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<p>Transportation infrastructure largely determines the sustainability of cities, especially in Baltimore, a city facing the challenges of an aging system and stakeholder conflicts. In this study, a multimodal transportation network model integrating roadway, traffic, and pedestrian data is constructed based on graph theory using GeoPandas and ArcGIS for geospatial analysis, aiming to analyze the region’s transportation conditions and propose optimization strategies. For the Francis Scott Key Bridge collapse, we identify 29 key nodes through annual average daily traffic (AADT) data filtering and betweenness centrality algorithm, and divide into three traffic areas using K-means clustering, constructing a traffic network model based on an undirected graph. Comparison of pre- and post-collapse traffic distributions shows that interregional traffic flow shifts, but the overall traffic stability in Baltimore remains unchanged. The collapse disproportionately and negatively affects stakeholders in the vicinity of Interstate I-95. To address transit inequities in Baltimore, we analyze its transit network and propose a comprehensive scoring algorithm incorporating passenger flow and centrality. By selecting 20 representative transit nodes (with radial effects and without large overlapping areas), the operational impact of the transit system is evaluated using Dijkstra’s algorithm and regional accessibility analysis, and the results show that the public transportation system plays a crucial role in Baltimore, with a peripheral coverage of 78.51% and an impact rate of 73.76% on the travel of the residents. In this regard, we propose a transportation network optimization strategy, suggesting the expansion of bus stops and the widening of main roads in the northwestern part of the city, which is densely populated and has an underdeveloped transportation network. Using K-means clustering and spatial correlation, we construct an experimental model through increasing bus stops from the perspective of urban strategy to verify the rationality of the above suggestions. An experimental analysis of before and after the addition of stations shows that the model can increase overall travel efficiency by 35.88%. This work provides a viable strategy for Baltimore’s 2030 Sustainable Transportation Plan that balances efficiency, equity, and safety.</p>

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Urban multimodal network resilience: graph-geospatial analysis of post-collapse impact and transit equity in Baltimore

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Zhuye Liu,
  • Kaiwen Dong,
  • Yang Xu,
  • Dong Lin,
  • Mohan Deng

摘要

Transportation infrastructure largely determines the sustainability of cities, especially in Baltimore, a city facing the challenges of an aging system and stakeholder conflicts. In this study, a multimodal transportation network model integrating roadway, traffic, and pedestrian data is constructed based on graph theory using GeoPandas and ArcGIS for geospatial analysis, aiming to analyze the region’s transportation conditions and propose optimization strategies. For the Francis Scott Key Bridge collapse, we identify 29 key nodes through annual average daily traffic (AADT) data filtering and betweenness centrality algorithm, and divide into three traffic areas using K-means clustering, constructing a traffic network model based on an undirected graph. Comparison of pre- and post-collapse traffic distributions shows that interregional traffic flow shifts, but the overall traffic stability in Baltimore remains unchanged. The collapse disproportionately and negatively affects stakeholders in the vicinity of Interstate I-95. To address transit inequities in Baltimore, we analyze its transit network and propose a comprehensive scoring algorithm incorporating passenger flow and centrality. By selecting 20 representative transit nodes (with radial effects and without large overlapping areas), the operational impact of the transit system is evaluated using Dijkstra’s algorithm and regional accessibility analysis, and the results show that the public transportation system plays a crucial role in Baltimore, with a peripheral coverage of 78.51% and an impact rate of 73.76% on the travel of the residents. In this regard, we propose a transportation network optimization strategy, suggesting the expansion of bus stops and the widening of main roads in the northwestern part of the city, which is densely populated and has an underdeveloped transportation network. Using K-means clustering and spatial correlation, we construct an experimental model through increasing bus stops from the perspective of urban strategy to verify the rationality of the above suggestions. An experimental analysis of before and after the addition of stations shows that the model can increase overall travel efficiency by 35.88%. This work provides a viable strategy for Baltimore’s 2030 Sustainable Transportation Plan that balances efficiency, equity, and safety.