Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (2): 566-576.doi: 10.13229/j.cnki.jdxbgxb.20230385

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Collaborative optimization for signals and trajectories of connected automated vehicles on dedicated bus lanes

Sheng JIN1(),Bo-lin LI1,Wei XUE2   

  1. 1.Institute of Intelligent Transportation Systems，Zhejiang University，Hangzhou 310058，China
    2.City Cloud Technology （China） Co. ，Ltd. ，Hangzhou 310000，China
  • Received:2023-04-19 Online:2025-02-01 Published:2025-04-16

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Abstract:

In order to improve the mixed traffic of connected automated vehicles and human-driving vehicles， this paper proposed a collaborative optimization method for signals and trajectories of connected automated vehicles on dedicated bus lanes. In the double-layer optimization model of signal and trajectory， this paper builds linear mathematical relations between trajectory variables and traffic signal variables， and compute the solutions in sections of time and space. Consequently， this model is used in real number case to verify the validity and operate comparative experiments between collaborative and non-collaborative optimization. It proves that efficiency of connected automated vehicles in collaborative optimization is 7.7% higher.

Key words: traffic information engineering and control, connected automated vehicle, dedicated bus lane, trajectory optimization, collaborative optimization

CLC Number: 

  • U491.5

Fig. 1

Flow chart of CAV entering dedictated bus lanes"

Fig. 2

Time-space graph for mix-use lane model"

Fig. 3

Two basic trajectories of CAV"

Fig. 4

Optimization range of signal"

Fig. 5

Change of CAV trajectories after and before optimization"

Fig. 6

Time-space graph of collaborative optimaization"

Fig. 7

Relations between signal sequence and CAV trajectory"

Fig. 8

Shape of the dedicated bus lane in the real case"

Table 1

CAV traffic volume in simulation"

流量

位置

低流量中流量高流量
进出进出进出
1140028004200
230506010089140
3100202004030060
412063240120357190
5119160240320355480
66683140170199250

Fig. 9

Average speed in optimizations of different signal change range"

Fig. 10

Average speed in optimizations of different CAV traffic demand"

Table 2

Average speed in optimizations of different signal change range and relative percentage"

公交停靠

时间/s

轨迹优化

/（m·s-1）

协同优化4 s

/（m·s-1）

优化幅度/%

协同优化7 s

/（m·s-1）

优化幅度

协同优化10 s

/（m·s-1）

优化幅度/%
309.309.583.019.876.11%10.108.60
459.8010.244.4510.436.43%10.779.90
6011.2011.421.9311.633.86%11.805.36
7511.7011.972.2712.123.59%12.305.13
9012.0012.312.6012.564.67%12.806.67
10511.0011.403.6411.807.27%12.009.09
12010.4010.763.4611.036.06%11.308.65
1359.7010.053.6310.366.80%10.8011.34
1509.8010.002.0410.092.96%10.305.10
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