Browsing by Subject "transportation planning"
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Item Dynamic Traffic Assignment Incorporating Commuters? Trip Chaining Behavior(2012-10-19) Wang, WenTraffic assignment is the last step in the conventional four-step transportation planning model, following trip generation, trip distribution, and mode choice. It concerns selection of routes between origins and destinations on the traffic network. Traditional traffic assignment methods do not consider trip chaining behavior. Since commuters always make daily trips in the form of trip chains, meaning a traveler?s trips are sequentially made with spatial correlation, it makes sense to develop models to feature this trip chaining behavior. Network performance in congested areas depends not only on the total daily traffic volume but also on the trip distribution over the course of a day. Therefore, this research makes an effort to propose a network traffic assignment framework featuring commuters? trip chaining behavior. Travelers make decisions on their departure time and route choices under a capacity-constrained network. The modeling framework sequentially consists of an activity origin-destination (OD) choice model and a dynamic user equilibrium (DUE) traffic assignment model. A heuristic algorithm in an iterative process is proposed. A solution tells commuters? daily travel patterns and departure distributions. Finally, a numerical test on a simple transportation network with simulation data is provided. In the numerical test, sensitivity analysis is additionally conducted on modeling parameters.Item Identification of the relationship between economic and land use characteristics and urban mobility at the macroscopic level in Texas urban areas(Texas A&M University, 2004-11-15) Schrank, David LynnTraffic congestion continues to be a growing problem for cities of all sizes in the United States. Transportation agencies in urban areas are facing the difficult challenges of providing an efficient and reliable transportation system for residents and businesses despite ever-diminishing resources. Agencies in these areas need the capability of determining the future benefits of transportation investments so they can communicate this information to the public. This capability is difficult for many agencies, especially some of the smaller ones, who may not have the resources to make these analyses without turning to expensive long-range models. This research uses readily available socio-economic, land use, and traffic congestion data from many of the Texas urban areas to create prediction models to estimate future traffic congestion levels. Many of the transportation agencies that could utilize this tool do not have the resources to deal with large complex databases. Thus, basic information such as income, employment, single family residences, or commercial properties, to name a few, is used to create the predictions models. Results from this research show that traffic congestion prediction models can be created from socio-economic and land use data. These models were created for eighteen individual Texas urban areas and several combinations of areas. Transportation agencies could use the results of this research to estimate future congestion in their respective areas.