Dynamic data-driven local traffic state estimation and prediction

Antoniou, Constantinos; Koutsopoulos, Haris N.; Yannis, George

doi:10.1016/j.trc.2013.05.012

Cited by 136 publications

(71 citation statements)

References 49 publications

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“…For traffic information forecasting methods, several studies used data mining techniques (e.g., linear regression [26,27], logistic regression (LR) [28], Bayesian classifier [29,30], k-nearest neighbors (kNN) [31][32][33], artificial neural network (ANN) [34][35][36][37], etc.) to analyze the historical traffic information and obtain the forecasted traffic information.…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

“…For traffic information forecasting methods based on regression approaches, some studies used and applied linear regression and LR approaches to predict the future vehicle speed, and the experimental results in these studies showed that the average accuracy of vehicle speed forecasting was higher than 90% for highways [26,28]. However, these regression approaches are unsuitable to analyze the non-linear distribution of data, so the accuracy of the vehicle speed prediction-based regression approach was only 73.3% for an urban road network in [27].…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

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Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.

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Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

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“…The reliability of each link was calculated using (1). The data collected in May and June is used as training data set, and the data collected in July is used as testing data set.…”

Section: Experimental Programmentioning

confidence: 99%

“…Antoniou et al [1] present an approach for local traffic state estimation and traffic state prediction; the method exploits all available (traffic and other) information and 2 Discrete Dynamics in Nature and Society uses data-driven computational techniques. The approach is advantageous as it can flexibly incorporate additional explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Two-Phase Model of Multistep Forecasting of Traffic State Reliability

Yang

Liu

Jiang

et al. 2018

Discrete Dynamics in Nature and Society

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Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures.

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“…The speed and density relationship determines the traffic fundamental diagram [1][2][3], so it is extremely important to analyse the speed and density relationship and this has received much attention within the past few decades. Several models including single-regime and multiple-regime ones were proposed by researchers.…”

Section: Introductionmentioning

confidence: 99%