Optimal Traffic Relief Road Design using Bilevel Programming and Greedy Seeded Simulated Annealing: A Case Study of Kinshasa

TL;DR

Kinshasa's traffic congestion is addressed with a bilevel TNDP under budget constraints. The study formulates a bilevel programming model with binary edge-augmentation decisions and a lower-level traffic assignment driven by origin–destination demands, using a BPR-type travel-time relation $t = t_0(1+\alpha x^4)$ with $x = V/C$, and compares unconstrained ($q=0$) and constrained ($q=1$) designs. It evaluates eight metaheuristics plus two Greedy-Seeded hybrids (Gr-SA, Gr-TS), showing the greedy-seeded hybrids deliver the best travel-time reductions and high stability; edge betweenness centrality improves by about 2.5x. The results yield policy guidance prioritizing junctions around the Bandundu and airport gateways and inner-city corridors, while highlighting the value of seed-based hybrids for budget-constrained urban planning.

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, Greedy) were used selectively and hybridised to achieve high quality, stable solutions. A greedy search seeded simulated annealing and Tabu search were devised to achieve the design goals. Results: Greedy Simulated Annealing and Greedy Tabu search yielded the best travel time reduction and the most stable solutions compared to other solvers, also improving network edge betweenness centrality by nearly a scale of two and a half. Conclusions: Road priorities were proposed, including junctions connecting the Bandundu and Kongo Central entry point to main attraction centres (Limete Poids Lourd, Gombe, Airport) and additional inner city areas (Ngaliema, Selembao, Lemba, Masina, Kimwenza).

Figures (20)

• Figure 1: Kinshasa Transport Network - Major Arterial Roads jica2019kinshasa
• Figure 2: Graph representation of Kinshasa Main Arteries
• Figure 3: Edge Betweenness centrality - Kinshasa Traffic Network
• Figure 4: Demand-based Traffic data in the Kinshasa Network as per O-D pair data in the Appendix and flow-based simulation in the lower level problem (Equation \ref{['eq:lvl_prob']})
• Figure 5: Correlation between network structural edge betweenness centrality and estimated traffic flow volume