In this article, we delve into the realm of ridesharing and its application in automated mobility-on-demand systems. We examine four distinct methods, each with its unique strengths and weaknesses, and analyze their performance under various scenarios. Our investigation sheds light on the intricacies of these approaches, making them more accessible to a broad audience.
Method Batch [s] Lim.
The article presents an in-depth analysis of four ridesharing methods: IH (insertion heuristic), VGA (vehicle grouping algorithm), ALNS-IH (ALNS initialized with the solution of the insertion heuristic), and ALNS-prop (ALNS initialized with the proposed method of batch 120 s). These methods are categorized into two groups: (i) heuristics, which use simplified algorithms to find near-optimal solutions, and (ii) metaheuristics, which incorporate various techniques to improve their performance.
5.3 Results
The results are presented in four tables for each area, with the most complex area (largest by demand) being analyzed first. The findings reveal that the proposed method is mostly faster and computational time growth is not as rapid compared to other methods. However, there are differences between method variants, with longer batches resulting in a linear computation time growth and shorter batches slowing down faster due to the lack of parallelization in chaining. Notably, the metaheuristic is the slowest one by far, indicating potential for optimization.
Conclusion
In conclusion, this study offers a comprehensive analysis of ridesharing methods for automated mobility-on-demand systems. By examining each method’s strengths and weaknesses under various scenarios, we demystify complex concepts and provide insights into their performance. Our findings highlight the potential of the proposed method and encourage further optimization to improve computational time. This analysis serves as a valuable resource for researchers and practitioners seeking to develop efficient and effective ridesharing strategies for automated mobility-on-demand systems.
