Title
Editorial: Emerging on-demand passenger and logistics systems: Modelling, optimization, and data analytics
Author
Ke, Jintao (The University of Hong Kong)
Wang, Hai (Singapore Management University)
Masoud, Neda (University of Michigan)
Schiffer, Maximilian (Technische Universität München)
Correia, Gonçalo (TU Delft Transport and Planning)
Date
2024
Abstract
The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, Picnik); and same-day courier services (e.g., Amazon Same-Day Delivery) has significantly enhanced both convenience and safety for customers, particularly during the COVID-19 pandemic.
Despite their rapid growth, on-demand transportation services bear several challenges for key stakeholders. The private sector, which includes online platforms, strives to optimize system efficiency and revenue through advanced artificial intelligence techniques and optimization methods. Meanwhile, the public sector aims to strike a balance between the interests of various stakeholders to create more sustainable, equitable, and eco-friendly mobility systems. As such, new mobility paradigms arise in which public authorities require decision support tools that offer realistic cost and benefit estimations for all parties involved. As these services continue to expand, researchers, operators, and policymakers can leverage the vast amount of data generated to better understand, model, analyze, and effectively coordinate both the supply and demand dynamics within these systems.
To reference this document use:
http://resolver.tudelft.nl/uuid:90ac8cd3-f09e-4b82-a586-f2cd70d64abb
DOI
https://doi.org/10.1016/j.trc.2024.104574
Embargo date
2024-09-25
ISSN
0968-090X
Source
Transportation Research. Part C: Emerging Technologies, 161
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
contribution to periodical
Rights
© 2024 Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Gonçalo Correia