AI SearchDynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty
Call Number: AIT Thesis no.TE-00-07 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. TE-00-07Publication details: Bangkok : Asian Institute of Technology, 2001Description: 68 leavesSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2001 Summary: The shortest path problems in transportation networks have been the subject of extensive research for many years. It is hypothesized that shortest path algorithm employed with the route choice model including individual behavior will give superior results compared to the general shortest path techniques. The empirical studies have shown that drivers use many criteria in pre-route decision. One important factor found in the previous study is travel time reliability. However, the travel time reliability has overlooked from many studies on shortest path areas. There have been few studies on identifying travel time uncertainty in real transportation network to develop route choice behavior model and use it for identifying the optimal path. With the teclmologies of Advance Transportation Information Systems (ATIS), real time data implementations supporting central to any in-vehicle Route Guidance Systems (RGS) can be used to find an optimal route from the origin to the destination m a transportation network which takes into account drivers ' route choice behavior. The main purposes of the thesis are to identify the characteristics of travel time unce1tainties in the context of ATIS, and combine them into the travel time approximation model for identifying the optimal path in dynamic and stochastic transportation network. The analytical mathematics shows that, in individual travel time forecast, there are two component of unce1tainty: mean travel time forecasting error and individual variance. The approximation model for individual travel time forecast and the prediction interval (PI) model are proposed in this thesis. The results using the Automatic Vehicle Identification (A VI) based travel time data show that the proposed models are more general than existing models in that they can be applied for both travel time estimate and travel time forecast
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Civil Engineering
Thesis (M.Eng.) - Asian Institute of Technology, 2001
The shortest path problems in transportation networks have been the subject of extensive research for many years. It is hypothesized that shortest path algorithm employed with the route choice model including individual behavior will give superior results compared to the general shortest path techniques. The empirical studies have shown that drivers use many criteria in pre-route decision. One important factor found in the previous study is travel time reliability. However, the travel time reliability has overlooked from many studies on shortest path areas. There have been few studies on identifying travel time uncertainty in real transportation network to develop route choice behavior model and use it for identifying the optimal path. With the teclmologies of Advance Transportation Information Systems (ATIS), real time data implementations supporting central to any in-vehicle Route Guidance Systems (RGS) can be used to find an optimal route from the origin to the destination m a transportation network which takes into account drivers ' route choice behavior. The main purposes of the thesis are to identify the characteristics of travel time unce1tainties in the context of ATIS, and combine them into the travel time approximation model for identifying the optimal path in dynamic and stochastic transportation network. The analytical mathematics shows that, in individual travel time forecast, there are two component of unce1tainty: mean travel time forecasting error and individual variance. The approximation model for individual travel time forecast and the prediction interval (PI) model are proposed in this thesis. The results using the Automatic Vehicle Identification (A VI) based travel time data show that the proposed models are more general than existing models in that they can be applied for both travel time estimate and travel time forecast
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