Electric micromobility services and transport network companies and others have emerged in the past decade. They offer shared e-micromobility platforms to help match riders with the micro-type mode of transportation, consisting of e-pedelec (e.g., e-bikes), e-scooters, and e-mopeds. Compared to conventional vehicles, micromobility vehicles require less driving and parking space. They are usually accessible in designated city areas or from a docking station. In addition, they are a potential solution to cover the first-and- last-mile trips and to bridge public transportation gaps in city centres, e.g., Paris and Sydney.
Currently, usage patterns, user profiles, the social and environmental impacts of micromobility services, and the best practices for regulating their use are merely analysed (Milakis et al., 2020). Almost all empirical studies focused on only one shared e-micromobility service (Liao & Correia, 2022). Therefore, an integrated perspective that accounts for multiple shared e-micromobility services and their relations with existing private and public vehicles is necessary to fully understand their demand and impact and facilitate synergy between different shared e-micromobility services.
Liao & Correia (2022) recently conducted a comprehensive analysis of the literature on shared e-mobility, including e-micromobility services. They divided all the studies into three research themes: performance description of existing systems, demand estimation, and identifying factors influencing the demand. They highlighted the significant gap in research: no studies in the literature try to consider all three themes in a single research project on e-micromobility services in urban areas. This thesis aims to address this gap to explore operational and strategic decision-making for shared e-micromobility platforms and their co-existence with other modes and how regulations from government agencies would create a less congested, greener, and more sustained transport ecosystem.
The research questions considered by this thesis are related to modelling, market design, and optimisation of operations of such competitive mixed-asset shared e-micromobility services, which are challenging and novel and have never been studied before.
This thesis intends to develop quantitative models and market design methods to fundamentally transform the analysis, optimisation and regulation of shared e-micromobility services as a dynamic competitive market to maximise social welfare. In particular, we aim to (i) design a management system and market model for the shared e-micromobility services; (ii) investigate different methodologies and algorithms for operating the sharing system and charging process (connection with grid) of e-micro type modes; (iii) identify the conditions under which the use of shared e-micromobility promotes a gradual modal shift towards more active modes of transport and contributes to a decrease in car use and car ownership.
One of the critical issues of e-micromobility services is safety issues, such as the impact of banning e-micromobility use on pavements or the weather impact on the usage rate of e-micromobility services. The proposed framework should represent multiple scenarios under various network conditions and demand profiles for the multimodal transportation system. In addition, different charging strategies and management policies will be designed and investigated.
To exploit the full potential of shared e-micromobility services, we must rethink micromobility models and market design and regulation. Existing models of micromobility services in networks are largely based on a steady-state analysis approach that neglects the within-day and day-to-day evolutions of congestion in the network. The emerging shared e-micromobility services require mathematically tractable and dynamic models and innovative planning and operational market design methods to address city congestion. To this end, we introduce the following specific steps below to achieve this aim:
1- Modelling the time-varying travel demand and dynamics of the services provided by e-micromobility. This step requires a comprehensive literature review of the mathematical and quantitative models applied to shared micromobility services or similar services such as ride-sharing with electric vehicles.
2- Optimizing the fleet management of micromobility modes during the service operation with dynamic dispatching and relocation of vacant modes.
3- Design a market and regulator including multiple operators for micromobility systems with heterogeneous pricing and operating policies. A day-to-day framework will be designed to represent the evolution of the transportation system and the market under the different demand scenarios.
4- Considering a stochastic demand and supply model to include safety issues and external factors (e.g., weather) to analyse city-scale scenarios. This analysis will deploy multi-agent simulation models (an agent-based simulator, e.g., MATSim, SM4T and SUMO), which detail the operation of several services in real-time while explaining one by one the passenger movements.
Existing tools and models
The GRETTIA lab has a long experience with transportation modelling and simulation (Ameli, 2021; Zargayouna et al., 2020a) especially for shared mobility modelling (Alisoltani et al., 2021). Recently, the ORNISIM project has been accomplished at GRETTIA to represent and visualise e-scooters in Paris based on survey data to address the modal share and safety issues of this micromobility. In addition, the University of Sydney's transport group has exceptional track record in traffic network modeling and shared and automated transport services, especially dynamics large-scale congestion models and novel ride-hailing models (Ramezani et al., 2018).
●Alisoltani, N., Leclercq, L., & Zargayouna, M. (2021). Can dynamic ride- sharing reduce traffic congestion? Transportation research part B: methodological, 145, 212-246.
●Ameli, M., Lebacque, J. P., & Leclercq, L. (2021). Computational Methods for Calculating Multimodal Multiclass Traffic Network Equilibrium: Simulation Benchmark on a Large-Scale Test Case. Journal of Advanced Transportation, 2021.
●Liao, F. and Correia, G., 2022. Electric carsharing and micromobility: A literature review on their usage pattern, demand, and potential impacts. International Journal of Sustainable Transportation, 16(3), pp.269-286.
●Milakis, D.; Gedhardt, L.; Ehebrecht, D.; Lenz, B. Is micro-mobility sustainable? An overview of implications for accessibility, air pollution, safety, physical activity and subjective wellbeing. In Handbook of Sustainable Transport; Curtis, C., Ed.; Edward Elgar Publishing: Cheltenham, UK, 2020; pp. 180–189.
●Ramezani, M., and Nourinejad, M. Dynamic modeling and control of taxi services in large-scale urban networks: A macroscopic approach. Transportation Research Part C: Emerging Technologies 94 (2018), 203–219.
●Zargayouna, M., & Zeddini, B. (2020). Dispatching Requests for Agent-Based Online Vehicle Routing Problems with Time Windows. Journal of computing and information technology, 28(1), 59-72.Benefits
Applicants must fulfil the following eligibility criteria:
One application per call per year is allowed.
Applicants must be available full-time to start the programme on schedule (November 1st 2023).
Application rules are enforced by the French doctoral system which specifies a standard duration of 3 years for a full-time PhD together with the MSCA standards and the OTM-R European rules as follows.
Citizens of any nationality may apply to the programme.
There is no age limit.Selection process
Please refer to the Guide for Applicants available on the CLEAR-Doc website: https: // clear-doc.univ-gustave-eiffel.fr/how-to-apply/useful-documents/Additional comments
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Computer science: Master Degree or equivalent
Mathematics: Master Degree or equivalent