Temporal Aggregation Effects in Spatiotemporal Traffic Modelling
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 acc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6931 |
| _version_ | 1797545759581667328 |
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| author | Dmitry Pavlyuk |
| author_facet | Dmitry Pavlyuk |
| author_sort | Dmitry Pavlyuk |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:19:30Z |
| format | Article |
| id | doaj.art-7b35261508b14f67a0c70ae99df4e6d2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:19:30Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7b35261508b14f67a0c70ae99df4e6d22023-11-20T23:28:12ZengMDPI AGSensors1424-82202020-12-012023693110.3390/s20236931Temporal Aggregation Effects in Spatiotemporal Traffic ModellingDmitry Pavlyuk0Transport and Telecommunication Institute, LV-1019 Riga, LatviaSpatiotemporal 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.https://www.mdpi.com/1424-8220/20/23/6931spatiotemporal modelstemporal aggregationforecasting accuracybig dataurban traffic modelling |
| spellingShingle | Dmitry Pavlyuk Temporal Aggregation Effects in Spatiotemporal Traffic Modelling Sensors spatiotemporal models temporal aggregation forecasting accuracy big data urban traffic modelling |
| title | Temporal Aggregation Effects in Spatiotemporal Traffic Modelling |
| title_full | Temporal Aggregation Effects in Spatiotemporal Traffic Modelling |
| title_fullStr | Temporal Aggregation Effects in Spatiotemporal Traffic Modelling |
| title_full_unstemmed | Temporal Aggregation Effects in Spatiotemporal Traffic Modelling |
| title_short | Temporal Aggregation Effects in Spatiotemporal Traffic Modelling |
| title_sort | temporal aggregation effects in spatiotemporal traffic modelling |
| topic | spatiotemporal models temporal aggregation forecasting accuracy big data urban traffic modelling |
| url | https://www.mdpi.com/1424-8220/20/23/6931 |
| work_keys_str_mv | AT dmitrypavlyuk temporalaggregationeffectsinspatiotemporaltrafficmodelling |