Origin-Destination Demand Prediction: An Urban Radiation and Attraction Perspective

--In recent years, origin-destination (OD) demand prediction has gained significant attention for its profound implications in urban development. Existing deep learning methods primarily focus on the spatial or temporal dependency between regions yet neglecting regions' fundamental functional difference. Though physical methods have characterised regions' functions by their radiation and attraction capacities, these functions are defined on numerical factors like population without considering regions' intrinsic nominal attributes, e.g., a region is a residential or industrial district. Moreover, the complicated relationships between two types of capacities, e.g., the radiation capacity of a residential district in the morning will be transformed into the attraction capacity in the evening, are totally missing from physical methods. In this paper, we not only generalize the physical radiation and attraction capacities into the deep learning framework with the extended capability to fulfil regions' functions, but also present a new model that captures the relationships between two types of capacities. Specifically, we first model regions' radiation and attraction capacities using a bilateral branch network, each equipped with regions' attribute representations. We then describe the transformation relationship of different capacities within the same region using a parameter generation method. We finally unveil the competition relationship of different regions with the same attraction capacity through adversarial learning. Extensive experiments on two city datasets demonstrate the consistent improvements of our method over the state-of-the-art baselines, as well as the good explainability of regions' functions using their nominal attributes. With the spread of ride-hailing platforms like Uber and Didi, intelligent transportation systems have emerged as a vibrant research domain [1]-[3]. These systems are designed to offer convenient ride services, improve public transportation efficiency through proactive order assignment, and optimize profitability by identifying high-profit routes based on historical passenger demands [4]. Among the wide spectrum of applications, traffic demand forecasting is the focal point due to its vital role in urban development, traffic control, and route planning [5]-[11]. The conventional task in this field involves the prediction of the potential number of passenger demands in a specific region [10], [12], [13]. However, such a task is unable to capture associations in inter-regional flows. Tieyun Qian is the corresponding author. Figure 1: (a) An illustration of the region partition in Manhattan, New Y ork, and (b) and (c) are visualizations of the taxi outflow and inflow demand in a designated region with a red mark in (a) on 2019-01-17, respectively.