GeoPrune: Efficiently Matching Trips in Ride-sharing Through Geometric Properties

Xu, Yixin; Qi, Jianzhong; Borovica-Gajić, Renata; Kulik, Lars

doi:10.1145/3400903.3400912

Cited by 5 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shen et al [12] partitioned the road network into grids, with every participant only being matched within their grid. Xu et al [13] made use of ellipses to bound possible locations for matches in order to avoid removing the optimal solution during pruning; however, this approach requires equal speed limits. A similar approach was taken by Masoud and Jayakrishnan [8].…”

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Snapshot-Optimal Real-Time Ride Sharing

Hassan,

Wallace,

Moser

et al. 2024

Information

View full text Add to dashboard Cite

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.

show abstract

Section: Previous Workmentioning

confidence: 99%

“…Many existing ridesharing studies use pruning mechanisms to speed up the matching of travellers. Approaches include the use of grids [33,34] and geometric shapes [8,13]. These approaches are approximations, and may miss candidates with longer journeys and more distant origins.…”

Section: Reachability Graphsmentioning

confidence: 99%

Snapshot-Optimal Real-Time Ride Sharing

Hassan,

Wallace,

Moser

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…In this context, a spatial indexing technique known as R-tree makes it possible to index objects dynamically in a tree and to optimize zone or node overlapping queries (11)(12)(13). The R-tree has been used for taxi ridesharing and has proven to be very efficient (6,14,15). In this paper, we propose to extend the scope of use of R-trees to the private driver ridesharing problem.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, the research focused on multi-hop ridesharing involving several vehicles to carry a rider ( 4 , 5 ). This form of ridesharing is much more efficient as it offers a wider range of solutions to the riders in the system ( 6 ). However, multi-hop ridesharing is much more complex to solve.…”

Section: Related Workmentioning

confidence: 99%

“…To do so, they maintain a trip schedule consisting of different tables storing spatio-temporal information. In this context, we extend the trip scheduling architecture presented in Tong et al and in two works by Xu et al by adding several new arrays ( 6 , 14 , 15 ). The above previous works’ architecture mainly describes three arrays: “ arr array” stores the shortest time needed to reach each stop, “ ddl array” stores the latest possible arrival time (deadline) of each stop to respect the constraints of the driver (latest arrival time) or the riders (waiting time and latest arrival time) carried, and “ slk array” stores the maximum available detour time before each stops (slack time).…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Preferential Optimization of Multi-Hop Dynamic Ridesharing Based on R-Trees and Multi-Agent Systems

Fèvre

Zgaya

Mathieu

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

For several years, we have been witnessing an increase in pollution and traffic jams in large cities. Research has therefore focused on the development of new, greener, and more flexible means of transport to complement the existing static transport offer. These means of transport are intended to be shared and dynamic to attract more people. One of these promising means of transportation is dynamic multi-hop ridesharing which makes it possible to route a rider through several drivers for a more flexible ridesharing offer. While the majority of papers on this topic deal with the global optimality of the system, we are interested in a preferential optimization for each individual in the system. In this context, this paper addresses an innovative solution to the on-demand multi-hop dynamic ridesharing problem where each actor (driver or rider) is represented by an autonomous and interactive entity called an “agent.” This ridesharing system is, therefore, a multi-agent system (MAS) in which each agent has a limited rationality. This MAS permits us to define the communications, perceptions, and preferences of drivers and riders. We detail a preference-weighted objective function for riders that allows the simulation of a wide range of behaviors. We propose the use of R-trees to optimize the computational cost of identifying candidate drivers and the best associated transfer nodes. We present various simulation scenarios by varying user preferences and instance parameters. Our experiments show that our model can efficiently simulate real-world behaviors and is able to optimize ridesharing on a case-by-case basis.

show abstract

A greedy approach for increased vehicle utilization in ridesharing platforms

Makhdomi,

Gillani

2024

Expert Systems with Applications

View full text Add to dashboard Cite

GeoPrune: Efficiently Matching Trips in Ride-sharing Through Geometric Properties

Cited by 5 publications

References 21 publications

Snapshot-Optimal Real-Time Ride Sharing

Snapshot-Optimal Real-Time Ride Sharing

Preferential Optimization of Multi-Hop Dynamic Ridesharing Based on R-Trees and Multi-Agent Systems

A greedy approach for increased vehicle utilization in ridesharing platforms

Contact Info

Product

Resources

About