User Portrait Method of Freeway Freight Car for Risk Identification of Freight Transportation

doi:10.12141/j.issn.1000-565X.220525

Abstract

Abstract:

At present, the freight car overload phenomenon is coming from bad to worse, in order to improve the efficiency of freight car control on the highway and the level of safety in the freight transport, a freight transport risk level identification model based on user portrait of freight risk was proposed. Firstly, based on highway toll data, taking freight car as the research object, a user portrait system for freight transport risk identification was developed from the aspects of driving behavior and operation status. Then，the sample data was cleaned and the label index was extracted and analyzed. Then, K-means++ algorithm was applied to obtain the classification results of freight transport risk feature portraits. Next, the entropy weight method was used to score the freight risk of all kinds of freight car to determine the risk level of all kinds of freight car. Finally, by combining with the relevant indicators of various types of vehicles, the vehicle portrait was completed. Based on the trucking toll data of the entire highway network in Guangdong Province from March to May 2022, the proposed model was used to divide the trucking vehicles into five categories. Among them, “the freight car of high risk and high workload” accounted for 5.42%, the freight car of higher risk and night-driving and overloaded ”accounted for 19.12%, “the freight car of medium-risk and overspeed” accounted for 12.85%, “ the freight car of low risk and low-frequency” accounted for 37.00%, and “ the freight car of low risk and high-frequency ” accounted for 25.61%. The validity of the model was verified by the data of an accident database in Guangdong Province in the same period. The data showed that the relative risk coefficient of high risk vehicles is much higher than that of low risk vehicles. The research shows that the proposed model can effectively identify trucks with high freight risk characteristics. Based on the results of risk grade identification, traffic management departments can carry out high-risk vehicle identification, key inspection of overload and over-limit, and specific message push to guide vehicle driving safety, so as to improve the safety management level of the industry.

Key words: traffic safety, freight car, freeway, network toll, clustering algorithm, user portrait

CLC Number:

U491

LIN Peiqun, GONG Minping, ZHOU Chuhao. User Portrait Method of Freeway Freight Car for Risk Identification of Freight Transportation[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 1-9.

Figures/Tables 11

Table 1

Fig.1

Table 2

Weight classification of driving time， transportation distance and overload times"

$h i$
$2 T c$ /h	评分	分级	权重	分级	权重	超载比例	扣分	分级	权重
4	0.83	<1 h	0.5	<50 km	0.5	未达30%	1分	<0.2	1.0
4	0.83	［1，4） h	1.0	［50，100） km	1.0	30%~50%	3分	［0.2，0.4）	1.5

5	0.73	［4，6） h	1.5	［100，200） km	1.5	50%以上	6分	［0.4，0.6）	2.0
7	0.53	［6，8） h	2.0	［200，300） km	2.0	50%以上	6分	$≥$ 0.6	2.5

9	0.39	［8，10） h	2.5	［300，400） km	2.5
9	0.39	$≥$ 10 h	3.0	$≥$ 400 km	3.0

Table 2

Fig.2

Fig.3

Fig.4

Table 3

Table 4

Table 5

Fig.5

Fig.6

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字段名称	字段代码		字段代码
通行标识	PASS_ID	车牌	VEHICLE_ PLATE
入口时间	EN_TIME	出口时间	EX_TIME
轴数	AXLE_COUNT	行程距离	MILEAGE
运输性质	TRANSPORT_ TYPE	核定总质量	TOP_WEIGHT
出口通行车型	VEHICLE_TYPE	机动车所有人	VEHICLE_ OWNER
出口总重	EX_WEIGHT	入口总重	EN_WEIGHT
出口收费站编码	EX_STATION_ID	入口收费站编码	EN_STATION_ID
出口收费站名称	EX_STATION_NAME	入口收费站名称	EN_STATION_NAME

货车类别	运距加权/km	运输量/kg	时长加权/s	夜间驾驶系数	超速系数	超载系数	OD 信息熵	收费站信息熵	车辆数	占比/%
类别1	48 458.4	1 425 468.2	2 968 208.2	0.212	0.045	1.224	4.252	4.175	109 354	5.42
类别2	9 134.4	1 679 334.2	584 295.6	0.250	0.066	1.345	3.309	3.591	385 817	19.12
类别3	1 637.1	286 471.5	52 675.6	0.051	0.524	0.359	1.965	2.340	259 224	12.85
类别4	3 990.0	654 244.2	185 591.3	0.039	0.129	0.580	4.535	4.402	516 904	25.61
类别5	1 190.0	301 792.3	59 276.8	0.030	0.074	0.518	1.782	2.262	746 783	37.00

货车类别	数量	上高速趟次	T12占比/%	W占比/%	货车私有占比/%	货车公有占比/%	周转量/吨公里
类别1	109 354	58.31	58.08	12.07	82.64	17.36	599 424.63
类别2	385 817	70.87	75.50	4.54	73.06	26.94	148 686.35
类别3	259 224	43.63	66.82	2.04	42.09	57.91	9 890.74
类别4	516 904	86.53	46.85	6.31	63.64	36.36	31 094.83
类别5	746 783	36.21	57.28	5.04	48.90	51.10	11 388.06

货运风险指标	权重	百分比/%
运距加权	0.197 144	19.71
时长加权	0.186 006	18.60
夜间驾驶系数	0.191 977	19.20
超速系数	0.142 827	14.28
超载系数	0.098 189	9.82
OD信息熵	0.031 958	3.20
收费站信息熵	0.015 763	1.58
车货总重	0.136 137	13.61

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