Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3458-3464.doi: 10.13229/j.cnki.jdxbgxb.20221192

Previous Articles    

Short⁃term traffic flow prediction algorithm for road network based on deep asynchronous residual network

Hai-long GAO1(),Yi-bo XU2,De-zao HOU2(),Xue-song WANG3   

  1. 1.Key Laboratory of Technology on Intelligent Transportation Systems，Research Institute of Highway，Ministry of Transport，Beijing 100088，China
    2.Key Laboratory of Technology on Intelligent Transportation Systems，Research Institute of Highway，Ministry of Transport，Beijing 100088，China
    3.School of Transportation Engineering，Tongji University，Shanghai 201804，China
  • Received:2022-09-15 Online:2023-12-01 Published:2024-01-12
  • Contact: De-zao HOU E-mail:ghl19650815@163.com;hdz0328@163.com

RICH HTML

 2   

PDF (PC)

108

Abstract:

The whole road network was divided into sub-networks that meet the requirements of short-term traffic forecast. Three temporal and spatial characteristics are extracted from the short-term traffic volume of the road network， namely time sequence， periodic similarity and spatial sequence， and residual units were added to convolutional neural networks to construct a deep asynchronous network model to realize the short-term traffic flow forecast of the road network. The experimental results show that the root mean square error and average absolute error of the proposed algorithm are small， the trend of traffic forecast value is almost consistent with the trend of real traffic value， and the traffic flow forecast performance is good.

Key words: intelligent transportation, deep asynchronous residual network, predictability of traffic flow, space time characteristics, convolutional neural network, residual unit

CLC Number: 

  • TP399

Fig.1

Structure diagram of relationship between generalized spatial distance and its influencing factors"

Fig.2

Residual unit diagram"

Table 1

Data division of traffic flow forecast"

数据集训练数据测试数据
南京Taxi4952456
杭州Bike3469269

Fig.3

Comparison of root mean square error values of various algorithms"

Fig.4

Comparison of the mean absolute error values of various algorithms"

Fig.5

Comparison of TaxiNJ traffic prediction values and various algorithms"

Fig.6

Comparison of BikeHZ traffic forecast value and various algorithms"

