Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research. © 2020, The Author(s)

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualizations and Performance Measures Considering Multiple Origin-Destination Paths

Emerging connected vehicle (CV) data sets have recently become commercially available that enable analysts to develop a variety of powerful performance measures without a need to deploy field infrastructure. This paper presents a several tools using CV data to evaluate the quality of signal progression. These include both performance measures for high-level analysis as well as visualizations to examine details of coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor, and the tools can be applied to select O-D paths or to all O-D paths in the corridor. Results for real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day and direction, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time space diagram, and an empirical platoon progression diagram. Comparing visualizations of only end-to-end journeys on the corridor with all journeys on the corridor reveals several features that are only visible with the latter. The study demonstrates the utility of CV trajectory data for obtaining high-level details as well as drilling down into the details

Learning control policies of driverless vehicles from UAV video streams in complex urban environments

© 2019 by the authors. The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the e°ciency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible tra°c conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning

Utilizing Simulated Vehicle Trajectory Data from Connected Vehicles to Characterize Performance Measures on an Arterial After an Impactful Incident

Traffic incidents are unforeseen events known to affect traffic flow because they reduce the capacity of an arterial corridor segment and normally generate a temporary bottleneck. Identification of retiming requirements to enhance traffic signal operations when an incident occurs depends on operations-oriented traffic signal performance measurements. When effective and real-time traffic signal performance metrics are employed at traffic control centers, delays, fuel use, and air pollution may all be decreased. The majority of currently available traffic signal performance evaluations are based on high-resolution traffic signal controller event data, which gives data on an intersection-by-intersection basis but requires a substantial upfront expenditure. The necessary detecting and communication equipment also involves costly and periodic maintenance. Additionally, the full manifestation of connected vehicles (CVs) is fast approaching with efforts in place to accelerate the adaptation of CVs and their infrastructures. CV technologies have enormous potential to improve traffic mobility and safety. CVs can provide abundant traffic data that is not otherwise captured by roadway detectors or other methods of traffic data collection. Since the observation is independent of any space restrictions and not impacted by queue discharge and buildup, CV data offers more comprehensive and reliable data that can be used to estimate various traffic signal performance measures. This thesis proposes a conceptual CV simulation framework intended to ascertain the effectiveness of CV trajectory-based measures in characterizing an arterial corridor incident, such as a vehicle crash. Using a four-intersection corridor with different signal timing plans, a microscopic simulation model was created in Simulation of Urban Mobility (SUMO), Vehicles in Network Simulation (Veins) and Objective Modular Network Testbed in C++ (OMNeT++) platforms. Furthermore, an algorithm for CVs that defines, detects and disseminates a vehicle crash incident to other vehicles and a roadside unit (RSU) was developed. In the thesis, it is demonstrated how visual performance metrics with CV data may be used to identify an incident. This thesis proposes that traffic signal performance metrics, such as progression quality, split failure, platoon ratios, and safety surrogate measures (SSMs), may be generated using CV trajectory data. The results show that the recommended approaches with access to CV trajectory data would help both performance assessment and operation of traffic control systems. Unlike the current state of the practice (fixed detection technology), the developed conceptual framework can detect incidents that are not captured by intersection-vicinity-limited detectors while requiring immediate attention