Optimal Traffic Relief Road Design using Bilevel Programming and Greedy Seeded Simulated Annealing: A Case Study of Kinshasa
Yves Matanga, Chunling Du, Etienne van Wyk

TL;DR
This paper develops a bilevel programming and greedy seeded simulated annealing approach to optimize traffic relief road design in Kinshasa, effectively prioritizing road projects within limited budgets to reduce congestion.
Contribution
It introduces a hybrid metaheuristic algorithm combining greedy seeded simulated annealing for efficient traffic network design in an emerging city.
Findings
Greedy seeded simulated annealing achieved the best travel time reduction.
Network edge betweenness centrality improved by nearly 2.5 times.
Proposed road priorities connect key city entry points and inner areas.
Abstract
Context: The city of Kinshasa faces severe traffic congestion, requiring strategic infrastructure capacity enhancements. Although a comprehensive master plan has been proposed, its implementation requires substantial financial investment, which remains constrained in the Democratic Republic of the Congo (DRC), an emerging economy. This research proposes a traffic flow based algorithm to support the development of priority road segments. The objective is to enable more effective prioritisation of road construction projects and facilitate the optimal allocation of limited infrastructure budgets. Methods: The study was conducted by formulating a standard transport network design problem (TNDP) that included estimated origin demand data specific to the city of Kinshasa. Given the high computational nature of the 30 node network design, TNDP relevant metaheuristics (GA, ACO, PSO, SA, TS,…
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Taxonomy
TopicsVehicle Routing Optimization Methods · Transportation Planning and Optimization · Optimization and Mathematical Programming
