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

Previous Articles    

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

RICH HTML

 4   

PDF (PC)

99

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
1 Shalaby A, Farhan A. Prediction model of bus arrival and departure times using AVL and APC data[J]. Journal of Public Transportation, 2004, 7(1): 41-61.
2 童小龙, 卢冬生, 张腾, 等. 基于时间序列法的公交车站间行程时间预测模型研究——以苏州1路公交为例[J]. 交通运输工程与信息学报, 2017, 15(4): 114-119, 126.
Tong Xiao-long, Lu Dong-sheng, Zhang Teng, et al. Research on bus inter-site travel time prediction model: a case study based on suzhou No.1 bus route[J].Journal of Transportation Engineering and Information, 2017, 15(4): 114-119, 126.
3 苗旭, 王忠宇, 吴兵, 等. 考虑前序路段状态的公交到站时间双层BPNN预测模型[J]. 交通运输系统工程与信息, 2020, 20(2): 127-133.
Miao Xu, Wang Zhong-yu, Wu Bing, et al. Bi-layer BPNN prediction model for bus arrival time considering preceding segment state[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 127-133.
4 Cong B, Peng Z R, Lu Q C, et al. Dynamic bus travel time prediction models on road with multiple bus routes[J]. Computational Intelligence & Neuroscience, 2015, 2015: 432389.
5 Farid Y Z, Christofa E, Paget-Seekins L. Estimation of short-term bus travel time by using low-resolution automated vehicle location data[J].Transportation Research Record Journal of the Transportation Research Board, 2016, 2539(1): 113-118.
6 梁士栋, 赵淑芝, 马明辉, 等. 路段直线式公交站点对公交车延误的影响[J]. 吉林大学学报: 工学版, 2016, 46(6): 1807-1817.
Liang Shi-dong, Zhao Shu-zhi, Ma Ming-hui, et al. Impacts of linear bus stop on bus delays[J]. Journal of Jilin University(Engineering and Technology Edition), 2016, 46(6): 1807-1817.
7 邝先验, 罗会超, 钟蕊, 等. 基于天牛须小波神经网络的公交到站时间预测[J]. 吉林大学学报: 工学版, 2022, 52(1): 110-117.
Kuang Xian-yan, Luo Hui-chao, Zhong Rui, et al. Bus arrival time prediction based on wavelet neural network optimized by beetle antennae search[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 110-117.
8 杨世军, 裴玉龙, 潘恒彦, 等. 城市公交车辆驻站时间特征分析及预测[J]. 吉林大学学报: 工学版, 2021, 51(6): 2031-2039.
Yang Shi-jun, Pei Yu-long, Pan Heng-yan, et al. Characteristics analysising and prediction of dwelling time of urban bus[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2031-2039.
9 Sharmila R B, Velaga N, Choudhary P. Bus arrival time prediction and measure of uncertainties using survival models[J]. IET Intelligent Transport Systems, 2020, 14(8): 900-907.
10 Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.
11 Hinton G E, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.
12 Hadgu A T, Nigam A, Diaz-Aviles E. Large-scale learning with AdaGrad on spark[C]∥IEEE International Conference on Big Data, Santa Clara, USA, 2015: 2828-2830.
[1] Wen-jing WU,Yong-bin ZHAN,Li-li YANG,Run-chao CHEN. Coordinated control method of variable speed limit in on⁃ramp area considering safety distance [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1315-1323.
[2] Zhi ZHENG,Bo GENG,Fu-min WANG,Jun-hong DONG,Si-si WEI. Improvement of protective ability for existing low⁃grade concrete guardrail [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1362-1374.
[3] Hong-fei JIA,Zi-han SHAO,Li-li YANG. Ride⁃sharing matching model and algorithm of online car⁃hailing under condition of uncertain destination [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 564-571.
[4] Xing-hua LI,Fei-yu FENG,Cheng CHENG,Wei WANG,Peng-cheng TANG. Choice preference analysis and modeling of ridesplitting service [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 578-584.
[5] Tian-jun FENG,Xue-lu SUN,Jia-sheng HUANG,Xiu-juan TIAN,Xian-min SONG. Two-phase signal intersection delay based on three crossing modes [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 550-556.
[6] Xian-tong LI,Wei QUAN,Hua WANG,Peng-cheng SUN,Peng-jin AN,Yong-xing MAN. Route travel time prediction on deep learning model through spatiotemporal features [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 557-563.
[7] Yan-feng JIA,Da-yi QU,Lu LIN,Rong-han YAO,Xiao-long MA. Coordinated speed control of connected mixed traffic flow based on trajectory [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2051-2060.
[8] Chang-fu ZONG,Long WEN,Lei HE. Object detection based on Euclidean clustering algorithm with 3D laser scanner [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 107-113.
[9] Liang JIANG,Yi HE. Risky driving behavior and influencing factors analysis for electric two⁃wheeler [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1107-1113.
[10] Xiao⁃qin LUO,Dian⁃hai WANG,Sheng JIN. Traffic signal actuated control at isolated intersections for heterogeneous traffic [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 695-704.
[11] Zhi⁃hui LI,Tao ZHONG,Yong⁃hua ZHAO,Yong⁃li HU,Hai⁃tao LI,Jing⁃wei ZHAO. Pedestrian tracking algorithm for autonomous driving [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 680-687.
[12] QU Da-yi,YANG Jing-ru,BING Qi-chun,WANG Wu-lin,ZHOU Jing-chun. Arterial traffic offset optimization based on queue characteristics at adjacent intersections [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1685-1693.
[13] LI Zhi-hui, HU Yong-li, ZHAO Yong-hua, MA Jia-lei, LI Hai-tao, ZHONG Tao, YANG Shao-hui. Locating moving pedestrian from running vehicle [J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[14] SUN Zong-yuan, FANG Shou-en. Hierarchical clustering algorithm of moving vehicle trajectories in entrances and exits freeway [J]. 吉林大学学报(工学版), 2017, 47(6): 1696-1702.
[15] ZHANG Zhe, JIA Li-min, QIN Yong, YUN Ting. Equalization-based feedback control model of pedestrian counter flow [J]. 吉林大学学报(工学版), 2017, 47(6): 1728-1737.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd