Editorial
Emerging on-demand passenger and logistics systems: Modelling, optimization, and data analytics
Jintao Ke (The University of Hong Kong)
Hai Wang (Singapore Management University)
Neda Masoud (University of Michigan)
Maximilian Schiffer (Technische Universität München)
Goncalo Homen de Almeida Correia (TU Delft - Transport and Planning)
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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.