Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (2): 427-435.doi: 10.13229/j.cnki.jdxbgxb.20220352

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Urban rail transit emergency risk level identification method

Bo-song FAN1,2(),Chun-fu SHAO2,3()   

  1. 1.School of Traffic Management，People's Public Security University of China，Beijing 100038，China
    2.Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport，Beijing Jiaotong University，Beijing 100044，China
    3.School of Transportation Engineering，Xinjiang University，Urumqi 830046，China
  • Received:2022-03-31 Online:2024-02-01 Published:2024-03-29
  • Contact: Chun-fu SHAO E-mail:fanbosong@bjtu.edu.cn;cfshao@bjtu.edu.cn

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Abstract:

In order to promote the accuracy of risk level identification， to solve the real-time risk control and emergency treatment problems of urban rail transit system， an improved feature selection algorithm （Im-F-score+XGB） on filtering features of the risk factors for emergencies was proposed. Through the analysis of the basic data of urban rail transit emergency， the feature importance degree of each risk feature was calculated， the influence degree of different features on the risk of emergencies was excavated， and the important features for determining the risk level of emergencies was obtained. Besides， the cyclic multi-time window scanning method and the weighted cascade residual forest model were used to obtain the mapping relationship between the emergency risk grade and the feature of risk features， and an improved emergency risk level identification model （Im-F-GCF） was established. Compared with RF， HGBDT， GCF and LightGBM models， the validity of the proposed model is verified.

Key words: transportation planning and management, urban rail transit, emergency, risk level, feature selection, weighted cascade residual forest

CLC Number: 

  • U121

Fig.1

Im-F-GCF model framework"

Table 1

30 risk features and their descriptions"

变量名称变量描述
日路网客运量/万人次每日轨道交通路网客运量
实际开行列数/列每日路网实际开行列车数
列车兑现率/%每日路网实际与计划开行列车比值
延误时长/min每起突发事件延误时间长度
2分晚点列车数/列每日路网晚点超过2 min的列车数
正点率/%每日路网实际开行列车正点到达比率
加开临客列车/列每日路网加开临时列车数量
工作日工作日、非工作日取值：0、1
天气恶劣天气、非恶劣天气取值：0、1
线路突发事件所发生的线路
专业类别突发事件所属的类别（信号故障、车辆故障等）
1号线断面满载率/%每日高峰时段各条线路断面满载率平均值
2号线断面满载率/%
5号线断面满载率/%
6号线断面满载率/%
7号线断面满载率/%
8号线断面满载率/%
9号线断面满载率/%
10号线断面满载率/%
13号线断面满载率/%
15号线断面满载率/%
昌平线断面满载率/%
房山线断面满载率/%
亦庄线断面满载率/%
八通线断面满载率/%
机场线断面满载率/%
4-大兴线断面满载率/%
14号线（西段）断面满载率/%
14号线（东段）断面满载率/%
16号线断面满载率/%

Table 2

Number of train adjustments to the network and each line on a given day"

运营企业及线路列车调整
停运通过清人掉线中折
路网81846
北京地铁1号线
2号线
5号线12
6号线7166
7号线
8号线
9号线
10号线22
13号线
15号线
昌平线
房山线
亦庄线
八通线
机场线
S1线
小计81846
京港地铁4-大兴线
14号线（西段）
14号线（东段）
16号线（北段）
小计

Fig.2

Importance and cumulative contribution of emergency features （Im-F-score model）"

Table 3

Important risk features"

变量名称变量描述
日路网客运量每日轨道交通路网客运量
实际开行列数每日轨道交通运营管理部门实际开行列车数
列车兑现率每日路网实际开行列车数与计划开行列车数比值
延误时长突发事件从产生影响到影响结束的持续时间
机场线断面满载率每日高峰时段各条线路断面满载率平均值
4-大兴线断面满载率
16号线断面满载率

Table 4

Classification matrix of prediction results of each risk level"

实际情况判别结果
正类负类
正类TPFN
负类FPTN

Table 5

Experimental results"

模型

Acc/

%

Pre/

%

Rec/

%

Spe/

%

F1/

%

Kap/

%

Im-F-GCF训练90.8694.7693.1383.7093.9487.78
测试89.7093.7792.5081.0793.1386.24
RF84.5089.7688.7173.4789.2379.59
HGBDT86.2590.7790.7373.3090.7581.76
GCF88.2492.5991.6977.7692.1484.40
Light GBM88.6593.5891.0581.5292.3084.88

Fig.3

ROC curves of each model"

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