Summary
Dr Jose Escribano is a Lecturer in the Department for Civil and Environmental Engineering at Imperial College London. His research group works on collaborative logistics, implementation of new technologies for humanitarian response operations, urban air mobility and the implementation of AI tools for air traffic management.
Dr Escribano joined Imperial College in 2015 and received his PhD in 2021 on the optimization of humanitarian relief operations using Unmanned Aerial Vehicles (UAVs). Dr Escribano is also a holder of the JSPS fellowship on the modelling of urban evacuation procedures and has worked as consultant for the United Nations World Food Programme in the development of UAV coordination model to improve the safety, efficiency and standardisation of UAV deployments in humanitarian contexts. To date, Dr Escribano has been part of several large scale academic and consultancy projects, including project SHIFT, Project D-Risk, among others. In addition, Dr Escribano has extensive experience as a reviewer for academic journals and research agencies.
Publications
Journals
Cheong H-I, Macias JJE, Karamanis R, et al., 2023, Policy and strategy evaluation of ridesharing autonomous vehicle operation: a london case study, Transportation Research Record: Journal of the Transportation Research Board, Vol:2677, ISSN:0361-1981, Pages:22-52
Escribano Macias J, Khalife C, Slim J, et al., 2023, An integrated vertiport placement model considering vehicle sizing and queuing: a case study in London, Journal of Air Transport Management, Vol:113, ISSN:0969-6997
Liu Y, Ye Q, Escribano J, et al., 2023, Route planning for last-mile deliveries using mobile parcel lockers: a hybrid q-learning network approach, Transportation Research Part E: Logistics and Transportation Review, Vol:177, ISSN:1366-5545, Pages:1-40
Ye Q, Feng Y, Macias JJE, et al., 2023, Adaptive road configurations for improved autonomous vehicle-pedestrian interactions using reinforcement learning, Ieee Transactions on Intelligent Transportation Systems, Vol:24, ISSN:1524-9050, Pages:2024-2034
Ngu E, Parada L, Macias JJE, et al., 2022, Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem, Transportation Research Record, Vol:2676, ISSN:0361-1981, Pages:385-395