Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Ren, Yi; Hou, Zhongsheng; Sirmatel, Isik Ilber; Geroliminis, Nikolas

doi:10.1016/j.trc.2020.102618

Cited by 71 publications

(41 citation statements)

References 59 publications

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“…At the same time, the explosive growth of road traffic travel requirements has made traditional traffic control methods stretched. People began to think about Traffic Control theory and method based on data-driven [22,23]. Because when it is difficult to develop a model for a controlled system, we can use the system input and output data to implement control and decision-making; In recent years, breakthroughs in artificial intelligence theory and methods and the evolution of largescale cloud computing and edge computing technologies have promoted the development of new types of intelligent control centered on artificial intelligence methods.…”

Section: Traditional Intersection Traffic Controlmentioning

confidence: 99%

Urban Intersection Signal Control Based on Time-Space Resource Scheduling

et al. 2021

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This paper improves the level of urban traffic control by creasing the dimension of control variables. It focuses on roads rather than vehicles. A new space-time resource scheduling model and a bilevel optimization control method for urban intersections are developed in this study. In traditional concept, the properties of lane are fixed. Nowadays, it changes with the development of new technologies, which increase the dimension of the control variables in the control model and expand the control capability. To this end, the space-time resource scheduling model for intersections includes spatial variables (lane genes, phases, and phase sequences) and time variables (green light time of phases). Then, a new bi-level optimization control method is developed, in which there are an upper layer for lane control based on reinforcement learning and a lower layer is a two-layer optimal control method of phase control based on the model predictive control idea. Finally, the proposed method is proved more efficient than traditional methods after comprehensive experiments. INDEX TERMS V2I; time-space resource scheduling model; lane genes; dual-layer optimization; reinforcement learning; model predictive control

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Section: Traditional Intersection Traffic Controlmentioning

confidence: 99%

Urban Intersection Signal Control Based on Time-Space Resource Scheduling

et al. 2021

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“…Although early applications of gating date from the 1960s (Wood, 1993), perimeter traffic flow control gained prominence some 50 years later after the proposition by Daganzo (2007) of this type of control based on the aggregated modeling of an urban network by a Macroscopic or Network Fundamental Diagram (MFD or NFD) boosted by the quasi-simultaneous verification of the diagram's existence with field data by Geroliminis and Daganzo (2008). Following this finding, there was a surge in NFD-based perimeter urban traffic flow control strategies for single regions (Haddad, 2017a;Keyvan-Ekbatani et al, 2012, 2015a, some considering expanding regions (Keyvan-Ekbatani et al, 2015b), or the presence of public transport (Ampountolas et al, 2017;Geroliminis et al, 2014), or the presence of freeways (Haddad et al, 2013), or the combination with other real-time urban traffic control strategies (Keyvan-Ekbatani et al, 2019), strategies for multiple regions (Aboudolas & Geroliminis, 2013;Geroliminis et al, 2013;Kouvelas et al, 2017), city-wide traffic control and the impacts of cordon queues (Ni & Cassidy, 2020), congestion pricing in a connected vehicle environment (Yang et al, 2019); and also model free perimeter control (Li & Hou, 2020;Ren et al, 2020). There are other streams of NFD applications which have been to a lesser extent in the spotlight, e.g., travel time reliability (Mahmassani et al, 2013), level of service and resilience of the network (Hoogendoorn et al, 2015), pedestrian dynamics (Hoogendoorn et al, 2017), traffic safety (Alsalhi et al, 2018), the effects of the public transport system on the NFD of corridors (Castrillon & Laval, 2018), and NFD for train traffic operations (Corman et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimizing distribution of metered traffic flow in perimeter control: Queue and delay balancing approaches

Keyvan‐Ekbatani

Carlson

Knoop

et al. 2021

Control Engineering Practice

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Perimeter traffic flow control based on the macroscopic or network fundamental diagram provides the opportunity of operating an urban traffic network at its capacity. Because perimeter control operates on the basis of restricting inflow via reduced green times at selected entry (gated) links, vehicles on those links may be subject to queuing and delay. The experienced delay or resulting queue lengths depend on the adopted policy for the distribution of the inflows and corresponding green times at the gated links. The chosen policy may have a significant impact on the traffic system under control. For example, managing queue lengths may reduce the interference with upstream traffic whereas the management of delays may improve users' perception with respect to equity and fairness. In this paper, an approach has been proposed to distribute the gated flow based on the queue lengths or experienced delay at the gated signalized junctions. This is in contrast to standard practice that distributes inflows proportionally to the gated links' saturation flows. Perimeter control is then evaluated in a microscopic simulator for a realistic traffic network and compared in three configurations against fixed-time: perimeter control without queue or delay management; perimeter control with relative queue balancing; and perimeter control with delay balancing. It has been found that managing the queues at the gated links not only improves the overall network performance but also reduces the possibility of queue propagation to the upstream junctions. This improves traffic flow outside the protected network by managing the queue propagation at the gated links and reducing the possibility of queue spillback to upstream intersections. In addition, the results indicate that perimeter control with delay balancing has a similar performance as the case without queue or delay management being a suitable approach for flow distribution among the gated links. In the scenarios with perimeter control with either queue or delay balancing the gap between the ordered flow by the controller and the actual flow crossing the stop-line at the gated links reduced remarkably.

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“…In each region, vehicles circulate at approximately the same average speed, and the traffic states are described by an MFD, that reflects the relationship between the number of vehicles (or accumulation) within the region and the average circulating flow (Daganzo, 2007;Geroliminis & Daganzo, 2008;Vickrey, 2020). The aggregated traffic models based on the MFD (Jin, 2020;Mariotte et al, 2020) have been used in a wide range of applications, including perimeter control strategies (Haddad & Zheng, 2018;He et al, 2019;Ren et al, 2020;Sirmatel & Geroliminis, 2019), route guidance Yildirimoglu & (c) example of paths on a regional network Geroliminis, 2014), pricing schemes (Gu et al, 2019;Yang et al, 2019), urban parking (Cao et al, 2019), and environmental control schemes (Ingole et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Gaussian sampling heuristic estimation model for developing synthetic trip sets

Batista

Cantelmo

Menéndez

et al. 2021

Computer aided Civil Eng

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In this paper, we develop a heuristic model based on Gaussian processes to determine synthetic sets of trips in urban networks, considering only supplyrelated information. This is an alternative to the benchmark method used in the literature, which consists of repeating several trials of Monte Carlo simulations and therefore requiring a complex calibration task and large computational resources. The developed heuristic model explicitly leverages the probabilistic nature of Gaussian processes and exploits their properties to iteratively select origin-destination (od) pairs of nodes in the city network. The model then determines the shortest trip in distance for the selected od pairs and appends it to the synthetic set. We discuss the implementation and performance of both the benchmark method and the developed heuristic model on two city networks.We show that the presented model is more robust and computationally efficient than the benchmark method. This is evidenced by its ability to determine synthetic sets with much smaller sizes, naturally reducing the computational burden, when compared to the benchmark method. We also discuss how the choice of the kernel function and calibration of the hyperparameters influence the performance of the presented heuristic model.

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Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Cited by 71 publications

References 59 publications

Urban Intersection Signal Control Based on Time-Space Resource Scheduling

Urban Intersection Signal Control Based on Time-Space Resource Scheduling

Optimizing distribution of metered traffic flow in perimeter control: Queue and delay balancing approaches

A Gaussian sampling heuristic estimation model for developing synthetic trip sets

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