Study on Traffic Flow Forecasting and Stakeholder Utility Assessment in Baltimore

DOI

10.2991/978-94-6463-845-5_110How to use a DOI?

Keywords

Traffic flow forecasting; stakeholder utility assessment; Baltimore; traffic optimization

Abstract

This study centers on the optimization of Baltimore's traffic system. We have developed a traffic flow prediction model using key factors such as population density, road betweenness centrality, and lane count to analyze the changes in traffic flow before and after a bridge collapse. By applying Monte Carlo simulation, we have quantified the impact of the bridge collapse on different stakeholders, revealing a significant decrease in government utility (-0.81 to -1.38) and a relatively minor decrease in residents’ utility (0.37 to 0.27). The results indicate that the bridge collapse has exacerbated congestion in the downtown area. Furthermore, this study delves into the potential of traffic improvement projects, such as the addition of bus stops and the construction of elevated bike lanes, to enhance overall traffic efficiency and improve the welfare of stakeholders. These findings provide valuable insights for urban traffic planning and policy-making.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yinan Cao
AU  - Yifan Xia
PY  - 2025
DA  - 2025/09/16
TI  - Study on Traffic Flow Forecasting and Stakeholder Utility Assessment in Baltimore
BT  - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025)
PB  - Atlantis Press
SP  - 1127
EP  - 1135
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-845-5_110
DO  - 10.2991/978-94-6463-845-5_110
ID  - Cao2025
ER  -