Objective This study explored the job choice preferences of Center for Disease Prevention and Control(CDC)workers to provide CDC management information and recommendations for optimizing employee retention and motivat...Objective This study explored the job choice preferences of Center for Disease Prevention and Control(CDC)workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies.Methods A discrete choice experiment was conducted in nine provinces across China.Seven key attributes were identified to analyze the job preferences of CDC workers.Mixed logit models,latent class models,and policy simulation tools were used.Results A valid sample of 5,944 cases was included in the analysis.All seven attributes significantly influenced the job choices of CDC workers.Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility.Income-prioritizers were concerned with income and opportunities for career development,whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits.The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives.Conclusion Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers.Heterogeneity in job preferences was also identified.Based on the preference characteristics of different subgroups,policy content should be skewed to differentiate the importance of incentives.展开更多
In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based trav...In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.展开更多
In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics a...In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics are obtained from statistical data,while the trip mode split data is collected through a trip survey in Bengbu.In addition,the discrete choice model is adopted to build the functional relationship between the mode choice and the travelers＇ personal characteristics,as well as family characteristics and trip characteristics.The model shows that the relationship between the mode split and the personal,as well as family and trip characteristics is stable and changes little as the time changes.Deduced by the discrete model,the mode split result is relatively accurate and can be feasibly used for trip mode structure forecasts.Furthermore,the proposed model can also contribute to find the key influencing factors on trip mode choice,and restructure or optimize the urban trip mode structure.展开更多
Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restric...Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.展开更多
Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteris...Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.展开更多
Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital w...Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.展开更多
This paper investigates the effectiveness of online reviews on addressing price endogeneity issue in an application to consumer demand for smartphone.We consider review variables as the substitutes of unobserved produ...This paper investigates the effectiveness of online reviews on addressing price endogeneity issue in an application to consumer demand for smartphone.We consider review variables as the substitutes of unobserved product quality in terms of a scalar variable as seen in previous methods.An aspect-based sentiment classification technique is designed to construct feature-related review variables from millions of review contents.We discuss the performance of review variables both in a hedonic pricing model and a conditional logit discrete choice model.Our results demonstrate that review variables show a good performance either as instruments for price or as explicit control variables in demand models.In detail,the pricing prediction accuracy increases 3.4%,which is considered as a significant improvement in the practice of forecasting.In the discrete choice model,the estimated price coefficient is biased in the positive direction without endogeneity correction.It is adjusted in the expected way after including review variables.The findings indicate that online reviews provide alternative sources of information in dealing with endogeneity in discrete choice models.We also analyze the differences in the preferences and needs of individual consumers to provide some practical implications of marketing.展开更多
Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little i...Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little is known about how rodents forage for seeds when multiple desirable and undesirable seed traits are available simultaneously.Here,we adopt a novel method of designing choice experiments to study how eastern gray squirrels(Sciurus carolinensis)select for 6 traits(caloric value,protein content,tannin concentration,kernel mass,dormancy period and toughness of shell)among seeds.From n=426 seed-pair presentations,we found that squirrels preferentially consumed seeds with short dormancy or tougher shells,and preferentially cached seeds with larger kernel mass,tougher shells and higher tannin concentrations.By incorporating random effects,we found that squirrels exhibited consistent preferences for seed traits,which is likely due to the fitness consequences associated with maintaining cached resources.Furthermore,we found that squirrels were willing to trade between multiple traits when caching seeds,which likely results in more seed species being cached in the fall.Ultimately,our approach allowed us to compute the relative values of different seed traits to squirrels,despite covariance among studied traits across seed species.In addition,by investigating how squirrels trade among different seed traits,important insights can be gleaned into behavioral mechanisms underlying seed caching(and,thus,seed survival)dynamics as well as evolutionary strategies adopted by plants to attract seed dispersers.We describe how discrete choice experiments can be used to study resource selection in other ecological systems.展开更多
