• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

Chen, Yanyan (Chen, Yanyan.) (Scholars:陈艳艳) | Zhang, Ye (Zhang, Ye.) | Sun, Haodong (Sun, Haodong.)

Indexed by:

CPCI-S

Abstract:

Intersections are the key node in the urban road network, so reasonable channelization at intersections is key for improving traffic efficiency of the entire urban network. However, traffic data of intersections that has been collected so far has great volatility and abnormality, which cannot provide an accurate data basis for further intersection optimization. This paper is based on historical traffic data of intersections for data processing and short-term traffic forecasting. First, the historical data is preprocessed by a time series method and short-term traffic prediction method to recover the missing data. We then performed short-term traffic forecasting based on SPSS and used an expert modeling method and ARIMA forecasting method to predict short-term traffic. After pretreatment, we performed time division of traffic data using the K-means clustering algorithm. Through the above methods, traffic data can be improved to provide accurate data support for intersection optimization.

Keyword:

Intersection Short-term traffic forecast Urban traffic k-means clustering analysis algorithm

Author Community:

  • [ 1 ] [Chen, Yanyan]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Ye]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Sun, Haodong]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 陈艳艳

    [Chen, Yanyan]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

Show more details

Related Keywords:

Related Article:

Source :

CICTP 2019: TRANSPORTATION IN CHINA-CONNECTING THE WORLD

Year: 2019

Page: 5189-5201

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

Online/Total:455/5440619
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.