Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2031-2039.doi: 10.13229/j.cnki.jdxbgxb20200676

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Characteristics analysising and prediction of dwelling time of urban bus

Shi-jun YANG(),Yu-long PEI(),Heng-yan PAN,Guo-zhu CHENG,Wen-hui ZHANG   

  1. School of Traffic and Transportation,Northeast Forestry University,Harbin 150040,China
  • Received:2020-09-03 Online:2021-11-01 Published:2021-11-15
  • Contact: Yu-long PEI E-mail:ysj0826@nefu.edu.cn;peiyulong@nefu.edu.cn

Abstract:

In order to accurately analyze the characteristics of bus dwelling time and forecast the dwelling time, this paper analyzes the time structure of bus dwelling process. The concept of station service time is introduced to quantify the length of time that a vehicle is used for passengers to get on and off the train in the process of station service. According to the analysis of manual survey data, it is concluded that the boarding time and alighting time are the influencing factors of the station service time and dwelling time. Compared with the alighting time, the correlations between boarding time and station service time, and between boarding time and dwelling time are stronger, and the maximum value of both has the strongest correlation with the station service time and dwelling time. This paper analyzes the impact of congestion situation inside the bus on the boarding time and alighting time of passengers, as well as the influence of the payment method, age and load of passengers on the boarding time. This paper introduces the concept of vehicle stop deviation distance, and analyzes the influence of the degree of vehicle stop deviation and the number of passengers on the boarding delay loss time, and the influence of vehicle waiting time on the departure delay loss time. Based on the above analysis results, a BP neural network model with eight input layers is constructed. The goodness of fit between the training results and the prediction results is 0.915 and 0.955 respectively, which has a good forecasting effect.

Key words: urban traffic, bus, dwelling time, time lost due to delay, station time, BP neural network, prediction of the dwelling time

CLC Number: 

  • U491.1

Fig.1

Composition of vehicle dwell time"

Table 1

Time parameter and its interpretation"

参数含义参数含义
t1车辆进站停车t5

乘客停止下车,

车辆后门关闭

t2

车辆后门开启,

乘客开始下车

t6

乘客上车结束,

车辆前门关闭

t3车辆前门开启t7前车起步出发
t4乘客开始上车t8车辆起步出站

Fig.2

Scatter diagram of boarding and alighting time and volumes"

Fig.3

Analysis chart of station service time"

Fig.4

Analysis chart of dwelling time"

Fig.5

Influence of the crowding degree in the car on the time of boarding and alighting"

Fig.6

Influence of payment method on boarding time"

Fig.7

Influence of age composition on boarding time"

Fig.8

Influence of luggage load on boarding time"

Fig.9

Schematic diagram of lost delay time"

Table 2

Parameter and its interpretation"

参数含义参数含义
Ci公交车辆编号xqd车辆停靠位置
Li后车与前车的实际距离xzs站台起点位置
Ls后车与前车的安全距离xze站台终点位置

Fig.10

Analysis on the lost time of boarding delay"

Fig.11

Analysis on the lost time of vehicle start delay"

Table 3

Input layer indicators"

编号符号指标释义
1NP停靠站停车位数量
2NL停靠站线路数量
3Nup站点上车人数
4Ndown站点下车人数
5AG站台乘客平均年龄
6LO站台乘客行李负重系数
7FE上车乘客中手机扫码支付的比例
8Bcro车内拥挤状态

Fig.12

Structure of BP neural networkmodel for dwell time"

Fig.13

Fitting chart of BPNN training andforecasting results"

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