The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change.To meet user needs,electric vehicle(EV)management for chargi...The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change.To meet user needs,electric vehicle(EV)management for charging operations is essential.This study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging infrastructures.The conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system data.One of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect decisions.Another objective was to estimate the number of electric vehicles in Turin and the characteristics of their users,both of which are helpful in understanding electric mobility within a city.Analysing these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications,indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.展开更多
Background:This study aimed to investigate physicians’preferences in relation to Internet hospital consultations and to explore the factors influencing their choices.The study also assessed physicians’willingness to...Background:This study aimed to investigate physicians’preferences in relation to Internet hospital consultations and to explore the factors influencing their choices.The study also assessed physicians’willingness to participate in Internet-based hospital consultation services and the demand among physicians to do so.Methods:A total of 119 physicians from two general hospitals and three specialized hospitals in Beijing were selected for a questionnaire survey using a discrete choice experiment design.Physician preferences were analyzed using conditional logit regression.Findings:In descending order of importance,physicians’willingness to engage in Internet hospital consultations was influenced by changes in doctors’share of online diagnosis and treatment performance compared with offline consultations,daily working hours,and response time.Physicians were more likely to choose Internet hospital consultations when there was a 20%increase in the proportion of online consultations compared to offline consultations,a 10%increase in the weight of online consultations in the annual assessment,a 1-hour reduction in offline working hours and a 1-hour increase in online working hours,and a response time of less than 24 h.The subgroup analysis revealed variations in physicians’preferences based on age,professional titles,working years,and department.Younger doctors,those with fewer professional titles,and those with less work experience had higher expectations.Compared to internal medicine and pediatric doctors,surgeons exhibited a greater willingness to work longer hours.Interpretation:Increasing the proportion of online consultations,including online consultations in the annual assessment,reducing offline working hours,extending online availability,and ensuring timely responses can incentivize physicians to choose online hospital consultations.This approach promotes the high-quality development of Internet hospitals by combining economic and noneconomic incentives and optimizing workload distribution.展开更多
A logit-based discrete choice model is proposed to study the exit choice behaviour of evacuees in rooms with internal obstacles and multiple exits. Several factors influencing the exit choice behaviour, including the ...A logit-based discrete choice model is proposed to study the exit choice behaviour of evacuees in rooms with internal obstacles and multiple exits. Several factors influencing the exit choice behaviour, including the information obtained by evacuees, the tendency of following others, the visibility and familiarity of exits and the physical conditions of nearby exits, are considered. Evacuees are allowed to re-select their target exits for minimizing the perceived disutility during evacuation process. Numerical results from applying the model to cellular automata simulation of evacuation are presented and the effects of some model parameters on evacuation time are investigated.展开更多
The article titled Job Preferences of Centers for Disease Control and Prevention Workers:A Discrete Choice Experiment in China(Yan Guo,Hanlin Nie,Hao Chen,Stephen Nicholas,Elizabeth Maitland,Sisi Chen,Lieyu Huang,Xium...The article titled Job Preferences of Centers for Disease Control and Prevention Workers:A Discrete Choice Experiment in China(Yan Guo,Hanlin Nie,Hao Chen,Stephen Nicholas,Elizabeth Maitland,Sisi Chen,Lieyu Huang,Xiumin Zhang,and Xuefeng Shi)was published in Biomedical and Environmental Sciences,2025,38(6):740-750.展开更多
This paper analyzes the characteristics of the destination distribution of trips and proposes a stratified sampling strategy for travel mode choice.The stratified sampling strategy can reduce the size of the alternati...This paper analyzes the characteristics of the destination distribution of trips and proposes a stratified sampling strategy for travel mode choice.The stratified sampling strategy can reduce the size of the alternative set;thus,the computation burden of simulation is decreased.Using the stratified sampling strategy,a combined choice model of the trip mode and destination is developed based on the Bayesian theory.Simulations are carried out to verify the proposed model.The results show that the combined choice model of the trip mode and destination can efficiently simulate travelers＇ choice behaviors.Furthermore,the forecasting accuracy of the combined choice model is higher than the one of the gravity model.Therefore,the proposed model is a powerful tool with which to analyze travelers＇ behaviors in selecting the trip mode.展开更多
