Software-Defined Approach for Communication in Autonomous Transportation Systems

Autonomous driving technology offers a promising solution to reduce road accidents, traffic congestion, and fuel consumption. The management of vehicular networks is challenging as it demands mobility, location awareness, high reliability and low latency of data traffic. In this paper, we propose a novel communication architecture for vehicular network with 5G Mobile Networks and SDN technologies to support multiple core networks for autonomous vehicles and to tackle the potential challenges raised by the autonomous driving vehicles. Data requirements are evaluated for vehicular networks with respect to number of lanes and cluster size, to efficiently use the frequency and bandwidth. Also, the network latency requirements are analysed, which are mandatory constraints for all the applications where real time end-to-end communication is necessary. A test environment is also formulated to evaluate improvement in vehicular network using SDN-based approach over traditional core networks

Congestion-aware traffic routing for large-scale mobile agent systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 191-201).Traffic congestion is a serious world-wide problem. Drivers have little knowledge of historical and real-time traffic congestion for the paths they take and often tend to drive suboptimal routes. Congestion phenomena are sure to be influenced by the coming of autonomous cars. This thesis presents route planning algorithms and a system for either autonomous or human-driven cars in road networks dealing with travel time uncertainty and congestion. First, a stochastic route planning algorithm is presented that finds the best path for a group of multiple agents. Our algorithm provides mobile agents with optimized routes to achieve time-critical goals. Optimal selections of agent and visit locations are determined to guarantee the highest probability of task achievement while dealing with uncertainty of travel time. Furthermore, we present an efficient approximation algorithm for stochastic route planning based on pre-computed data for stochastic networks. Second, we develop a distributed congestion-aware multi-agent path planning algorithm that achieves the social optimum, minimizing aggregate travel time of all the agents in the system. As the number of agents grows, congestion created by agents' path choices should be considered. Using a data-driven congestion model that describes the travel time as a function of the number of agents on a road segment, we develop a practical method for determining the optimal paths for all the agents in the system to achieve the social optimum. Our algorithm uses localized information and computes the paths in a distributed manner. We implement the algorithm in multi-core computers and demonstrate that the algorithm has a good scalability. Third, a path planning system using traffic sensor data is then implemented. We predict the traffic speed and flow for each location from a large set of sensor data collected from roving taxis and inductive loop detectors. Our system uses a data-driven traffic model that captures important traffic patterns and conditions using the two sources of data. We evaluate the system using a rich set of GPS traces from 16,000 taxis in Singapore and show that the city-scale congestion can be mitigated by planning drivers' routes, while incorporating the congestion effects generated by their route choices.by Sejoon Lim.Ph.D

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

How Effective are Toll Roads in Improving Operational Performance?

The main focus of this research is to develop a systematic analytical framework and evaluate the effect of a toll road on region’s traffic using travel time and travel time reliability measures. The travel time data for the Triangle Expressway in Raleigh, North Carolina, United States was employed for the assessment process. The spatial and temporal variations in the travel time distributions on the toll road, parallel alternate route, and near-vicinity cross-streets were analyzed using various travel time reliability measures. The results indicate that the Triangle Expressway showed a positive trend in reliability over the years of its operation. The parallel route reliability decreased significantly during the analysis period, whereas the travel time reliability of cross-streets showed a consistent trend. The stabilization of travel time distributions and the reliability measures over different years of toll road operation are good indicators, suggesting that further reduction in performance measures may not be seen on the near vicinity corridors. The findings from link-level and corridor-level analysis may help with transportation system management, assessing the influence of travel demand patterns, and evaluating the effect of planned implementation of similar projects

Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%