On-demand ridesharing with optimized pick-up and drop-off walking locations

Fielbaum, Andrés; Bai, Xiaoshan; Alonso–Mora, Javier

doi:10.1016/j.trc.2021.103061

Cited by 72 publications

(68 citation statements)

References 54 publications

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“…These phenomena can be observed in Fig. 2 (from Fielbaum et al (2021)), which shows the quality of service when modeling a ridepooling system (based on the model by Alonso-Mora et al (2017a)) during one hour in Manhattan. It reveals that the level of rejected requests, the average waiting time, and the average delay are all higher at the center of the network, precisely the most demanded zone.…”

Section: Introductionmentioning

confidence: 94%

“…Many studies deal with the problem of how to assign passengers and vehicles, either through agent-based models (like (Merlin 2017;Fagnant and Kockelman 2018;Lokhandwala and Cai 2018;Vosooghi et al 2019) among others) or through centralized algorithms (like (dOrey et al 2012;Pelzer et al 2015;Alonso-Mora et al 2017a;Gao et al 2017;Qian et al 2017;Wang et al 2018;Tsao et al 2019;Simonetto et al 2019;Fielbaum et al 2021;Levin et al 2017;Lin et al 2018) among others). Some of these assignment methods have rebalancing or anticipatory techniques, which are described in the following subsection.…”

Section: On-demand Ridepooling Systemsmentioning

confidence: 99%

“…They all show that the most demanded zone, at the center of the network, faces a worse quality of service. Source: Fielbaum et al (2021) in those same high-demanded zones so some vehicles might be sent towards them. Several specific implementations for each technique are analyzed and compared.…”

Section: Introductionmentioning

confidence: 99%

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Anticipatory routing methods for an on-demand ridepooling mobility system

2021

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On-demand mobility systems in which passengers use the same vehicle simultaneously are a promising transport mode, yet difficult to control. One of the most relevant challenges relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We propose, formally discuss and experimentally evaluate several formulations for both approaches. We test these techniques in combination with a state-of-the-art trip-vehicle assignment method, using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users’ traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there.

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Section: Introductionmentioning

confidence: 94%

Section: On-demand Ridepooling Systemsmentioning

confidence: 99%

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Anticipatory routing methods for an on-demand ridepooling mobility system

2021

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“…As such, although numerous implementation strategies in determining pick-up and drop-off (PUDO) zones to improve taxi, ridesharing and ride hailing operation management, PUDO station location placement is not explored extensively [19]. Many studies tend to propose a pre-determined set of "meeting points" to which all passengers have to walk [15,17,[20][21][22]. This is because optimizing placement constraints (i.e., minimum capacity, walking radius, the distance towards adjacent facilities, etc.)…”

Section: Pudo Locationmentioning

confidence: 99%

“…This is because optimizing placement constraints (i.e., minimum capacity, walking radius, the distance towards adjacent facilities, etc.) stretches the complexity of the PUDO location optimization [15,21]. Czioska, P. et al [19] introduced a GIS-based method to identify potential meeting points and proposed assessment criteria to rate the suggested meeting points.…”

Section: Pudo Locationmentioning

confidence: 99%

A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

Gunawan

Susilawati

2021

Smart Cities

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The location placement of pick-up/drop-offs of ride hailing usually only considers spatial distribution within a certain area. Previous studies often mapped out the potential hotspots for pick-up/drop-offs, benefitting the ride-hailing company and not considering the passengers. Therefore, in this study, we incorporated the spatiotemporal distribution of mobility-on-demand on generating pick-up/drop-off location placement using a genetic algorithm considering the walking distance and minimum demand data served within the radius. The data collected are analyzed through the clustering method, and the resulting cluster centers are fed into the location optimization algorithm. The genetic algorithm is used to optimize the location placement with the consideration of the user’s walking distance and demand. The algorithm is able to find an appropriate placement and determine reliable pick-up/drop-off stations within the study area based on observed spatiotemporal demand despite the difference in demand distribution through different time periods.

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A Proportional Pricing Mechanism for Ridesharing Services with Meeting Points

Cipolina-Kun

Yazdanpanah²,

Stein³

et al. 2022

PRIMA 2022: Principles and Practice of Multi-Agent Systems

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Ridesharing is a promising approach for reducing congestion and pollution, and many variants have been studied in the literature over the past decades. In this paper, we consider a novel setting where individuals walk to a common pick-up point and ride together to a single drop-off point from where they walk to their final destination. This setting requires finding the optimal composition of riders and pick-up and drop-off meeting points, as well as an equitable distribution of the costs whereby riders are incentivised to participate. Based on game-theoretic principles, we propose a methodology to determine the optimal pick-up and drop-off points, together with a cost allocation method that is equitable in the sense that it ensures proportionality for sharing the costs, i.e., those who walk more should pay less. We present a formal evaluation of our cost allocation method and empirical evaluation against the Shapley value using realworld and simulated data. Our results show that our approach is computationally more tractable than the Shapley value, as it is linear in time while guaranteeing individual rationality under certain conditions.

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On-demand ridesharing with optimized pick-up and drop-off walking locations

Cited by 72 publications

References 54 publications

Anticipatory routing methods for an on-demand ridepooling mobility system

Anticipatory routing methods for an on-demand ridepooling mobility system

A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

A Proportional Pricing Mechanism for Ridesharing Services with Meeting Points

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