Coordination of Urban On-Demand Transportation Services

Context

The rise of on-demand transportation services has significantly altered urban mobility patterns. While these services offer convenience to users, if not properly managed or coordinated, they may worsen urban congestion, increase the travel time, energy consumption in the end. For instance, in the absence of coordination, two people that go in the same direction within nearly the same time slot require two vehicles whereas they can share the same vehicle. This research aims to evaluate the different strategies of coordinating on-demand services provided by different companies, in terms of number vehicle-miles for given demands. It is worth noting that minimizing the number of vehicle-miles is in line with minimizing the congestion and energy consumption.  Centralized optimization which uses all the resources (vehicles, drivers, …) initially owned by different service providers (i.e., pools the resources they provide) to satisfy all the requests obviously is the best strategy in terms of the number of vehicle-miles. But it may hurt the interests of some and more crucially, it requires that the centralized platform have complete access to the information of the service providers and therefore creates reluctance of the latter. As a consequence, such a strategy may be inapplicable in practice.

This research investigates two categories of service provision: cooperative-direct, and cooperative-indirect, examining how these strategies create values to customers and society in terms of cost (vehicle-miles) and congestion, respectively. Note that centralized optimization strategy applies to monopoly case and cooperative-direct category. Monopoly corresponds for instance to situations where the service is provided by government as part of public service. In case of cooperative-direct, although the service providers are willing to share the information and resources, each of them has its own financial interests. As a result, it is necessary to develop proper mechanism to share the value created due to this cooperation. 

The main parts of an on-demand service are the passengers, the ride provider, and the matching algorithm. The passenger seeks a ride to pick her/him up at the origin point and drop her/him off at the desired destination within a time interval. The ride provider has a fleet of vehicles (taxi, van, autonomous car, etc.) that is ready to serve the passengers’ requests. The matching algorithm orchestrates the pairing of passengers with available vehicles, seeking to optimize both travel time and vehicle-miles.

We classify this system into two categories, considering the interactions between different fleet providers and the performance of the matching algorithm. Fleet providers can work competitively or cooperatively. In the case of cooperative interaction, they share the fleet information so the passenger may get the ride easier, and the benefits of ride-sharing will be achievable more easily. In the competitive scenario, each provider tries to gain a higher profit even at the expenses of others. The matching algorithm communicates with the providers directly or indirectly. This classification can be expanded for other new mobility services as well.

Existing literature has predominantly explored the mechanics of ride-sourcing and ride-splitting while inadequately addressing their impact on urban congestion and energy consumption. This research aims to bridge this knowledge gap by investigating the influence of competitive and cooperative strategies, coupled with direct and indirect dispatching algorithms, on the performance of the whole service that can be measured in vehicle-miles that heavily impact traffic congestion and energy consumption. This study holds paramount importance for devising targeted optimization strategies that reduce the vehicle-miles and hence create values for passengers as well as the whole society through reduced congestion and energy consumption.

Objectives:

• On-demand service providers categorization: We aim to establish a clear taxonomy of operational models based on competitiveness and dispatching strategies.
• Optimization algorithms and value-sharing mechanism design: We aim to develop and propose optimization strategies for each category and, if necessary, mechanism of sharing the value created due to the cooperation among service providers so as to incentivize their willingness for cooperation.
• Comparative analysis and sensitivity analysis: We aim to analyze the impacts of each operational model and compare them on different network scales.

Methodologies:

To develop effective optimization and value-sharing methods for different operational strategies of on-demand transportation services and to assess their impact on key performance indicators such as vehicle-miles and congestion levels, we will undertake a comprehensive approach. This involves modeling the intricate dynamics of service provision, optimizing fleet management, designing an inclusive market and regulation framework, and incorporating rolling-horizon framework and robustness analysis to cope with the intrinsic uncertainty in practice. The following steps outline the methodology:

We will conduct a detailed literature review to identify dynamic and mathematically tractable models that are applicable to on-demand transportation services. Then, we will focus on optimizing the fleet management for each category of service provision, considering real-time dispatching algorithms that respond to changing demand and traffic conditions, vehicle relocation strategies to ensure optimal service coverage and minimize idle times, and algorithmic approaches to balance fleet size with operational costs.

