DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

Road traffic open data in Sweden: Availability and commercial exploitation - A research study on the state of open transportation data in Sweden

This chapter includes a description of how the study was conducted. In order to explore the possibilities for private companies to use open data, an extensive literature review was conducted. Furthermore, this helped to get familiarized with the subject of open data and understand how it is utilized today by public companies. While researching different methods for data analytics that are being used in transportation, it was found that predictive analytics was one of the most prominent methods as it can be used in numerous ways in order to improve predictions and planning within organizations. The use of predictive analytics in transportation includes predicting delays and traffic conditions which were found to be appropriate areas of analytics with regards to the types of open data that are commonly available. Hence, these will be the areas of transport analytics that will be focused on in this study. In order to analyze the full potential of open transport data, both as a means of improving existing businesses as well as to allow for new business opportunities to originate, the methodology had to be considered accordingly. To scope out opportunities for improvement of business activities, research projects were reviewed where a number of types of open transport-related data were used to predict future outcomes of traffic conditions and events in public transportation that could have potential impacts on how daily activities within transportation organizations are performed. The projects were chosen based on the potential accessibility that the data used for the analysis has in Swedish open data sources, in order to make sure that corresponding solutions to the problems are feasible to perform in Sweden. Furthermore, in order to analyze the potential for new businesses to arise from available open data, several existing companies that have gained their success through the use of such data were studied to gain an insight into how value can be extracted from it. To analyze the accessibility of relevant open data in Sweden, Trafiklab, and Trafikverket, two open data sources for transportation-related data have been used. These were chosen in a screening method of the biggest open data sources that offer a large amount of data publicly in Sweden.Incomin

Road traffic open data in Sweden: Availability and commercial exploitation - A research study on the state of open transportation data in Sweden

This chapter includes a description of how the study was conducted. In order to explore the possibilities for private companies to use open data, an extensive literature review was conducted. Furthermore, this helped to get familiarized with the subject of open data and understand how it is utilized today by public companies. While researching different methods for data analytics that are being used in transportation, it was found that predictive analytics was one of the most prominent methods as it can be used in numerous ways in order to improve predictions and planning within organizations. The use of predictive analytics in transportation includes predicting delays and traffic conditions which were found to be appropriate areas of analytics with regards to the types of open data that are commonly available. Hence, these will be the areas of transport analytics that will be focused on in this study. In order to analyze the full potential of open transport data, both as a means of improving existing businesses as well as to allow for new business opportunities to originate, the methodology had to be considered accordingly. To scope out opportunities for improvement of business activities, research projects were reviewed where a number of types of open transport-related data were used to predict future outcomes of traffic conditions and events in public transportation that could have potential impacts on how daily activities within transportation organizations are performed. The projects were chosen based on the potential accessibility that the data used for the analysis has in Swedish open data sources, in order to make sure that corresponding solutions to the problems are feasible to perform in Sweden. Furthermore, in order to analyze the potential for new businesses to arise from available open data, several existing companies that have gained their success through the use of such data were studied to gain an insight into how value can be extracted from it. To analyze the accessibility of relevant open data in Sweden, Trafiklab, and Trafikverket, two open data sources for transportation-related data have been used. These were chosen in a screening method of the biggest open data sources that offer a large amount of data publicly in Sweden.Incomin

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars