Traffic Prediction Using Multivariate Nonparametric Regression

Clark, Stephen

doi:10.1061/(asce)0733-947x(2003)129:2(161)

Cited by 301 publications

(132 citation statements)

References 8 publications

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…In [5], Clark proposes a non-parametric regression model to predict traffic based on the observed traffic data. In [7] and [2], authors use microscopic models upon trajectories of individual vehicles to simulate overall traffic data and further conduct prediction.…”

Section: ) Simulation Modelsmentioning

confidence: 99%

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Pan

Demiryurek

Shahabi

2012

2012 IEEE 12th International Conference on Data Mining

170

View full text Add to dashboard Cite

Abstract-For the first time, real-time high-fidelity spatiotemporal data on transportation networks of major cities have become available. This gold mine of data can be utilized to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of the 21st century. As a first step towards the utilization of this data, in this paper, we study the real-world data collected from Los Angeles County transportation network in order to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. In particular, we utilized the spatiotemporal behaviors of rush hours and events to perform a more accurate prediction of both shortterm and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Our result shows that taking historical rush-hour behavior we can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, we can incorporate the impact of an accident to improve the prediction accuracy by up to 91%.

show abstract

Section: ) Simulation Modelsmentioning

confidence: 99%

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Pan

Demiryurek

Shahabi

2012

2012 IEEE 12th International Conference on Data Mining

170

View full text Add to dashboard Cite

show abstract

“…So after the analysis of passenger data, we will build passenger flow distribution model. [3] Passenger Flow Forecasting based on BP Neural Network Model. This model use passenger flow data in each monitoring period over the past four days to forecast the data of the next weekday.…”

Section: Data Preprocessing and Statistical Analysismentioning

confidence: 99%

Analysis and Comparison of Urban Rail Transit Passenger Flow Forecast based on Multiple Methods

Cui¹,

Li²

2017

Proceedings of the 2017 6th International Conference on Energy, Environment and Sustainable Development (ICEESD 2017)

View full text Add to dashboard Cite

Abstract. Passenger flow forecast is an important and functional part of urban rail transit operation and management, excellent and accurate passenger flow forecast can guide the operation management to achieve high efficiency and safety. This article mainly discusses the three forecasting models commonly used for passenger flow forecast. In addition, passenger flow data of Beijing urban rail transit were used to do statistical analysis and prediction. Finally, the prediction accuracy, advantages and disadvantages of the three models are compared.

show abstract

“…The most commonly used nonparametric method is the artificial neural network (ANN), which has been used widely in traffic forecasting (Van Lint et al, 2005), (Vlahogianni et al, 2005). Other commonly used methods include various forms of nonparametric regression (Smith et al, 2002); (Clark, 2003) and kernel methods (Chun-Hsin Wu et al, 2004); (Castro-Neto et al, 2009)). Each approach has its strengths and weaknesses, and (Karlaftis and Vlahogianni, 2011) provide a good overview, but the task is the same: to create a model that can effectively describe the spatio-temporal evolution of the process.…”

Section: Space-time Forecastingmentioning

confidence: 99%

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Haworth

Cheng

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London's road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

show abstract

Traffic Prediction Using Multivariate Nonparametric Regression

Cited by 301 publications

References 8 publications

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Analysis and Comparison of Urban Rail Transit Passenger Flow Forecast based on Multiple Methods

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Contact Info

Product

Resources

About