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Related Experiment Video

Updated: Jan 11, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Short-Term driving speed prediction under consecutive Variable speed Limits: An interpretable deep learning approach

Junhua Wang¹, Yiwei Ren¹, Ting Fu¹

¹The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; College of Transportation, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.

Accident; Analysis and Prevention

|November 18, 2025

Summary

This study introduces a deep learning model to predict driver speed under Variable Speed Limits (VSLs). Heavy vehicles consistently slow down, while light vehicles adapt more at lower VSLs, with lane position significantly impacting responses.

Keywords:

Bidirectional Long Short-Term Memory (Bi-LSTM)Driving Speed Prediction Spatio-Temporal Attention Mechanism Variable Speed Limits (VSLs)Wide-Area Trajectory Data

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Transportation Engineering
Artificial Intelligence
Traffic Safety

Background:

Current Variable Speed Limit (VSL) research often lacks real-world microscopic trajectory data.
Understanding driver behavior under VSLs is crucial for traffic safety and efficiency.

Purpose of the Study:

To develop an interpretable deep learning framework for predicting short-term driving speeds under consecutive VSL controls.
To quantitatively analyze driver behavior and the influence of spatiotemporal features on responses to VSLs.

Main Methods:

Utilized wide-area vehicle trajectory data from a 2.2 km freeway segment with two successive VSL signs.
Developed a Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-BiLSTM) model incorporating a Multi-View Spatio-Temporal Attention Mechanism (MSTAM).

Main Results:

Heavy vehicles consistently decelerated under VSLs; light vehicles showed varied responses based on VSL level and lane position.
Drivers in the left lane responded more promptly and decisively than those in the right lane.
The second VSL sign demonstrated greater regulatory effectiveness than the first, with the MSTAM model outperforming the baseline CNN-BiLSTM.

Conclusions:

The proposed MSTAM model accurately predicts driver speeds and captures spatiotemporal attention patterns, offering insights into adaptive driver responses.
Findings support enhanced VSL deployment and lane-specific speed control strategies for improved traffic management.