1 周永强, 王波, 钱欢,等. 智能交通系统中CMIMO-SM-ARQ协议性能建模分析[J]. 计算机应用研究, 2021, 38(8): 2449-2453.
Zhou Yong-qiang, Wang Bo, Qian Huan, et al. Performance modeling and analysis of CMIMO-SM-ARQ protocol in ITS[J]. Application Research of Computers, 2021, 38(8): 2449-2453.
2 陈喜群, 曹震, 沈楼涛, 等. 融合路段传输模型和深度学习的城市路网短时交通流状态预测[J]. 中国公路学报, 2021, 34(12): 203-216.
Chen Xi-qun, Cao Zhen, Shen Lou-tao, et al. Short-term traffic-state prediction of urban road networks based on the fusion of a link-transmission model and deep learning[J]. China Journal of Highway and Transport, 2021, 34(12): 203-216.
3 蒲悦逸, 王文涵, 朱强, 等. 基于CNN-ResNet-LSTM模型的城市短时交通流量预测算法[J]. 北京邮电大学学报, 2020, 43(5): 9-14.
Pu Yue-yi, Wang Wen-han, Zhu Qiang, et al. Urban short-term traffic flow prediction algorithm based on CNN-ResNet-LSTM model[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 9-14.
4 刘铭, 鱼昕. 基于改进LSTM算法的短时交通流量预测[J]. 桂林理工大学学报, 2021, 41(2): 409-414.
Liu Ming, Yu Xin. Short-term traffic flow prediction based on MVF-LSTM algorithm[J]. Journal of Guilin University of Technology, 2021, 41(2): 409-414.
5 代亮, 梅洋, 钱超, 等. 基于生成对抗网络的大规模路网交通流预测算法[J]. 控制与决策, 2021, 36(12): 2937-2945.
Dai Liang, Mei Yang, Qian Chao, et al. Traffic flow forecasting algorithm for large-scale road network based on GAN[J]. Control and Decision, 2021, 36(12): 2937-2945.
6 钟异莹, 陈坚, 邵毅明, 等. 强制性换道的空间特征对分流区交通流的影响[J]. 交通运输系统工程与信息, 2020, 20(5): 114-120.
Zhong Yi-ying, Chen Jian, Shao Yi-ming, et al. Spatial feature of mandatory lane changing and its impact on traffic flow at diverging area[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 114-120.
7 温长鹏, 周磊, 潘兵宏. 基于交通流稳定距离的匝道连续入口最小间距研究[J]. 中外公路, 2021, 41(3): 392-397.
Wen Chang-peng, Zhou Lei, Pan Bing-hong. Research on minimum spacing of ramp continuous entrance based on traffic flow steady distance[J]. Journal of China & Foreign Highwa, 2021, 41(3): 392-397.
8 雷毅, 张善关, 谢云驰, 等. 基于流形距离的高速公路短时交通流预测模型[J]. 科学技术与工程, 2020, 20(18): 7312-7317.
Lei Yi, Zhang Shan-guan, Xie Yun-chi, et al. Short-term traffic flow prediction model of the highway based on manifold distance[J]. Science Technology and Engineering, 2020, 20(18): 7312-7317.
9 程浩, 王起琮, 杨奕华, 等. R-Q因子分析在海相石灰岩岩相识别中的应用——以塔里木盆地巴楚-塔中地区奥陶系石灰岩为例[J]. 西安石油大学学报: 自然科学版, 2020, 35(4): 1-10, 18.
Cheng Hao, Wang Qi-cong, Yang Yi-hua, et al. Application of R-Q factor analysis in lithofacies identification of marine limestone: taking the ordovician limestone in Bachu-Tazhong area of Tarim basin as an example[J]. Journal of Xi'an Shiyou University(Natural Science), 2020, 35(4): 1-10, 18.
10 吴晋武, 张海峰, 冉旭东. 基于数据约减和支持向量机的非参数回归短时交通流预测算法[J]. 公路交通科技, 2020, 37(7): 129-134.
Wu Jin-wu, Zhang Hai-feng, Ran Xu-dong. Nonparametric regressive short-term traffic flow forecast algorithm based on data reduction and SVM[J]. Journal of Highway and Transportation Research and Development, 2020, 37(7): 129-134.
11 黄振盛, 汪玉美, 韩江洪, 等. 基于MLS-SVM和时空特性的短时交通流量预测方法[J]. 合肥工业大学学报: 自然科学版, 2020, 43(1):57-63.
Huang Zhen-sheng, Wang Yu-mei, Han Jiang-hong, et al. Short-term urban traffic flow prediction based on MLS-SVM and spatiotemporal features[J]. Journal of Hefei University of Technology (Natural Science), 2020, 43(1): 57-63.
12 向乔怡, 祁辉. 有向图上高维时间序列模型及其在交通网络中的应用[J]. 数学杂志, 2022, 42(4): 345-358.
Xiang Qiao-yi, Qi Hui. High-dimensional time series model on directed graph and its application in traffic network[J]. Journal of Mathematics, 2022, 42(4): 345-358.
13 熊炜, 管来福, 王传胜,等. 基于预测和残差细化网络的道路提取算法研究[J]. 计算机工程与科学, 2020, 42(4): 683-690.
Xiong Wei, Guan Lai-fu, Wang Chuan-sheng, et al. Road extraction algorithm based on prediction and residual refinement networks[J]. Computer Engineering and Science, 2020, 42(4): 683-690.
14 张永凯, 武志昊, 林友芳, 等. 面向交通流量预测的时空超关系图卷积网络[J]. 计算机应用, 2021, 41(12): 3578-3584.
Zhang Yong-kai, Wu Zhi-hao, Lin You-fang, et al. Spatio-temporal hyper-relationship graph convolutional network for traffic flow forecasting[J]. Journal of Computer Applications, 2021, 41(12): 3578-3584.
15 李洪均, 丁宇鹏, 李超波, 等. 基于特征融合时序分割网络的行为识别研究[J]. 计算机研究与发展, 2020, 57(1): 145-158.
Li Hong-jun, Ding Yu-peng, Li Chao-bo, et al. Action recognition of temporal segment network based on feature fusion[J]. Journal of Computer Research and Development, 2020, 57(1): 145-158.
[1] Xiang-jiu CHE,Huan XU,Ming-yang PAN,Quan-le LIU. Two-stage learning algorithm for biomedical named entity recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2380-2387.
[2] Zhen-hai ZHANG,Kun JI,Jian-wu DANG. Crack identification method for bridge based on BCEM model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1418-1426.
[3] Pei-yong LIU,Jie DONG,Luo-feng XIE,Yang-yang ZHU,Guo-fu YIN. Surface defect detection algorithm of magnetic tiles based on multi⁃branch convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1449-1457.
[4] Jun-jie WANG,Yuan-jun NONG,Li-te ZHANG,Pei-chen ZHAI. Visual relationship detection method based on construction scene [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 226-233.
[5] Xuan-jing SHEN,Xue-feng ZHANG,Yu WANG,Yu-bo JIN. Multi⁃focus image fusion algorithm based on pixel⁃level convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1857-1864.
[6] Ming-hua GAO,Can YANG. Traffic target detection method based on improved convolution neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1353-1361.
[7] Huai-jiang YANG,Er-shuai WANG,Yong-xin SUI,Feng YAN,Yue ZHOU. Simplified residual structure and fast deep residual networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1413-1421.
[8] Xue-zhi WANG,Qing-liang LI,Wen-hui LI. Spatio⁃temporal model of soil moisture prediction integrated with transfer learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 675-683.
[9] Xiang-jun LI,Jie-ying TU,Zhi-bin ZHAO. Validity classification of melting curve based on multi⁃scale fusion convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 633-639.
[10] 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.
[11] Wen-zhi GAO,Yan-jun WANG,Xin-wei WANG,Pan ZHANG,Yong LI,Yang DONG. Real⁃time diagnosis for misfire fault of diesel engine based on convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 417-424.
[12] Long ZHANG,Tian-peng XU,Chao-bing WANG,Jian-yu YI,Can-zhuang ZHEN. Gearbox fault diagnosis baed on convolutional gated recurrent network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 368-376.
[13] You-jun ZHANG,Shun-yan CHENG. Structure parameter estimation of microstrip filter based on convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 3022-3028.
[14] Jun YANG,Zhi-ming GAO. Dense correspondence calculation of 3D models based on unsupervised deformed network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2971-2983.
[15] Xuan-jing SHEN,Tong-zhuang LIU,Yu WANG,Jia-wei LIU. Detection algorithm for parking space status based on convolution network structural re⁃parameterization [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2898-2905.
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