Short-term prediction of traffic flow using a binary neural network

Hodge, Victoria J.; Krishnan, Rajesh; Austin, Jim; Polak, John; Jackson, Thomas W.

doi:10.1007/s00521-014-1646-5

Cited by 48 publications

(38 citation statements)

References 23 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 ANN, some studies proposed and applied ANN to forecast short-term traffic information, and the experimental results showed that the accuracy of using ANN was better than using other approaches [35][36][37]. Although a previous study classified road segments into several road types for reducing ANN models [35], the diverse traffic characteristics of road segments may exist in the same type of road.…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

“…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]. Furthermore, the regression approaches are the special cases of ANN without hidden layers, and the accuracy of the traffic information forecasting method based on ANN may be higher than regression approaches [34][35][36][37].…”

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

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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%

Section: Traffic Information Forecasting Methodsmentioning

confidence: 99%

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

2016

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“…Zheng and Su 22 developed a k-NN-LSPC (k-NN-linearly sewing principle component) for prediction of traffic volume, which outperformed eight other algorithms. Hodge et al 23 addressed the problem of short-term prediction of traffic flow through a scalable neural network-based k-NN predictor.…”

Section: Literature Reviewmentioning

confidence: 99%

Soft Computing-Based Traffic Density Estimation Using Automated Traffic Sensor Data under Indian Conditions

Raj

Bahuleyan²,

Ramesh³

et al. 2017

Current Science

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Traffic density is an indicator of congestion and the present study explores the use of data-driven techniques for real time estimation and prediction of traffic density. Data-driven techniques require large database, which can be achieved only with the help of automated sensors. However, the available automated sensors developed for western traffic may not work for heterogeneous and lane-less traffic. Hence, the performance of available automated sensors was evaluated first to identify the best inputs to be used for the chosen application. Using the selected data, implementation was carried out and the results obtained were promising, indicating the possibility of using the proposed methodology for real time traveller information under such traffic conditions. Keywords: Automated traffic sensors, artificial neural network, k-nearest neighbour, traffic density.WITH the fast growing urban population and increasing vehicle population, it is becoming difficult to implement efficient traffic management for Indian roads. Intelligent transportation systems (ITS) is viewed as an option to handle some of these issues and is becoming more popular under this scenario. ITS enable gathering of data and providing timely feedback to traffic managers and roadusers based on the real time data. Advanced traveller information system (ATIS) is a major functional area of ITS and it deals with providing real time traffic information to travellers for making informed travel decisions. The information provided can include expected travel time, congestion condition, locations of incidents, weather and road conditions, optimal routes, recommended speeds and lane restrictions.Traffic congestion information is most sought after, followed by travel time and travel speed information. However, congestion being qualitative in nature, there is a need to identify the best measure to quantify it. As per the Highway Capacity Manual (HCM) 1 , traffic density on freeways, delays at signalized intersections and walking speed for pedestrians are examples of measures of effectiveness that characterize traffic conditions on a facility.Of these, traffic density is the primary measure for quantifying congestion on roadways, other than the intersection areas. Traffic density is the number of vehicles occupying a given length of roadway. Density being a spatial variable makes it difficult to carry out direct measurements. Aerial photography is the primary approach to measure density directly from field, which is very difficult to implement. Hence, it is usually estimated from other location-based parameters such as speed, flow or occupancy, making it a challenging research problem. This problem of estimation of density from location based parameters such as volume and time mean speed (TMS) obtained using selected sensors is considered in this study. This becomes more challenging under Indian conditions, with heterogeneous and lane less-traffic, leading to high variability and randomness. In addition, these traffic conditions make automated data collection ...

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“…Thus verifies the favorable estimated performance of neural network. Reference [7] utilized binary neural network to predict traffic flow and reached the effect of "training once, repeatedly use". But the BPNN remains to be improved on account of mainly three weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Research of Prediction Scheme Based on Adaptive Particle Swarm Wavelet Neural Network Model

Ye¹,

Zhang²,

Li³

et al. 2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Abstract-Traffic flow prediction have the characteristics of complexity, non-linearity and randomness. Researchers are resorting to hybrid neural networks fusing more effective algorithms into forecast process. This paper advances a fresh algorithm with both the thought of variation and adaptive to conquer the poor global optimization capability and slow convergent rate of traditional back propagation neural network (BPNN). Mathematically, this algorithm constructs an adjustable formula to generate acceleration factors based on a logistic curve equation. Variable random initialization strategy has been introduced simultaneously. Simulations reveal that in contrast to auto-regressive integrated moving average model (ARIMA), back propagation neural network and wavelet neural network (WNN), the mean relative error reduced from about 10% to nearly 5%, the running time dropped from 5 seconds above to 2.5 seconds below, and the quality of the traffic flow prediction has a great improvement, which verifies the superiority of the brand new method proposed in this paper.

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Short-term prediction of traffic flow using a binary neural network

Cited by 48 publications

References 23 publications

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

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

Soft Computing-Based Traffic Density Estimation Using Automated Traffic Sensor Data under Indian Conditions

Research of Prediction Scheme Based on Adaptive Particle Swarm Wavelet Neural Network Model

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