AN APPROACH OF TRAFFIC FLOW PREDICTION USING ARIMA MODEL WITH FUZZY WAVELET TRANSFORM

It is essential for intelligent transportation systems to be capable of producing an accurate forecast of traffic flow in both the short and long terms. However, the counting datasets of traffic volume are non-stationary time series, which are integrally noisy. As a result, the accuracy of traffic prediction carried out on such unrefined data is reduced by the arbitrary components. A prior study shows that Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) models convey demand of noise-free dataset for model construction. Therefore, this study proposes to overcome the noise issue by using a hybrid approach that combines the ARIMA model with fuzzy wavelet transform. In this approach, fuzzy rules are developed to categorize traffic datasets according to influencing factors such as the time of a day, the season of a year, and weather conditions. As the input of linear data series for ARIMA model needs to be converted into linear time series for traffic flow prediction, the discrete wavelet transform is applied to help separating the nonlinear and linear part of the time series along with denoised time series traffic data

A Hybrid Approach of Traffic Flow Prediction Using Wavelet Transform and Fuzzy Logic

The rapid development of urban areas and the increasing size of vehicle fleets are causing severe traffic congestions. According to traffic index data (Tom Tom Traffic Index 2016), most of the larger cities in Canada placed between 30th and 100th most traffic congested cities in the world. A recent research study by CAA (Canadian Automotive Association) concludes traffic congestions cost drivers 11.5 million hours and 22 million liters of fuel each year that causes billions of dollars in lost revenues. Although for four decades’ active research has been going on to improve transportation management, statistical data shows the demand for new methods to predict traffic flow with improved accuracy. This research presents a hybrid approach that applies a wavelet transform on a time-frequency (traffic count/hour) signal to determine sharp variation points of traffic flow. Datasets in between sharp variation points reveal segments of data with similar trends. These sets of data, construct fuzzy membership sets by categorizing the processed data together with other recorded information such as time, season, and weather. When real-time data is compared with the historical data using fuzzy IF-THEN rules, a matched dataset represents a reliable source of information for traffic prediction. In addition to the proposed new method, this research work also includes experiment results to demonstrate the improvement of accuracy for long-term traffic flow prediction

Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

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

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

From statistical- to machine learning-based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large-scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical- and Machine Learning (ML)-based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.publishedVersio

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method