a r t i c l e i n f o A bi-level decision support system (BDSS) is proposed for a normative road network design with uncertain travel demand. A bi-level decision support model with link capacity expansion is developed to simultaneously reduce travel delay to road users and mitigate vulnerability of road network. A tractable solution scheme for BDSS is developed. Due to some hierarchy in decisio...

Over the past half-century, the progress of travel behavior research and travel demand forecasting has been spear headed and continuously propelled by the micro-economic theories, specifically utility maximization. There is no denial that the travel demand models today are sophisticated and in most cases capable of forecasting the main stream of travel behavior in urban areas. However, a quick ...

We propose a model and solution methods, for locating a fixed number of multiple-server, congestible common service centers or congestible public facilities. Locations are chosen so to minimize consumers’ congestion (or queuing) and travel costs, considering that all the demand must be served. Customers choose the facilities to which they travel in order to receive service at minimum travel and...

Supported by general revenues from the State of Texas. 16. Abstract In the United States, a significant number of individuals depend on the auto mode of transportation. The high auto dependency, in turn, has resulted in high auto travel demand on highways. The resulting traffic congestion levels, surging oil prices, the limited ability to address increased auto travel demand through building a...

This paper uses contingent behavior and contingent pricing analyses to explore the proper valuation of time and transport costs within the context of recreation demand. The contingent behavior analysis poses hypothetical increases in access fees, travel time, and travel distance and considers recreation demand responses. As a useful complement, the contingent pricing analysis poses hypothetical...

While transportation funding can be collected in a variety of direct (e.g., fares, tolls, and gas taxes) or indirect property sales tax) ways, dynamic demand responsive pricing not only collects revenue but also incentivizes travelers to avoid peak-demand periods, thus utilizing infrastructure capacity more efficiently. Unfortunately, the response price changes, called elasticity demand, is gen...