Smart Mobility and Transportation Analytics Research Interest Group (SMTA-RIG)
Co-Leader: Dr Dong Li
Co-Leader: Professor Jiyin Liu
With the ever-increasing population in urban areas and urgent demand for accessible, green, and sustainable transport, a lot of new mobility solutions have emerged. The car sharing, ride-hailing, bike/scooter sharing, and on demand transport services are no more uncommon. Governments around the world provide support and incentives for smarter and greener mobility options. Innovation and new technologies in mobility emerge all the time.
The Smart Mobility and Transportation Analytics research interest group focuses on research, development and application of multi-disciplinary works that address the strategical, operational, and behavioural challenges arising from these disruptive innovations in mobility and transportation.
The members of the RIG utilise both qualitative tools (for problem exploration) and analytical tools (for problem solving). The group members are experienced in optimisation, modelling, heuristics, behavioural operations management, forecasting, reinforcement learning, and data analytics.
The group works with various stakeholders including academics, industrialists, businesses and policy makers, and is interested in the following research themes.
· Consumer behaviours in car/bike/scouter/vehicle sharing
· Ride-matching and routing optimisation in car hailing services
· Revenue management in car rental/vehicle sharing
· Vehicle routing (with drones) in last mile logistics
· Vehicle routing for service delivery
· Facility location in transportation network
· Planning and scheduling in transportation and logistics systems
Baibing Li – Professor
- Grammatoula Papaioannou – Lecturer
- Rupal Rana – Lecturer
- Richlove Frimpong – Doctoral Researcher
- Vidura Sooriyaarachchi- Doctoral Researcher
- Naami Sharma - Doctoral Researcher
- Shanshan Meng – Visiting Doctoral Researcher
Seminars and workshops will be added once they are being scheduled.
Title: "Retail Facility Location using Public Data"
Speaker: Kalyan Talluri, Professor of Operations Management in the Department of Management, Imperial College London
Date: 1st May 2019
Over the past few years a number of optimization models for the location of retail facilities based on spatial customer-choice behavioural models have been proposed in the Operations Research (OR) literature. The main obstacle in their implementation has been the availability of relevant data to estimate the demand for each hypothetical location. Specifically, the models require estimates of how demand will expand and shift when we locate a new facility. This is a difficult task as (i) the firm does not observe demands for the existing retail facilities as they are owned by others, and yet, (ii) needs to estimate a structural model of how demand will change as a function of location, price and design features of its planned facility. In this paper, we present (a) a model of competition and location for retail facilities and (b) tackle this estimation challenge in the case of restaurants, an industry where the econometrics is particularly challenging as unobservable tastes and quality and value determine to a large extent the success of an establishment.
Kalyan Talluri is a Professor of Operations Management in the Department of Management. He obtained his PhD in Operations Research from MIT, a Masters in Industrial Engineering from Purdue University and a B.Tech in Mechanical Engineering from Osmania University, Hyderabad. He had previously taught at Kellogg Graduate School of Management, Northwestern University and the Dept. of Economics at the Universitat Pompeu Fabra, Barcelona. He also worked at US Airways for three years prior to that. He has held visiting positions at the Indian School of Business, New York University, INSEAD and Dartmouth College.
Professor Talluri's research interests are in network and service design, data analytics, revenue management and pricing. He serves as the Department Editor of Revenue Management and Market Analytics, for the journal of Management Science.
· C. Ji, R. Mandania, J. Liu, A. Liret, Scheduling on-site service deliveries to minimise the risk of missing appointment times. Transportation Research Part E: Logistics and Transportation Review, 158, 2022
· D. Zhang, D. Li, H. Sun, L. Hou, The vehicle routing problem with distribution uncertainty in deadlines, European Journal of Operational Research, 292(1), 311-326, 2020
· B. Li, Measuring travel time reliability and risk: A nonparametric approach, Transportation Research Part B: Methodological, 130, 152-171, 2019
· D. Yu, D. Li, M. Sha, D. Zhang, Carbon-efficient deployment of electric Rubber-tyred gantry cranes in container terminals with workload uncertainty, European Journal of Operational Research, 275(2), 552-569, 2018
· L. Hou, D. Li, D. Zhang, Ride-matching and routing optimisation: models and a large neighbourhood search heuristic, Transportation Research Part E: Logistics and Transportation Review, 118:143-162, 2018
· D. Li, Z. Pang, Dynamic booking control for car rental revenue management: a decomposition approach, European Journal of Operational Research, 256(3):850-867, 2017
Doctoral students join a lively and supportive community of research students, becoming an integral part of the School’s research culture. We welcome approaches from suitably qualified graduates, particularly those with a relevant Master's degree and sufficient funding, who may wish to undertake research projects in the specialist areas of the group, leading to a PhD. As a research student, you will be encouraged to attend conferences to present your work and develop joint publications with your supervisors.
The group has great supervisory experience and is keen to supervise high quality research students in members’ specialist research fields. Recognition of the quality of supervision offered by the group members has resulted in funding for doctoral students from Research Councils. For students interested in further information on potential PhD projects and supervisors, please stay tuned to our webpages.
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