03703nas|a2200397 i 4500 20260818094238.0 170701s2001 th uzm rtt 00| a1eng d .b11829734 AIT Thesis no.TE-00-07 Parichart Pattanamekar Dynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty Bangkok : Asian Institute of Technology, 2001 68 leaves Thesis ; no. TE-00-07 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 Travel time (Traffic engineering) Park, Dong Joo, Chairperson Yordphol Tanaboriboon, Examination Committee Takahashi, Kiyoshi, Examination Committee Pannapa Herabat, Examination Committee Royal Thai Government / Human Resource Development Program (Phase II), Scholarship Donor Asian Institute of Technology. Thesis ; no. TE-00-07 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B11660 .b11829734 mnait u 240405 0 010716 m a u 0 mnait mnarc 22 40 Barcode : 30050120810642 CREATED : 2014-10-04 RECORD # : i12788715 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050160042452 CREATED : 2016-03-23 RECORD # : i12904120 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 31911 31911 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT Thesis no.TE-00-07 30050120810642 2026-08-18 09:42:38 3 2026-08-18 22 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.TE-00-07 30050160042452 2026-08-18 09:42:38 1 2026-08-18 40