Optimizing a vehicle’s route in an on-demand ridesharing system in which users might walk

Fielbaum, Andrés

doi:10.1080/15472450.2021.1901225

Cited by 12 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To analyze the qualitative effects of stop pooling on the collective dynamics of ride sharing and in particular how stop pooling changes fluctuations in the travel time, we introduce an event-based model (details in supplementary note 1 and 2 citing [16][17][18][19][20][21][22][23][24] and in [15]) with three different events: (i) users request trips from an origin to a destination, (ii) ride sharing buses pickup users and (iii) deliver them. New users request trips while buses serve other users.…”

Section: Methodsmentioning

confidence: 99%

Taming travel time fluctuations through adaptive stop pooling

Lotze,

Marszal,

Schröder

et al. 2024

J. Phys. Complex.

View full text Add to dashboard Cite

Ride sharing services combine trips of multiple users in the same vehicle and may provide more sustainable transport than private cars. As mobility demand varies during the day, the travel times experienced by passengers may substantially vary as well, making the service quality unreliable.We show through model simulations that such travel time fluctuations may be drastically reduced by stop pooling. Having users walk to meet at joint locations for pick-up or drop-off allows buses to travel more direct routes by avoiding frequent door-to-door detours, especially during high demand.We in particular propose adaptive stop pooling by adjusting the maximum walking distance to the temporally and spatially varying demand.The results highlight that adaptive stop pooling may substantially reduce travel time fluctuations while even improving the average travel time of ride sharing services, especially for high demand. Such quality improvements may in turn increase the acceptance and adoption of ride sharing services.

show abstract

Section: Methodsmentioning

confidence: 99%

Taming travel time fluctuations through adaptive stop pooling

Lotze,

Marszal,

Schröder

et al. 2024

J. Phys. Complex.

View full text Add to dashboard Cite

show abstract

“…When taking a shared trip with multiple riders, the PUDO optimization is vital to prevent pointless detours. Whereas in the conventional system, the vehicle is frequently obliged to pick up passengers at certain locations [15]. Detours are made in order to pick up additional riders frequently result in longer travel times and higher costs.…”

Section: B Motivationmentioning

confidence: 99%

Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

Saputra,

Sihabudin

2023

IJACSA

View full text Add to dashboard Cite

It is important to extract hotspots in urban traffic networks to improve driver route efficiency. This research aims to identify hotspot pick-up and drop-off (PUDO) areas in ridehailing transportation services using a clustering approach. However, there are challenges in applying clustering algorithms to trajectory data in the coordinates of the Global Positioning System (GPS). So this research proposes modifications to the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm by considering the radius from the center of the cluster to determine the presence of amenities around the cluster. We used a dataset containing 55,988 trip trajectories of Grab drivers over a two-week period in Jakarta. A preliminary statistical analysis was carried out to understand the distribution of trips. Next, we identify the PUDO point of each trip for use in the clustering analysis. The research explores the various parameters and settings of the clustering method and their impact on the results. The study found that the results obtained from the clustering method are sensitive to parameter selection, including epsilon radius and minimum number of points needed to form a cluster. The optimal cluster with the best parameters (eps: 0.25, minpts: 100) in the pick-up (PU) location analysis produced 17 clusters with the silhouette coefficient of 0.752, while in the drop-off (DO) location there are 18 clusters with a silhouette coefficient of 0.694. Overall, the research highlights the potential of the clustering analysis method for ride-hailing transportation.

show abstract

“…Such an approach was tested by Fielbaum et al (2021), showing that it can improve the level of service and decrease the total travel time. Later, Fielbaum (2021) tried optimizing pickup and drop-off positions of precomputed vehicle plans to measure the benefits exactly. He demonstrated that we could decrease the travel cost by almost 19% when optimizing the locations with a heuristic method and 24% with a slower optimal solution method.…”

Section: Related Workmentioning

confidence: 99%

Large-scale online ridesharing: the effect of assignment optimality on system performance

Fiedler

Čertický

Alonso–Mora

et al. 2022

Journal of Intelligent Transportation Systems

View full text Add to dashboard Cite

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57% compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20% reduction in vehicle distance driven and 5% lower average passenger travel delay compared to a system that uses insertion heuristic.

show abstract

Optimizing a vehicle’s route in an on-demand ridesharing system in which users might walk

Cited by 12 publications

References 39 publications

Taming travel time fluctuations through adaptive stop pooling

Taming travel time fluctuations through adaptive stop pooling

Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

Large-scale online ridesharing: the effect of assignment optimality on system performance

Contact Info

Product

Resources

About