@inproceedings{a7f82acccfc64fa79b9d066a8fdd5e0f,
title = "An Improved K-Nearest Neighbor Model for Road Speed Forecast Based on Spatiotemporal Correlation",
abstract = "Most k-nearest neighbor models only focus on single step short-Term traffic forecasting andcan't perform well when extended to multi-stepforecast. To enhance accuracy, this paper presents an improved k-nearest neighbor model considering thespatiotemporal correlation. The proposed model defines the current conditionsbythetwo-dimensionalspatiotemporalstate matrices, instead of the one-dimensional state vectorof thetime series. Moreover, this paperdeterminesthe weights by Gaussian functionto adjust the matching distance andmanage the data of the nearest neighbors. The original speed data used in this paper arenormalized in order to apply the proposed model to different types of road segments. The case showsthe improved model performs more desirablethan the original k-nearest neighbormodels and demonstrates more appropriate for multi-step forecast ofthe roadspeed.",
keywords = "K-nearest neighbor, Spatiotemporal correlation, Speed forecast, Weights",
author = "Pinlong Cai and Yunpeng Wang and Guangquan Lu and Peng Chen",
note = "Publisher Copyright: {\textcopyright} ASCE.; 15th COTA International Conference of Transportation Professionals: Efficient, Safe, and Green Multimodal Transportation, CICTP 2015 ; Conference date: 24-07-2015 Through 27-07-2015",
year = "2015",
doi = "10.1061/9780784479292.031",
language = "英语",
series = "CICTP 2015 - Efficient, Safe, and Green Multimodal Transportation - Proceedings of the 15th COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "342--351",
editor = "Xuedong Yan and Yu Zhang and Yafeng Yin",
booktitle = "CICTP 2015 - Efficient, Safe, and Green Multimodal Transportation - Proceedings of the 15th COTA International Conference of Transportation Professionals",
address = "美国",
}