A greedy approach for increased vehicle utilization in ridesharing networks (2304.01225v2)

Abstract: In recent years, ridesharing platforms have become a prominent mode of transportation for the residents of urban areas. As a fundamental problem, route recommendation for these platforms is vital for their sustenance. The works done in this direction have recommended routes with higher passenger demand. Despite the existing works, statistics have suggested that these services cause increased greenhouse emissions compared to private vehicles as they roam around in search of riders. This analysis provides finer details regarding the functionality of ridesharing systems and it reveals that in the face of their boom, they have not utilized the vehicle capacity efficiently. We propose to overcome the above limitations and recommend routes that will fetch multiple passengers simultaneously which will result in increased vehicle utilization and thereby decrease the effect of these systems on the environment. As route recommendation is NP-hard, we propose a k-hop-based sliding window approximation algorithm that reduces the search space from entire road network to a window. We further demonstrate that maximizing expected demand is submodular and greedy algorithms can be used to optimize our objective function within a window. We evaluate our proposed model on real-world datasets and experimental results demonstrate superior performance by our proposed model.

• The paper introduces a greedy approach using a k-hop sliding window approximation to maximize expected passenger demand.
• The study leverages submodularity to justify the use of greedy algorithms for efficient route recommendations in scalable networks.
• Experimental results with real-world data validate enhanced vehicle utilization and a reduced environmental footprint.

The paper "A greedy approach for increased vehicle utilization in ridesharing networks" addresses a critical challenge in the optimization of ridesharing platforms: route recommendation. This paper is framed within the context of the significant role that ridesharing has assumed in urban transportation, aiming to enhance the efficiency of vehicle usage and mitigate the environmental impact of these services.

Traditional route recommendation strategies in ridesharing often focus on routes with higher passenger demand. However, these methods have led to unintended consequences, such as increased greenhouse gas emissions, as ridesharing vehicles typically spend considerable time roaming in search of riders. The core insight of this paper is that despite the booming popularity of ridesharing, the existing systems have not leveraged vehicle capacity to its full potential.

To address this issue, the authors propose an innovative route recommendation model aimed at increasing vehicle utilization by fetching multiple passengers simultaneously. The underlying challenge is that finding the optimal routes in such a dynamic environment is an NP-hard problem, making exact solutions impractical for large-scale networks. Consequently, the authors introduce a k-hop-based sliding window approximation algorithm. This technique significantly reduces the search space from the entire road network to a more manageable window, thereby enhancing computational efficiency.

A critical aspect of the proposed model is its reliance on submodularity, a property that allows for the application of greedy algorithms. By demonstrating that the objective function—maximizing expected passenger demand—is submodular within a window, the authors validate the use of greedy algorithms to optimize their proposed solution.

The model's efficacy is tested using real-world datasets, and experimental results illustrate that this greedy approach yields superior performance compared to previous methods. The findings underscore that this method not only improves vehicle utilization but also holds the potential to decrease the environmental footprint of ridesharing systems.

In summary, this paper offers a significant advancement in ridesharing route optimization by leveraging a greedy approximation strategy to enhance vehicle occupancy and thus reduce greenhouse emissions, making ridesharing a more sustainable urban transportation option.