Activity based travel demand models

The relationship between travel growth, increased congestion and effectiveness of traffic management measures can be better understood by examination of change in people's travel patterns due to congestion and its mitigation policies. The studies suggested that combined models ...

The reduction of logistics costs is very important for Mexican companies, especially for those from freight transport industry. They must manage their operations efficiently and effectively to increase the revenue, which is reduced if trucks are not being productive. The objective of this research is to provide a model for a Mexican freight transport company, which lets the company reduce transportation costs, based on assignment models to make daily assignment programme of trucks. One particularity of the model being built is the fact that it will not only include general costs as in a Generalised Assignment Procedure (GAP), but also penalty costs caused by truck drivers. The main problem consisted in minimize the total cost of assigning 100 vehicles owned by the company to 10 routes (in which it was required to be transported molasse, and sand), considering that trucks cannot be assigned to some routes due to their characteristics, as well as they must assign different operators t...

The rise of urban Big Data has made it possible to use demand data at an operational level, which is necessary to directly measure the economic welfare of operational strategies and events. GIS is the primary visualization tool in this regard, but most current methods are based on scalar objects that lack directionality and rate of change-key attributes of travel. The few studies that do consider field-based time geography have largely looked at vector fields for individuals, not populations. A population-based vector field is proposed for visualizing time-geographic demand momentum. The field is estimated using a vector kernel density generated from observed trajectories of a sample population. By representing transport systems as vector fields that share the same time-space domain, demand can be projected onto the systems to visualize relationships between them. This visualization tool offers a powerful approach to visually correlate changes in the systems with changes in demand, as demonstrated in a case study of the Greater Toronto Area using data from the 2006 and 2011 Transportation Tomorrow Surveys. As a result, it is now possible to measure in real time the effects of disasters on the economic welfare of a population, or quantify the effects of operational strategies and designs on the behavioural activity patterns of the population.

Professor Barnhardt: "There must be alternatives. You must have some technology that could solve our problem." Klaatu: "Your problem is not technology. The problem is you. You lack the will to change." This exchange between a scientist and an alien in the 2008 remake of The Day the Earth Stood Still summarizes the dilemma that is facing urban transportation today and that will continue to be a major issue in determining our ability to create a sustainable future. Many engineers and technologists think that they have the answers, while the crux of the matter lies in another popular quote provided by Walt Kelly in his April 1970 Earth Day comic strip Pogo: "We have met the enemy and they is us." The solutions to future issues of congestion, air pollution, and mobility reside as much with behavioral economists as with more traditional players. While engineers, planners, and operators of the transport system push for technology solutions, behavioral economists tell us that we need to understand the psychology behind individual decision making. The thesis of this paper on the future of public transportation and the role of transportation demand management (TDM) is that in order to affect the kind of change necessary to create a sustainable future, we need to craft the kinds of incentives, disincentives, and related messaging that impact travelers' decision making to use sustainable modes, including bus, rail, carpool, vanpool, bike, and walk. In this paper we will look back at the reactionary nature of past responses, identify some areas of current success, and then look into our crystal ball to speculate what might be expected and what might be needed in the future.

A multitude of studies have been motivated on the association between land use, urban settings and transport infrastructure to assist policy makers in sustainable planning. Alike, incorporation of cross-border trips have been an integral part of transportation demand models through external surveys. The present study seeks to explore the Incoming Cross Border Traffic (ICBT) into a study area based on the characteristics of a study area that attracts cross-border trips from outside region. This paper presents an analysis of cross-border trips in Karachi Metropolis, largest city of Pakistan, through Household Individual Survey (HIS-2010) and land use data from alternative resources. Results reveal that land use particulars, socioeconomic characteristics and travel attributes of individuals significantly influences cross-border trips and this effect varies spatially. Work, shopping and Education trips are discussed through separate models in this paper with a number of practical insights to policy makers for sustainable development of city. This study contribute in elucidating travel behaviour through land use parameters and also persuade professionals to integrate estimation of cross-border trips by socioeconomic parameters, in transport forecasting models.

