Distributed Integrated Control of a Mixed Traffic Network With Urban and Freeway Networks

Wu, Na; Li, Dewei; Xi, Yugeng

doi:10.1109/tcst.2021.3055071

Cited by 12 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its predictions can be utilized by the MPC of following CAVs for improved control accuracy, riding comfort, and energy efficiency. Wu et al developed a distributed control framework for the cooperative control of mixed urban and freeway traffic networks [28]. A MPC strategy was applied to optimize the traffic signals in the urban network and the variable speed limits in the freeway network.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

View full text Add to dashboard Cite

Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.

show abstract

Section: Related Workmentioning

confidence: 99%

“…where Eqs. (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) are iterated until the traffic density is set for the road segment connecting l and m, and approval statuses are marked for the EVs in the prerouting and rerouting reservation queues.…”

Section: Real-time Congestion Control Of the Urban Traffic Control Centermentioning

confidence: 99%

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

View full text Add to dashboard Cite

show abstract

“…There is limited research on macro-coordinated control of arterial networks and expressways. The methods in [26]- [27] focus on modeling and controlling urban intersections and road segments, which, due to computational constraints, are more suitable for localized mixed road networks. Additionally, the coordinated control scheme combining perimeter control within subregions and ramp metering has been proposed in [1] and [28], but the positive role of traffic guidance in solving traffic congestion has not been considered.…”

Section: Introductionmentioning

confidence: 99%

Integrated Control for Mixed CAV and CV Traffic Flow in Expressway Merge Zones Combined with Variable Speed Limit, Ramp Metering, and Lane Changing

Zhang

Ding

et al. 2023

J. Transp. Eng., Part A: Systems

View full text Add to dashboard Cite

Abstract-Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it is crucial to clear the interaction between expressways and arterial networks and achieve orderly coordination between them. This study employs the multi-class cell transmission model (CTM) combined with the macroscopic fundamental diagram (MFD) to model the traffic dynamics of expressway systems and arterial subregions, enabling vehicle path tracking across these two systems. Consequently, a comprehensive traffic transmission model suitable for mixed road networks has been integrated. Utilizing the SUMO software, a simulation platform for the mixed road network is established, and the average trip lengths within the model have been calibrated. Based on the proposed traffic model, this study constructs a route guidance model for mixed road networks and develops an integrated model predictive control (MPC) strategy that merges route guidance, perimeter control, and ramp metering to address the challenges of mixed road networks' traffic flow control. A case study of a scenario in which a bidirectional expressway connects two subregions is conducted, and the results validate the effectiveness of the proposed cooperative guidance and control (CGC) method in reducing overall congestion in mixed road networks.

show abstract

Multidimensional-Taylor-network-based robust optimal tracking control for MIMO nonlinear discrete-time systems

Sun

Yan

et al. 2023

ISA Transactions

View full text Add to dashboard Cite

Distributed Integrated Control of a Mixed Traffic Network With Urban and Freeway Networks

Cited by 12 publications

References 29 publications

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Integrated Control for Mixed CAV and CV Traffic Flow in Expressway Merge Zones Combined with Variable Speed Limit, Ramp Metering, and Lane Changing

Multidimensional-Taylor-network-based robust optimal tracking control for MIMO nonlinear discrete-time systems

Contact Info

Product

Resources

About