Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles

Car‐following prevails in daily traffic under various scenarios. However, most connected automated vehicle (CAV) car‐following algorithms in the literature only apply to one‐dimensional CAV control, which limits the application scenarios. This paper presents a two‐dimensional (2D) CAVs car‐following strategy on curved roads in the spatial domain with vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication to expand the car‐following application scenarios. This control strategy constructs a 2D car‐following equilibrium policy by extending the simplified Newell's car‐following model. Based on that, a constrained optimal CAV car‐following control in the spatial domain is constructed to maintain the inter‐vehicle spacing and speed difference while regulating the lane deviation and avoiding work zones. As demonstrated by numerical results, our proposed algorithm maintains a high degree of consistency between equilibrium spacing and vehicle speed while duplicating the trajectory of the leading vehicle, implying empirical string stability. The results also suggest the robustness of handling different scenarios.

Section: Discussionmentioning

confidence: 99%

“…Li et al, 2022;Yue et al, 2022;Zhu et al, 2022). A more complex scenario such as CAV control for intersections (e.g., Ding et al, 2022;Z. Zhang et al, 2022) could also be studied.…”

Section: Scenario 3: Comparison With Temporal Lqrmentioning

confidence: 99%

A 2D‐connected automated vehicle car‐following control algorithm

Ran

Zhou

et al. 2023

“…CAVs not only provide high-resolution, multidimensional data for traffic control (Massaro et al, 2017;Z. Zhang et al, 2022) but also render it possible to jointly optimize traffic signals and vehicle trajectories (Q.…”

Section: )mentioning

confidence: 99%

“…A promising avenue in transportation system evolution is the application of connected and automated vehicles (CAVs), foreseen as harboring profound implications for traffic management, control, and other aspects (Du et al., 2023; Jiang & Adeli, 2004; Mahmassani, 2016; Shi et al., 2022). CAVs not only provide high‐resolution, multidimensional data for traffic control (Massaro et al., 2017; Z. Zhang et al., 2022) but also render it possible to jointly optimize traffic signals and vehicle trajectories (Q. Guo et al., 2019). Numerous control methods have emerged, with a common goal of enhancing signal operations through the consideration of approaching vehicle trajectories (Feng et al., 2015; W. Li & Ban, 2019; D. Li et al., 2023; Liang et al., 2020; W. Ma et al., 2020; Wang et al., 2021) or planning vehicle trajectories based on signal status (He et al., 2015; S. E. Li et al., 2015; Stebbins et al., 2017; X. Wu et al., 2015; Zhou et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions

Dai,

Ding,

et al. 2023

With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process.

“…Investigating the causal relationship between real‐time on‐ramp flow and freeway bottleneck conditions can facilitate the evaluation of the metering effects. The availability of massive traffic data can aid in precise traffic perception as demonstrated by prior studies (Yang et al., 2022; Z. Zhang et al., 2022). Data‐driven approaches have been effectively utilized to model the intricate interplay between bottleneck traffic conditions and diverse traffic and linguistic variables (Adeli & Ghosh‐Dastidar, 2004; Jiang & Adeli, 2004a, 2004b).…”

Section: Introductionmentioning

confidence: 97%

A coordinated ramp metering framework based on heterogeneous causal inference

Peng

2023

The coordinated ramp metering strategy aims to enhance the traffic flow on freeways by integrating the effects of multiple on-ramps. The success of this strategy depends heavily on the metering effects of each on-ramp and the cooperation pattern. To ensure the effectiveness of coordination control, it is essential to design the coordinated strategy based on the causal effect of each ramp flow on the freeway bottleneck. Therefore, this study investigated the heterogeneous causal effects of ramp flow on freeway bottleneck and proposed a coordinated ramp metering framework based on these causal effects. Causal inference was conducted using loop detector data. The estimated heterogeneous causal effect, which varies across traffic conditions, measures the change of bottleneck occupancy resulting from a unit increase in each on-ramp outflow. This information quantifies the importance of each ramp in real time and can be used to update the ramp weight and metering rate of each ramp controller. To comprehensively test the proposed coordination framework, two instances with different road networks were selected. In the first instance, limiting the on-ramp flow downstream of the bottleneck was found to be helpful in mitigating the bottleneck. Compared with isolated ramp metering, the proposed algorithm reduced travel delay by 26.0% by considering the heterogeneous causal effects of on-ramp flow both upstream and downstream of the freeway bottleneck. In the next instance, the proposed algorithm reduced travel delay by 5.8%, compared to a well-performed feedback-based coordinated ramp metering algorithm. The results of this study suggest that the use of heterogeneous causal inference is effective in improving the performance of coordinated ramp metering algorithms.