Real-time regional path decision method in cooperative vehicle infrastructure system
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摘要:
为解决车辆行驶数据缺失和滞后造成路径规划系统不稳定问题,建立了基于车路协同系统(CVIS)的新型区域路径实时决策方法。首先,通过获取网联车辆的实时行驶数据,结合交通信号配时和路径转向信息,并考虑车辆在途经交叉口时可能遇到的非自由流行驶情况,动态计算当前路段路阻值;其次,根据当前时刻各路段的路阻统计数据,以及区域路网拓扑结构,实时预测各备选路线的行程时间,选择行程时间最少的路线作为车辆最优行驶路径;最后,选取北京市望京地区的典型区域路网数据进行验证。在150组实验过程中,计算得出不同时段下按所提方法得到的最优路线用时平均比常规导航系统推荐最优路线用时分别短9.52 s、13.39 s及20.65 s,证明了所提方法的有效性。
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关键词:
- 智能交通系统 /
- 车路协同系统(CVIS) /
- 实时路径决策 /
- 网联汽车 /
- 城市交通诱导
Abstract:To solve the instability of the path planning system caused by vehicle driving data loss and lag, a novel real-time regional path decision method based on the cooperative vehicle infrastructure system (CVIS) was presented in this paper. Firstly, the current road section resistance value was calculated dynamically through acquiring the real-time driving data of connected vehicles, combing with the traffic signal timing and path steering information, and considering the non-free flow situation which vehicles may encounter when passing through the intersection. Secondly, the travel time of each alternative route was predicted in real time according to the current road resistance statistics and the road network topology structure. After that, the predicted route with the least travel time was selected as the optimal vehicle driving path. Finally, the typical regional road network data of Wangjing area in Beijing was selected as the test scenario. 150 sets of tested results show that the average travel time in different periods of the optimized route obtained by this method is 9.52 seconds, 13.39 seconds and 20.65 seconds shorter than recommended route of the navigation system respectively, which proves the feasibility of the proposed method.
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表 1 车载及路侧单元采集数据
Table 1. Data collected in OBU and RSU
数据类型 数据含义 平均行驶速度v 一定时间内该路段上所有车辆的平均行驶速度 平均排队车辆数N 一定时间内该路段上每个周期平均排队车辆数 信号控制周期C 该交叉口对应信号控制周期 相位绿灯起始时间Ps 在信号周期中该路段所处相位绿灯起亮时间 相位绿灯持续时间Pd 在信号周期中该路段所处相位绿灯持续时间 当前所处时间c 目前已处于信号控制的第几秒 表 2 实验路线参数
Table 2. Parameters of experimental route
路线编号 途经灯控交叉口/个 行驶里程/km 预测行程时间/min 行驶路线 06:00—08:00 08:00—10:00 10:00—12:00 路线1 5 2.0 9 7 6 路线2 5 2.1 10 9 7 路线3 5 2.2 10 9 7 表 3 路侧终端采集数据
Table 3. Data collected in RSU
路段 平均行驶速度v/(km·h-1) 平均排队车辆数N 路段长度
L/m信号控制
周期C/s绿灯起止时
间Ps~Pd/s06:00—08:00 08:00—10:00 10:00—12:00 06:00—08:00 08:00—10:00 10:00—12:00 24 28 38 15 14 12 431 150 0~42 26 32 43 9 8 6 374 150 0~88 22 30 38 7 6 5 415 150 52~76 20 28 34 6 5 4 368 60 28~54 21 33 37 9 8 7 443 90 48~84 20 26 33 9 8 6 431 150 49~69 15 19 24 8 7 6 418 70 0~38 14 20 25 0 0 0 422 70 0~70 16 22 27 7 6 5 387 70 0~36 12 15 22 9 8 7 430 90 0~32 24 28 38 15 14 12 431 150 0~42 18 25 31 11 10 7 374 150 0~88 12 15 19 10 9 8 381 80 0~39 13 18 21 0 0 0 502 70 0~70 12 15 22 9 8 7 430 90 0~32 表 4 路阻计算参数
Table 4. Calculation parameters of road resistance
参数 典型值 a/(m·s-2) 3 dl/m 4.3 dp/m 0.5 dg/m 6 表 5 06:00—08:00路线结果对比
Table 5. Comparison of route results from 06:00 to 08:00
路线 平均预测
行程时间/s平均预测
排队次数平均预测
排队时间/s最优路线
次数路线1 510.54 4.28 306.61 126 路线2 556.37 3.99 261.19 20 路线3 611.48 3.69 298.61 4 表 6 08:00—10:00路线结果对比
Table 6. Comparison of route results from 08:00 to 10:00
路线 平均预测
行程时间/s平均预测排
队次数平均预测
排队时间/s最优路线
次数路线1 440.84 3.63 236.91 127 路线2 478.31 4 183.13 22 路线3 497.64 3.11 184.77 1 表 7 10:00—12:00路线结果对比
Table 7. Comparison of route results from 10:00 to 12:00
路线 平均预测
行程时间/s平均预测
排队次数平均预测
排队时间/s最优路线
次数路线1 392.62 3.45 188.69 105 路线2 416.05 3.89 120.87 23 路线3 411.66 2.74 98.79 22 -
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