Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often...Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often adopt the user equilibrium(UE) model for select link analysis.However,empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model.This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model,to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression.To enhance the applicability in large-scale networks,Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model.The features of the proposed method are twofold:(1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and(2) applicability in large-scale networks due to the link-based stochastic loading method.An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information.A visualizationmethod is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.展开更多
Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/appr...Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.展开更多
基金supported by National Natural Science Foundation of China(51408433)Fundamental Research Funds for the Central Universities of Chinathe Chenguang Program sponsored by Shanghai Education Development Foundation and Shanghai Municipal Education Commission
文摘Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often adopt the user equilibrium(UE) model for select link analysis.However,empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model.This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model,to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression.To enhance the applicability in large-scale networks,Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model.The features of the proposed method are twofold:(1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and(2) applicability in large-scale networks due to the link-based stochastic loading method.An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information.A visualizationmethod is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.
文摘Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.
