A Strategic User Equilibrium for Independently Distributed Origin‐Destination Demands

Wen, Tao; Gardner, Lauren; Dixit, Vinayak; Waller, S. Travis; Chen, Fang

doi:10.1111/mice.12292

Cited by 10 publications

(10 citation statements)

References 77 publications

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“…The common modeling device by which the network flow pattern can be estimated is the traffic assignment under UE conditions. The notion of UE was initially proposed by Wardrop (), followed by intensive research in the field of traffic assignment modeling (Ziliaskopoulos et al., ; Meng and Wang, ; Bekhor et al., ; Unnikrishnan and Waller, ; Wen et al., ; Lu et al., ). Recently the UE traffic assignment considering EVs’ recharge needs—which incorporates vehicles’ distance constraints—has attracted increasing attention.…”

Section: Literature Reviewmentioning

confidence: 99%

Multitype Recharge Facility Location for Electric Vehicles

Zhang

Rey

Waller

2018

Computer aided Civil Eng

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The motivation of this study is to minimize the system-level travel time costs and greenhouse emissions, which include tailpipe emissions by internal combustion engine vehicles (ICEVs) and smokestack emissions indirectly caused by electric vehicles (EVs), while satisfying EVs' replenishment need in transport networks subject to financial restraints for infrastructure development. In this study, we address recharge facility locations of EVs, where two types of recharge services are taken into account, that is, traditional charging stations and modern charging lanes. The multitype recharge facility location problem is formulated by employing the bilevel framework of the network design problem. In the lower-level program, the mixed-vehicular traffic assignment problem with en-route multitype recharge is employed, which accounts for both ICEVs and EVs with various driving ranges. The upper-level program aims to minimize the total system travel costs by selecting the optimal solution from a set of infrastructure design options considering both expansions of road capacities and provisions of multitype recharge facilities for EVs. In the algorithmic framework, we propose a tailored metaheuristic to solve medium to large instances. Systematic evaluation is conducted to test the efficacy of the proposed approach. The results highlight the impacts of traffic composition, distance ranges of EVs, budget levels and facility expenses on the project selection and evaluation. The results indicate that the two design objectives, to respectively minimize the network-wide travel time and greenhouse emissions, are conflicting for certain scenarios. Additionally, the results demonstrate the advantages of the network design problem (NDP) considering both multitype recharge service provision and road capacity

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Section: Literature Reviewmentioning

confidence: 99%

Multitype Recharge Facility Location for Electric Vehicles

Zhang

Rey

Waller

2018

Computer aided Civil Eng

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“…Finally, these sampled O-D matrices are assigned to each link according to the assignment map matrix. The resulted simulated link flows are used to represent observed day-today link flow discussed in (9) and (10). The impact of the weight parameters δ 1 and δ 2 are explored, in conjunction with how the sparsity regularisation will facilitate improving the estimation.…”

Section: Numerical Demonstrationmentioning

confidence: 99%

Estimation of sparse O–D matrix accounting for demand volatility

Wen

Gardner

Waller

et al. 2018

IET Intelligent Transport Systems

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A critical issue in origin-destination (O-D) demand estimation is under-determination: the number of O-D pairs to be estimated is often much greater than the number of monitored links. In real world, some centroids tend to be more popular than others, and only few trips are made for intro-zonal travel. Consequently, a large portion of trips will be made for a small portion of O-D pairs, meaning many O-D pairs have only a few or even zero trips. Mathematically, this implies that the O-D matrix is sparse. Also, the correlation between link flows is often neglected in the O-D estimation problem, which can be obtained from day-to-day loop detector count data. Thus, sparsity regularisation is combined with link flow correlation to provide additional inputs for the O-D estimation process to mitigate the issue of under-determination and thereby improve estimation quality. In addition, a novel strategic user equilibrium model is implemented to provide route choice of users for the O-D estimation problem, which explicitly accounts for demand and link flow volatility. The model is formulated as a convex generalised least squares problem with regularisation, the usefulness of sparsity assumption, and link flow correlation is presented in the numerical analysis.

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“…The strategic user equilibrium (Dixit et al., ) effectively accounts for the impact of demand uncertainty subject to Wardrop's UE conditions, and under the static user equilibrium, the computation tractability and simplicity are preserved. The model was extended to the dynamic traffic assignment (Waller et al., ), road pricing scheme (Duell et al., ), and independently distributed O–D demands (Wen et al., ).…”

Section: Literature Reviewmentioning

confidence: 99%

Two Methods to Calibrate the Total Travel Demand and Variability for a Regional Traffic Network

Wen

Gardner

Dixit

et al. 2017

Computer aided Civil Eng

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This article proposes a novel methodology that uses the bi‐level programming formulation to calibrate the expected total demand and the corresponding demand variability of traffic networks. In the bi‐level formulation the upper‐level is either a new maximum likelihood estimation method or a least squares method and the lower‐level is the strategic user equilibrium assignment model (StrUE) which accounts for the day‐to‐day demand volatility. The maximum likelihood method proposed in this article has the ability to utilize information from day‐to‐day observed link flows to provide a unique estimation of the total demand distribution, whereas the least squares method is capable of capturing link flow variations. The lower‐level StrUE model can take the total demand distribution as input, and output a set of link flow distributions which can then be compared to the link‐level observations. The mathematical proof demonstrates the convexity of the model, and the sensitivity to the prediction error is analytically derived. Numerical analysis is conducted to illustrate the efficiency and sensitivity of the proposed model. Some possible future research is discussed in the conclusion.

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A Strategic User Equilibrium for Independently Distributed Origin‐Destination Demands

Cited by 10 publications

References 77 publications

Multitype Recharge Facility Location for Electric Vehicles

Multitype Recharge Facility Location for Electric Vehicles

Estimation of sparse O–D matrix accounting for demand volatility

Two Methods to Calibrate the Total Travel Demand and Variability for a Regional Traffic Network

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