Temporal Aggregation Effects in Spatiotemporal Traffic Modelling

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.

Estimação das características de tráfego em tempo próximo ao real para uso em sistemas de previsão e controle de tráfego.

“…Por fim, um dos grandes desafios de modelos espaço temporal autorregressivos (STAR) relaciona-se à dinâmica do fenômeno estudado, como no caso da ST-STTF, onde as condições de fluxo de tráfego se alteram constantemente (PAVLYUK, 2020). Nesse sentido podem ser consideradas matrizes dinâmicas, que de um lado incorporam as constantes alterações do fluxo, mas que de outro lado tornam o modelo mais complexo.…”

“…Com relação ao Horizonte de previsão, como era esperado, à partir da literatura revisada (ERMAGUN; LEVINSON, 2018;LAÑA et al, 2018;PAVLYUK, 2020), verificou-se que os modelos com horizontes maiores apresentaram menor acurácia. Nos dias típicos a acurácia das previsões permitiu erros medidos pelo MAPE a partir de 7% na via expressa e 10% na via arterial para o horizonte de 1 passo (4 min).…”