Doctor of Philosophy (PhD)
Civil and Environmental Engineering
First Committee Member
Shashi K. Sathisan
Number of Pages
The imbalance between demand and supply on transportation networks, especially during peak periods, leads to significant level of congestion. Potential solutions to alleviate congestion problems include enhancing system capacity and effective utilization of available capacity--i.e., traffic demand management. Intelligent transportation system (ITS) initiatives such as travel demand management systems (TDMS) and traveler information systems (TIS) refer to demand management as an objective. The success of these initiatives rely heavily on an ability to accurately estimate the temporal variations in travel demand in near real-time. The focus of this dissertation is on developing a methodology for estimating temporal variations in travel demand in urban areas; A significant portion of daily congestion on urban transportation networks occur during peak periods. A majority of trips during peak periods are work trips. The peak study period is divided into several time slices to facilitate simulation and modeling. A methodology is developed to estimate origin-destination (O-D) trip tables for each time slice. Trip attractions during each time slice, for each traffic analysis zone (TAZ), are estimated using pertinent characteristics of the TAZ. The O-D trip tables for each time slice are estimated as a function of trip attractions for the time slice, total trip productions during the peak period and the travel time matrix for the peak period. These O-D trip tables for each time slice and the existing network conditions can be used to assign trips in near real-time; The algorithm is coded using C++ programming language. The model is first tested on various small hypothetical cases with 5 TAZs, 10 TAZs, 15 TAZS and 20 TAZs respectively. The results obtained are as expected. The robustness of the model is tested using the hypothetical case with 10 TAZs. Since, testing and validating the model on large real world networks is important, the model is tested with 1995 data obtained for the Las Vegas valley. The results are consistent with that obtained for the hypothetical cases. The model is tested on Silicon Graphics IP 27 with IRIX version 6.4 as the operating system. For almost all the scenarios, the run time is less than 3 minutes. This strengthens the notion that the model can be implemented in real time.
Demand; Intelligent; Intelligent Transportation; Modeling; System; Temporal; Transportation; Travel; Travel Demand; Temporal Variations
Civil engineering; Transportation; Operations research
University of Nevada, Las Vegas
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Pulugurtha, Srinivas Subrahmanyam, "Modeling temporal variations in travel demand for intelligent transportation systems" (1997). UNLV Retrospective Theses & Dissertations. 3043.