Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

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.

A comparison of model choice strategies for logistic regression

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.

The Ethical Choice of Business English Interpreters under Chesterman’s Model of Translation Ethics

This paper explores the ethical challenges encountered by business English interpreters using Chesterman’s Model of Translation Ethics,set against the context of economic globalization and the“Belt and Road”initiative.With the increasing demand for interpreters,the paper delves into the ongoing discussion about the role of AI in translation and its limitations,especially concerning cultural subtleties and ethical issues.It highlights the importance of human interpreters’cross-cultural understanding and the ethical principles that inform their work,such as the Ethics of Representation,Service,Communication,Norm-based Ethics,and Commitment.Moreover,the paper examines how these ethical models are applied in practical business situations,including business banquets,business negotiations,business talks and business visits,etc.,and investigates the cultural misunderstandings that may occur during these interactions.The study concludes that although AI provides efficiency and cost savings,human interpreters are essential for their capacity to handle the intricacies of cross-cultural communication with cultural awareness and ethical discernment.

Discrete Choice Analysis of Temporal Factors on Social Network Growth

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.

Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Modeling commuters' route choice behavior under pre-trip information system

Research about the auto commuter＇s pre-trip route choice behavior ignores the combined influence of the real-time information and all respondents＇ historical information in the existing documents.To overcome this shortcoming,an approach to describing the pre-trip route choice behavior with the incorporation of the real-time and historical information is proposed.Two types of real-time information are investigated,which are quantitative information and prescriptive information.By using the bounded rationality theory,the influence of historical information on the real-time information reference process is examined first.Estimation results show that the historical information has a significant influence on the quantitative information reference process,but not on the prescriptive information reference process.Then the route choice behavior is modeled.A comparison is also made among three route choice models,one of which does not incorporate the real-time information reference process,while the others do.Estimation results show that the route choice behavior is better described with the consideration of the reference process of both quantitative and prescriptive information.

An empirical study on travel demand management modeling based on discrete choice method

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.

Application of discrete choice model in trip mode structure forecast:a case study of Bengbu

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.

农户经济视角下的我国农村土地流转影响因素——基于Multiple Choice Model的调查研究

利用农户土地流转问卷调查所得到的微观数据,采用统计分析、Probit multiple choicemodel方法,将农户经济行为中的保障性行为、投资行为、就业行为和产业选择行为引入土地流转的实证研究领域。研究结果显示:目前的土地流转多为暂时性流转,土地流转结构性供需矛盾严重;农户对土地的需求目的更多来自于保障性需要;农户的投资行为、就业行为、社会保障行为对土地流转有着重要影响。

Modeling the criminal's location choices in urban areas

With the method of dynamic programming, two spatial variables,the expected utility and the probability of success of each crime, are used to model the criminal's location choices in urban areas in this paper.The modeling results show that a criminal optimizes his crime locations according to the expected utility and the success probability during his planned period A criminal usually commits his first offense in the district that has the highest probability of success but a lower expected utility, and commits his last crime in the district where the expected utility is the highest and success probability is lower.If a location has both an expected utility and a higher probability of success, the criminal might commit all his offenses in thes place. The model also suggests that crime prevention measures should be adopted in accordance with local conditions. 'Covering' measures, such as patrolling, should be taken in the poor residential districts or juvenile delinquency districts, while more sophisticated and advanced measures should be introduced in the richer districts or the districts where professional criminals haunt.

Comparative study on mode split discrete choice models

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.

