Transportation foundation models are regarded as key infrastructure for achieving the leap from informatization to intellectualization in road transportation systems, and thus have become one of the most focused research directions among transportation researchers. Starting from first principles and based on the fundamental transportation data currently available, this paper abstracts two types of primitives as the core and fundamental data building blocks for transportation foundation models. Specifically, individual travel data are represented by vehicle trajectories as the first type of primitive data, with travel attribute data serving as labels for trajectory data. For data at specific spatial scales, traffic flow spatiotemporal series data of people or vehicles within a given time window are adopted as the second type of primitive data. Furthermore, this paper discusses how to construct a World Model for Transportation that generates primitive data representing future traffic states. This world model takes as input both sensor-acquired primitive data describing historical traffic states and time-series data of potential control and management actions (e.g., signal timing, route guidance, or lane closure plans), enabling it to simulate the outcomes of different intervention strategies. These primitive data will provide critical data foundations for reasoning, simulation, and decision-making in upper-level applications of transportation foundation models, serving as a bridge connecting data perception and intelligent decision-making.

The advent of drones is leading to a paradigm shift in courier services, while their large-scale deployment is confined by a limited range. Here, we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles. With this, we propose a ground-air cooperation (GAC) based business model for parcel delivery in an urban environment. As per our case study using real-world data in Beijing, the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost, but also reduce road traffic by 8.6%, leading to an annual social benefit of 6.67 billion USD for Beijing. The proposed model utilizes the currently “wasted or unused” rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers, thus fundamentally addressing the congestion, noise, pollution, and road wear and tear problems caused by trucks, and bringing in immense social benefit.

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents' travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed.