Jul 2026· TOP - An Official Journal of the Spanish Society of Statistics and Operations Research· 0 citations· 17 references
Abstract
This paper addresses the challenge of congestion in time-expanded networks, focusing on a case study related to maritime evacuations. The problem is made complex by an endogenous relationship between inputs and outputs, where the assignment of flow to an edge leads to increased congestion, which reflects in later arrivals and changes on the overall network topology. This dynamic interaction between flow and congestion is central to the problem, as it results in a feedback loop that complicates the identification of optimal evacuation paths. The study presents an iterative algorithm inspired by the network-simplex method, designed to handle the evolving nature of congestion while minimizing evacuation time. While the primary case study involves cruise ship evacuations, the approach is generalizable to other cases where congestion and nonlinear flow dynamics are significant factors. By considering lifeboat capacity, passenger mobility restrictions, and the impact of congestion on network structure, this work provides a practical initial plan for an evacuation off-shore, considering a congested, time-expanded network setting.
In response to the issue of nonlinear mutations and increased vulnerability of urban traffic networks caused by disturbances such as emergencies, traditional allocation methods based on expected travel time or deterministic impedance are unable to reflect the differentiated preferences of travelers for reliability and delay risks. This paper, from the perspective of road network vulnerability, introduces the description of traffic impedance under event influence, constructs a user allocation framework considering individual utility differences, and validates the normal and vulnerable states on a sample network with 6 nodes, 10 road sections, and 5 pairs of origins-destinations. The results show that the effective paths for the same OD pair satisfy the equilibrium characteristics of equal travel time budget and the minimum value, and the model solution is reasonable; different types of travelers exhibit differentiated flow allocation in both states, indicating that considering heterogeneity helps to depict the distribution law of traffic flow in vulnerable scenarios. The research can provide a reference for traffic organization and road network optimization under emergency conditions.
Lei Wu, Jinglong Dai· The 2026 International Confe...· 0 citations
A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases and providing a scalable and intelligent solution for modern smart city traffic systems.
Friday Idakwo David, S. T. Apeh, Oduware Okosun· E3S Web of Conferences· 0 citations
This paper addresses morning commute congestion caused by concentrated school-related trips in urban networks. We propose a bi-level optimization framework for regulating school start times in a multi-region urban network characterized by Macroscopic Fundamental Diagrams (MFDs), explicitly coupling system-level regulation with multi-class user-equilibrium-based departure-time choices. The Upper-Level problem jointly minimizes total time spent and deviations from current school schedules, while the Lower-Level problem models commuter behavior through a deterministic dynamic multi-class user equilibrium formulation incorporating alpha-beta-gamma preferences for travel time, earliness, and lateness costs. To address the computational challenges arising from the bilevel structure, non-convex traffic dynamics, and endogenous demand responses, an iterative algorithm alternating between the Upper- and Lower-Level problems is developed. The Upper-Level problem is approximated through a formulation solvable with standard mathematical programming solvers, while an iterative algorithm provides an approximate solution to the Lower-Level equilibrium problem. Numerical results demonstrate substantial congestion reductions and characterize the trade-off between school start-time flexibility and traffic efficiency. Sensitivity analyses further examine the effects of MFD uncertainty and scheduling preferences.
A. Georgantas, S. Timotheou, Christos G. Panayiotou· 0 citations
Traffic congestion in urban centres is a persistent challenge with long-term socioeconomic, environmental, and health consequences. Lagos, Nigeria, one of Africa's fastest-growing megacities, faces severe gridlock across its road network managed by the Lagos State Transport Authority (LAMATA), which serves over twenty million daily commuters. This paper applies linear programming (LP) to develop an optimisation model that minimises total vehicular delay time across five key corridors of the Lagos metropolitan road network, subject to road capacity, signal-cycle, and modal-split constraints. Traffic count data were gathered during peak and off-peak periods over twelve weeks on the Apapa-Oshodi, Lagos Island-Victoria Island, Ojota-Ketu, Lekki-Ajah, and Ikeja Along-Agege corridors. The simplex method was applied to solve the LP model, and sensitivity analysis was conducted to assess solution stability under demand variations of ±20%. Results show that average peak-hour delay and total vehicular hours lost (VHT) per day can be reduced by 34.7% and 28.4% respectively, through optimised reallocation of green-signal time and promotion of bus rapid transit (BRT) and non-motorised transport (NMT) modes. The model remains stable within demand fluctuations of ±15%, confirming its practical utility. The findings offer actionable policy support for LAMATA planners and demonstrate the significant potential of mathematical programming in evidence-based urban transport policy.
Anthony O. Adekoya, W. Adedeji, S. Oyelami et al.· R E M (Rekayasa Energi Manuf...· 0 citations
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
Jayesh G. Priolkar, G. Kunkolienkar· International Journal of Ele...· 0 citations
Addressing the issue of road network vulnerability caused by the failure of critical transportation nodes, this study focuses on traffic flow reconstruction and adaptive optimization of the public transportation system following a major bridge collapse. The study first constructs a traffic flow model for urban transportation networks based on graph theory and uses traffic balancing equations and segment capacity constraints to quantitatively assess the impact of core corridor failures on the distribution of traffic flow across the entire network. By introducing a shortest-path optimization objective function, the study minimizes commuting costs while satisfying physical topological constraints. Building on this foundation, the study further proposes data-driven optimization strategies for the public transit system. The K-means clustering algorithm is used to identify high-density clusters of urban transportation demand, and a normalization model based on traffic distribution ratios is established to achieve precise allocation of public transit resources. To quantitatively evaluate the optimization results, this section designed a multi-criteria weighted scoring system covering traffic satisfaction, improvements in commuting efficiency, and safety margins. Empirical analysis indicates that this coupled model can effectively identify and alleviate secondary congestion points caused by traffic shifts, with a traffic distribution accuracy rate of 92%. It provides a scientific computational framework for the resilient recovery of road networks and the allocation of public transport resources following sudden infrastructure failures in cities.
Yunhao Hao, Youbang Wang, Huixin Zhao et al.· International Conference on...· 0 citations
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