Multi-Objective Stop Location Optimization Model for Minimizing Social, User, and Operator Costs in Urban Tram Systems

The relocation of stops can enhance the quality of a transit system, leading to increased usage of sustainable transport modes and thus helping to achieve the sustainability goals of a particular region. Nevertheless, determining optimal stop locations is a relatively intricate task. It involves striking a balance between two competing goals of accessibility and efficiency. Previous research has mainly focused on examining stop relocations on specific line segments using optimisation models. However, these models often make crucial assumptions, particularly regarding transit demand, thereby limiting their applicability.In this paper a multi-objective optimisation model is presented to make the trade-offs between several factors influencing stop locations explicit. Detailed socio-economic data of zones is used in the model, alongside current travel behaviour, to estimate transit demand precisely. Additionally, the effects of stop relocation on operations are estimated using running time data. As a result, optimal stop locations for an entire transit system can be determined.The results of a case study of the tram system of The Hague indicate that in areas where trip distances are short and near the end of a tram line, stop spacing should be denser compared to other parts of the system. Moreover, it is concluded that stops are not always optimal where two transit lines intersect. Only in case of a high share of transfer passengers, a stop should be located irrespective of the other factors. Finally, it is concluded that the potential speed on a line section does not affect the optimal stop spacing significantly.

Language

  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01904211
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-24-00238
  • Files: TRIS, TRB
  • Created Date: Jan 7 2024 12:57PM