Short-term traffic flow forecasting for urban roads

Ya Chen Hsieh, Ka-Io Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The interests and applications of short-term traffic forecasting have been growing in the recent years. Many of the applications in Advance Traveler Information System (ATIS) and Advance Traffic Management Systems (ATMS) require an estimation and forecasting of the traffic conditions of the network. With a historical database of past traffic data from various types of vehicle detectors installed in the roads, real-time traffic information is collected which will be used to estimate the current traffic conditions and predict the condition in near future. Whereas most of the literature focused on the traffic flow prediction on the freeways, modeling traffic flow in urban arterials is more challenging as there are disturbances such as motorcycles and traffic signals in urban area. In this study, the traffic flow forecasting for urban arterials is investigated. Seasonal ARIMA and neural network are considered as the algorithms, and two arterials in the Taipei city, Taiwan, are studied in our numerical example.

Original languageEnglish
Title of host publicationProceedings of the 14th HKSTS International Conference
Subtitle of host publicationTransportation and Geography
EditorsDG Wang, SM Li
PublisherHONG KONG SOC TRANSPORTATION STUDIES LTD
Pages779-788
Number of pages10
ISBN (Print)9789889884772
StatePublished - 1 Dec 2009
Event14th HKSTS International Conference: Transportation and Geography - Kowloon, Hong Kong
Duration: 10 Dec 200912 Dec 2009

Publication series

NameProceedings of the 14th HKSTS International Conference: Transportation and Geography
Volume2

Conference

Conference14th HKSTS International Conference: Transportation and Geography
CountryHong Kong
CityKowloon
Period10/12/0912/12/09

Keywords

  • NEURAL-NETWORKS
  • PREDICTION
  • MODEL

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