详情

Development of Bayesian Multi-State Travel Time Reliability Models

项目名称：	Development of Bayesian Multi-State Travel Time Reliability Models
摘要：	The objective of this project is to develop a Bayesian multi-state travel time reliability approach for modeling travel time uncertainty under various traffic conditions. The reliability of travel time is a key performance index of transportation system and has been a major transportation research area. Reliability is one of the four key focus areas of the Strategic Highway Research Plan (SHRP2). Travel time is affected by multiple factors such as traffic condition, weather, incidents etc. Many of these factors are random in nature and stochastic modes should be used in modeling the uncertainty associated with travel time. Traditionally, uni-mode distributions have been adopted for travel time reliability modeling and the log-normal distribution has been the most popular model. In recent years, the multi-state travel time reliability model has been proven to be a superior alternative by providing substantial improved data fitting, scientifically sound interpretation, as well as close relationship with the underline traffic flow characteristics. Majority of the current stochastic models, however, focus primarily on provide the best fitting for the travel time data. Limited researches have been conducted to link travel time uncertainty with traffic conditions and other external factors. Part of the reason is that traditionally used uni-mode distributions lack the flexibility to accommodate variation in travel time, let alone the complex interaction with external factors. On contrast, the multi-state model used a two-level structure to represent 1) the probability of encountering a traffic delay, and 2) the distribution characteristics of travel time in both delay or non-delay conditions. Previous studies have shown that the parameters of the multi-state model are directly related to the time of day. The results fit the intuition that the probability of encountering traffic delay is much higher during peak hours. However, previous studies are exploratory in nature and had not quantitatively evaluated the relationship between traffic conditions and parameters of the multi-state models. To establish quantitative relationship between traffic condition and the key parameters of mult-state models, i.e., the probability of encountering delay and the distribution parameters for each travel time state, will significantly increase our understanding of the relationship between traffic condition and travel time. Due to the complexity of the problem, traditional Expectation-Maximization (EM) algorithm for model fitting is not computationally attractive. A full Bayesian approach will be more appealing for both research and practical purposes. The results of this study will benefit congestion management for traffic management authorities as well travel time prediction for individual travelers.
状态：	Active
资金：	90560.00
资助组织：	Mid-Atlantic Universities Transportation Center<==>Research and Innovative Technology Administration
执行机构：	Virginia Polytechnic Institute and State University, Blacksburg
开始时间：	20120901
实际结束时间：	20131231
主题领域：	Highways；Operations and Traffic Management；Planning and Forecasting