Type of Document Dissertation Author Kim, Changkyun URN etd-06062008-164007 Title Development and evaluation of traffic prediction systems Degree PhD Department Civil Engineering Advisory Committee
Advisor Name Title Hobeika, Antoine G. Committee Chair Sivanandan, R. Committee Chair Foutz, Robert Committee Co-Chair Trani, Antonio A. Committee Member Walker, Richard D. Committee Member Keywords
- Traffic flow
Date of Defense 1994-04-12 Availability restricted Abstract
Developing real-time traffic diversion strategies is a major issue of Advanced Traffic Management Systems (ATMS), a component of Intelligent Vehicle Highway Systems (IVHS). Traffic diversion utilizes available capacity in the urban network during a congestion-causing event. If an alternative route selected for diversion is not congested at the current time, a certain part of the route may become congested by the time the diverted drivers reach that part of the network. Thus the ability to forecast future traffic variables on each link along various routes in a prompt and accurate fashion may be necessary to ensure the success of a diversion strategy. Forecasting future traffic variables would also be helpful for urban traffic control. In addition, the forecasting model may help assign the vehicles onto the alternate roads, if the information on driver destinations and the routes between a diversion point and the destinations are available.
This dissertation is aimed at developing and evaluating two prediction models: link-based model and network-based model. The link-based prediction model has two components. One component is an Auto Regressive Integrated Moving Average (ARIMA) time series model based on the latest (current) traffic data. The other component is the smoothed historical traffic volume (historical average) for that period as obtained from previous days. These two components are combined to represent the dynamic fluctuations in the traffic flow behavior. The combined model is designed to produce the predicted traffic volumes for a look-ahead period of 30 minutes, divided into 6-minute time intervals. The results show that the combined model is promising for light to medium congested traffic conditions.
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