Search

This paper presents a comprehensive and innovative evaluation framework for identifying a reliable population synthesis for agent-based modeling–transportation-oriented simulations (ABM–TOS). We show, via this framework and different metrics for the analysis of the generated distribution of the individuals’ attributes, that population synthesizers may fail to correctly replicate the real population heterogeneity owing to diverse control variables, data limitations, and post-simulation computation of certain parameter distributions. To show these shortcomings, the authors propose a systematic classification of different types of distributions crucial for mobility simulations. The proposed framework aims to provide a comprehensive overview of the population and serve as a rapid ’debugging’ tool to identify and rectify any flaws in a specific population during the calibration of the activity-based mobility simulation models. To prove the effectiveness of this framework, we applied it to synthetic populations generated through MOBIUS (mobility optimization based on iterative user synthesis), a newly developed synthetic population generator, which in this case was employed to create different variants of the Luxembourg population (1%, 10%, 30%). The application of our framework to these populations not only provided an effective method for assessing their goodness of fit, but also helped highlight the distributions that are most critical to the successful implementation of the methodology.

Calibration is a critical aspect of model development that has long been recognized by researchers as a challenging issue. In particular, difficulties arise when the observed data used for calibration do not match the model output, which is the case in the majority of transport planning models. In the traditional calibration process, the origin–destination (OD) matrices are the key interface between demand and supply models, which could lead to issues when observed traffic link counts are used to update the OD matrix, causing a loss of key demand characteristics in the process. Developing a unified structure for modeling both demand and supply requires a calibration process that meets the requirements of both types of models, a serious issue which has received less attention in the literature. In this paper, the existing processes of developing and integrating demand and supply models are discussed and then examined using a case study in the Melbourne area. The numerical results show that the standard OD calibration procedure causes unrealistic changes in the OD matrix. Finally, some possible solutions to address the current limitations in development of a unified structure are discussed.

Modeling travel-related decisions of transport system users is the core of many behavioral travel demand models. It is of great significance to use these models for planning purposes in which decisions of individuals are simulated for specific time intervals or on a continuous dimension until the target year. Discrete choice and survival analysis methods are two popular econometric structures to model and forecast time-dependent outcomes. This paper elaborates the conceptual and practical differences between these two methods in the context of vehicle ownership modeling. There are meaningful differences between these two methods including data preparation approaches, interpretations of the variable of interest in the model, and the simulation procedures. Further, this paper shows how negligent application of discrete choice methods for modeling time-to-event variables results in specification bias. This discussion paves the path for using hazard-based models in travel demand modeling, as the application of these models have been quite limited compared with their capacity.

•A spectrum of perceptions towards the effectiveness of policies in restricting the spread of the disease is observed.•Australia, USA, Brazil and Norway indicate no statistically significant perception related to the effectiveness of policies in restricting the spread of COVID19 for leisure activities.•A gap is indicated between individuals' mobility decisions and their perceptions about the effectiveness of policies for controlling the disease that needs to be addressed.•Most countries exhibit lesser tendencies of travelling alone if the individuals perceive that the probability of contracting the disease is higher while travelling in public transport.

•Respondents tend to be more multimodal after subscribing to MaaS bundles.•The usage of taxi is encouraged after subscribing to MaaS bundles.•Risk-averse individuals are hesitant to subscribe when facing uncertainties.•Travelers seek a new balance between travel time and cost after subscription.•Travelers have higher value of travel time and flexibility when facing uncertainties.

One of the major challenges associated with the analysis of route choice modeling is the formulation of the choice set of alternatives that may allow a relatively accurate prediction of demand for travel routes. The subset of route alternatives in the choice set should be relevant and feasible and include the attributes considered most by travelers when they choose a route. This research investigated the role and significance of route choice set formations with a focus on the perspectives of the modeler and of travelers. Revealed preference data were collected from Sydney, Australia, residents about their choice of route for their most recent commuting trip. The survey tool was programmed to use the Google Maps application programming interfaces to collect the route choice information, including the selected route and the set of routes that were considered. Three discrete choice models were used to investigate the traveler’s inclination toward certain attributes of routes, considering both car and public transit routes with the master choice set. The effect of possible bias generated because of the formation of route choice from the perspective of the modeler was also analyzed and presented with the results. The results show the intuitive signs of various attributes, with travel time being the significant factor for route choice. The difference between the choice sets considered by the traveler and by the modeler also suggests that those considered by the modeler possess enough variation to offer the possibility of better capturing important factors affecting route choice behavior.

