Passenger request prediction is essential for operations planning, control, and management in ride-sharing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) ﬂow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network framework along with the Attention Mechanism to predict the OD ﬂow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originat-ing from diﬀerent locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.

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GNN-based Passenger Request Prediction

Spatiotemporal forecasting has been attracting tremendous interest in various fields, among which traffic flow prediction is a representative example. Existing methods typically deal with the complex spatial and temporal dependencies in traffic flow through graph neural networks (GNNs) and temporal neural networks (TNNs), respectively. However, these works still fall short due to: 1) deep GNNs have the over-smoothing problem that hinders the handling of higher-order spatial correlations; 2) TNNs have difficulty in extracting the temporal dependencies with different localities. To this end, this paper proposes Higher-Order Masked Graph Neural Networks (HOMGNNs) to model and predict the traffic flow data. Concretely, we design the spatial graph learning layer to adaptively characterize the dependency correlations of different orders, and the higher-order GNN (HOGNN) is further proposed to deal with these correlations. Furthermore, we define and construct a temporal graph to represent the temporal dynamics in traffic data. The masked GNN (MGNN) is further proposed to extract these dynamics based on the temporal graph. To validate the superiority of the proposed HOMGNNs, we conduct extensive experiments on METR-LA and PEMS-BAY datasets. Experimental results demonstrate the remarkable performance of our method compared with 11 state-of-the-art baselines.

Higher-Order Masked Graph Neural Networks for Traffic Flow Prediction

Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals. As a result, recent years have witnessed an explosion of research interest in the RSODP (Origin-Destination Prediction for Ridesharing) problem with the goal of predicting the future ridesharing requests and providing schedules for vehicles ahead of time. Most of the existing prediction models utilise Deep Learning. However, they fail to effectively consider both spatial and temporal dynamics. In this paper the Baselined Gated Attention Recurrent Network (BGARN), is proposed, which uses graph convolution with multi-head gated attention to extract spatial features, a recurrent module to extract temporal features, and a baselined transferring layer to calculate the final results. The model is implemented with PyTorch and DGL (Deep Graph Library) and is experimentally evaluated using the New York Taxi Demand Dataset. The results show that BGARN outperforms all the other existing models in terms of prediction accuracy.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09858119.pdf

A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.

Origin-Destination Travel Time Oracle for Map-based Services

Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in dif...

WoTianyu

Papers

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs