Designing Smarter Transit: A Hybrid Optimization Framework for Congestion-Aware Public Transport Networks

Yajat Agarwal

Abstract: We present an integrated mathematical framework to design and optimize urban public-transport routes and service frequencies while accounting for dynamic road congestion and stochastic passenger demand. The model couples a Transit Network Design Problem (TNDP) - formulated as a mixed-integer program that selects routes and frequencies to minimize a weighted sum of operator cost and passenger generalized travel cost - with a dynamic traffic model (Cell Transmission Model / Lighthill?Whitham?Richards family) to capture congestion-dependent travel times. Passenger demand is treated stochastically (Poisson or time-varying origin?destination rates) and assigned using a user-equilibrium / capacity-constrained assignment. Solution approaches combine exact MILP for small instances and heuristics/metaheuristics (route-generation + genetic algorithms / local search + simulation-based evaluation) for large networks. We describe model formulation, numerical solution strategy, and evaluation metrics; and provide a case study plan and recommended data sources. The framework balances operator cost, passenger waiting and in-vehicle times, and system robustness to demand uncertainty.

Keywords: Urban transport planning, transit network design, dynamic traffic modeling, heuristic optimization, stochastic demand

How to Cite?: Yajat Agarwal, "Designing Smarter Transit: A Hybrid Optimization Framework for Congestion-Aware Public Transport Networks", Volume 14 Issue 10, October 2025, International Journal of Science and Research (IJSR), Pages: 1048-1050, https://www.ijsr.net/getabstract.php?paperid=SR251017172248, DOI: https://dx.doi.org/10.21275/SR251017172248

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