Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4–10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

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Short-term traffic flow prediction using seasonal ARIMA model with limited input data

Transportation systems might be heavily affected by factors such as accidents and weather. Specifically, inclement weather conditions may have a drastic impact on travel time and traffic flow. This study has two objectives: first, to investigate a correlation between weather parameters and traffic flow and, second, to improve traffic flow prediction by proposing a novel holistic architecture. It incorporates deep belief networks for traffic and weather prediction and decision-level data fusion scheme to enhance prediction accuracy using weather conditions. The experimental results, using traffic and weather data originated from the San Francisco Bay Area of California, corroborate the effectiveness of the proposed approach compared with the state of the art.

Improving Traffic Flow Prediction with Weather Information in Connected Cars: A Deep Learning Approach

Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.

Road Traffic Forecasting: Recent Advances and New Challenges

Traffic flow prediction using LSTM with feature enhancement

Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network

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 input variables.

Short term traffic flow prediction in heterogeneous condition using artificial neural network

Road traffic accident prediction models play a critical role to the improvement of traffic safety planning. The focus of this study is to extract key factors from the collected data sets which are responsible for majority of accidents. In this paper urban traffic accident analysis has been done using support vector machines (SVM) with Gaussian kernel. Multilayer perceptron (MLP) and SVM models were trained, tested, and compared using collected data. The results of the study reveal that proposed model has significantly higher predication accuracy as compared with traditional MLP approach. There is a good relationship between the simulated and the experimental values. Simulations were carried out using LIBSVM (library for support vector machines) integrated with octave.

Traffic Accident Prediction Model Using Support Vector Machines with Gaussian Kernel

This study applies artificial neural network (ANN) for the determination of optimized height of a highway noise barrier. Field measurements were carried out to collect traffic volume, vehicle speed, noise level, and site geometry data. Barrier height was varied from 2 to 5 m in increments of 0.1 m for each measured data set to generate theoretical data for network design. Barrier attenuation was calculated for each height increment using Federal Highway Administration model. For neural network design purpose, classified traffic volume, corresponding traffic speed, and barrier attenuation data have been taken as input parameters, while barrier height was considered as output. ANNs with different architectures were trained, cross validated, and tested using this theoretical data. Results indicate that ANN can be useful to determine the height of noise barrier accurately, which can effectively achieve the desired noise level reduction, for a given set of traffic volume, vehicular speed, highway geometry, and site conditions.

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Optimized height of noise barrier for non-urban highway using artificial neural network

Noise is one of the environmental pollutant that is encountered in daily life. Noise pollution has become a major concern of communities living in the vicinity of major highway corridors. In view of the rapid development it is essential to study highway noise with respect to various causative factors. In the present paper a road traffic noise prediction model for Indian conditions is developed using regression analysis which is based on Calixto model. Data collected has been analyzed and compared with the values predicted by Calixto model. After comparison of results it was observed that Calixto Model could be satisfactorily applied for Indian conditions as they give accepted results with a good value.

Kranti Kumar

Papers

Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network

Short term traffic flow prediction in heterogeneous condition using artificial neural network

Traffic Accident Prediction Model Using Support Vector Machines with Gaussian Kernel

Optimized height of noise barrier for non-urban highway using artificial neural network

Mathematical modeling of road traffic noise prediction