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- 01HCVN1QP9KNMSHABS5CP7E07X classification C1.
- 01HCVN1QP9KNMSHABS5CP7E07X date "2023".
- 01HCVN1QP9KNMSHABS5CP7E07X language "eng".
- 01HCVN1QP9KNMSHABS5CP7E07X type conference.
- 01HCVN1QP9KNMSHABS5CP7E07X hasPart 01HCVNA4A1Z0A5YRZEC1C0TXDN.pdf.
- 01HCVN1QP9KNMSHABS5CP7E07X subject "Technology and Engineering".
- 01HCVN1QP9KNMSHABS5CP7E07X doi "10.1109/ica58538.2023.10273080".
- 01HCVN1QP9KNMSHABS5CP7E07X isbn "9798350301274".
- 01HCVN1QP9KNMSHABS5CP7E07X issn "2639-5045".
- 01HCVN1QP9KNMSHABS5CP7E07X presentedAt urn:uuid:3bd6348b-4a3a-420f-a517-b9bf036d4dc2.
- 01HCVN1QP9KNMSHABS5CP7E07X abstract "As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant.".
- 01HCVN1QP9KNMSHABS5CP7E07X author 1826d5a2-b3ce-11ed-91a8-e3ba1a2a0cc2.
- 01HCVN1QP9KNMSHABS5CP7E07X author A3271364-9B2A-11E7-B6A4-767EAD28A064.
- 01HCVN1QP9KNMSHABS5CP7E07X author D6E7F804-FEE3-11E2-98EE-0C9910BDE39D.
- 01HCVN1QP9KNMSHABS5CP7E07X author F4D0DA5C-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HCVN1QP9KNMSHABS5CP7E07X author urn:uuid:2d942256-54d9-4ff5-9bd4-83dae0d4611d.
- 01HCVN1QP9KNMSHABS5CP7E07X author urn:uuid:f53f2110-5eef-47ac-ad28-4151e923df9b.
- 01HCVN1QP9KNMSHABS5CP7E07X dateCreated "2023-10-16T07:08:48Z".
- 01HCVN1QP9KNMSHABS5CP7E07X dateModified "2024-10-29T18:26:27Z".
- 01HCVN1QP9KNMSHABS5CP7E07X name "Enhancing traffic flow forecasting with spatio-temporal information : a case study on highway networks in Belgium".
- 01HCVN1QP9KNMSHABS5CP7E07X pagination urn:uuid:3f9d3606-5d44-4102-9425-bffb004ba2ce.
- 01HCVN1QP9KNMSHABS5CP7E07X publisher urn:uuid:1c29aec5-1bbc-4fbe-8b7b-261d5fc1702a.
- 01HCVN1QP9KNMSHABS5CP7E07X sameAs LU-01HCVN1QP9KNMSHABS5CP7E07X.
- 01HCVN1QP9KNMSHABS5CP7E07X sourceOrganization urn:uuid:08ba319e-3238-43b0-9a37-3f323c9527f2.
- 01HCVN1QP9KNMSHABS5CP7E07X type C1.