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39
Support vector regression
 Neural Information Processing Letters and Reviews
, 2007
"... Abstract − Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space ..."
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Abstract − Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.
Traveltime prediction using Gaussian process regression: A trajectorybased approach
 in Proc. of the 9th SIAM international conference on Data Mining (SDM), 2009
"... This paper is concerned with the task of traveltime prediction for an arbitrary origindestination pair on a map. Unlike most of the existing studies, which focus only on a particular link (road segment) with heavy traffic, our method allows us to probabilistically predict the travel time along an ..."
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This paper is concerned with the task of traveltime prediction for an arbitrary origindestination pair on a map. Unlike most of the existing studies, which focus only on a particular link (road segment) with heavy traffic, our method allows us to probabilistically predict the travel time along an unknown path (a sequence of links) if the similarity between paths is defined as a kernel function. Our first innovation is to use a string kernel to represent the similarity between paths. Our second new idea is to apply Gaussian process regression for probabilistic traveltime prediction. We tested our approach with realistic traffic data. 1
Travel time estimation for ambulances using Bayesian data augmentation. Annals of Applied Statistics, to appear
, 2013
"... Estimates of ambulance travel times on road networks are critical for effective ambulance base placement and realtime ambulance dispatching. We introduce new methods for estimating the distribution of travel times on each road segment in a city, using Global Positioning System (GPS) data recorded ..."
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Estimates of ambulance travel times on road networks are critical for effective ambulance base placement and realtime ambulance dispatching. We introduce new methods for estimating the distribution of travel times on each road segment in a city, using Global Positioning System (GPS) data recorded during ambulance trips. Our preferred method uses a Bayesian model of the ambulance trips and GPS data. Due to sparseness and error in the GPS data, the exact ambulance paths and travel times on each road segment are unknown. To estimate the travel time distributions using the GPS data, we must also estimate each ambulance path. This is called the mapmatching problem. We consider the unknown paths and travel times to be missing data, and simultaneously estimate them and the parameters of each road segment travel time distribution using Bayesian data augmentation. We also introduce two alternative estimation methods using GPS speed data that are simple to implement in practice. We test the predictive accuracy of the three methods on a subregion of Toronto, using simulated data and data from Toronto EMS. All three methods perform well. Point estimates of ambulance trip durations from the Bayesian method outperform estimates from the alternative methods by roughly 5 % in root mean squared error. Interval estimates from the Bayesian method for the Toronto EMS data are substantially better than interval estimates from the alternative methods. Mapmatching estimates from the Bayesian method are robust to large GPS location errors, and interpolate well between widely spaced GPS points.
Unsupervised Learning Based Performance Analysis of νSupport Vector Regression for Speed Prediction of A Large Road Network
"... 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 ..."
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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 νSVR 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 kmeans 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 νSVR method. I.
Comparison of Parametric and Nonparametric Techniques for Nonpeak Traffic Forecasting
"... Abstract—Accurately predicting nonpeak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LSSVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in ..."
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Abstract—Accurately predicting nonpeak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LSSVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two nonparametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LSSVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. Keywords—Parametric and Nonparametric Techniques, Nonpeak Traffic Forecasting
Spatio and temporal patterns in largescale traffic speed prediction
 IEEE Transactions on Intelligent Transportation Systems
"... Abstract—The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, datadriven methods, such as support vector regression (SVR), can predict traffic with high accuracy becaus ..."
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Abstract—The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, datadriven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as kmeans clustering, principal component analysis, and selforganizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVRbased algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR. Index Terms—Largescale network prediction, spatiotemporal error trends. I.
Predicting Link Travel Times from Floating Car
, 2013
"... We study the problem of predicting travel times for links (road segments) using floating car data. We present four different methods for predicting travel times and discuss the differences in predicting on congested and uncongested roads. We show that current travel time estimates are mainly useful ..."
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We study the problem of predicting travel times for links (road segments) using floating car data. We present four different methods for predicting travel times and discuss the differences in predicting on congested and uncongested roads. We show that current travel time estimates are mainly useful for prediction on links that get congested. Then we examine the problem of predicting link travel times when no recent probe car data is available for estimating current travel times. This is a serious problem that arises when using probe car data for prediction. Our solution, which we call geospatial inference, uses floating car data from nearby links to predict travel times on the desired link. We show that geospatial inference leads to improved travel time estimates for congested links compared to standard methods.
A Decision Support System for Predicting Traffic Diversion Impacts across Transportation Networks using Support Vector Regression * Corresponding Author
, 2007
"... Networks using Support Vector Regression This paper describes followup research to a previous study by the authors which used CaseBased Reasoning (CBR) and Support Vector Regression (SVR) to evaluate the likely impacts of implementing diversion strategies in response to incidents on highway networ ..."
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Networks using Support Vector Regression This paper describes followup research to a previous study by the authors which used CaseBased Reasoning (CBR) and Support Vector Regression (SVR) to evaluate the likely impacts of implementing diversion strategies in response to incidents on highway networks. In the previous study, the training and testing of the CBR and SVR tools were performed on a single transportation network from South Carolina, which limited the applicability of the developed tool to the specific network for which it was developed. To address this limitation, the current study investigates the feasibility of developing a generic decision support system (DSS) capable of predicting traffic diversion impacts for new transportation networks that the tool has not previously seen. In such cases, users need only input the geometric and traffic variables, via a Graphical User Interface (GUI), and the tool, which uses a SVR model, will predict the benefits of diverting traffic for a specific incident on the new site. To illustrate the feasibility of developing such a tool, two different highway networks covering portions of I85 and I385 in South Carolina were used to train the SVR model, which was then tested on a third network covering portions of I89 in Vermont. The study found only a 15 percent difference between the predictions of the SVR model and those of a detailed simulation counterpart, demonstrating the feasibility of developing a generic DSS. Adding more sites and parameters to train the software is also expected to improve the prediction accuracy of the DSS.
Compressed prediction of largescale urban traffic
 in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
, 2014
"... Traffic prediction lies at the core of many intelligent transport systems (ITS). Commonly deployed prediction methods such as support vector regression and neural networks achieve good performance by explicitly predicting the traffic variables (e.g., traffic speed or volume) at each road segment in ..."
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Traffic prediction lies at the core of many intelligent transport systems (ITS). Commonly deployed prediction methods such as support vector regression and neural networks achieve good performance by explicitly predicting the traffic variables (e.g., traffic speed or volume) at each road segment in the network. For large traffic networks, predicting traffic variable at each road segment may be unwieldy, especially in the setting of realtime prediction. To tackle this problem, we propose an alternative approach in this paper. We first generate lowdimensional representation of the network, leveraging on the columnbased (CX) decomposition of matrices. The lowdimensional model represents the large network in terms of a small subset of road segments. The future state of the lowdimensional network is predicted by standard procedures, i.e., support vector regression. The future state of the entire network is then inferred by extrapolating the predictions of the subnetwork, using the CX decomposition. Numerical results for a largescale road network in Singapore demonstrate the efficiency and accuracy of the proposed algorithm. Index Terms — Prediction in large networks, lowdimensional models
A support vector machine with the tabu search algorithm for freeway incident detection
 Int. J. Appl. Math. Comput. Sci
"... Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To ..."
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Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.