Cyber-physical models for distributed CAV data intelligence in support of self-organized adaptive traffic signal coordination control
Introduction
Traffic signal coordination control is designed to lead to grouping vehicles as a platoon fleet that can pass through a series of consecutive intersections along a corridor with minimal stops. Handling several consecutive intersections along a corridor has been proved a more efficient and tactic control scheme than conventional isolated signal control schemes in last decades. Since the implementation of infrastructure-based coordinated signal control highly relies on reliability of fixed-point sensors, the performance of the coordinated signal control is often limited against our desired level of effectiveness due to the natures of fixed-point sensors. Some problems may be associated with the limited coverage of the detection areas, high cost of installation and maintenance, and low accuracy of the detected traffic. All of those limitations greatly degrade the performance of quickly capturing the spatiotemporal traffic patterns along the corridor. According to the Traffic Detector Handbook published by Federal Highway Administration (FHWA), loop failures may be resulted from individual loop malfunctions, pavement cracks, and electricity or electronic components issues (Klein, Mills, & Gibson, 2006). Poor sealants, inadequate sealant application, or inadequate splices may cause loop malfunctions. Pavement deteriorates (i.e., pavement cracking and moving) or problems caused by other external factors (i.e., constructions, crashes) are another reason for reliability issues to the loop performance. In addition, breakdown of wire insulation and electronic units, electrical surges, or improper electronics unit tuning often incur the loop failure. Therefore, the high risk of the loop malfunctions or failures is a barrier to reliable signal coordination for a period of time. Traditional data sources are not sufficient to enable the signal coordination to achieve the desired level of effective mobility or congestion mitigation.
With the introduction of emerging connected vehicle (CV) and/or autonomous vehicle (AV) technologies (or CAV technologies), especially, vehicle-to-everything (V2X)-dependent CAV technologies enable vehicles a “floating sensor” data source to seamlessly cover concerned highways over continuous time horizon for traffic monitoring while making the vehicular trajectories trackable (Lin et al., 2022b, Wei et al., 2018). In this circumstance, the CAV-generated data source has a great potential to overcome the weakness of the traditional fixed-point sensor data sources for coordinated signal coordination. The feature of the naturalized “floating sensing mobility” data source that will be potentially available ubiquitously provides the opportunity of capturing critical traffic patterns on a cycle-to-cycle basis. Moreover, cooperative driving with the support of CAV technologies will make it possible to execute self-organized or self-configured adaptive signal coordination control by using the CAV-generated data source.
Despite the optimistic anticipation, little in-depth research progress has been reported on this topic in literature. Most of related literatures concentrate on traditional fixed-time coordinated control and actuated or adaptive coordinated control plans wherein all signal timing parameters are predetermined and fixed, or cycle length is fixed while the green time is varying on a cycle-to-cycle basis in responding to detected traffic demand. While a few researchers have proposed self-organized adaptive signal coordination control strategies by using the CAV-generated data, there is still lack of underlying mechanism and associated intelligent CAV-generated data models that can be applied at scalable scales of the distributed adaptive signal control systems. Supportive data process and analytics technique is still yet mature in self-organized adaptive traffic signal coordination control. To address the challenges, the study presented in this paper aims to systemize an innovated mechanism and data process models to enable adaptive signal coordination with the aid of CAV-enabled cooperative driving functions. The data process models are developed to generate CAV-enabled parameters that can be used as core inputs to adaptive signal controllers for the self-organized adaptive traffic signal coordination control. An arterial corridor in Uptown Cincinnati, OH is targeted as a case study corridor for testing of the developed models.
The remainder of this paper is organized as follows. Section 2 presents a literature review. Section 3 provides methodological framework for distributed intelligent CAV-generated data fusion. Section 4 presents the proof-of-concept study with testing of the developed models. Lastly, Section 5 includes the discussions and conclusions.
Section snippets
Traffic signal coordination control
Effinger (2023) gives a good summary of signal control schemes. More valuably, he pointed out obvious different features of the existing signal controls, namely, pretimed traffic signal, actuated traffic signal, responsive traffic signal and adaptive traffic signal, as summarized in Table 1. This summary is beneficial to better understanding of the features of various types of signal control schemes under varied traffic and roadway conditions.
