Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity

Cheng, Shifen; Lü, Feng; Peng, Peng; Wu, Sheng

doi:10.1016/j.compenvurbsys.2018.05.009

Cited by 120 publications

(46 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with parametric methods, non‐parametric methods does not need to make any strict restrictions on the data, but relies on the existing data to determine the relationship between output and input. Examples of non‐parametric methods include support vector regression (SVR) [9, 10], Kalman flter [11], artificial neural network (ANN) [12–19] and k‐nearest neighbour [20, 21]. Note that ANNs have excellent memory and self‐learning ability, they are widely used for traffic flow prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Short‐term traffic flow prediction of road network based on deep learning

Han

Huang

2020

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Short‐term traffic flow prediction of road network based on deep learning

Han

Huang

2020

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…Anbaroglu et al [ 26 ] proposed a Non-Recurrent Congestion (NRC) events detection methodology to support the accurate detection of NRC events on large urban road networks. Cheng et al [ 27 , 28 ] used a data-driven approach to predict changes in traffic flow to alleviate traffic congestion. However, the abovementioned methods mainly focus on high-density crowd detection in outdoor environments.…”

Section: Related Workmentioning

confidence: 99%

Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data

Wang

Gao

Zhao

et al. 2020

Sensors

View full text Add to dashboard Cite

Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification–Reconstruction–Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance.

show abstract

“…Such algorithms have advantages that they are free of any assumptions regarding the underlying model formulations and the uncertainty involved in estimating the model parameters. These algorithms include K nearest neighbor (KNN) [12,13], Support Vector Machine (SVM) [14], Random Forest (RF) regression [15], and Artificial Neural Network (AI/NN) [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Short‐Term Traffic Volume Forecasting with Asymmetric Loss Based on Enhanced KNN Method

Wang

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Short-term traffic volume forecasting is one of the most essential elements in Intelligent Transportation System (ITS) by providing prediction of traffic condition for traffic management and control applications. Among previous substantial forecasting approaches, K nearest neighbor (KNN) is a nonparametric and data-driven method popular for conciseness, interpretability, and real-time performance. However, in previous related researches, the limitations of Euclidean distance and forecasting with asymmetric loss have rarely been focused on. This research aims to fill up these gaps. This paper reconstructs Euclidean distance to overcome its limitation and proposes a KNN forecasting algorithm with asymmetric loss. Correspondingly, an asymmetric loss index, Imbalanced Mean Squared Error (IMSE), has also been proposed to test the effectiveness of newly designed algorithm. Moreover, the effect of Loess technique and suitable parameter value of dynamic KNN method have also been tested. In contrast to the traditional KNN algorithm, the proposed algorithm reduces the IMSE index by more than 10%, which shows its effectiveness when the cost of forecasting residual direction is notably different. This research expands the applicability of KNN method in short-term traffic volume forecasting and provides an available approach to forecast with asymmetric loss.

show abstract

Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity

Cited by 120 publications

References 27 publications

Short‐term traffic flow prediction of road network based on deep learning

Short‐term traffic flow prediction of road network based on deep learning

Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data

Short‐Term Traffic Volume Forecasting with Asymmetric Loss Based on Enhanced KNN Method

Contact Info

Product

Resources

About