Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1792-1799.doi: 10.13229/j.cnki.jdxbgxb20210248

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Fluctuation characteristics and prediction method of bus travel time between stations

Xian-min SONG1(),Shu-tian YANG1,Ming-xin LIU2(),Zhi-hui LI1   

  1. 1.College of Transportation，Jilin University，Changchun 130022，China
    2.The Architectural Design & Research Institute of Zhejiang University Co. ，Ltd. ，Hangzhou 310028，China
  • Received:2021-03-25 Online:2022-08-01 Published:2022-08-12
  • Contact: Ming-xin LIU E-mail:songxm@jlu.edu.cn;517301017@qq.com

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Abstract:

In this paper， the volatility index of travel time based on bus headway is firstly proposed， and the method of dividing bus operating environment is established by using fast search and density peak clustering algorithm， which can be divided into low volatility， medium volatility and high volatility. The influencing factors of bus travel time between stations were analyzed， and the selection method of model input variable set based on embedding method was designed， and the deep neural network prediction method （CFDP-DNN） considering bus operating environment was established. In order to verify the effectiveness of this method， it was compared with SVM， ANFIS， BP neural network and other methods. The experimental results show that the correlation of CFDP-DNN prediction results is 0.9354， and the MAPE error range is 11%~22%， indicating that the division of traffic operating environment based on bus headway can effectively improve the prediction accuracy of travel time. The prediction method proposed in this paper can realize real-time and accurate bus travel time prediction between stations and provide theoretical support for bus dynamic scheduling.

Key words: engineering of communication and transportation system, bus travel time, deep neural network, volatility, operating environment

CLC Number: 

  • U491

Fig.1

Spatio-temporal distribution chart of working day volatility index"

Fig.2

Spatio-temporal distribution chart of rest day volatility index"

Fig.3

Contour coefficient plot of CFDP experimental results"

Table 1

Contour coefficient table of experimental results of four algorithms"

聚类算法轮廓系数最佳簇目数量
CFDP0.75373
K-Means0.49354
SOM0.41505
FCM0.54443

Table 2

Model parameter description table"

模型参数含义描述
i时段索引
j路段索引
k公交车索引
n前溯路段数量
p天的数量
q公交车的数量
ATTi,ji时段j路段的历史平均公交行程时间
vj-nk第k辆公交车在前n个路段的行驶速度
Ti,jpi时段j路段的前p天的公交行程时间
Tjk-q第k辆公交车前通过路段j的q辆公交车的行程时间
Tem当前时间的天气温度

Fig.4

CFDP-DNN prediction model process"

Fig.5

Schematic diagram of prediction effect of different methods in working days"

Fig.6

Schematic diagram of prediction effect of different methods in rest days"

Fig.7

Scatter plot of predicted value and actual value distribution of four prediction methods"

Table 3

Prediction error table of different prediction methods"

指标DNNBPANFISSVM
—+CFDP—+CFDP—+CFDP—+CFDP
MSE912.83712.011423.991288.681739.031347.911327.721178.21
RMSE30.2126.6837.7435.9041.7036.7136.4434.33
MAPE0.210.140.270.260.340.290.260.23

Table 4

MAPE table of four prediction methods in different sections"

起始站点终到站点CFDP-DNN/%CFDP-BP/%CFDP- ANFIS/%CFDP-SVM/%
靖宇路信息学院15302618
信息学院湖光路16312922
湖光路南湖广场20333526
南湖广场延安大街11202223
延安大街高科技宿舍13222920
高科技宿舍湖西路15262819
湖西路同德路14232223
同德路新民广场13192325
新民广场吉大三院14252927
吉大三院吉大一院18343931
吉大一院省邮政公司22303423
省邮政公司同志街13212626
同志街解放大路12262722
解放大路人民广场14242920
人民广场般若寺15232618
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