Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Gu, Ziyuan; Waller, S. Travis; Saberi, Meead

doi:10.1111/mice.12444

Cited by 38 publications

(14 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any node is conditionally independent of other nodes if its parent is known. Bayesian network is a probabilistic model that describes both continuous and discrete distributions [16]. Since discrete variables are more practical in credit data and their probability distributions are simpler to express, this study will focus on the discrete case.…”

Section: Design and Construction Of A Networkmentioning

confidence: 99%

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

Zhou¹

2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource-sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.

show abstract

Section: Design and Construction Of A Networkmentioning

confidence: 99%

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

Zhou¹

2022

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…where the term CF r (CF m ) is the commonality factor of path r(m) which describes the degree of overlapping with other alternative paths [34]. C r (n) is the sum of the travel costs of all links in path r.…”

Section: Simulation-based Dynamic Assignmentmentioning

confidence: 99%

“…w min is set as 0 AUD/h, and w max is set as 15 AUD/ h. e smoothing parameter α is set as 1/3 × (1 − 0) � 0.3, while β is set as 1/3 × (15 − 0) � 5. One can refer to the study by Gu et al [34], wherein the analysis on the smoothing parameters is conducted. e threshold e is set as 2.45 veh/ km/lane which is nearly one-fourth of the value (9.8 veh/km/ lane) in the no-toll scenario.…”

Section: Experiments Setupmentioning

confidence: 99%

Differential Congestion Pricing Strategies for Heterogeneous Users in the Mixed Traffic Condition

Chen

Zhang

2022

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

Congestion pricing is one effective demand management strategy to alleviate traffic congestion. This work investigates pricing schemes for mixed traffic flow systems where the human-driven vehicles (HVs) and autonomous vehicles (AVs) coexist. The emerging and integration of autonomous vehicles can help improve the overall transportation efficiency and safety. Given the coexistence of HVs and AVs in the near future, there is need to adjust the existing traffic management strategies to adapt to the mixed traffic conditions. In this study, congestion pricing is imposed on the HVs and the AVs differently, that is, a distance-based toll to the HVs while a delay-based toll to the AVs. We consider six user groups based on the value of time (VOT) and the vehicle types. Compared with the unified distance-based toll, the advantage of delay-based toll is demonstrated first. Then, a surrogate-based optimization framework, namely the regressing Kriging (RK) model, is formulated. Three pricing schemes are investigated and compared: equity-oriented (EQ), environment friendliness-oriented (EN), and revenue-oriented (RE) schemes. Results show that the RE scheme collects the highest revenues; however, its cost-efficiency is weakened. The EQ scheme reduces the variance in the average travel costs among user groups, thus solving the equity issue.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Cited by 38 publications

References 65 publications

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

Differential Congestion Pricing Strategies for Heterogeneous Users in the Mixed Traffic Condition

A Gaussian sampling heuristic estimation model for developing synthetic trip sets

Contact Info

Product

Resources

About