Multitime Resolution Hierarchical Attention-Based Recurrent Highway Networks for Taxi Demand Prediction

Baiping Chen,Wei Li

doi:10.1155/2020/4173094

Abstract

Taxi demand forecasting is an important consideration in building up smart cities. However, complex nonlinear spatiotemporal relationships in demand data make it difficult to construct an accurate prediction model. Considering that a single time resolution may not enable accurate learning of the time pattern of taxi demand, we expand the time series prediction model in our proposed multitime resolution hierarchical attention-based recurrent highway network (MTR-HRHN) model, using three time resolutions to model temporal closeness, period, and trend properties of demand data to capture a more comprehensive time pattern. We evaluate the MTR-HRHN on a taxi trip record dataset and the results show that the forecasting performance of the MTR-HRHN exceeds that of eight well-known methods in the short-term demand prediction in some high-demand regions.

Highlights

With the increasing travel demand of urban dwellers, taxis have become much more popular in urban areas, especially through the use of ride hailing services such as Didi Chuxing and Uber
Each X is linked to a sequence of historical exogenous data, and each demand forecast part (DP) is linked to a sequence of historical target data. e attention mechanism of the hierarchical attention-based recurrent highway network (HRHN) further learns the association between the target and exogenous data
It can be found that the predicted results of MTRHRHN are relatively accurate at most times

Summary

Introduction

With the increasing travel demand of urban dwellers, taxis have become much more popular in urban areas, especially through the use of ride hailing services such as Didi Chuxing and Uber. For the demand forecasting problem, the common method for taxi demand prediction is to consider the impact of historical demand data on future demand; that is, predict demand y􏽢T at time T, given a series of historical demands E time interval T is a short-term time, which is often a few hours or even shorter For data such as taxi demand with nonlinear, unstable, and spatiotemporal related properties, linear or nonlinear methods considering only historical demand are insufficient. In this regional forecasting problem, exogenous data are often selected from other regions

Methods

Results

Conclusion