DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction

TL;DR

This paper introduces DeepSTCL, a deep learning framework using ConvLSTM to simultaneously capture spatial and temporal dependencies in urban travel demand prediction, outperforming traditional models in accuracy and speed.

Contribution

The paper proposes a novel Deep Spatio-Temporal ConvLSTM model that effectively models both spatial and temporal dependencies for travel demand forecasting.

Findings

Outperforms traditional models in accuracy.

Demonstrates faster prediction times.

Effectively captures spatial-temporal dependencies.

Abstract

Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources. Currently, a series of problems with transportation resources such as unbalanced distribution and road congestion disrupt the scheduling discipline. Therefore, it is significant to predict travel demand for urban resource dispatching. Previously, the traditional time series models were used to forecast travel demand, such as AR, ARIMA and so on. However, the prediction efficiency of these methods is poor and the training time is too long. In order to improve the performance, deep learning is used to assist prediction. But most of the deep learning methods only utilize temporal dependence or spatial dependence of data in the forecasting process. To address these limitations, a novel deep learning traffic demand forecasting framework which based on Deep Spatio-Temporal ConvLSTM is proposed in this paper. In order to evaluate the performance of the framework, an end-to-end deep learning system is designed and a real dataset is used. Furthermore, the proposed method can capture temporal dependence and spatial dependence simultaneously. The closeness, period and trend components of spatio-temporal data are used in three predicted branches. These branches have the same network structures, but do not share weights. Then a linear fusion method is used to get the final result. Finally, the experimental results on DIDI order dataset of Chengdu demonstrate that our method outperforms traditional models with accuracy and speed.