高速公路隧道区域纵向风险驾驶行为时空特征

Spatiotemporal Characteristics of Longitudinal Risky Driving Behaviors in Highway Tunnel Sections

为了精准定位纵向风险驾驶行为在隧道路段的形态、位置及时间,增强交通管理部门主动预防交通事故的能力,针对传统时空分析维度分离的局限性,研究建立了时空维度结合的时空核密度估计模型(spatio-temporal kernel density estimation,STKDE),采用最小交叉二乘验证(least squares cross-validation,LSCV)确定模型最佳带宽。构建了基于轨迹数据的纵向风险驾驶行为识别方法,提取超速、超低速、急加速、急减速共4种纵向风险驾驶行为的时空位置;将隧道时空域分割为时空单元后,应用STKDE计算各时空单元内纵向风险驾驶行为时空核密度估计值ψ;结合时空立方体(space-time cube,ST-Cube)对STKDE结果可视化。基于下细腰隧道全域高精度轨迹数据进行实例分析,研究发现:高速驾驶行为在隧道出口100 m区域内高发,超速高发于16:00与09:00;低速驾驶行为在隧道入口前200 m高发,超低速高发于02:00与14:00;在进入隧道前100 m和隧道0~1500 m区域,急加速与急减速行为的ψ始终大于0.5,处于高发状态,且在隧道区间内每隔150~200m,2种急变速驾驶行为会同步出现波动,在驶离隧道后2种行为均迅速减少,且不再高发。通过与传统时空分析方法对比,结果表明:结合ST-Cube的STKDE分析方法,能实现耦合时空的特征分析,并量化估计全时空域内风险驾驶行为发生的可能性,其在对急加减速驾驶行为的特征分析中存在一定优势。

To pinpoint the patterns,locations,and timings of longitudinal risky driving behaviors in tunnel sections,enhancing the ability of traffic management departments to proactively prevent accidents,this study addresses the limitations of conventional separate spatio-temporal analysis dimensions by developing a spatio-temporal kernel density estimation model(STKDE).The model's optimal bandwidth is determined using least squares cross-validation(LSCV).A method for identifying longitudinal risky driving behaviors based on trajectory data is constructed,extracting spatio-temporal locations for four risky driving behaviors:speeding,extreme low speed,rapid acceleration,and rapid deceleration.By partitioning the spatio-temporal domain of the study area into units,STKDE is applied to compute the spatio-temporal kernel density estimation value,ψ,within each unit.The results of STKDE are visualized using a space-time cube model(ST-Cube).An empirical analysis based on high-precision trajectory data from the Xiaxiyao Tunnel reveals that high-speed driving behavior frequently occurs within 100 meters of the tunnel exit,with speeding peaking at 16:00 and 09:00.Low-speed driving behavior is frequent within 200 meters before the tunnel entrance,with extreme low speed peaking at 02:00 and 14:00.Within 100 meters before entry and throughout the first 1500 meters of the tunnel,theψvalues for rapid acceleration and deceleration remain above 0.5,indicating high-frequency occurrences..Additionally,every 150~200 meters within the tunnel,these two types of sudden speed changes show simultaneous fluctuations,but significantly decrease and are no longer frequent once exiting the tunnel.A comparison with conventional spatio-temporal analysis methods shows that the STKDE method,combined with ST-Cube,achieves integrated spatio-temporal feature analysis and provides a quantifiable estimation of the likelihood of risky driving behaviors across the entire spatio-temporal domain,demonstrating a particular advantage in characterizing rapid acceleration and deceleration behaviors.