智能网联客货混合交通流特性及集聚换道策略

doi:10.13229/j.cnki.jdxbgxb.20231205

摘要/Abstract

摘要：

为研究车联网环境下客货混合交通流特性和适用于该环境的换道策略，引入相对熵以量化客货混合车流的有序性，并在此基础上提出条件集聚换道策略。通过元胞自动机仿真研究条件集聚换道策略对混合车流通行能力、有序性等的影响。结果表明：相比于无集聚换道策略，集聚换道策略在不同智能网联自动驾驶汽车（CAV）渗透率、客货比例的混合车流中可将道路最大通行能力提高5.5%~10.5%，且能显著降低车流相对熵，增大协同自适应巡航控制（CACC）队列车辆数，从而提升车流有序性；条件集聚换道策略的CACC最小队列规模为4~5辆时，对道路通行能力的提升效果最为显著。

关键词: 智能交通, 智能网联车, 换道策略, 混合交通流, 数值仿真, 相对熵

Abstract:

To study the characteristics of passenger-cargo mixed traffic flow in vehicle-connected environment and the lane change strategy suitable for the mixed traffic flow environment， the relative entropy is introduced to quantify the order of passenger-cargo mixed traffic flow， and on this basis， conditional agglomeration lane change strategy is proposed. The effect of conditional lane change strategy on the capacity and order of mixed traffic flow was studied by cellular automata simulation. The results show that compared with non-agglomeration lane change strategy， CDA（Conditional aggregation） lane change strategy can improve the maximum road capacity by 5.5%~10.5% in mixed traffic flow with different CAV（Connected and autonomous vehicle） penetration rate and passenger-cargo ratio. It can significantly reduce the relative entropy of traffic flow， increase the number of vehicles in CACC（Cooperative adaptive cruise control） queue and improve the orderliness of traffic flow. When the CACC minimum queue size of the conditional lane change strategy is 4~5 vehicles， the road capacity can be improved the most.

Key words: intelligent transportation, connected and autonomous vehicle, lane change strategy, heterogeneous traffic flow, numerical simulation, relative entropy

中图分类号:

U495

吴德华,陈荣峰. 智能网联客货混合交通流特性及集聚换道策略[J]. 吉林大学学报(工学版), 2025, 55(8): 2588-2596.

De-hua WU,Rong-feng CHEN. Characteristics of passenger-cargo mixed traffic flow in intelligent network and agglomeration lane-change strategy[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2588-2596.

图/表 10

图1

图2

图3

表1

IDM参数取值"

参数	取值
$a m a x / (m · s - 2)$	1
$v f / (m · s - 1)$	33.3
$s 0 / m$	2
$b / (m · s - 2)$	2

表1

图4

图5

表2

图6

图7

图8

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换道策略	p=0.2	p=0.4	p=0.6	p=0.8
CVA	-1.0	1.3	2.6	1.8
CDA	-0.1	5.7	7.3	7.0