Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions

2026-01-06

arXiv.org

AI Summary

Time pressure is a critical yet latent cognitive factor influencing risky behaviors and crash risk among two-wheeled motor vehicle riders, yet it remains challenging to model in real time within intelligent transportation systems. To address this gap, this study constructs the first large-scale, multivariate, time-series riding dataset annotated with time pressure labels and proposes MotoTimePressure, a deep learning model integrating convolutional preprocessing, a two-stage temporal attention mechanism, and Squeeze-and-Excitation modules. Experimental results demonstrate that the model achieves 91.53% accuracy and 98.93% ROC AUC in time pressure recognition. Furthermore, incorporating time pressure estimates into collision warning systems improves prediction accuracy from 91.25% to 93.51%, thereby validating time pressure as a practically valuable new dimension for proactive safety interventions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Time-Series/Data Streams
• Application Domains: Transportation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored in intelligent transportation systems. We present a large-scale dataset of 129,000+ labeled multivariate time-series sequences from 153 rides by 51 participants under No, Low, and High Time Pressure conditions. Each sequence captures 63 features spanning vehicle kinematics, control inputs, behavioral violations, and environmental context. Our empirical analysis shows High Time Pressure induces 48% higher speeds, 36.4% greater speed variability, 58% more risky turns at intersections, 36% more sudden braking, and 50% higher rear brake forces versus No Time Pressure. To benchmark this dataset, we propose MotoTimePressure, a deep learning model combining convolutional preprocessing, dual-stage temporal attention, and Squeeze-and-Excitation feature recalibration, achieving 91.53% accuracy and 98.93% ROC AUC, outperforming eight baselines. Since time pressure cannot be directly measured in real time, we demonstrate its utility in collision prediction and threshold determination. Using MTPS-predicted time pressure as features, improves Informer-based collision risk accuracy from 91.25% to 93.51%, approaching oracle performance (93.72%). Thresholded time pressure states capture rider cognitive stress and enable proactive ITS interventions, including adaptive alerts, haptic feedback, V2I signaling, and speed guidance, supporting safer two-wheeler mobility under the Safe System Approach.

Problem

• time pressure
• powered two-wheeler
• proactive safety
• intelligent transportation systems
• collision prediction

Innovation

• time pressure prediction
• powered two-wheeler safety
• deep learning for ITS
• temporal attention mechanism
• proactive safety intervention