One of the major challenges associated with the analysis of route choice modeling is the formulation of the choice set of alternatives that may allow a relatively accurate prediction of demand for travel routes. The subset of route alternatives in the choice set should be relevant and feasible and include the attributes considered most by travelers when they choose a route. This research investigated the role and significance of route choice set formations with a focus on the perspectives of the modeler and of travelers. Revealed preference data were collected from Sydney, Australia, residents about their choice of route for their most recent commuting trip. The survey tool was programmed to use the Google Maps application programming interfaces to collect the route choice information, including the selected route and the set of routes that were considered. Three discrete choice models were used to investigate the traveler’s inclination toward certain attributes of routes, considering both car and public transit routes with the master choice set. The effect of possible bias generated because of the formation of route choice from the perspective of the modeler was also analyzed and presented with the results. The results show the intuitive signs of various attributes, with travel time being the significant factor for route choice. The difference between the choice sets considered by the traveler and by the modeler also suggests that those considered by the modeler possess enough variation to offer the possibility of better capturing important factors affecting route choice behavior.

To improve the level of service of traffic and predict travel demand, it is essential to analyze the behavioral factors that affect transportation mode choice and route choice at the individual level. Such analysis requires detailed data on the behavior of people in the selection of different modes and routes. This paper presents a unique data collection endeavor, intended to observe the formation of the choice set from which the final alternative is selected. The revealed preference information about the routes people considered and used for their last work or study trip was targeted. Google Maps API (application programming interface), which has the capacity to calculate and return car and public transportation routes, was employed to program the survey and adaptively show the respondents the routes according to the reported origin and destination. A pilot survey was conducted with this survey tool at the University of New South Wales, Sydney, Australia, with a sample of 200 respondents. A preliminary analysis was carried out to analyze the effectiveness of the survey tool and the specification of the choice set. Three modeling structures—multinomial logit (MNL), nested logit, and mixed MNL—were used to estimate the parameters of the preliminary analysis. The results were fairly intuitive as far as the signs of the parameters were concerned, and travel time significantly influenced route choice.

The use of active modes of travel in school trips has sharply declined for a number of reasons, some of which are rooted in urban design, location of schools, and parental reservations. Thus, policy makers are keen to find practical ways to promote active modes of travel, particularly walking. This study was an effort to determine a tolerable walking distance for school trips and identify factors that made walking trips more reasonable for students. In the vein of understanding the tolerable walking distance, a two-step framework encompassing a random utility method and a hazard-based model was introduced. This framework was applied to a set of data that were collected from middle and high school students in the city of Tehran, Iran. The framework was intended to find a more accurate estimate of tolerable walking distance in school trips by addressing data censorship problems. The hazard-based model showed that 85% of students failed to walk when their travel distance exceeded 1,900 m. In Western countries, some studies have found this distance to be around 1,500 m. This study found girls were less likely to walk compared with boys. Furthermore, safety, time, and comfort of the trip turned out to be influential in parental objections, such that they decrease the critical walking distance by 18%, 13%, and 11%, respectively.

•Overlooking the interdependency of activity type and accompaniment decisions misleads policies.•Elasticities of independent model vary up to 24 times compared with the joint model.•1% increase in trip duration will escalate escorted healthcare trips by 0.16%.•1% increase in age of disabled persons leads to a decrease of 1.2% in unescorted work trips.

The use of active modes of travel in school trips has sharply declined for a number of reasons, some of which are rooted in urban design, location of schools, and parental reservations. Thus, policy makers are keen to find practical ways to promote active modes of travel, particularly walking. This study was an effort to determine a tolerable walking distance for school trips and identify factors that made walking trips more reasonable for students. In the vein of understanding the tolerable walking distance, a two-step framework encompassing a random utility method and a hazard-based model was introduced. This framework was applied to a set of data that were collected from middle and high school students in the city of Tehran, Iran. The framework was intended to find a more accurate estimate of tolerable walking distance in school trips by addressing data censorship problems. The hazard-based model showed that 85% of students failed to walk when their travel distance exceeded 1,900 m. In Western countries, some studies have found this distance to be around 1,500 m. This study found girls were less likely to walk compared with boys. Furthermore, safety, time, and comfort of the trip turned out to be influential in parental objections, such that they decrease the critical walking distance by 18%, 13%, and 11%, respectively.

To improve the level of service of traffic and predict travel demand, it is essential to analyze the behavioral factors that affect transportation mode choice and route choice at the individual level. Such analysis requires detailed data on the behavior of people in the selection of different modes and routes. This paper presents a unique data collection endeavor, intended to observe the formation of the choice set from which the final alternative is selected. The revealed preference information about the routes people considered and used for their last work or study trip was targeted. Google Maps API (application programming interface), which has the capacity to calculate and return car and public transportation routes, was employed to program the survey and adaptively show the respondents the routes according to the reported origin and destination. A pilot survey was conducted with this survey tool at the University of New South Wales, Sydney, Australia, with a sample of 200 respondents. A preliminary analysis was carried out to analyze the effectiveness of the survey tool and the specification of the choice set. Three modeling structures—multinomial logit (MNL), nested logit, and mixed MNL—were used to estimate the parameters of the preliminary analysis. The results were fairly intuitive as far as the signs of the parameters were concerned, and travel time significantly influenced route choice.

