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.

翻译：时间压力是影响电动两轮车骑手危险操作与事故倾向的关键因素，然而其在智能交通系统中的预测研究仍显不足。本研究构建了一个大规模数据集，包含来自51名参与者在无、低、高三种时间压力状态下153次骑行产生的超过12.9万条标注多元时间序列。每条序列涵盖车辆运动学、控制输入、违规行为及环境上下文等63维特征。实证分析表明：与无时间压力状态相比，高时间压力状态导致平均速度提升48%，速度变异度增加36.4%，交叉路口危险转弯行为增加58%，紧急制动频率提高36%，后轮制动力增强50%。为建立数据集基准，我们提出MotoTimePressure深度学习模型，该模型融合卷积预处理、双阶段时序注意力机制与Squeeze-and-Excitation特征重校准模块，实现了91.53%的准确率与98.93%的ROC AUC，性能优于八种基线模型。鉴于时间压力无法实时直接测量，我们进一步验证其在碰撞预测与阈值判定中的应用价值：将MTPS预测的时间压力作为特征，可将基于Informer的碰撞风险预测准确率从91.25%提升至93.51%，逼近理论最优值（93.72%）。通过阈值划分的时间压力状态能够有效捕捉骑手认知负荷，为主动式智能交通干预提供依据，包括自适应预警、触觉反馈、车路协同信号传输及速度引导等策略，在安全系统框架下助力电动两轮车安全出行。