BACKGROUND Patients with Crohn’s disease(CD)are at risk of developing complications such as perianal fistulas.Patients with Crohn’s perianal fistulas(CPF)are affected by fecal incontinence(FI),bleeding,pain,swelling...BACKGROUND Patients with Crohn’s disease(CD)are at risk of developing complications such as perianal fistulas.Patients with Crohn’s perianal fistulas(CPF)are affected by fecal incontinence(FI),bleeding,pain,swelling,and purulent perianal discharge,and METHODS This cross-sectional observational study was conducted in patients with CD aged 21-90 years via a web-enabled questionnaire in seven countries(April-August 2021).Patients were recruited into three cohorts:Cohort 1 included patients without perianal fistulas;cohort 2 included patients with perianal fistulas without fistula-related surgery;and cohort 3 included patients with perianal fistulas and fistula-related surgery.Validated patient-reported outcome measures were used to assess quality of life.Drivers of treatment preferences were measured using a discrete choice experiment(DCE).RESULTS In total,929 patients were recruited(cohort 1,n=620;cohort 2,n=174;cohort 3,n=135).Short Inflammatory Bowel Disease Questionnaire scores were worse for patients with CPF(cohorts 2 and 3)than for those with CD without CPF(cohort 1):Mean score 3.8 and 3.7 vs 4.1,respectively,(P<0.001).Similarly,mean Revised FI and FI Quality of Life scores were worse for patients with CPF than for those with CD without CPF.Quality of Life with Anal Fistula scores were similar in patients with CPF with or without CPF-related surgery(cohorts 2 and 3):Mean score 41 and 42,respectively.In the DCE,postoperative discomfort and fistula healing rate were the most important treatment attributes influencing treatment choice:Mean relative importance 35.7 and 24.7,respectively.CONCLUSION The burden of illness in CD is significantly higher for patients with CPF and patients rate lower postoperative discomfort and higher healing rates as the most desirable treatment attributes.展开更多
Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all ...Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all aspects of travel behavior, including travel frequency, travel distances, mode choice and time-of-day choice. At the same time, car ownership affects residential location choices, as households owning cars are less likely to choose urban neighborhoods than households without cars. This paper describes a new microscopic auto-ownership model that has been estimated with survey data. The model is fully integrated with a land use and a transportation model to capture： （1） how owning a car affects travel behavior and location choice; and （2） how the built environment and the transportation needs affect auto-ownership decisions. The model has been validated against census data and is fully operational.展开更多
Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors pos...Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.展开更多
Insurance companies do not differentiate their customers based on their driving behavior in Iran that leads to reckless driving and thereby imposes significant costs to the society. We provided a model that can predic...Insurance companies do not differentiate their customers based on their driving behavior in Iran that leads to reckless driving and thereby imposes significant costs to the society. We provided a model that can predict effects of different policies and discounts on market share of insurance companies. The usage of model has been tested in Tehran insurance market and the results provided showed that people would consider changing their insurance plans based on discounts and costs. Further, the first movers (i.e. the insurance companies that offer the discounts first) would absorb safer drivers and thereby stand in a superior financial position. This could significantly change the dynamics and the role of major players in the insurance market.展开更多
This paper estimates the consumer surplus that Uber brings for consumers.The estimation uses three datasets:individual-level choice dataset—the National Household Travel Survey(NHTS)data of 2008–2009,origin-destinat...This paper estimates the consumer surplus that Uber brings for consumers.The estimation uses three datasets:individual-level choice dataset—the National Household Travel Survey(NHTS)data of 2008–2009,origin-destination level dataset—Uber data,and Google data of 2017.Firstly,we use NHTS data to identify consumer's preferences in 2008 under a discrete-choice framework.Assuming unchanged preferences of consumers,we use the coefficients of the discrete-choice model to reveal passengers'demand on different transportation modes in 2017.After revealing the demand curve,this paper calculates the consumer surplus by differencing the consumer surplus in the circumstance where Uber is available with the consumer surplus of the scenario if Uber is not available.We find that Uber brings at least$0.76 gains for each trip.The overall consumer surplus generated by Uber in San Francisco is around$100 million per year.展开更多
基金supported by the Major Program of the National Social Science Foundation of China(no.2022YFC3600801)the Operation of Public Health Emergency Response Mechanisms of the Chinese Center for Disease Control and Prevention(no.102393220020010000017)。
