为了评价混合交通环境中的行人和车辆冲突的安全性,对比分析了目前常用的行人车辆冲突参数,提出了个体行人和车辆之间冲突的安全评价模型,并将模型应用于不同的混合交通场景,验证模型的鲁棒性和适用性.结果表明,冲突时间差(Time differe...为了评价混合交通环境中的行人和车辆冲突的安全性,对比分析了目前常用的行人车辆冲突参数,提出了个体行人和车辆之间冲突的安全评价模型,并将模型应用于不同的混合交通场景,验证模型的鲁棒性和适用性.结果表明,冲突时间差(Time difference to collision,TDTC)和车辆速度对行人车辆冲突的安全性影响最大.TDTC越接近于零,行人越危险;车辆速度越快,行人越危险.以此建立的行人车辆冲突评价模型可正确评估86.2%的行人车辆冲突安全,对于危险冲突的漏检率为2.1%.展开更多
The accurate prediction of vehicle speed plays an important role in vehicle's real-time energy management and online optimization control. However, the current forecast methods are mostly based on traffic conditio...The accurate prediction of vehicle speed plays an important role in vehicle's real-time energy management and online optimization control. However, the current forecast methods are mostly based on traffic conditions to predict the speed, while ignoring the impact of the driver-vehicle-road system on the actual speed profile. In this paper, the correlation of velocity and its effect factors under various driving conditions were firstly analyzed based on driver-vehicle-road-traffic data records for a more accurate prediction model. With the modeling time and prediction time considered separately, the effectiveness and accuracy of several typical artificial-intelligence speed prediction algorithms were analyzed. The results show that the combination of niche immunegenetic algorithm-support vector machine(NIGA-SVM) prediction algorithm on the city roads with genetic algorithmsupport vector machine(GA-SVM) prediction algorithm on the suburb roads and on the freeway can sharply improve the accuracy and timeliness of vehicle speed forecasting. Afterwards, the optimized GA-SVM vehicle speed prediction model was established in accordance with the optimized GA-SVM prediction algorithm at different times. And the test results verified its validity and rationality of the prediction algorithm.展开更多
文摘为了评价混合交通环境中的行人和车辆冲突的安全性,对比分析了目前常用的行人车辆冲突参数,提出了个体行人和车辆之间冲突的安全评价模型,并将模型应用于不同的混合交通场景,验证模型的鲁棒性和适用性.结果表明,冲突时间差(Time difference to collision,TDTC)和车辆速度对行人车辆冲突的安全性影响最大.TDTC越接近于零,行人越危险;车辆速度越快,行人越危险.以此建立的行人车辆冲突评价模型可正确评估86.2%的行人车辆冲突安全,对于危险冲突的漏检率为2.1%.
基金supported by the Nanjing University of Aeronautics and Astronautics Research Funding(Grant No.NS2015028)
文摘The accurate prediction of vehicle speed plays an important role in vehicle's real-time energy management and online optimization control. However, the current forecast methods are mostly based on traffic conditions to predict the speed, while ignoring the impact of the driver-vehicle-road system on the actual speed profile. In this paper, the correlation of velocity and its effect factors under various driving conditions were firstly analyzed based on driver-vehicle-road-traffic data records for a more accurate prediction model. With the modeling time and prediction time considered separately, the effectiveness and accuracy of several typical artificial-intelligence speed prediction algorithms were analyzed. The results show that the combination of niche immunegenetic algorithm-support vector machine(NIGA-SVM) prediction algorithm on the city roads with genetic algorithmsupport vector machine(GA-SVM) prediction algorithm on the suburb roads and on the freeway can sharply improve the accuracy and timeliness of vehicle speed forecasting. Afterwards, the optimized GA-SVM vehicle speed prediction model was established in accordance with the optimized GA-SVM prediction algorithm at different times. And the test results verified its validity and rationality of the prediction algorithm.
