Multiclass dynamic system optimum solution for mixed traffic of human-driven and automated vehicles considering physical queues

Ngoduy, Dong; Hoang, Nam H.; Vu, Hai L.; Watling, David

doi:10.1016/j.trb.2020.12.008

Cited by 42 publications

(6 citation statements)

References 47 publications

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“…As a result, a multi-class dynamic system optimal (DSO) model with the presence of EVs and WCL is utilised to compute an approximate representation of the dynamic traffic flows. Due to the linear structure of DSO, it assists the development of tractable optimisation formulation and solution approaches and better facilitates investigating the network's traffic dynamics and flow propagation (Ngoduy et al, 2021;Chakraborty et al, 2021). As such, the previous bi-level approach must be reformulated as a single-level program in the case of dynamic traffic assignment.…”

Section: Model Travellers' Route Choice Behavioursmentioning

confidence: 99%

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Dynamic wireless charging lanes location model in urban networks considering route choices

Tran

Keyvan‐Ekbatani

Ngoduy

et al. 2022

Transportation Research Part C: Emerging Technologies

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Section: Model Travellers' Route Choice Behavioursmentioning

confidence: 99%

“…In this paper, we adopt one of the state-of-the art approaches to formulate the DSO as an optimisation problem using the Two-regime Transmission Model (TTM) as the underlying traffic flow model (Ngoduy et al, 2016(Ngoduy et al, , 2021.…”

Section: Multi-class Dso Model With Evs and Wclmentioning

confidence: 99%

Dynamic wireless charging lanes location model in urban networks considering route choices

Tran

Keyvan‐Ekbatani

Ngoduy

et al. 2022

Transportation Research Part C: Emerging Technologies

Self Cite

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“…A MPC strategy was applied to optimize the traffic signals in the urban network and the variable speed limits in the freeway network. Ngoduy et al proposed a dynamic system optimum formulation for the multi-class dynamic traffic assignment (DTA) problem of fixed traffic flow [29]. The proposed method used the concept of link based approach to develop a multi-class DTA model that equally distributes the total physical queues over the links while considering explicitly the variations in capacity and backward wave speeds due to class proportions.…”

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

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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.

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“…However, most of these works are based on an analytical model (e.g., (J. Liu et al, 2020;Ngoduy et al, 2021;Shen et al, 2006;Shen & Zhang, 2009;Tajtehranifard et al, 2018;Wie et al, 1990)) and very few studies utilize a simulation-based model which either are only microscopic or only mesoscopic (Ameli et al, 2020a;Hu et al, 2018;Mansourianfar et al, 2021;Sbayti et al, 2007;Yang & Jayakrishnan, 2012). Nevertheless, previous studies have demonstrated the superior performance of the simulation-based dynamic traffic assignment (SB-DTA) model (Ameli et al, 2020a), but many open-source (e.g., simulation of urban mobility (SUMO)) and commercial (e.g., Aimsun) traffic simulation software products do not provide SB-DSO traffic assignment algorithms.…”

Section: Introductionmentioning

confidence: 99%

Proposing a Simulation-Based Dynamic System Optimal Traffic Assignment Algorithm for SUMO: An Approximation of Marginal Travel Time

et al. 2022

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User equilibrium (UE) and system optimal (SO) are among the essential principles for solving the traffic assignment problem. Many studies have been performed on solving the UE and SO traffic assignment problem; however, the majority of them are either static (which can lead to inaccurate predictions due to long aggregation intervals) or analytical (which is computationally expensive for large-scale networks). Besides, most of the well-known micro/meso traffic simulators, do not provide a SO solution of the traffic assignment problem. To this end, this study proposes a new simulation-based dynamic system optimal (SB-DSO) traffic assignment algorithm for the SUMO simulator, which can be applied on large-scale networks. A new swapping/convergence algorithm, which is based on the logit route choice model, is presented in this study. This swapping algorithm is compared with the Method of Successive Average (MSA) which is very common in the literature. Also, a surrogate model of marginal travel time was implemented in the proposed algorithm, which was tested on real and abstract road networks (both on micro and meso scales). The results indicate that the proposed swapping algorithm has better performance than the classical swapping algorithms (e.g. MSA). Furthermore, a comparison was made between the proposed SB-DSO and the current simulation-based dynamic user equilibrium (SB-DUE) traffic assignment algorithm in SUMO. This proposed algorithm helps researchers to better understand the impacts of vehicles that may follow SO routines in future (e.g., Connected and Autonomous Vehicles (CAVs)).

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Multiclass dynamic system optimum solution for mixed traffic of human-driven and automated vehicles considering physical queues

Cited by 42 publications

References 47 publications

Dynamic wireless charging lanes location model in urban networks considering route choices

Dynamic wireless charging lanes location model in urban networks considering route choices

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

Proposing a Simulation-Based Dynamic System Optimal Traffic Assignment Algorithm for SUMO: An Approximation of Marginal Travel Time

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