Performance reliability of road networks has become a more prevailing concern for engineers and planners involved in network design. To improve network reliability the most, engineers and planners may need to decide which links need improvement and at the same time account for the route choice behavior of network users. Using a simple network example, a new reliability version of Braess’s paradox is presented showing that increasing the capacity or decreasing the variability of travel time for some links may actually lead to a less reliable network. A bilevel programming model is proposed through which the reliability paradox can be avoided and an optimal improvement scheme for the existing links may be determined to achieve the most reliable network, subject to a budget constraint. Finally, a numerical example is presented to illustrate the model together with a sensitivity analysis–based algorithm.

In Vehicle Routing Problems (VRPs) the decisions to be taken concern the assignment of customers to vehicles and the sequencing of the customers assigned to each vehicle. Additional decisions may need to be jointly taken, depending on the specific problem setting. In this paper, after discussing the different kinds of decisions taken in different classes of VRPs, the class where the decision about when the routes start from the depot has to be taken is considered and the related literature is reviewed. This class of problems, that we call VRPs over time, includes the periodic routing problems, the inventory routing problems, the vehicle routing problems with release dates, and the multi-trip vehicle routing problems.

The choice of 'dining out with friends' or 'wrapping up unfinished tasks at work' depends on the utility/satisfaction gained from performing each activity while being constrained by time and physical resources. In fact, such parameters as 'type', 'time of day', 'duration', 'location', 'companionship', and etc. are defining factors in quantifying the utility of activities-a challenging problem which has been the focus of research for many years. This paper, proposes a methodology to estimate the parameters of utility distributions for joint and solo activities, along with the penalty values associated with the deviation from the mode of activity start time and duration. The study utilizes travel survey data collected in Frauenfeld, Switzerland, over the period of six weeks in 2003. The proposed model is a bi-level optimization model, where the upper level maximizes the accuracy of the activity scheduling in the aggregate level and is measured using the outputs of lower level optimization models. Each lower level model is a variation of pickup and delivery problem and schedules activities for each individual in the population using the parameters of utility distribution and penalty values generated by the Genetic Algorithm. The results indicate that travelers are trying to be more consistent with their arrival time to work, school and pickup/drop off activities, also associated penalty values for deviation from the mode value for work and school activities are high. Additionally, significant differences in the estimated utility distribution for joint and solo activities are observed. The proposed methodology has the potential to be applied to any multiday travel survey data, which due to advancements made in handheld smart devices and mobile applications is becoming more convenient to collect.

Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data.

Maintaining consistency between individuals' activity choices and realistic network conditions is made possible through a unified framework of activity-based and dynamic traffic assignment models. In this study, a model formulation and solution approach is presented for an integrated system of household activity schedule adjustment and multimodal dynamic traffic assignment model. In the proposed framework, individuals are initially assigned an activity pattern that includes their activity start time, end time, sequence, and number of intermediate and final destinations. The planned schedules are loaded to the network and executed to obtain the dynamic traffic conditions. Next, the individuals who experience schedules inconsistent with the planned ones try to adjust their activity durations or trip departure times to accommodate the fluctuations. In some cases, however, the schedule inconsistencies may not be resolved through departure time or duration adjustments; therefore, the traveler may cancel an activity or trip as a response to the fluctuations. Strategies for selecting activities to be canceled are discussed, and the influence of incorporating activity cancellation on numerical convergence is presented.

Understanding the movements of people is essential for the design and management of urban areas. This article presents a novel approach to understanding the asymmetry in route choice (i.e., the degree to which people choose different walking routes for their outbound and return journeys). The study utilizes a large volume of traces of individual routes, captured using a smartphone application. The routes are aggregated to a regular grid, and matrix statistics are developed to estimate the aggregate degree of route asymmetry for different types of route (shortest, longest, weekday, weekend, etc.). The results suggest that people change their route approximately 15% of the time. Although this varied little when observing trips made at the weekend or on a weekday, people taking journeys that deviated substantially from the shortest possible path were 6 percentage points less likely to change their routes than those taking journeys that were closest to the shortest path (14 and 20% asym...

