In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driv...In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.展开更多
近年来,清洁低碳的电-气综合能源系统(electricity-gas integrated energy system,EGIES)受到了广泛关注。然而,EGIES涉及不同能源形式的设备量测与信息传输,数据误差的产生因素复杂且不确定性突出,导致确定性的点状态估计可信度不足,...近年来,清洁低碳的电-气综合能源系统(electricity-gas integrated energy system,EGIES)受到了广泛关注。然而,EGIES涉及不同能源形式的设备量测与信息传输,数据误差的产生因素复杂且不确定性突出,导致确定性的点状态估计可信度不足,为系统安全稳定运行带来严峻挑战。针对此问题,提出基于模型-数据联合驱动的EGIES区间状态估计方法。建立EGIES加权最小二乘(weighted least square,WLS)点状态估计模型,并利用人工鱼群算法(artificial fish swarms algorithm,AFSA)求解;考虑估计结果置信水平,利用核密度估计(kernel density estimation,KDE)构造点状态估计误差区间,进而得到区间状态估计结果;基于量测量样本与区间状态估计结果样本,训练长短期记忆(long short term memory,LSTM)神经网络得到EGIES快速区间状态估计模型。以30节点电力系统与14节点天然气系统耦合的EGIES进行算例分析,结果表明,所提区间状态估计方法的区间覆盖概率均保持在置信度水平之上。同时所提方法测试集在线估计时间仅为13.97s,相比于WLS-NR-KDE方法与WLS-AFSA-KDE方法分别降低76.44%与94.00%。展开更多
文摘In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.
文摘近年来,清洁低碳的电-气综合能源系统(electricity-gas integrated energy system,EGIES)受到了广泛关注。然而,EGIES涉及不同能源形式的设备量测与信息传输,数据误差的产生因素复杂且不确定性突出,导致确定性的点状态估计可信度不足,为系统安全稳定运行带来严峻挑战。针对此问题,提出基于模型-数据联合驱动的EGIES区间状态估计方法。建立EGIES加权最小二乘(weighted least square,WLS)点状态估计模型,并利用人工鱼群算法(artificial fish swarms algorithm,AFSA)求解;考虑估计结果置信水平,利用核密度估计(kernel density estimation,KDE)构造点状态估计误差区间,进而得到区间状态估计结果;基于量测量样本与区间状态估计结果样本,训练长短期记忆(long short term memory,LSTM)神经网络得到EGIES快速区间状态估计模型。以30节点电力系统与14节点天然气系统耦合的EGIES进行算例分析,结果表明,所提区间状态估计方法的区间覆盖概率均保持在置信度水平之上。同时所提方法测试集在线估计时间仅为13.97s,相比于WLS-NR-KDE方法与WLS-AFSA-KDE方法分别降低76.44%与94.00%。