Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (4): 1275-1286.doi: 10.13229/j.cnki.jdxbgxb.20230784

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Data-driven prediction of departure state for tail vehicles in queues at signalized intersections

Kai-ming LU1,2(),Yan-yan CHEN1,2,Yao TONG1,2,Jian ZHANG1,2,Yong-xing LI1,2(),Ying LUO1,2   

  1. 1.School of Urban Transportation，Urban Construction Department，Beijing University of Technology，Beijing 100124，China
    2.Beijing Key Laboratory of Traffic Engineering，Beijing University of Technology，Beijing 100124，China
  • Received:2013-07-26 Online:2025-04-01 Published:2025-06-19
  • Contact: Yong-xing LI E-mail:luakaiming@emails.bjut.edu.cn;liyx@bjut.edu.cn

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Abstract:

Aiming at the problem that the traditional queue tail vehicle departure state prediction model is difficult to adapt to the uncertainty of queue dissipation， a queue tail vehicle departure state prediction model driven by trajectory data is proposed. By analyzing the shapes of queue dissipation trajectories and potential influencing factors， the uncertainty of departure state of tail vehicles is uncovered. Starting from the two stages of queue waiting and vehicle start-up， a feature set that influences the tail vehicle departure state is proposed. The extreme gradient boosting algorithm is employed to construct the prediction model， incorporating the SHapley Additive exPlanations（SHAP） interpretable machine learning framework to dissect the contributions of features， and to determine the optimal feature combination and model parameters. The research results indicate that the proposed XGBoost-based departure time prediction model achieves an average mean absolute percentage error（MAPE） of 5.74%， which is improved by 10% approximately compared with the kinematic model. The MAPE for the queue departure speed is 9.98%， improved about 6% over the kinematic model. Furthermore， the performance of the proposed model surpasses three commonly used machine learning methods of random forest， decision trees， and multi-layer perceptron neural networks. The research outcomes provide technical support for adjusting the minimum green light time of intersection signals and eco-driving of connected vehicles in the vehicle-road cooperative environment.

Key words: engineering of communications and transportation system, queue dissipation characteristic, the departure time of tail vehicles in queues, the departure speed of tail vehicles in queues, XGBoost, SHAP

CLC Number: 

  • U491.1

Fig.1

Video acquisition area"

Fig.2

Vehicle trajectory smoothing sample during queue dissipation process"

Fig.3

Time-spatial diagrams of four typical queue dissipation trajectories"

Fig.4

Relationship diagram of tail vehicle departure time with queue length（sample size： 583）"

Fig.5

Relationship diagram of tail vehicle departure speed with queue length（sample size： 583）"

Table 1

Feature set influencing the tail vehicle departure state"

符号时刻/阶段特征变量变量类型
Y1—驶离时间/s连续
Y2—驶离速度/（km·h-1）连续
x1S1尾车车型离散
x2S1尾车相邻前车车型离散
x3T0排队长度/veh连续
x4T0尾车前方车队大车比连续
x5T0尾车前方车辆间距平均值/m连续
x6T0尾车前方车辆间距标准差/m连续
x7S2尾车启动车速/（km·h-1）连续
x8S2尾车相邻前车启动车速/（km·h-1）连续
x9S2尾车前方车辆启动车速平均值/（km·h-1）连续
x10S2尾车前方车辆启动车速标准差/（km·h-1）连续
x11S2头车启动车速/（km·h-1）连续
x12T1尾车前方车辆速度平均值/（km·h-1）连续
x13T1尾车前方车辆速度标准差/（km·h-1）连续
x14T1尾车前方车辆间距平均值/m连续
x15T1尾车前方车辆间距标准差/m连续
x16T1尾车前方车队大车比连续
x17T1尾车前方车队长度/m连续

Fig.6

Schematic diagram of start-up speed"

Fig.7

SHAP diagram of feature importance to departure time prediction of tail vehicle"

Fig.8

SHAP diagram of feature importance to departure speed prediction of tail vehicle"

Table 2

Feature consists for departure time prediction of tail vehicle"

组合编号特征组成
1所有特征
2T0时刻所有特征+头车启动车速
3T0时刻特征
4

初始排队长度+所有车间距相关特征+

头车启动车速

5T0时刻排队长度+所有车间距相关特征
6排队长度
7Based_3
8Based_5
9Based_6
10Based_8

Table 3

Feature consists for departure speed prediction of tail vehicle"

组合编号特征组成
1所有特征
2T0时刻所有特征
3T0时刻排队长度+所有车间距相关特征
4T0时刻所有特征+头车启动车速
5Based_8
6Based_6
7Based_5
8Based_4
9Based_1

Fig.9

Prediction comparison of tail vehicle departure time under different feature consists"

Fig.10

Prediction comparison of tail vehicle departure speed under different feature consists"

Table 4

Optimal values of main parameters of model"

模型主要参数取值范围模型参数最优取值

排队尾车

驶离时间

排队尾车驶离速度
基本学习器的深度［2，10］38
基本学习器的数量［100，800］700400
学习率［0.01，0.5］0.20.03
损失减少阈值［0，1］0.70.1
L1正则化［0.0001，100］10.000 1
L2正则化［0.0001，100］1010
子集最小权重［2，200］610
样本子采样［0.6，0.9］0.70.8
列子采样［0.6，1］0.80.9

Table 5

Parameter calibration results of theory model"

平均期望加速

度/（m·s-2）

标定

取值

平均消散速率

/（s·veh-1）

标定

取值

ad,50.691wˉ51.277
ad,60.647wˉ61.277
ad,70.607wˉ71.308
ad,80.594wˉ81.318
ad,90.538wˉ91.346
ad,100.496wˉ101.356
ad,110.492wˉ111.356
ad,120.465wˉ121.363
ad,130.464wˉ131.388
ad,140.454wˉ141.401
ad,150.438wˉ151.379
ad,160.434wˉ161.342
ad,170.421wˉ171.315
ad,180.389wˉ181.313

Table 6

Performance comparison of tail vehicle departure time prediction models"

模型MAPE/%RMSE （km·h-1）
XGBoost5.741.74
随机森林10.583.75
决策树12.845.01
MLP神经网络30.397.33
理论模型14.645.78

Fig.11

Comparison of departure time of tail vehicle predicted by XGBoost-based model and theoretical model"

Table 7

Performance comparison of tail vehicle departure speed prediction models"

模型MAPE/%RMSE （km·h-1）
XGBoost9.983.48
随机森林10.063.44
决策树13.344.73
MLP神经网络13.274.45
理论模型15.783.50

Fig.12

Comparison of departure speed of tail vehicle predicted by XGBoost-based model and theoretical model"

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