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Article

Optimizing Urban Mobility Through Complex Network Analysis and Big Data from Smart Cards

Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA
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Author to whom correspondence should be addressed.
Submission received: 29 June 2025 / Revised: 2 August 2025 / Accepted: 4 August 2025 / Published: 6 August 2025
(This article belongs to the Special Issue IoT-Driven Smart Cities)

Abstract

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning.
Keywords: smart-card data; complex networks; network robustness; network characteristics analysis; transit optimization smart-card data; complex networks; network robustness; network characteristics analysis; transit optimization

Share and Cite

MDPI and ACS Style

Sun, L.; Ashrafi, N.; Pishgar, M. Optimizing Urban Mobility Through Complex Network Analysis and Big Data from Smart Cards. IoT 2025, 6, 44. https://doi.org/10.3390/iot6030044

AMA Style

Sun L, Ashrafi N, Pishgar M. Optimizing Urban Mobility Through Complex Network Analysis and Big Data from Smart Cards. IoT. 2025; 6(3):44. https://doi.org/10.3390/iot6030044

Chicago/Turabian Style

Sun, Li, Negin Ashrafi, and Maryam Pishgar. 2025. "Optimizing Urban Mobility Through Complex Network Analysis and Big Data from Smart Cards" IoT 6, no. 3: 44. https://doi.org/10.3390/iot6030044

APA Style

Sun, L., Ashrafi, N., & Pishgar, M. (2025). Optimizing Urban Mobility Through Complex Network Analysis and Big Data from Smart Cards. IoT, 6(3), 44. https://doi.org/10.3390/iot6030044

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