Prioritization of transportation projects, a process of selecting projects for funding given multiple constraints, is cumbersome and time-consuming. This study presents a simplified methodology for ranking transportation projects with an integrated multiple-criteria decision-making (MCDM) process for prioritizing transportation projects when multiple decision makers present various opinions and biases. This study applies the analytic hierarchy process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to help multiple decision makers select transportation projects in an environment where vagueness and subjectivity are replaced with numerical values and processed in an automated fashion. The AHP is used to weigh a set of criteria by pair-wise comparisons, and TOPSIS is used to obtain final project rankings. Even though AHP and TOPSIS are widely accepted MCDM methods, few transportation policy boards have used this unique, integrated approach for ranking transportation projects. The El Paso, Texas, Metropolitan Planning Organization's Transportation Improvement Plan was used as a case study for this research.
Many urban university campuses are considered major trip attractors. Considering the multimodal and complex nature of university campus transportation planning and operation, this paper proposes a dynamic traffic simulation and assignment analysis approach and demonstrates how such a methodology can be successfully applied. Central to the research is the estimation of trip originÁ destinations and the calibration of a parking lot choice model. Dynamic simulation is utilized to simulate multiple modes of transportation within the transportation network while further assigning these modes with respect to various mode-specific roadway accessibilities. A multiple vehicle-class simulation analysis for planning purposes becomes a critical capability to predict how faculty and staff who once parked within the campus core choose other nearby alternate parking lots. The results highlight the effectiveness of the proposed approach in providing integrated and reliable solutions for challenging questions that face urban university campus planners and local transportation jurisdictions.
The integration of mesoscopic and microscopic simulation models provide expanded dimensions of modeling capabilities by taking the strengths of both model resolutions. Many transportation agencies, practitioners and researchers are beginning to see the advantages of using multiple levels of resolution when analyzing corridor specific problems. Mesoscopic models use dynamic traffic assignment to reroute traffic given various traffic conditions. Microscopic models are used to analyze traffic conditions at the individual car or lane level. Models are calibrated and validated using data collected in the field. Most practitioners validate their models to existing conditions and then forecast future conditions to predict traffic congestion. Once simulation runs are finished, results are presented to hosting transportation agencies and the project is completed. Very few practitioners collect future field data and compare it to the simulated forecasts. This sort of reverse model validation is referred to as back casting. This paper outlines the complete modeling process from model development, conversion, calibration, consistency, validation and ultimately -model back casting. A case study involving user class restrictions on Interstate 10 in El Paso, Texas was used to analyze how accurate the models were at predicting future conditions. Researcher's simulated truck restricted lanes on the freeway to determine how effective the user class restrictions were on overall traffic speeds and travel times. One year later when the truck lane restrictions were in place, field data was collected to determine how accurate the models were at predicting traffic conditions.
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