基于基因表达式编程的高架道路事故实时预测

doi:10.13229/j.cnki.jdxbgxb.20221029

Abstract

Abstract:

In order to effectively predict crash on elevated expressway， taking Yan'an elevated expressway in Shanghai as the research object， based on its traffic flow and crash data， an improved Gene Expression Programming algorithm with additional elite gene bank and extinction mechanism was applied to dig out ‘Crash Prediction Empirical Formula’. The prediction accuracy and interpretability of the empirical formula were verified by comparing with the results of machine learning and statistical analysis. The crash of another expressway was predicted by empirical formula without retraining and calibration， and the portability of the empirical formula was verified. The research results indicated that the prediction performance of the empirical formula on the Yan'an elevated expressway dataset is significantly improved compared with the traditional Logistics regression， and the receiver operating characteristic curve area and F1-score indexes are consistent with the artificial neural network model， identifying 74% of the crashes correctly. The good performance of the empirical formula on Hangzhou Shangtang elevated expressway dataset shows that the empirical formula has basic portability. In conclusion， the gene expression programming algorithm considers both high accuracy and interpretability for the crash risk prediction problem， and shows portability， which is helpful to establish a low-cost and efficient crash prediction system.

Key words: engineering of traffic and transportation system, crash prediction, gene expression programming, elevated expressway

CLC Number:

U491.31

Xiao-chi MA,Jian LU. Real⁃time crash prediction of elevated expressway based on gene expression programming algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 719-726.

Figures/Tables 8

Fig.1

Table 1

Table 2

Data structure and statistical description"

变量	含义	最小值	最大值	均值
crash	是否发生事故	0.0	1.0	0.28
CI₁	拥堵指数	16.9	74.2	47.9
AS₁/（km·h^-1）	速度	11.3	70.0	36.4
SDV₁/（km·h^-1）	速度标准差	0.0	2.3	0.4
SDV₂/（km·h^-1）	速度标准差	0.0	2.4	0.4
VOL₁/辆	车道交通量	45.5	145.0	108.0
AS $2 U$ /（km·h^-1）	速度	10.0	72.2	35.9
SDV $1 U$ /（km·h^-1）	速度标准差	0.0	2.8	0.5
VOL $1 U$ /辆	车道交通量	5.0	150.0	90.8
AS $1 D$ /（km·h^-1）	速度	10.5	68.4	39.3
SDV $1 D$ /（km·h^-1）	速度标准差	0.0	2.7	0.4
SDV $2 D$ /（km·h^-1）	速度标准差	0.0	2.2	0.4
VOL $1 D$ /辆	车道交通量	27.5	148.0	102.7

Table 2

Table 3

Table 4

Table 5

Estimation results of logistics regression"

变量名	参数估计B	显著性	参数估计指数值
AS₁	-0.053	0.000	0.948
AS $1 D$	0.022	0.094	1.022
SDV $1 D$	0.852	0.069	2.345
SDV $2 D$	-0.998	0.056	0.368
VOL $1 D$	-0.017	0.018	0.982
常量	1.657	0.046	5.245

Table 5

Fig.2

Table 6

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分类结果	预测值1	预测值0
真实值1	TP	FN
真实值0	FP	TN

参数	数值	参数	数值
初始种群大小	100	根插串率	0.1
函数符集F	+ - * / sin sqrt sigmoid	插串位数	1~3
终结符集T	表2的12个自变量和常数	单点重组率	0.4
染色体基因长度	18	两点重组率	0.2
染色体数量	4	基因重组率	0.1
常数集	随机常数	精英基因采集代数	20
变异率	0.035	精英基因投放代数	40
插串率	0.1	精英基因投放比例/%	10

	GEP经验公式	Logistics回归	ANN模型
AUC	0.702	0.664	0.714
Sensitivity	0.742	0.510	0.697
F₁-score	0.717	0.469	0.681
约登指数	0.083	0.227	0.332

参数	GEP经验公式	Logistics回归
AUC	0.683	0.653
Sensitivity	0.756	0.515
F₁-score	0.679	0.462
约登指数	0.070	0.199

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[3]	JIANG Yue, TANG Chang-jie, LI Chuan, LI Sheng-zhi, YE Shang-yu, WU Jiang . Identification of Hammerstein model based on gene expression programming [J]. 吉林大学学报(工学版), 2008, 38(05): 1114-1119.

Real⁃time crash prediction of elevated expressway based on gene expression programming algorithm

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