A New Vehicle Arrival Prediction Model for Adaptive Signal Control in a Connected Vehicle Environment

Abstract

With the development of connected vehicles (CVs) technology, it has become a new research topic to capture the dynamic traffic system by using CVs data. The traditional vehicle arrival prediction models are limited to the fixed detectors, which can only recognize the passing information of vehicles, but cannot identify the state of vehicles. This paper proposed a new vehicle arrival prediction model of traffic signal control in a connected vehicle environment. First, the vehicle's identification number (ID), position, velocity, and acceleration were obtained in a CV environment. Second, a new vehicle arrival prediction model was developed by the joint probability distribution calibrated based on CV data. Then, the simulation experiment was designed for analyzing influence factors on model performance. The results show that the average relative error of each factor is less than 10%. Finally, the reliability of the proposed model was verified based on an adaptive signal control algorithm. Therefore, the proposed model can be used in the adaptive signal control system.

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

“…Later, their model was extended in 2020 [46, 47] by including an individual vehicle speed‐based dispersion model, where the proposed model is based on vehicle speed data and used to produce optimal timing plans. Recently in 2021, they presented the latest model extension [48] by adopting a joint probability distribution using both vehicle speed and location in the CV environment.…”

Reducing Congestion in an Intelligent Traffic System With Collaborative and Adaptive Signaling on the Edge

“…We used the PPO algorithm that takes the ratio of a new policy with the learned FIGURE 5: Advantage Function policy to ensure the steps that are close to the old policy. The formulation is given in (7), prepared in accordance to [47].…”

“…However, the development of road infrastructure is not sufficient alone, in our opinion. Instead, recent developments in remote sensing and Information and Communication technologies [5,6,7] can be leveraged to address the traffic congestion problem.…”

“…Because SPaT can be modeled as a Markov Decision Process (MDP), dynamic programming is applied to traffic control systems in many recent studies [6,7,15,16,17]. These dynamic programming-based schemes require a mathematical traffic model which is based on the vehicles' parameters acquired from In-Vehicle sensors.…”

Intention of Risk-Taking Behavior at Unsignalized Intersections Under the Connected Vehicle Environment

“…Furthermore, some researchers have proven that CV can help drivers to avoid being involved in crashes with the provision of real-time information by enhancing their awareness and attentiveness of risky situations [7,8]. Therefore, improving traffic safety and operation has been the most important goal of CV technologies [9,10].…”