A market and regulatory framework will be designed to encompass multiple operators, reflecting diverse pricing strategies and operational policies, a day-to-day dynamic framework to simulate the evolution of the transportation system under various demand and operational scenarios, and mechanisms to foster cooperative strategies that create value for all stakeholders (passengers, service providers and society).

The designed optimization algorithms will be assessed for their potential to create values by implementing the algorithms in realistic environments and evaluating their performance against current operational strategies. We will analyze key performance indicators such as average travel time, average waiting time, and road utilization rates.

In addition, we will conduct sensitivity analyses to determine the robustness of the algorithms under different traffic conditions and demand levels.

In conclusion, based on the findings from the above steps, this research will yield detailed optimization strategies for each operational category to guide service providers in enhancing their operational efficiency. Furthermore, we will propose a set of policy recommendations for urban transport regulators to incentivize cooperative behaviors that lead to value creation for all stakeholders.

References

Yan, P., Lee, C.Y., Chu, C., Chen, C. and Luo, Z., 2021. Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability. Omega, 102, p.102351.

Alisoltani, N., Leclercq, L. and Zargayouna, M., 2021. Can dynamic ride-sharing reduce traffic congestion?. Transportation research part B: methodological, 145, pp.212-246.

Mourad, A., Puchinger, J. and Chu, C., 2019. A survey of models and algorithms for optimizing shared mobility. Transportation Research Part B: Methodological, 123, pp.323-346.

Pandey, V., Monteil, J., Gambella, C. and Simonetto, A., 2019. On the needs for MaaS platforms to handle competition in ridesharing mobility. Transportation Research Part C: Emerging Technologies, 108, pp.269-288.

Mourad, A., Puchinger, J. and Chu, C., 2019. Owning or sharing autonomous vehicles: comparing different ownership and usage scenarios. European Transport Research Review, 11, pp.1-20.

The Gustave Eiffel University is a national multi-campus university. It was created on 1 January 2020 as a result of the merger of the University of Paris-Est Marne la Vallée, Ifsttar, and several schools and engineering schools: EIVP, ENSG, ESIEE, and Ecole d'architecture Paris Est. Gustave Eiffel University has more than 2,500 agents and 17,000 students at nine sites in France. The university hosts a quarter of the national research and development on sustainable cities and is home to the largest transport research center in Europe.It is a university on a human scale, bringing together multi-disciplinary skills that enable it to conduct quality research for the benefit of society, to offer training courses adapted to the socio-economic world, and to support changes in society and public policies.

The GRETTIA research lab is part of Université Gustave Eiffel and carries out its research activity in the field of land transport systems. It is interested in all aspects of road and guided transport modes, from systemic aspects, modeling and simulation to the dynamic aspects of vehicles, including management, diagnosis and maintenance. GRETTIA contributes to the development of transport network and system engineering, taking into consideration the issues of integration, intermodality, reliability and system analysis. Within this framework, the research unit conducts a transversal activity of development of models and generic tools based on the field of mathematical engineering, advanced computer science and mechanics; contributes to the modeling, design, management,evaluation and maintenance of intelligent transport and infrastructure operation systems; studies the conditions of functional and social acceptability of new transport services.

The candidate must:

• Have a Master 2 degree or equivalent in transportation engineering, civil engineering, computer science, urban planning, operations research or other field strongly related to transportation.
• Have experience in mathematical modeling and optimization.
• Have very good programming skills.
• Have excellent analytical and communication skills in written and spoken English.
• Be able to work independently and take responsibility for the progress and quality of the project.