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Document Type

Dissertation

Degree

Doctor of Philosophy

Department

Civil Engineering

First Advisor's Name

Albert Gan

First Advisor's Title

Committee Member

Second Advisor's Name

David L. Shen

Second Advisor's Title

Committee Member

Third Advisor's Name

Fang Zhao

Third Advisor's Title

Committee Member

Fourth Advisor's Name

Mohammed Hadi

Fourth Advisor's Title

Committee Member

Fifth Advisor's Name

Prasad V. Bidarkota

Fifth Advisor's Title

Committee Member

Keywords

Traffic Forecasting, Short-term Forecasting, Time Series Model, ARIMA Model, VARMA Model

Date of Defense

5-16-2008

Abstract

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

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