文摘Objective This study explored the job choice preferences of Center for Disease Prevention and Control(CDC)workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies.Methods A discrete choice experiment was conducted in nine provinces across China.Seven key attributes were identified to analyze the job preferences of CDC workers.Mixed logit models,latent class models,and policy simulation tools were used.Results A valid sample of 5,944 cases was included in the analysis.All seven attributes significantly influenced the job choices of CDC workers.Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility.Income-prioritizers were concerned with income and opportunities for career development,whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits.The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives.Conclusion Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers.Heterogeneity in job preferences was also identified.Based on the preference characteristics of different subgroups,policy content should be skewed to differentiate the importance of incentives.
文摘In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.
基金The National Natural Science Foundation of China (No.50738001,51078086)
文摘In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics are obtained from statistical data,while the trip mode split data is collected through a trip survey in Bengbu.In addition,the discrete choice model is adopted to build the functional relationship between the mode choice and the travelers＇ personal characteristics,as well as family characteristics and trip characteristics.The model shows that the relationship between the mode split and the personal,as well as family and trip characteristics is stable and changes little as the time changes.Deduced by the discrete model,the mode split result is relatively accurate and can be feasibly used for trip mode structure forecasts.Furthermore,the proposed model can also contribute to find the key influencing factors on trip mode choice,and restructure or optimize the urban trip mode structure.
基金supported by the Science&Technology pillar project(No.0556)of Guangzhou
文摘Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.
文摘Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.
文摘Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.
文摘This paper investigates the effectiveness of online reviews on addressing price endogeneity issue in an application to consumer demand for smartphone.We consider review variables as the substitutes of unobserved product quality in terms of a scalar variable as seen in previous methods.An aspect-based sentiment classification technique is designed to construct feature-related review variables from millions of review contents.We discuss the performance of review variables both in a hedonic pricing model and a conditional logit discrete choice model.Our results demonstrate that review variables show a good performance either as instruments for price or as explicit control variables in demand models.In detail,the pricing prediction accuracy increases 3.4%,which is considered as a significant improvement in the practice of forecasting.In the discrete choice model,the estimated price coefficient is biased in the positive direction without endogeneity correction.It is adjusted in the expected way after including review variables.The findings indicate that online reviews provide alternative sources of information in dealing with endogeneity in discrete choice models.We also analyze the differences in the preferences and needs of individual consumers to provide some practical implications of marketing.
基金The Fred M.Van Eck Forest Foundation for Purdue University and the McIntire-Stennis program provided funding.
文摘Seeds of many hardwood trees are dispersed by scatter-hoarding rodents,and this process is often mediated by the traits of seeds.Although numerous studies have linked seed traits to seed preference by rodents,little is known about how rodents forage for seeds when multiple desirable and undesirable seed traits are available simultaneously.Here,we adopt a novel method of designing choice experiments to study how eastern gray squirrels(Sciurus carolinensis)select for 6 traits(caloric value,protein content,tannin concentration,kernel mass,dormancy period and toughness of shell)among seeds.From n=426 seed-pair presentations,we found that squirrels preferentially consumed seeds with short dormancy or tougher shells,and preferentially cached seeds with larger kernel mass,tougher shells and higher tannin concentrations.By incorporating random effects,we found that squirrels exhibited consistent preferences for seed traits,which is likely due to the fitness consequences associated with maintaining cached resources.Furthermore,we found that squirrels were willing to trade between multiple traits when caching seeds,which likely results in more seed species being cached in the fall.Ultimately,our approach allowed us to compute the relative values of different seed traits to squirrels,despite covariance among studied traits across seed species.In addition,by investigating how squirrels trade among different seed traits,important insights can be gleaned into behavioral mechanisms underlying seed caching(and,thus,seed survival)dynamics as well as evolutionary strategies adopted by plants to attract seed dispersers.We describe how discrete choice experiments can be used to study resource selection in other ecological systems.
