Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Enhanced K-Nearest Neighbor Model For Multi-steps Traffic Flow Forecast in Urban Roads
Short-term flow forecast is a fundamental key in intelligent transportation planning. Often accurate predictions are provided by the predictive models the most adapted to the nature of the addressed problem. In this paper we present a k-Nearest Neighbor approach (E-KNN) enhanced by taking advantage of traffic attributes. The proposed model is applied to 11 weeks of non-processed data, recorded by 7 inductive loop detectors installed on urban roads located in downtown of Bremen (Germany). The performance of E-KNN is tested on 3 weeks of data and reported following different day-hours categories, including rush hours. Excluding early day-hours where traffic is insignificant, E-KNN performs 6-steps (1h) prediction with an average absolute relative error of 17% on test-set.
Enhanced K-Nearest Neighbor Model For Multi-steps Traffic Flow Forecast in Urban Roads
Short-term flow forecast is a fundamental key in intelligent transportation planning. Often accurate predictions are provided by the predictive models the most adapted to the nature of the addressed problem. In this paper we present a k-Nearest Neighbor approach (E-KNN) enhanced by taking advantage of traffic attributes. The proposed model is applied to 11 weeks of non-processed data, recorded by 7 inductive loop detectors installed on urban roads located in downtown of Bremen (Germany). The performance of E-KNN is tested on 3 weeks of data and reported following different day-hours categories, including rush hours. Excluding early day-hours where traffic is insignificant, E-KNN performs 6-steps (1h) prediction with an average absolute relative error of 17% on test-set.
Enhanced K-Nearest Neighbor Model For Multi-steps Traffic Flow Forecast in Urban Roads
Mallek, Amin (Autor:in) / Klosa, Daniel (Autor:in) / Buskens, Christof (Autor:in)
26.09.2022
1467514 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Impact of Data Loss on Multi-Step Forecast of Traffic Flow in Urban Roads Using K-Nearest Neighbors
| DOAJ | 2022
| British Library Conference Proceedings | 2005
Traffic Incident Duration Prediction Based on K-Nearest Neighbor
| British Library Conference Proceedings | 2013
Composite Nearest Neighbor Nonparametric Regression to Improve Traffic Prediction
| British Library Online Contents | 2007