Inefficiency of Logit-Based Stochastic User Equilibrium in a Traffic Network Under ATIS

Huang, Hai; Liu, Tianliang; Guo, Xiaotian; Yang, Hai

doi:10.1007/s11067-009-9118-7

Cited by 19 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…route choice proportions on route j between OD pair i in the information and no-information cases, efficient travel times on route j between OD pair i in the information and no-information cases, respectively, and w ij T is the random travel time on route j between OD pair i in the information case.Note that the information provision does not necessarily give positive effect, asHuang et al (2011) and others have discussed.The total travel time…”

mentioning

confidence: 84%

“…To date, many studies have been conducted to model the effect of a traffic information provision in the network equilibrium framework (e.g. Arnott et al, 1991;Yang, 1998;Jha et al, 1998;Yang & Meng, 2001;Lo & Szeto, 2002;Yin & Yang, 2003;Yang & Huang, 2004;de Palma & Picard, 2006;Huang & Li, 2007;Huang et al, 2008Huang et al, & 2011Wu et al, 2010;de Palma et al, 2012). User equilibrium (UE), stochastic user equilibrium (SUE), and system optimum (SO) are used in most of these studies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of providing traffic information estimated by a stochastic network equilibrium model with stochastic demand

Nakayama

2016

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

To estimate travel times through road networks, in this study, we assume a stochastic demand and formulate a stochastic network equilibrium model whose travel times, flows, and demands are stochastic. This model enables us to examine network reliability under stochastic circumstances and to evaluate the effect of providing traffic information on travel times. For traffic information, we focus on travel time information and propose methods to evaluate the effect of providing that information. To examine the feasibility and validity of the proposed model and methods, we apply them to a simple network and the real road network of Kanazawa, Japan. The results indicate that providing ambulance drivers in Kanazawa with travel time information leads to an average reduction in travel time of approximately three minutes. Keywords:stochastic network equilibrium, emergency vehicles, travel time information provision stochastic route choice, and developed a second-order stochastic network equilibrium with stochastic flows.A main cause of network uncertainty is variation in travel demand; thus, stochastic demand should be incorporated into network equilibrium models. Shao et al. (2006) and Siu & Lo (2008) introduced stochastic demands into network equilibrium models and explicitly considered stochastic flows. Lam et al. (2008) and Sumalee et al. (2010) considered network flow under stochastic supply and demand, and examined the impacts of adverse weather conditions. Zhang et al. (2011), who similarly considered stochastic supply and demand, introduced the expected residual minimization into stochastic-flow network equilibrium. Nakayama & Watling (2014) proposed stochastic network equilibrium models with stochastic flows, in which stochastic travel time, network flow, and demand are treated consistently. However, they assumed that the stochastic travel demands are independent among origin-destination (OD) pairs.In this study, we improve upon the model of Nakayama & Watling (2014), alleviating the independent demand and other assumptions, and formulate a stochastic network equilibrium model under stochastic demands. Using the proposed stochastic network equilibrium model, we develop methods to evaluate the effect of providing travel time information. Then, we apply the model and methods to a simple network and the real road network of Kanazawa, Japan, and examine their applicability and validity. Assumptions for Drivers and NotationThe notation used in this study is as follows: I = the number of OD pairs A = the total number of links J i = the number of routes between OD pair i J = the total number of all routes (= J 1 + J 2 +⋅⋅⋅+ J I ) K = the total number of latent drivers δ a,ij = link route incidence variable (δ a,ij = 1 if link a is part of route j between OD pair i; 0 otherwise) σ σ σ J J J J J J J J J J

show abstract

mentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Effect of providing traffic information estimated by a stochastic network equilibrium model with stochastic demand

Nakayama

2016

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…Considering the monotone condition and that the feasible domain is a bounded closed convex set, with the help of Brouwer's fixed-point theory, it is easy to prove the existence and uniqueness of the solution. Besides, after substituting (18) into (25) and with transformation, the following VI problem is obtained: (25). There is no need to prove again its equivalence with equilibrium conditions (20), (21), and (22) and the existence and uniqueness of its solutions.…”

Section: Complexitymentioning

confidence: 99%

“…Moreover, travelers are influenced by the guidance information. Ren et al [24], Huang et al [25], and Sun et al [26] divided travelers into two groups according to whether they assembled the advanced traffic information system (ATIS). Zhong et al [27], Liao and Chen [28], and Zhong et al [29] studied the route choice behavior in the ATIS context.…”

Section: Introductionmentioning

confidence: 99%

Unified Framework for Optimal Routing Choice under Guidance Information

Sun

et al. 2018

Complexity

View full text Add to dashboard Cite

In order to satisfy the diverse demand of travel service in the context of big data, this paper puts forward a unified framework for optimal routing choice under guidance information. With consideration of the influence of big data, the scenario analysis of routing choice is implemented, and the routing choice under guidance information is discussed. The optimal routing choice problem is abstracted into the collaboration optimization model of travel route choice, departure time choice, and travel mode choice. Based on some basic assumptions, the collaboration optimization model is formulated as a variational inequality model. The method of successive averages is applied to solve the proposed model. A case study is carried out to verify the applicability and reliability of the model and algorithm.

show abstract

“…Interestingly, this latter definition refers to the information service's underlying technology being advanced, whereas the former two use the label "advanced" to describe a service's functionality. In addition, where some studies (e.g., [30]) use the label to refer to a highly specific bit of information (e.g., travel time information for a given route) being of very high quality, other studies (e.g., [24]) use it to refer to the integration of different sources and functionalities within one device. Taken together, it appears that a high level of heterogeneity exists in terms of the meanings attached by scholars to the label "advanced" (travel information services).…”

Section: "Advanced" Travel Informationmentioning

confidence: 99%

Travel Information: Time to Drop the Labels?

Chorus

2012

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

This paper argues that the literature on travel information and its potential as a travel demand management tool suffers from confusing use of terminology. It shows how labels such as "advanced," "personalized," and "dynamic"-when referring to travel information services-have been either implicitly or explicitly assigned a variety of often quite different meanings in various contexts. As a result, these labels, and similar labels such as "predictive" and "intelligent," often obfuscate rather than explain what kind of travel information is actually being discussed. Several options for avoiding this confusion in future research are presented. For example, one option is to entirely forego the use of labels and instead describe travel information services in terms of their relevant characteristics and functionalities. Another option is to start using recently proposed definitions and International Standards Organization standards, with the aim of achieving a more formal building ground for travel-information-related research than is currently available.

show abstract

Inefficiency of Logit-Based Stochastic User Equilibrium in a Traffic Network Under ATIS

Cited by 19 publications

References 23 publications

Effect of providing traffic information estimated by a stochastic network equilibrium model with stochastic demand

Effect of providing traffic information estimated by a stochastic network equilibrium model with stochastic demand

Unified Framework for Optimal Routing Choice under Guidance Information

Travel Information: Time to Drop the Labels?

Contact Info

Product

Resources

About