基金This work was partially supported by the EU Horizon 2020 project“INCIT-EV”,with Grant agreement ID:875683.
文摘The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change.To meet user needs,electric vehicle(EV)management for charging operations is essential.This study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging infrastructures.The conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system data.One of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect decisions.Another objective was to estimate the number of electric vehicles in Turin and the characteristics of their users,both of which are helpful in understanding electric mobility within a city.Analysing these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications,indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.
基金funded by the Natural Science Foundation of Beijing Municipality(grant number:9222003).
文摘Background:This study aimed to investigate physicians’preferences in relation to Internet hospital consultations and to explore the factors influencing their choices.The study also assessed physicians’willingness to participate in Internet-based hospital consultation services and the demand among physicians to do so.Methods:A total of 119 physicians from two general hospitals and three specialized hospitals in Beijing were selected for a questionnaire survey using a discrete choice experiment design.Physician preferences were analyzed using conditional logit regression.Findings:In descending order of importance,physicians’willingness to engage in Internet hospital consultations was influenced by changes in doctors’share of online diagnosis and treatment performance compared with offline consultations,daily working hours,and response time.Physicians were more likely to choose Internet hospital consultations when there was a 20%increase in the proportion of online consultations compared to offline consultations,a 10%increase in the weight of online consultations in the annual assessment,a 1-hour reduction in offline working hours and a 1-hour increase in online working hours,and a response time of less than 24 h.The subgroup analysis revealed variations in physicians’preferences based on age,professional titles,working years,and department.Younger doctors,those with fewer professional titles,and those with less work experience had higher expectations.Compared to internal medicine and pediatric doctors,surgeons exhibited a greater willingness to work longer hours.Interpretation:Increasing the proportion of online consultations,including online consultations in the annual assessment,reducing offline working hours,extending online availability,and ensuring timely responses can incentivize physicians to choose online hospital consultations.This approach promotes the high-quality development of Internet hospitals by combining economic and noneconomic incentives and optimizing workload distribution.
基金Projects supported by the National Basic Research Program of China (Grant No. 2006CB705503)the National Natural Science Foundation of China (Grant No. 70521001)
文摘A logit-based discrete choice model is proposed to study the exit choice behaviour of evacuees in rooms with internal obstacles and multiple exits. Several factors influencing the exit choice behaviour, including the information obtained by evacuees, the tendency of following others, the visibility and familiarity of exits and the physical conditions of nearby exits, are considered. Evacuees are allowed to re-select their target exits for minimizing the perceived disutility during evacuation process. Numerical results from applying the model to cellular automata simulation of evacuation are presented and the effects of some model parameters on evacuation time are investigated.
文摘The article titled Job Preferences of Centers for Disease Control and Prevention Workers:A Discrete Choice Experiment in China(Yan Guo,Hanlin Nie,Hao Chen,Stephen Nicholas,Elizabeth Maitland,Sisi Chen,Lieyu Huang,Xiumin Zhang,and Xuefeng Shi)was published in Biomedical and Environmental Sciences,2025,38(6):740-750.