Household’s decisions are much more complex than where to live and work while also choosing the mode of transport to be used. Housing decisions are dependent on the workplace location, as people reside close to working locations so as to reduce time and cost. The paper has focused on the residential location choice for the Mumbai Metropolitan Region (MMR). The discrete choice model’s one of the family multinomial logit models is used for the analysis. Results indicate that the distance to the CBD, the price of the house, and family income are the most significant factors that influence the residential location choice.

In the United States as in many nations, there are often differences between the travel patterns of men and women with regards to the differences in travel. Traditionally, women make shorter work trips, make greater use of public transit, make more trips for the purpose of serving another person's travel needs, and drive far fewer miles per year than men. These differences in travel are delineated by the separate social responsibilities of men and women. However, in the past few decades, women have been participating more in the labor force. In addition, women still retain their family obligations as nurturers, shoppers, and homemakers. Given the changes in the transit patterns of women in recent decades, women's travel patterns still differed substantially from those of men. In fact, these emerging trends from transit patterns of women, their actual vehicle miles traveled (VMT) are starting to increase and may surpass the VMT of men. Additionally, it is speculated by transp...

Stop frequency prediction model is one of the components of the activitybased travel demand models. Most of the previous studies have considered stops during commutes regardless of their purposes. This approach does not yield the contribution of the explanatory variables to the likelihood of making stops of different purposes. Besides, most of the former studies have been conducted in larger metropolitan areas. This study attempts to cover these gaps by using 2012 travel data of Fargo-Moorhead medium-sized US metropolitan area and classifying stops on work tours into escort, non-escort, and a combination of all stops. The results of logit models indicate that personal characteristics of the commuters do not contribute to the escort stop participation likelihood. In addition, household size variables have a large impact on the likelihood of participating in escort stops and participating in the combined stops on the outbound leg of the commutes. Contrary to several previous studies, the significance and sign of the coefficient of income level vary for different stop purposes. Commuters seemed to be more likely to make more than one non-escort stop close to their workplace on the outbound legs of their commutes. The general results suggest separating the stop purposes yields more illustrative results rather than using one model for the combined stops.

turning to land planning and urban design for help in reducing automobile use and related social and environmental costs. The effects of such strategies on travel demand have not been generalized in recent years from the multitude of available studies. Purpose: We conducted a meta-analysis of the built environment-travel literature existing at the end of 2009 in order to draw generalizable conclusions for practice. We aimed to quantify effect sizes, update earlier work, include additional outcome measures, and address the methodological issue of self-selection. Methods: We computed elasticities for individual studies and pooled them to produce weighted averages. Results and conclusions: Travel variables are generally inelastic with respect to change in measures of the built environ-ment. Of the environmental variables considered here, none has a weighted average travel elasticity of absolute magni-tude greater than 0.39, and most are much less. Still, the combined effect of several ...

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they ar...

An analysis of factors associated with nonwork trips was conducted. The key variable of interest was proximity to transit-oriented development areas around train stations in New Jersey. The data were derived from a questionnaire of households around eight train stations and stratified by distance from a station. The focus was on the mode used for trips to restaurants-coffee shops and grocery-food stores, two of the most common and frequent nonwork trips. The analysis focused on the choice of walking versus driving for these trips by using an instrumental variable probit model aimed at controlling for the endogeneity associated with self-selection bias (i.e., people who prefer not to drive choose more walkable neighborhoods). Results showed strong associations between proximity to transit-oriented development areas and walking trips, and these results hold when endogeneity is controlled for. Local street density and per capita vehicle ownership also had the expected effects on walking trips.

Personal travel demand behaviour (TDB) will change in the future due to a range of factors including high price, supply reduction, insecurity and perception. We have proposed that adaptation will depend on a new metric called essentiality as well as more common variables such as income and transport networks. A new type of virtual reality, immersive game travel survey has been developed with the purpose of probing adaptive behaviour change. There are two issues addressed by the survey tool; (1) rapid, low-cost travel surveys to track TDB change, and (2) a way to survey adaptation behaviour before fuel shortages.

