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 2022-06-01 to 2026-06-01.)
ArticleCitations
Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne39
A real-time mixed autonomy traffic signal optimization model for Continuous Flow Intersections39
A simulation-based testing framework for autonomous driving: ensuring realism and priority of test scenarios36
ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage36
Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles33
Optimal lane-based pre-timed signal timing with sub-cycles for maximal capacity at an isolated intersection29
Robust real-time traffic light detector on small-form platform for autonomous vehicles23
Optimization of hard shoulder running on highways using multi-agent reinforcement learning considering emergency vehicles23
Inferring the number of vehicles between trajectory-observed vehicles22
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
The mathematical algorithms for maintaining vehicle platoons in unpredictable situations20
Adaptive bidirectional spatial-temporal prediction model for traffic speed in large-scale road networks20
Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices19
Forecasting short-term subway passenger flow using Wi-Fi data: comparative analysis of advanced time-series methods17
Follow the leader: a deep reinforcement learning framework for safe and efficient autonomous car-following17
Risky scooter riders pay more: a usage-based insurance study of a scooter risk-aware fingerprint model without privacy issues15
Road traffic attributes prediction using deep learning hybridization by the traffic fundamental diagram15
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