文摘This paper analyzes the characteristics of the destination distribution of trips and proposes a stratified sampling strategy for travel mode choice.The stratified sampling strategy can reduce the size of the alternative set;thus,the computation burden of simulation is decreased.Using the stratified sampling strategy,a combined choice model of the trip mode and destination is developed based on the Bayesian theory.Simulations are carried out to verify the proposed model.The results show that the combined choice model of the trip mode and destination can efficiently simulate travelers＇ choice behaviors.Furthermore,the forecasting accuracy of the combined choice model is higher than the one of the gravity model.Therefore,the proposed model is a powerful tool with which to analyze travelers＇ behaviors in selecting the trip mode.
文摘BACKGROUND Patients with Crohn’s disease(CD)are at risk of developing complications such as perianal fistulas.Patients with Crohn’s perianal fistulas(CPF)are affected by fecal incontinence(FI),bleeding,pain,swelling,and purulent perianal discharge,and METHODS This cross-sectional observational study was conducted in patients with CD aged 21-90 years via a web-enabled questionnaire in seven countries(April-August 2021).Patients were recruited into three cohorts:Cohort 1 included patients without perianal fistulas;cohort 2 included patients with perianal fistulas without fistula-related surgery;and cohort 3 included patients with perianal fistulas and fistula-related surgery.Validated patient-reported outcome measures were used to assess quality of life.Drivers of treatment preferences were measured using a discrete choice experiment(DCE).RESULTS In total,929 patients were recruited(cohort 1,n=620;cohort 2,n=174;cohort 3,n=135).Short Inflammatory Bowel Disease Questionnaire scores were worse for patients with CPF(cohorts 2 and 3)than for those with CD without CPF(cohort 1):Mean score 3.8 and 3.7 vs 4.1,respectively,(P<0.001).Similarly,mean Revised FI and FI Quality of Life scores were worse for patients with CPF than for those with CD without CPF.Quality of Life with Anal Fistula scores were similar in patients with CPF with or without CPF-related surgery(cohorts 2 and 3):Mean score 41 and 42,respectively.In the DCE,postoperative discomfort and fistula healing rate were the most important treatment attributes influencing treatment choice:Mean relative importance 35.7 and 24.7,respectively.CONCLUSION The burden of illness in CD is significantly higher for patients with CPF and patients rate lower postoperative discomfort and higher healing rates as the most desirable treatment attributes.
文摘Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all aspects of travel behavior, including travel frequency, travel distances, mode choice and time-of-day choice. At the same time, car ownership affects residential location choices, as households owning cars are less likely to choose urban neighborhoods than households without cars. This paper describes a new microscopic auto-ownership model that has been estimated with survey data. The model is fully integrated with a land use and a transportation model to capture： （1） how owning a car affects travel behavior and location choice; and （2） how the built environment and the transportation needs affect auto-ownership decisions. The model has been validated against census data and is fully operational.
文摘Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.
文摘Insurance companies do not differentiate their customers based on their driving behavior in Iran that leads to reckless driving and thereby imposes significant costs to the society. We provided a model that can predict effects of different policies and discounts on market share of insurance companies. The usage of model has been tested in Tehran insurance market and the results provided showed that people would consider changing their insurance plans based on discounts and costs. Further, the first movers (i.e. the insurance companies that offer the discounts first) would absorb safer drivers and thereby stand in a superior financial position. This could significantly change the dynamics and the role of major players in the insurance market.
文摘This paper estimates the consumer surplus that Uber brings for consumers.The estimation uses three datasets:individual-level choice dataset—the National Household Travel Survey(NHTS)data of 2008–2009,origin-destination level dataset—Uber data,and Google data of 2017.Firstly,we use NHTS data to identify consumer's preferences in 2008 under a discrete-choice framework.Assuming unchanged preferences of consumers,we use the coefficients of the discrete-choice model to reveal passengers'demand on different transportation modes in 2017.After revealing the demand curve,this paper calculates the consumer surplus by differencing the consumer surplus in the circumstance where Uber is available with the consumer surplus of the scenario if Uber is not available.We find that Uber brings at least$0.76 gains for each trip.The overall consumer surplus generated by Uber in San Francisco is around$100 million per year.
