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<p>In this study, the transportation network design problem (NDP) is investigated by integrating geometric and topological indices—continuity, regularity, betweenness centrality, and closeness centrality—alongside the traditional total travel time minimization objective. Each index is individually optimized within a bi-level optimization framework, enabling a comprehensive assessment of its impact on network performance. Unlike conventional travel time-based optimization, these indices exhibit lower sensitivity to factors such as time-of-day demand variations, as they primarily capture the intrinsic structural properties of the network. Applications to the Sioux Falls and Tehran networks under different peak-hour scenarios demonstrate that incorporating these indices enhances network cohesion, improves accessibility, and promotes sustainable mobility. Notably, closeness centrality consistently delivers superior performance while maintaining computational simplicity, making it a promising metric for future transportation planning. Furthermore, a multi-objective optimization approach was applied to the Tehran network, where the weighting scheme was derived from a distance-based analysis. Interestingly, the results reveal that solving the NDP with a single-objective function based on closeness centrality yields outcomes remarkably close to those obtained from the multi-objective formulation. By integrating these alternative indices, this research advocates for a paradigm shift in network design that extends beyond operational efficiency, fostering more resilient, accessible, and user-centric transportation systems.</p>

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Optimizing geometric and topological indices for sustainable mobility: a network design approach

  • 期刊论文

  • 发表日期：

    2025年4月17日

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  • Mohammad Nayeri,
  • Abbas Babazadeh,
  • Mehrdad Gholami Shahbandi

摘要

In this study, the transportation network design problem (NDP) is investigated by integrating geometric and topological indices—continuity, regularity, betweenness centrality, and closeness centrality—alongside the traditional total travel time minimization objective. Each index is individually optimized within a bi-level optimization framework, enabling a comprehensive assessment of its impact on network performance. Unlike conventional travel time-based optimization, these indices exhibit lower sensitivity to factors such as time-of-day demand variations, as they primarily capture the intrinsic structural properties of the network. Applications to the Sioux Falls and Tehran networks under different peak-hour scenarios demonstrate that incorporating these indices enhances network cohesion, improves accessibility, and promotes sustainable mobility. Notably, closeness centrality consistently delivers superior performance while maintaining computational simplicity, making it a promising metric for future transportation planning. Furthermore, a multi-objective optimization approach was applied to the Tehran network, where the weighting scheme was derived from a distance-based analysis. Interestingly, the results reveal that solving the NDP with a single-objective function based on closeness centrality yields outcomes remarkably close to those obtained from the multi-objective formulation. By integrating these alternative indices, this research advocates for a paradigm shift in network design that extends beyond operational efficiency, fostering more resilient, accessible, and user-centric transportation systems.