Impact of Shared Lanes on Performance of the Combined Flexible Lane Assignment and Reservation-based Intersection Control

Benefiting from opportunities offered by connected and autonomous vehicles (CAVs), a concept called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC) was proposed recently for management of directionally unrestricted traffic flows in urban environments. In CADLARIC, resolution of vehicular conflicts is distributed between links and intersections to prevent intersections from turning into traffic bottlenecks. Although CADLARIC has shown promising results, it has been observed that, once traffic volume on a certain lane reaches “physical capacity,” adding more traffic on that lane degrades performance of the entire system, as each lane is exclusively dedicated to a particular movement. To overcome this problem, Combined Flexible Lane Assignment and Reservation-based Intersection Control (CFLARIC) is proposed, which offers more flexible lane assignment possibilities. While CFLARIC allows left- and right-turning lanes to be shared with through traffic, it is unclear how much through traffic should be assigned to turning lanes. Thus, this study investigates which strategy is the most beneficial when reassigning extra through traffic to the turning lanes. This goal is divided into two objectives: 1. Identify which lanes should be shared, and 2. Find a close-to-optimal amount of through traffic that should be assigned to the identified shared lane. The proposed CFLARIC strategies are compared with Fixed-Time Control (FTC), Full Reservation-based Intersection Control (FRIC), and CADLARIC for multiple demand scenarios. The results show that the best performing CFLARIC strategies outperform FTC, FRIC, and CADLARIC for delay and number of stops, and reduce the number of conflicting situations compared with FRIC and CADLARIC.

Safety Assessment of Urban Intersection Sight Distance Using Mobile LiDAR Data

This paper proposes an automated framework that utilizes Light Detection and Ranging (LiDAR) point cloud data to map and detect road obstacles that impact drivers’ field of view at urban intersections. The framework facilitates the simulation of a driver’s field of vision to estimate the blockage percentage as they approach an intersection. Furthermore, a collision analysis is conducted to examine the relationship between poor visibility and safety. The visibility assessment was used to determine the blockage percentage as a function of intersection control type. The safety assessment indicated that intersections with limited available sight distances (ASD) exhibited an increased risk of collisions. The research also conducted a sensitivity analysis to understand the impact of the voxel size on the extraction of intersection obstacles from LiDAR datasets. The findings from this research can be used to assess the intersection without the burden of manual intervention. This would effectively support transportation agencies in identifying hazardous intersections with poor visibility and adopt policies to enhance urban intersections’ operation and safety.

Rhythmic Control of Automated Traffic—Part I: Concept and Properties at Isolated Intersections

Leveraging the accuracy and consistency of vehicle motion control enabled by the connected and automated vehicle technology, we propose the rhythmic control (RC) scheme that allows vehicles to pass through an intersection in a conflict-free manner with a preset rhythm. The rhythm enables vehicles to proceed at a constant speed without any stop. The RC is capable of breaking the limitation that right-of-way can only be allocated to nonconflicting movements at a time. It significantly improves the performance of intersection control for automated traffic. Moreover, the RC with a predetermined rhythm does not require intensive computational efforts to dynamically control vehicles, which may possibly lead to frequent accelerations or decelerations. Assuming stationary vehicle arrivals, we conduct a theoretical investigation to show that RC can considerably increase intersection capacity and reduce vehicle delay. Finally, the performance of RC is tested in the simulations with both stationary and nonstationary vehicle arrivals at both symmetric and asymmetric intersections.

Research on Urban Traffic Active Control in Cooperative Vehicle Infrastructure

Urban intersection control mainly undertakes two tasks: traffic safety and traffic efficiency. Traditional intersection control models and methods have been insufficient in improving traffic efficiency, which is composed of the increase in traffic demand and the complexity of demand at present. In this paper, we propose a novel model and method called ATCM, which is based on the advanced technology of cooperative vehicle infrastructure. In this paper, a novel active traffic control model (ATCM) is proposed, which is based on the advanced technology of cooperative vehicle infrastructure. ATCM increases the intersection control model variables from the traditional two dimensions to five dimensions. It reshapes intersection control from the perspective of road designers and managers, so it can achieve more flexible and efficient traffic control. To this end, a multivariable active traffic control model is constructed, which includes road speed, lane control, sequence, phase, and green light time; a D-double layer optimization method is designed for this model. The first part of this optimization method combines speed control and dynamic phase sequence control. The second part is realized by the combination of lane control and dynamic phase sequence control. By conducting comprehensive experiments, the results demonstrate that the proposed approach is more flexible and efficient than traditional methods.