Tour-based travel demand forecasting model for a university campus

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Background and aimThe interest in investigating transportation issues at university campuses has been growing worldwide. Given the university travelers’ unique travel patterns and characteristics influenced mainly by students, forecasting the travel demand of university travelers provides a unique opportunity to understand these patterns and seek solutions for transportation problems at such major trip generators. This study aims at developing a travel demand forecasting model (TDFM) for a university campus, which can be vital for testing alternative solutions, strategies, and policies to tackle transportation problems on university campuses, especially those related to sustainable transportation. MethodologyThe proposed methodology was applied to Sharjah University City (SUC) as a case study. Thus, a tour-based TDFM was developed to replicate the patterns of activity-based travels at SUC. The model was established using the state-of-the-art modeling tool PTV VISUM. Subsequently, it was validated for the baseline year and then applied to forecast future traffic demands. FindingsWith the aim to reduce vehicular traffic volumes, the model was employed to evaluate two scenarios: introducing parking permits and pricing and establishing a park-and-ride facility to serve travelers to campus. These scenarios were compared in terms of several traffic and environmental performance measures. Based on predefined criteria, results highlighted that parking permit enforcement outperformed the park-and-ride facility. The present research may be considered one of the few studies to integrate a tour-based TDFM with a university's entire transportation network, a detailed traffic microsimulation, and an emission evaluation model. ConclusionResults highlighted the effectiveness of the proposed modeling framework in providing reliable solutions for challenging questions that face urban university campus planners under different scenarios. Other universities can follow the methodology presented in this work to develop similar travel forecasting models for their campuses based on their specific travel characteristics.