As early as the 1960s, Morgan and Little (1964)
Overall description of the proposed methodology
Fig. 1 shows the conceptual architecture and data flowchart to configure the CV/AV-generated data settings that can be used to describe traffic characteristics for modeling self-organized adaptive traffic signal coordination control. The C-V2X technologies enable wireless communication among CVs/AVs and CVs/AVs and roadside units data processors (RSU-DPs) with the support of IoT under standard protocols. Among intersections along a concerned corridor, each individual one is referred to as a V2X
Experiment scenario design
To test the developed models, a proof-of-concept study of a real corridor, Martin Luther King Drive East (simply named MLK) in Uptown Cincinnati, Ohio was conducted within the microsimulation VISSIM platform. As shown in Fig. 8, the tested MLK corridor consists of three intersections at Highland Avenue, Burnet Avenue, and Harvey Avenue. To facilitate the discussion, those intersections from left to right are simply named intersection 1, 2 and 3, respectively. According to the Average Annual
Conclusions
The research presented in the paper aims to develop cyber-physical models for making the CV/AV-generated data interoperable with the signal controllers to enhance the self-organized adaptive traffic signal coordination control. The accountable and quality of inputs involved in the phase timing determine the efficiency of the adaptive traffic signal coordination. The traditional fixed-point detection systems have been approved with some inherent limitations such as low coverage and costly
CRediT authorship contribution statement
Wei Lin: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Heng Wei: Conceptualization, Methodology, Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The data used for the paper was partially adopted from the Kleingers group. Any opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Agency; therefore, no official endorsement should be inferred.
References (77)
- et al.
Hierarchical traffic signal optimization using reinforcement learning and traffic prediction with long-short term memory
Expert Systems with Applications
(2021) - et al.
Self-organizing traffic signals using secondary extension and dynamic coordination. Transportation research
Part C, Emerging technologies
(2014) - et al.
Real-time Traffic Signal Control for Isolated Intersection, using Car-following Logic under Connected Vehicle Environment
Transportation Research Procedia
(2017) - et al.
Optimization Method of Intersection Signal Coordinated Control Based on Vehicle Actuated Model
Mathematical problems in engineering
(2015) - et al.
Grey models for short-term queue length predictions for adaptive traffic signal control
Expert Systems with Applications
(2021) - et al.
A real-time adaptive signal control in a connected vehicle environment. Transportation research
Part C, Emerging technologies
(2015) - et al.
Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest
Accident Analysis & Prevention
(2023) - et al.
Coordinated distributed adaptive perimeter control for large-scale urban road networks. Transportation research
Part C, Emerging technologies
(2017) - et al.
Distributed coordinated signal timing optimization in connected transportation networks. Transportation research
Part C, Emerging technologies
(2017) - et al.
Graph neural network for traffic forecasting: A survey
Expert systems with applications
(2022)
Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection
Sensors
Computer vision-guided intelligent traffic signaling for isolated intersections
Expert Systems with Applications
Intelligent traffic control for autonomous vehicle systems based on machine learning
Expert Systems with Applications
Adaptive coordinated traffic control for stochastic demand. Transportation research
Part C, Emerging technologies
An equitable traffic signal control scheme at isolated signalized intersections using Connected Vehicle technology. Transportation research
Part C, Emerging technologies
A driving-style-oriented adaptive control strategy based PSO-fuzzy expert algorithm for a plug-in hybrid electric vehicle
Expert Systems with Applications
Multi-stage stochastic program to optimize signal timings under coordinated adaptive control. Transportation research
Part C, Emerging technologies
A consensus-based distributed trajectory control in a signal-free intersection
Transportation Research Part C Emerging Technologies
Reinforcement learning in urban network traffic signal control: A systematic literature review
Expert Systems with Applications
A modified reinforcement learning algorithm for solving coordinated signalized networks. Transportation research
Part C, Emerging technologies
A mathematical model for the fixed-time traffic control problem
European journal of operational research
Meta-learning based spatial-temporal graph attention network for traffic signal control
Knowledge-based systems
DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles. Transportation research
Part C, Emerging technologies
Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles
Transportation Research Part C Emerging Technologies
An inductive heterogeneous graph attention-based multi-agent deep graph infomax algorithm for adaptive traffic signal control
Information fusion
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control
Neural networks
Signal adaptive cooperative control of two adjacent traffic intersections using a two-stage algorithm
Expert Systems with Applications
Real-time offset transitioning algorithm for coordinating traffic signals
Transportation research record
Signal Coordination and Arterial Capacity in Oversaturated Conditions
Transportation research record
Headways Groupings
Transportation research record
Self-Organizing Control Logic for Oversaturated Arterials
Transportation research record
Investigation of Self-Organizing Traffic Signal Control with Graphical Signal Performance Measures
Transportation Research Record
Long Green Times and Cycles at Congested Traffic Signals
Transportation research record
Real-Time Self-Adaptive Q-learning controller for Energy Management of Conventional Autonomous Vehicles
Expert Systems with Applications
Optimization of Traffic Signal Settings by Mixed-Integer Linear Programming - 1
The Network Coordination Problem. Transportation science
Optimum Control of a System of Oversaturated Intersections
Operations research
Cited by (3)
Mitigating traffic oscillation through control of connected automated vehicles: A cellular automata simulation
2024, Expert Systems with ApplicationsCAV-enabled data analytics for enhancing adaptive signal control safety environment
2023, Accident Analysis and PreventionDedicated Bus Arterial Coordination Control Based on Particle Swarm Optimization
2023, 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023