Real-Time Multimodal Network Optimization Considering Micromobility Preferences

Description

Urban transportation systems are undergoing significant transformations driven by technological advancements and shifting user behaviors. The rise of micromobility services, such as bike-sharing and scooter-sharing, has introduced new dynamics into urban mobility, presenting opportunities and challenges for city planners and transport operators. To create a more responsive and resilient urban transportation ecosystem, there is an urgent need to develop adaptive and efficient transportation networks that seamlessly integrate these emerging modes of transport.

Micromobility services have gained popularity due to their convenience, low cost, and environmental benefits. These services offer an alternative for short-distance trips, typically replacing car journeys and contributing to reduced traffic congestion and lower emissions. However, the dynamic nature of micromobility usage, characterized by fluctuating demand and varying travel patterns, requires a transportation network that can adapt in real time to these changes. Real-time adjustments to a multimodal transportation network involve using advanced algorithms and real-time data to enable the network to respond swiftly to demand shifts, thereby enhancing the efficiency and reliability of urban transportation.

Integrating micromobility with traditional transportation modes, such as buses, subways, and ride-sharing services, is essential for addressing the "first-mile" and "last-mile" challenges, ensuring that users can efficiently transition between different modes of transport. By utilizing real-time data and optimization techniques, this research aims to develop strategies that facilitate these transitions, ultimately improving the overall user experience and promoting sustainable urban mobility. A comprehensive understanding of micromobility user preferences and decision-making processes is crucial for successfully integrating these services into the broader transportation framework. Factors influencing individuals' transportation choices include travel time, cost, convenience, and environmental considerations. By developing detailed choice models that account for these variables, the research will provide valuable insights into user behavior, informing urban mobility planning and policy decisions.

Existing research highlights the potential benefits and challenges associated with integrating multiple transportation modes. Mobility-on-demand transit and micromobility services offer a promising solution for connecting urban centers with low-demand suburban areas, optimizing vehicle utilization through intermodal transfers [1,2]. Additionally, micromobility services can replace a substantial portion of short car trips, thereby reducing congestion and emissions, although their impact on overall energy use and emissions requires further measures [3]. Understanding user preferences is critical for the successful integration of micromobility services. Discrete choice modeling reveals that users prefer conventional bikes over e-bikes and e-scooters, with parking type and socio-demographic factors significantly influencing their choices [4]. Furthermore, micromobility services can effectively address the last-mile problem from subway stations to destinations, with user preferences varying based on demographic and socio-economic attributes [5].

By focusing on developing real-time adjustment strategies and understanding user preferences, this PhD aims to simulate a comprehensive multimodal transportation system that adapts in real-time to demand changes and incorporates user preferences for micro-mobility to contribute to creating more efficient, sustainable, and user-friendly urban transportation networks.

Objectives

The primary objectives of this PhD research are to:

• Develop real-time adjustment strategies for multimodal transportation networks: use real-time data and advanced algorithms to enable transportation networks to respond swiftly to shifts in demand patterns. The research will focus on optimizing the integration of micromobility services with traditional transportation modes to enhance the efficiency and reliability of urban transportation.
• Understand micromobility user preferences and decision-making processes: investigate the factors influencing individuals' choices between different transportation modes, including micromobility options. By developing comprehensive choice models, the study aims to inform urban mobility planning and policy decisions.
• Enhance urban mobility through integrated multimodal solutions: Create and test strategies that integrate micromobility services into a broader multimodal transportation framework. These strategies will focus on reducing congestion, minimizing environmental impacts, and promoting sustainable mobility choices.
• Consider scalability issues and propose algorithms and systems capable of handling real-time scenarios and backcasting processes.

Methodology

To achieve these objectives, the research will employ a multi-method approach:

• The research will collect and analyze real-time data from various transportation modes, including micromobility services. Advanced algorithms will be developed to optimize the integration of these modes and enable real-time adjustments to the transportation network.
• The research will develop choice models that incorporate variables such as travel time, cost, convenience, and environmental considerations. We will also conduct surveys and analyze data to understand micromobility user preferences and decision-making processes.
• The research will use simulation tools to model the interactions between different transportation modes and their impact on urban mobility. Optimization techniques will be applied to develop strategies that enhance the efficiency and sustainability of the transportation network.
• The research will work on simplified models to improve the scalability of the proposals.

References  [1] Alisoltani, Negin, Ludovic Leclercq, and Mahdi Zargayouna. "Can dynamic ride-sharing reduce traffic congestion?." Transportation research part B: methodological 145 (2021): 212-246.  [2] Luo, Qi, Shukai Li, and Robert C. Hampshire. "Optimal design of intermodal mobility networks under uncertainty: Connecting micromobility with mobility-on-demand transit." EURO Journal on Transportation and Logistics 10 (2021): 100045.  [3] Fan, Zhufeng, and Corey D. Harper. "Congestion and environmental impacts of short car trip replacement with micromobility modes." Transportation Research Part D: Transport and Environment 103 (2022): 103173.  [4] Jaber, Ahmed, Jamil Hamadneh, and Bálint Csonka. "The Preferences of Shared Micro-Mobility Users in Urban Areas." IEEE Access (2023).  [5] Eom, Jin Ki, Kwang-Sub Lee, and Jun Lee. "Exploring micromobility mode preferences for last-mile trips from subway stations." Journal of Public Transportation 25 (2023): 100054.

Présentation établissement et labo d'accueil

UNIVERSITÉ GUSTAVE EIFFEL - SITE DE MARNE-LA-VALLÉE

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.

Profil du candidat

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.

Date limite de candidature

19/07/2024