Over the past decade, policy makers and researchers in various disciplines have increasingly focused on school trip behavior. Active transportation modes have been promoted to students to improve their health and help mitigate traffic congestion and air pollution in urban areas. Policies that advocate greater use of active transport modes should account for child safety, which has been shown to be parents’ primary concern. This paper focuses on the reciprocal impact between mode choice and escorting decisions for school trips. Decisions on escorting and modes of transport were modeled jointly through use of a data-mining method, a random forest method, and an econometric (nested logit) approach. The nested logit model has the advantage of allowing interpretation of independent variables; the random forest method provides a close fit to the data. Results show that the prediction potential of the random forest model is around twice as high as that of the nested logit model. To predict the probability of choosing the walking mode, for instance, a policy maker might underestimate results by about 40% if using the nested logit model. Results also demonstrate that independent consideration of these two decisions can result in a misleading assessment of policies.

Over the past decade, policy makers and researchers in various disciplines have increasingly focused on school trip behavior. Active transportation modes have been promoted to students to improve their health and help mitigate traffic congestion and air pollution in urban areas. Policies that advocate greater use of active transport modes should account for child safety, which has been shown to be parents' primary concern. This paper focuses on the reciprocal impact between mode choice and escorting decisions for school trips. Decisions on escorting and modes of transport were modeled jointly through use of a data-mining method, a random forest method, and an econometric (nested logit) approach. The nested logit model has the advantage of allowing interpretation of independent variables; the random forest method provides a close fit to the data. Results show that the prediction potential of the random forest model is around twice as high as that of the nested logit model. To predict the probability of choosing the walking mode, for instance, a policy maker might underestimate results by about 40% if using the nested logit model. Results also demonstrate that independent consideration of these two decisions can result in a misleading assessment of policies.

At the beginning of 2011, the first generation of more than 77 million baby boomers began to turn 65. In this study, researchers explore the situation in which those baby boomers who are currently 55 to 64 years old will replace current senior citizens, 65- to 74-year-olds. This study presents a detailed descriptive analysis of activity generation and the planning and scheduling behavior of these two age groups. Global Positioning System–based data from a prompted recall activity-travel survey (the Urban Travel Route and Activity Choice Survey) are used in this study. This highly disaggregate survey with detailed activity attributes has made it possible to distinguish the preferences and flexibilities of preretirement baby boomers (55 to 64) and senior citizens (65 to 74) with respect to their daily activities. The study focuses on a diverse set of activity categories that include the following: work, school, personal, religious, health care, services, errands, discretionary, and shopping. For these activities, activity durations, times of day, and planning time horizons were studied, compared, and analyzed for both age groups. It was revealed that the main difference between these two age groups was the difference in the participation in mandatory activities. Although the two age groups had very similar behavior in choice of activity duration, their time-of-day choice behavior was very different. In addition, both age groups executed a major part of their activities impulsively. Seniors and baby boomers planned 61.6% and 56.9%, respectively, of their activities on “less than 1 h” and “same-day” planning time horizons.

Hazard models are used only moderately in the transportation field compared with their levels of application in other areas, such as medicine, political science, and economics. Many specifications of the hazard models are not well known, whereas the primary application of hazard models is limited to a basic proportional continuous hazard formulation with continuous failure time and Weibull baseline hazard function. Besides the recent advances in the methodology of hazard-based models, the basic proportional hazard formulation remains the proffered hazard-based modeling approach. This paper aims to present some popular specifications of hazard-based models and also discusses some formulations that are less frequently used in practical models. In this study, the Weibull baseline hazard, which is a monotonic function, is replaced by the nonmonotonic log logistic function, and the modified formulation is presented. The log logistic and Weibull formulations are also presented, not only in continuous form but also in discrete hazard formulations for applications in which the failures are observed in discrete time intervals. Finally, all four combinations of discrete and continuous formulations with Weibull and log logistic baselines are discussed with the option of unobserved heterogeneity of type of gamma distribution. The accelerated failure time model, which is an alternative to the proportional hazard models, is discussed. This paper attempts to present a comprehensive overview of the applications of the duration model in the field of transportation.

Dymium is a microsimulation framework written in R for integrated urban modelling. It aims to solve technical challenges faced by microsimulation modellers and make the approach more accessible to a broad range of users. With numerous packages that are available for free in R, including Dymium, all the stages – data preparation, microsimulation, calibration, and model validation – in building a microsimulation model can be done effectively under one programming environment. Although we developed the framework for modelling urban systems, i.e., demography, firmography and transport modelling, it has all necessary functionalities that can be readily adopted for other contexts.