Portfolio Choice under the Mean-Variance Model with Parameter Uncertainty

Assuming the investor is uncertainty-aversion,the multiprior approach is applied to studying the problem of portfolio choice under the uncertainty about the expected return of risky asset based on the mean-variance model. By introducing a set of constraint constants to measure uncertainty degree of the estimated expected return,it built the max-min model of multi-prior portfolio,and utilized the Lagrange method to obtain the closed-form solution of the model,which was compared with the mean-variance model and the minimum-variance model; then,an empirical study was done based on the monthly returns over the period June 2011 to May 2014 of eight kinds of stocks in Shanghai Exchange 50 Index. Results showed,the weight of multi-prior portfolio was a weighted average of the weight of mean-variance portfolio and that of minimumvariance portfolio; the steady of multi-prior portfolio was strengthened compared with the mean-variance portfolio; the performance of multi-prior portfolio was greater than that of minimum-variance portfolio. The study demonstrates that the investor can improve the steady of multi-prior portfolio as well as its performance for some appropriate constraint constants.

How Users Perceive Infrastructure Development Affects Their Transport Mode Choice

Transport infrastructure development and perception vary across and within countries, influencing mode choice among road users. This study explores how road users perceive the development of infrastructure modes, service attributes, embedded safety levels, and commuting modes. Additionally, the research examines whether participants’ environmental backgrounds impact their mode choice patterns. The study gathered responses from 1169 participants residing in two regions of Amman, Jordan, each with distinct infrastructure development and population densities. Participants completed a standardized questionnaire, and several statistical techniques were employed for analysis. The findings revealed that facilities’ infrastructure attributes, development, and safety were assessed using three indices. Both participant groups perceived these indices differently on average. Residents of low population density areas with relatively developed infrastructure showed more consistent assessments, irrespective of their most frequently used mode of transportation, tending towards lower scores. Interestingly, subjective ratings of infrastructure development were higher (4.96) than attribute-based ratings (4.32). Despite their generally low-quality perception, public transportation services received the highest appraisal (4.9). Conversely, pedestrian infrastructure complementing public transport received the lowest assessment (4.57), only slightly higher than street environments (4.59). The study found weak associations between subjective service characteristics ratings. Traveler and trip characteristics influenced mode choice and trips more than infrastructure perception. In conclusion, the study suggests that policies should be developed to encourage green transportation, ensure social equality and safety. In addition, the study contributes to understanding perceptions about transport infrastructure, modes of transportation, and the factors that influence sustainable and equitable transportation systems.

Investigating impacts of different building contents on the post-earthquake evacuation time using an agent-based model with considering turning behavior

Accurate assessment of crowd evacuation inside the post-earthquake environment is critical from many perspectives,but this issue receives much less attention compared to the seismic losses of structural and non-structural components.This could be attributed to the fact that post-earthquake evacuation analysis is complex due to the interaction between human behavior and the actual built environment induced by different building contents.This study attempts to tackle this problem by investigating the impacts of different building contents on post-earthquake evacuation time by using an agent-based model that considers turning behavior.To this end,the agent-based model is first described,including:properties of the agent-based model with turning behavior,key aspects in its formulation considering different evacuation stages,and influence of different building contents(namely,debris from partition walls and ceiling systems,and various types of equipment)on the agent’s behavior.Subsequently,a school building is used as a benchmark problem to validate the model without earthquake,and the findings indicate that the agent-based model can match the real safety drill results reasonably well.After the validation,the school building is subsequently designed in accordance with modern seismic design codes,and the influence of debris and equipment on post-earthquake evacuation time is quantitatively studied using a suite of pulse-type ground motions as input.Based on this case study,recommendations are made for structural and architectural designers in an effort to reduce the potential evacuation time.Specifically,debris induced by partition walls or ceiling systems should be controlled as it has the greatest impact on the total evacuation time.

Prediction of lute acoustic quality based on soundboard vibration performance using multiple choice model

The vibrational performance of wood materials critical affects the acoustic quality of a lute. The purpose of this research was to apply a multiple choice model to predict the quality of musical instruments based on data on lute soundboard vibrational properties of Paulownia wood. In the lute production, lute material selection mainly depends on the subjective evaluation of technicians, which is not only inefficient, but inaccurate. In this study, nine lutes were fabricated. Using the multiple selection model, the lute tone quality was predicted by the soundboard wood vibration data. Compared with the actual value, the dependent value predicted by the count of observations with the maximum probability had 22 erroneous judgments. The model precision is 87.78%. The results confirmed that the prediction model can be used as a guideline for the selection of the soundboard wood in musical instrument plants.

