SHORT-TERM TRAFFIC FLOW FORECASTING FOR URBAN ROADS

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.