Temporal Aggregation Effects in Spatiotemporal Traffic Modelling.

Dmitry Pavlyuk

doi:10.3390/s20236931

Abstract

Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.

Highlights

Traffic flow forecasting is a classic problem of transportation engineering
The primary research question relates to the forecasting accuracy of spatiotemporal models for different temporal aggregation levels
This study is devoted to the empirical analysis of temporal aggregation effects of forecasting accuracy of spatiotemporal models

Summary

Introduction

Traffic flow forecasting is a classic problem of transportation engineering. The mainstream of academic research into traffic flows shifted from univariate modelling of spatially disconnected road segments to spatiotemporal analysis, which utilizes potential dependencies between different locations [2]. Spatiotemporal traffic analysis requires careful identification of the spatial structure—a set of dependencies between traffic characteristics at neighbour or remote road segments [3]. Modern spatiotemporal models provide various treatments for the incorporation of spatial information, the problem is still emerging for dynamic spatial environments. Since Okutani and Stephanedes [4] directed attention to spatial dependencies between traffic flows and practical utility of this information for traffic forecasting, many researchers utilized information about the spatial structure in their methodologies. Applied methodologies could be tentatively classified into two approaches, as is classical for spatial econometrics—specific-to-general and general-to-specific

Methods

Results

Conclusion