Short-term traffic volume prediction using neural networks (original) (raw)
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A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL
Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper.
Short-Term Traffic Volume Prediction Techniques: Review
2020
The monitoring and controlling of road traffic is becoming a major problem in many countries. With the ever increasing number of vehicles on the road, the Traffic Monitoring Authority has to find new methods of overcoming such a problem. Now a day’s many intelligent transport systems use modern technologies to predict traffic flow, to minimize accidents on road, to predict speed of a vehicle and etc. There have been various Neural Network based approaches proposed for short-term traffic state prediction that are surveyed in this paper. IndexTerms Intelligent Transport Systems; Neural Network; Deep Learning.
Short Term Traffic Flow Prediction in Heterogeneous Condition Using Artificial Neural Network
TRANSPORT, 2013
Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as in...
SHORT TERM TRAFFIC FLOW FORECASTING USING ARTIFICIAL NEURAL NETWORKS
Traffic flow forecasting is a critical issue in detection of the traffic congestions. Better forecasts provide better routes, less travel time and less traffic bottlenecks. In this study, an existing traffic dataset is used for forecasting by Artificial Neural Networks (ANN), which is a commonly used method in this research area. At first, statistical analysis is conducted to reveal the structure of the data such as seasonality, trend, etc. Then for the organized data, backpropagation artificial neural network model is set up for forecasting the traffic flow. Finally, the forecast values are compared with the real data and different studies which use the same datasets.
Optimization of Dynamic Neural Network Performance for Short-Term Traffic Prediction
Transportation Research Record, 2003
This paper presents an approach to optimize the short-term traffic prediction performance using multiple topologies of dynamic neural networks and various network-related and traffic-related settings. The study emphasizes the potential benefit of optimizing the prediction performance by deploying multi-model approaches under parameters and traffic condition settings. The emphasis of the paper is on the application of temporal-processing topologies in short-term speed predictions in the range of 5 to 20 minute-horizons. Three network topologies are utilized: Jordan/Elman, partially recurrent networks, and time-lagged feedforward networks. The input patterns were constructed from data collected at the target location, as well as the upstream and downstream locations. However, various combinations were also considered. To encourage the networks to associate with historical information on recurrent conditions, a time factor was attached to the input patterns to introduce time recognition capabilities, in addition to information encoded in the recent past data. The optimal prediction settings (type of topology and input settings) were determined such that the performance is maximized under different traffic conditions at the target and adjacent locations. The optimized performance of the dynamic neural networks was compared to that of a statistical non-linear time series approach, which was outperformed in most cases. The study also shows that no single topology has consistently outperformed the others for all prediction horizons considered. However, the results show that the significance of introducing the time factor was more pronounced under longer prediction horizons. A comparative evaluation of performance between optimal and non-optimal settings shows substantial improvement in most of the cases. The procedure applied can also be used to identify the prediction reliability of information dissemination systems.
SHORT-TERM TRAFFIC PREDICTION USING A BINARY NEURAL NETWORK
2011
This paper presents a binary neural network algorithm for short-term traffic flow prediction. The algorithm can process both univariate and multivariate data from a single traffic sensor using time series prediction (temporal lags) and can combine information from multiple traffic sensors with time series prediction ( spatial-temporal lags). The algorithm provides Intelligent Decision Support (IDS) for road network managers to proactively manage problems on the network as the predictions generated may be used to determine if traffic control interventions need to be applied. The algorithm can operate in near-real-time and dynamically; using data from UTC or UTMC systems. It is based on the Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour prediction algorithm, which is designed for scalability and fast performance. The AURA k-NN predictor outperforms other machine learning techniques with respect to prediction accuracy and is able to train and predict rapidly. The basic AURA k-NN time series prediction algorithm was extended by incorporating average daily profiles and variable weighting into the prediction in this paper. The average daily profile of a variable is calculated as the average reading of the variable for a particular time of day and day of the week after removing outliers. When data vectors are matched in the AURA k-NN, the daily profile adds an extra dimension to the match. This process was further enhanced by weighting the profile using variable weighting to vary the profile's significance. It is shown that incorporating these two additional aspects improves the accuracy of the prediction compared to the standard AURA k-NN, resulting in a very fast and accurate traffic prediction tool
Short-term traffic forecasting: Where we are and where we’re going
Transportation Research Part C: Emerging Technologies, 2014
Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work.
Transportation Research Record: Journal of the Transportation Research Board
The prediction of high-resolution hourly traffic volumes of a given roadway is essential for transportation planning. Traditionally, automatic traffic recorders (ATR) are used to collect these hourly volume data. These large datasets are time-series data characterized by long-term temporal dependencies and missing values. Regarding the temporal dependencies, all roadways are characterized by seasonal variations that can be weekly, monthly or yearly, depending on the cause of the variation. Traditional time-series forecasting models perform poorly when they encounter missing data in the dataset. To address this limitation, robust, recurrent neural network (RNN)-based, multi-step-ahead forecasting models are developed for time-series in this study. The simple RNN, the gated recurrent unit (GRU) and the long short-term memory (LSTM) units are used to develop the forecasting models and evaluate their performance. Two approaches are used to address the missing value issue: masking and im...
IEEE Transactions on Intelligent Transportation Systems, 2012
This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.
Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems, 2017
One of the most challenging goals of the modern Intelligent Transportation Systems comprises the accurate and real-time short-term traffic prediction. The achievement of this goal becomes even more critical when the presence of atypical traffic conditions is concerned. In this paper, we propose a novel hybrid technique for short-term traffic prediction under both typical and atypical conditions. An Automatic Incident Detection (AID) algorithm, based on Support Vector Machines (SVM), is utilized to check for the presence of an atypical event (e.g. traffic accident). If such an event occurs, the k-Nearest Neighbors (k-NN) non-parametric regression model is used for traffic prediction. Otherwise, the Autoregressive Integrated Moving Average (ARIMA) parametric model is activated for the same purpose. In order to evaluate the performance of the proposed model, we use open real world traffic data from Caltrans Performance Measurement System (PeMS). We compare the proposed model with the unitary k-NN and ARIMA models, which represent the most commonly used non-parametric and parametric traffic prediction models. Preliminary results show that the proposed model achieves larger accuracy under both typical and atypical traffic conditions.