Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.

TRAFFIC SIGNAL RECOGNITION ON AN IMAGE USING CONVOLUTIONAL NEURAL NETWORK

The purpose of this work is to develop an intelligent system for recognizing traffic signals. To achieve this, DetectNet was applied, using an interface for learning, which was developed by NVIDIA. With their help, the disadvantages of this approach were identified, and therefore it was necessary to consider another option for solving this problem.

Developing Signal Warrants for Restricted Crossing U-Turn Intersections

The restricted crossing U-turn (RCUT) intersection is a form of innovative intersection design that reroutes left-turn and through traffic from the minor road to U-turn crossovers on the major road. When implemented correctly, an RCUT intersection can provide significant safety and operational benefits over the conventional intersection configuration. The RCUT may be controlled by traffic signals, STOP control, merges and diverges, or a combination of these. There is currently no concrete guidance in relation to when the use of traffic signal control is warranted at an RCUT intersection. This study investigated traffic volume conditions that may warrant consideration of traffic signal control at an RCUT intersection. Simulation experiments including two geometric configurations and three traffic control schemes were designed and run in VISSIM to evaluate the effects of traffic conditions on intersection delay and queue lengths. Traffic was varied by changing the composition, approach volumes, and origin–destination flow patterns to reflect different conditions that may occur at the intersection on any given day. For the range of conditions studied, the results of the simulation analysis suggested that the RCUT intersection may operate better with traffic signals (at all junctions) when the minor roadway traffic volume is more than 450 vehicles per hour (vph) and the major roadway has two through lanes. The corresponding minor roadway volume threshold increases to 575 vph when the major roadway has four through lanes.

Smart Traffic Light System

Traffic signal control frameworks are generally used to monitor and control the progression of cars through the intersection of roads. Moreover, a portable controller device is designed to solve the issue of emergency vehicles stuck in overcrowded roads. The main objective of this paper is to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The framework created can detect the presence or nonappearance of vehicles within a specific reach by setting appropriate duration for traffic signals to react accordingly. By employing mathematical functions and algorithms to ascertain the suitable timing for the green signal to illuminate, the framework can assist with tackling the issue of traffic congestion. The explanation relies on recent fixed programming time. Keywords: Smart Traffic Light System, Smart City, Traffic Monitoring.

Understanding E-Scooter Incidents Patterns in Street Network Perspective: A Case Study of Travis County, Texas

Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in U.S. urban environments remains unexplored due to the lack of traffic and crash data related to E-scooters. Our study objective is to better understand E-scooter crashes from a street network perspective. New parcel level street network data are obtained from Zillow and curated in Geographic Information System (GIS). We conducted local Moran’s I and independent Z-test to compare where and how the street network that involves E-scooter crash differs spatially with traffic incidents. The analysis results show that there is a spatial correlation between E-scooter crashes and traffic incidents. Nevertheless, E-scooter crashes do not fully replicate characteristics of traffic incidents. Compared to traffic incidents, E-scooter incidents tend to occur adjacent to traffic signals and on primary roads.