Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing
This work addresses a practical but underexplored challenge in ridesharing platforms, enabling better resource allocation and service efficiency.
The paper tackles the problem of predicting fine-grained origin-destination demand matrices for ridesharing, which is crucial for optimizing supply-demand balance, and introduces OD-CED, a model that reduces root-mean-square error by up to 45% and weighted mean absolute percentage error by 60% compared to traditional methods in highly sparse data scenarios.
Accurate spatial-temporal prediction of network-based travelers' requests is crucial for the effective policy design of ridesharing platforms. Having knowledge of the total demand between various locations in the upcoming time slots enables platforms to proactively prepare adequate supplies, thereby increasing the likelihood of fulfilling travelers' requests and redistributing idle drivers to areas with high potential demand to optimize the global supply-demand equilibrium. This paper delves into the prediction of Origin-Destination (OD) demands at a fine-grained spatial level, especially when confronted with an expansive set of local regions. While this task holds immense practical value, it remains relatively unexplored within the research community. To fill this gap, we introduce a novel prediction model called OD-CED, which comprises an unsupervised space coarsening technique to alleviate data sparsity and an encoder-decoder architecture to capture both semantic and geographic dependencies. Through practical experimentation, OD-CED has demonstrated remarkable results. It achieved an impressive reduction of up to 45% reduction in root-mean-square error and 60% in weighted mean absolute percentage error over traditional statistical methods when dealing with OD matrices exhibiting a sparsity exceeding 90%.