urban network performance under link disruptions: Impacts of information provisions in different street configurations32
Analysis on autonomous vehicle detection performance according to various road geometry settings32
A real-time mixed autonomy traffic signal optimization model for Continuous Flow Intersections30
Massively parallelizable approach for evaluating signalized arterial performance using probe-based data29
ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage26
A simulation-based testing framework for autonomous driving: ensuring realism and priority of test scenarios24
Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles23
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” scenario22
Optimization of hard shoulder running on highways using multi-agent reinforcement learning considering emergency vehicles20
Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods18
Robust real-time traffic light detector on small-form platform for autonomous vehicles18
Adaptive bidirectional spatial-temporal prediction model for traffic speed in large-scale road networks17
The mathematical algorithms for maintaining vehicle platoons in unpredictable situations17
A self-enforced optimal framework for inter-platoon transfer in connected vehicles16
Inferring the number of vehicles between trajectory-observed vehicles15
Road traffic attributes prediction using deep learning hybridization by the traffic fundamental diagram15
Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices15
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