The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice

The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.

Means-End Chain Model Framework for Measuring Housing Environment Choice Behavior

Housing and housing space has been a place for personal development, recreation and self accentuation. The need for housing and housing space quality can therefore not be overemphasized. The need for housing remains a constant index for all societies through the ages. Housing is a complex and heterogeneous product in its setting, the cognitive structures of housing users for housing attributes is also complex as well as their choice behaviors. Means-End Chain （MEC） model has been found to be very effective and potent in measuring these complexities. This conceptual paper explores from literature the MEC model and attempts to propagate its use as a research model for housing research, environment-behavior studies and person-environment congruence. It also presents the methodology employed by MEC for data collection and data management. It will suggest an extension to the traditional methodology that MEC utilizes. The possibility of extending the previous methods and their applicability in design process is herein presented; and to make a case for the usability of MEC model as a research tool for housing researchers. In dealing with user preference of housing, there is a need for research for a development of a technological tool for the identification of user needs and preference, and the kind of decision support that are required to identify these needs.

Influence of land use characteristics and trip attributes on commuting mode choice: a case of Nanjing

The effects of socio-demographics, land use characteristics and trip attributes on the commute mode choice are studied with a nested logit （NL） model. Based on the random utility maximum theory, the NL model is formulated. The analysis is carried out in the main area of Nanjing. Direct and cross elasticities are calculated to analyze the effects of travel time and travel cost on the selection of travel mode choice. The results reveal that the non-motorized travel modes are more attractive in the areas with higher housing and employment accessibility and car owners are found to be more likely to commute to work by car. The bus and subway choice probabilities are more sensitive to changes in travel times than to changes in travel costs. In conclusion, a comprehensive public transit system and effective integration of land use and transportation policies help to relieve the traffic congestion levels caused by the increasing urban sprawl.

Investigation on transformation process of trip mode choice for planned special events

This paper aims to provide a decision-making method for the transportation management strategies in guiding the transformation of trip mode choice during planned special events. The Expo 2010 Shanghai is taken as an example, and a structural equation model is employed to analyze the dynamic mechanism of trip mode choice behavior and the effectiveness of the transportation management measures at different stages. Based on the difference between the objective-oriented stated preference （SP） survey results and the objectives, together with the feedback from the previous stage survey, some adjustments on the transportation management measures are made in the next stage of the planning process until the objectives are eventually achieved. The results indicate that the adjustments on transportation management measures at different stages can effectively raise the transit share to 88.6%. Nonlocal visitors are inclined to choose nonstop modes of transportation and the companion attributes have the most significant effects on the trip mode choices of visitors. The research method is proved to be an effective way to support the decision making process of transportation management measures during planned special events in the future.

Parking choice behaviour analysis of rural residents based on the latent variable random forest model

The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution,which has attracted the attention of many scholars as well as policymakers.However,most of the current research on parking choice focuses on urban business and residential areas rather than on rural parking choice behaviour,and focuses on the analysis of observable factors,ignoring the internal relationship with potential variables.This study considers the heterogeneity of individuals and uses the random forest(RF)algorithm to construct a model of rural residents’willingness to choose parking with both latent and explicit variables,to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’parking choices,and to explore parking planning programmes and strategies that are truly applicable to rural areas.The results of the study suggest that the safety and convenience of the parking environment are key factors influencing the parking choice behaviour of rural residents,and can greatly improve the predictive accuracy of the parking willingness model.Upon comparison,it is found that the application of the RF algorithm is also significantly better than the logit model in terms of prediction effect,indicating that there is a nonlinear effect among the factors influencing the parking choice behaviour of rural residents and that the RF model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behaviour of rural residents.