Dynamic Speed Limit for Self-Identifying Platoons of Mixed Vehicular Traffic on Freeways under Connected Environment 2024-01-1996

About 200,000 miles (~8 times around the earth) comprise the National Highway System, which carries most of the highway freight and traffic in the U.S. The core of the nation’s highway system is the 48,254 miles of Interstate Highways, which comprise just over 1 percent of highway mileage but carry over 25% of all highway traffic. Americans traveled a total of 5.3 trillion miles by all transportation modes in 2016, an average of 16,400 miles per person. About 80 percent was by automobile, truck, or motorcycle. Due to a high contribution to mobility and energy consumption, freeways and highway have been attracting researchers to move more vehicles faster and in energy-efficient manner. The research interest in motorways and highways has been driven by their significant impact on transportation efficiency and energy consumption, as they facilitate the movement of vehicles at higher speeds while optimizing energy usage. This entails the development of enhanced control techniques capable of effectively detecting and responding to dynamic traffic conditions, environmental variables, and attributes of the road network in real-time. In conventional control systems, the sensors are often stationary and possess restricted detection capabilities, with the controls being activated by the vehicle itself. The integration of connected vehicles (CVs) has resulted in the enhancement of fixed sensor technologies through the incorporation of vehicle-based sensing capabilities. Furthermore, pedestrians are now being connected through their smart devices and non-motorized vehicles, such as scooters and bikes, to have a more comprehensive understanding of the current traffic conditions. There has been a subsequent movement in the field of research and development, wherein the focus has transitioned from just relying on infrastructure-based sensing to incorporating vehicle augmented sensing. In a similar vein, control methods are becoming increasingly sophisticated and adept at using information obtained by CV.

This study presents a novel control system that utilizes linked vehicles to enhance safety and increase traffic flow efficiency on a motorway facility. The conventional infrastructure-based controls for vehicles rely on vehicle detection and assume that the arrival of vehicles follows a Poisson distribution. However, these controls are unable to accurately recognize platoons of vehicles. In the context of conventional control techniques, it is common for vehicle platoons originating from the upstream to encounter interruptions in their movement because of conflicting demands made by vehicles on different approaches. Hence, because of its fundamental constraint, namely the lack of prompt responsiveness to incoming traffic, the performance of the system can be significantly subpar. The advent of connected vehicle technology has led to the availability of copious volumes of high-resolution data. The utilization of trajectory data derived by connected/autonomous vehicles presents a more dependable means of obtaining real-time traffic information, hence serving as a crucial data resource for an increasing range of applications, such as signal control techniques. Enhanced information leads to increased potential for deeper understanding of operations, hence resulting in enhanced control and management capabilities. This work introduces the application of the Density-based spatial clustering of applications with noise (DBSCAN) clustering technique for the purpose of identifying platoons of cars on a transportation facility. Dynamic speed limits (DSL) are employed to communicate the ideal speed to the recognized platoons. In contrast to conventional DSL, the transmission of speed information to linked and automated vehicles is facilitated using V2X connection. In this paper, variable message signs do not communicate speed limits and, as a result, speed limitations are not restricted to specific locations. Moreover, it should be noted that each platoon possesses a distinct ideal speed, which is determined by the specific flow characteristics that are currently prevailing. A series of experiments were conducted using a microscopic tool to evaluate the impact of a strategy on ten distinct traffic mixtures. The implementation of automation resulted in a concurrent increase in the rate of flow. With an increase in automation, an increase in flow was also observed