Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network

Abstract

Abstract-Many intelligent transportation systems (ITS)applications require accurate prediction of traffic parameters. Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. However, these studies focus on highways, or a few road segments. We propose a robust and scalable method using ν-SVR to tackle the problem of speed prediction of a large heterogenous road network. The traditional performance measures such as mean absolute percentage error (MAPE) and root mean square error (RMSE) provide little insight into spatial and temporal characteristics of prediction methods for a large network. This inadequacy can be a serious hurdle in effective implementation of prediction models for route guidance, congestion avoidance, dynamic traffic assignment and other ITS applications. We propose unsupervised learning techniques by employing k-means clustering, principal component analysis (PCA), and self organizing maps (SOM) to overcome this insufficiency. We establish the effectiveness of the developed methods by evaluation of spatial and temporal characteristics of prediction performance of the proposed variable window ν-SVR method.

Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

“…Based on some work that has tried to construct a traffic model and predict congested situations, a further step has been taken to provide routing strategy and avoid traffic congestion. Asif et al 18 introduced a variable window-support vector regression (SVR) method for predicting vehicle speed over an artificial neural network (ANN). This method can be utilized for route guidance and congestion avoidance.…”

Review on techniques for traffic jam detection and congestion avoidance

“…The implementation of route determination provides the adaptive routes for traffic conditions, as well as scalable routing services for user's preferences [28]. Reference [29] shows the effectiveness of a variable window n-SVR method for prediction of vehicle speed over ANN. This method can be used for route guidance to avoid congestion.…”

Large-Scale Station-Level Crowd Flow Forecast with ST-Unet

“…Here are some examples of the former type: support vector regression (SVR) methods for traffic flow predictions [8][9][10], gradient boosting regression tree (GBRT) and multi-similarity-based inference models for bike-sharing demand forecasting [11,12], ensemble framework with time-varying Poisson models and the auto-regressive integrated moving average (ARIMA) model for taxi-passenger demand forecasting [13]. For multi-output models, some examples include: the probabilistic graphical models (PGM)-based hybrid framework for citywide traffic volume estimation [14], intrinsic Gaussian Markov random field (IGMRF) model, one of the PGM models with cluster-based adjustment for cluster-level crowd flow forecast [1], vector auto-regressive moving average (VARMA) with a spatio-temporal correlations matrix for real-time traffic predictions [15], ν-SVR (the modified multi-output SVR (M-SVR) method) for traffic speed predictions in large road networks [16], deep spatio-temporal residual networks (with convolutional neural network (CNNs) as kernels) for region-level crowd flow predictions [2], and multi-graph convolutional networks for station-level bike flow predictions [17].…”

“…Because they model the spatio-temporal dependence of targets carefully, the number of training parameters is often k times the product of the amount of features and the amount of target variables. To reduce complexity, target variables are grouped by cluster algorithms [16] or part of training parameters are set according to rules (like in Reference [15]), which sacrifices some forecast performance. With abundant designs of structures and mature training techniques to handle large-scale problems well, deep neural networks (DNNs) are currently subject to much research (References [2,17,18], as mentioned above).…”