Regulating mobility-on-demand services: Tri-level model and Bayesian optimization solution approach

Dandl, Florian; Engelhardt, Roman; Hyland, Michael; Tilg, Gabriel; Bogenberger, Klaus; Mahmassani, Hani S.

doi:10.1016/j.trc.2021.103075

Cited by 40 publications

(8 citation statements)

References 37 publications

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“…[25] considered an intermodal AMoD system that coordinates with public transit, and designed a tolling scheme to achieve the social optimum in a perfect market with selfish agents. [53] developed a tri-level framework to capture the interactions among regulatory agencies, AMoD service providers, and travelers.…”

Section: Regulations On Avs and Tncsmentioning

confidence: 99%

Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network

Li¹

2023

Preprint

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This paper assesses the equity impacts of for-hire autonomous vehicles (AVs) and investigates regulatory policies that promote the spatial and social equity in future autonomous mobility ecosystems. To this end, we consider a multimodal transportation network, where a ride-hailing platform operates a fleet of AVs to offer mobility-on-demand services in competition with a public transit agency that offers transit services on a transportation network. A game-theoretic model is developed to characterize the intimate interactions between the ride-hailing platform, the transit agency, and multiclass passengers with distinct income levels. An algorithm is proposed to compute the Nash equilibrium of the game and conduct an ex-post evaluation of the performance of the obtained solution. Based on the proposed framework, we evaluate the spatial and social equity in transport accessibility using Theil index, and find that although the proliferation of for-hire AVs in the ride-hailing network improves overall accessibility, the benefits are not fairly distributed among distinct locations or population groups, implying that the deployment of AVs will enlarge the existing spatial and social inequity gaps in the transportation network if no regulatory intervention is in place. To address this concern, we investigate two regulatory policies that can improve transport equity: (a) a minimum service-level requirement on ride-hailing service, which improves the spatial equity in the transport network; (b) a subsidy on ride-hailing trips that serve as first/last-mile connection to public transit, which promotes the use of public transit and improves the social equity of the transport network. We show that the minimum service-level requirement entails a trade-off: as a higher minimum service level is imposed, the spatial inequity reduces, but the social inequity will be exacerbated. In contrast, subsidies on bundled trips can enhance accessibility, increase transit ridership, improve the profit of the ride-hailing platform, and more crucially, simultaneously bridge spatial and social inequity gaps. These results are validated through realistic numerical studies for San Francisco.

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Section: Regulations On Avs and Tncsmentioning

confidence: 99%

Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network

Li¹

2023

Preprint

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“…With a growing market share of today's MOD services, negative externalities of user-centric ridehailing can be observed (Henao and Marshall, 2019;Schaller, 2021). Therefore, the regulation of MOD (Li et al, 2019;Zhang and Nie, 2019)-with part of the focus on the regulatory protection of drivers-and of AMOD services (Simoni et al, 2019;Dandl et al, 2021;Mo et al, 2021) is becoming increasingly relevant. Simoni et al (2019) study various congestion pricing models in the presence of an AMOD system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Simoni et al (2019) study various congestion pricing models in the presence of an AMOD system. Dandl et al (2021) consider an AMOD ridepooling service which is regulated such that it cannot offer guaranteed single-passenger rides. Moreover, they introduce a regulatory tri-level framework optimizing a congestion-based road toll, parking fees, public transport frequency, and an AMOD fleet limit, where the reaction of an AMOD provider to changed regulatory settings is taken into account.…”

Section: Literature Reviewmentioning

confidence: 99%

Competition and Cooperation of Autonomous Ridepooling Services: Game-Based Simulation of a Broker Concept

Engelhardt

Malcolm

Dandl

et al. 2022

Front. Future Transp.

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With advances in digitization and automation, autonomous mobility on demand services have the potential to disrupt the future mobility system landscape. Ridepooling services in particular can both decrease land consumption by reducing the need for parking and increase transportation efficiency by increasing the average vehicle occupancy. Nevertheless, because ridepooling services require a sufficient user base for pooling to take effect, their performance can suffer if multiple operators offer such a service and must split the demand. This study presents a simulation framework for evaluating the impact of competition and cooperation among multiple ridepooling providers. Two different kinds of interaction via a broker platform are compared with the base cases of a single monopolistic operator and two independent operators with divided demand. In the first, the broker presents trip offers from all operators to customers (similar to a mobility-as-a-service platform), who can then freely choose an operator. In the second, a regulated broker platform can manipulate operator offers with the goal of shifting the customer-operator assignment from a user equilibrium towards a system optimum. To model adoptions of the service design depending on the different interaction scenario, a game setting is introduced. Within alternating turns between operators, operators can adapt parameters of their service (fleet size and objective function) to maximize profit. Results for a case study based on Manhattan taxi data, show that operators generate the highest profit in the broker setting while operating the largest fleet. Additionally, pooling efficiency can nearly be maintained compared to a single operator. The regulated competition benefits not only operators (profit) and cities (increased pooling efficiency), but customers also experience higher service rate, though they need to accept slightly increased waiting and travel time due to increased pooling efficiency. Contrarily, when users can decide freely, the lowest pooling efficiency and operator profit is observed.

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“…However, the easy to use apps and public availability of MSP vehicles has significantly induced additional demand contributing to an increase of vehicle kilometers traveled (VKT) and possibly an increase in congestion (Henao and Marshall, 2019;Kaltenhäuser et al, 2020). To reduce the overall VKT, spatially and temporally overlapping customers can be combined into single trips, i.e., shared-ride Mobility on Demand (MOD), which could be strengthened by regulation (Dandl et al, 2021). However, non-shared rides still contribute the most in current MOD services (Henao and Marshall, 2019;Hyland and Mahmassani, 2020).…”

Section: Introductionmentioning

confidence: 99%

Density Based Distribution Model for Repositioning Strategies of Ride Hailing Services

Syed

Dandl

Kaltenhäuser

et al. 2021

Front. Future Transp.

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Ride hailing (RH) services have become a common mode of transportation in the last decade. Usually, statistical tools are used to improve their performance, whereby the tools typically divide the whole operational area into multiple regions. These tools usually assume that the regions are independent of each other even though vehicles from one region can be used to serve neighboring regions, thereby a method is required that consistently relates vehicle demand and supply between geographically neighboring regions or to the whole operating area. This hinders tapping into the full potential of region-based performance improvement techniques like the repositioning of idle vehicles. Therefore, we develop an innovative reachability function-based method that coherently builds a relation among all regions in the form of a spatial density of the measured quantity. We use it to calculate the differences of vehicle supply and demand for the whole operational area in the form of an imbalance density. Based on this, we derive a novel repositioning formulation that significantly reduces both the overall vehicle imbalances and the total distance of repositioning trips, and thus improves the long-term RH performance. We test the approach in an agent-based simulation for an RH scenario with automated vehicles, using open-source New York City taxi data as demand. The approach shows a remarkable improvement over the state of the art repositioning strategies that balance the fleet over the individual regions. Furthermore, we introduce kernel-based key performance indicators (KPIs) that can be calculated at the time of making repositioning decisions. We also show the correlation of the KPIs with long-term performance gains. We expect that these KPIs can benefit future statistical (machine learning) approaches for repositioning.

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Regulating mobility-on-demand services: Tri-level model and Bayesian optimization solution approach

Cited by 40 publications

References 37 publications

Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network

Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network

Competition and Cooperation of Autonomous Ridepooling Services: Game-Based Simulation of a Broker Concept

Density Based Distribution Model for Repositioning Strategies of Ride Hailing Services

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