This article develops a rapid response ridership forecast model, based on the combined use of Geographic Information Systems (GIS), distance-decay functions and multiple regression models. The number of passengers boarding at each station in the Madrid Metro network is estimated as a function of the characteristics of the stations (type, number of lines, accessibility within the network, etc.) and of the areas they serve (population and employment characteristics, land-use mix, street density, presence of feeder modes, etc.). The paper considers the need to evaluate the distance threshold used (not the choice of a fixed distance threshold by assimilation from other studies), the distance calculation procedure (network distance versus straight-line distance) and, above all, the use of distance-decay weighted regression (so that the data from the bands nearer the stations have a greater weighting in the model than those farther away). Analyses carried out show that weighting the variables according to the distance-decay functions provides systematically better results. The choice of distance threshold also significantly improves outcomes. When an all-or-nothing function is used, the way the service area is calculated (straight-line or network distances) does not seem to have a decisive influence on the results. However, it seems to be more influential when distance-decay weighting is used.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they ar...

In an effort to reduce traffic congestion and excess dependency on private vehicles in Riyadh City, authorities are considering introducing public transport infrastructure and enforcement policies. This study was conducted to evaluate whether the current Riyadh City transport policy measures are adequate to encourage public transport ridership and identify relevant transport policy measures to encourage a shift from use of private cars to public transport. A series of semi-structured interviews were conducted in Riyadh during June and July 2015. Seventeen study participants were selected to represent local stakeholders including transport experts and representatives of Riyadh City authorities. It was found that existing transport policy measures involve conventional approaches e.g. increasing road infrastructure to match growth in private vehicle ownership. There is, however, considerable support for reshaping the existing Travel Demand Management policies and measures along with introducing a public transport system. Improving infrastructure to enhance accessibility to and from transit stations, selecting appropriate Transit-Oriented Development sites, parking charges, separate carriages for families, and promoting the safety of public transport facilities are perceived as the most effective strategies.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they ar...

The interest in activity-travel patterns has gained huge momentum lately. Most studies focus on analysis of particular aspects of such patterns and examine the relationships between aspects of activity patterns and sociodemographic variables. To complement this work, a study was conducted to examine whether and how activity-travel patterns vary across spatial settings. A set of descriptors was developed, and tests were conducted to determine whether these descriptors are significantly different across 8 cities and 17 neighborhoods. The results indicate that the organization of activity-travel patterns is relatively constant across urban settings. Characteristics of urban settings have a modest impact at best on descriptors of activity-travel patterns.

The relationship between travel growth, increased congestion and effectiveness of traffic management measures can be better understood by examination of change in people's travel patterns due to congestion and its mitigation policies. The studies suggested that combined models ...

The City of Edmonton in Canada conducted a stated preference survey where over 1,200 respondents were asked to consider tradeoffs involving a wide range of elements of urban form and transportation, including mobility, air quality, traffic noise, treatment of neighbourhood streets, development densities and funding sources such as taxes. Respondents were to imagine moving to a new home location and to indicate preferences among hypothetical alternatives for this new location, with these alternatives described in terms of attributes related to the elements of interest. The observations of choice behaviour thus obtained were then used to estimate choice model parameters indicating the sensitivities to these attributes. As such, these parameter estimates provide indications of the relative importance of the corresponding elements and they also provide insights into the influences of the specific home location attributes considered. It is these insights into the influences of home location attributes that is of particular interest in this book presenting a collection of modelling treatments of household behaviour.

In this study, the authors apply an integrated land use and transportation model, the Sacramento MEPLAN model, to evaluate transit investment alternatives combines with supportive land use policies and pricing policies in the Sacramento region. The current study builds upon the year-one study (Johnson et. al., 2000) in two important respects. First, the study employs a second version of the Sacramento MEPLAN model that explicitly represents floorspace consumption in the land use component of the model. Second, the transit, land use and pricing policies evaluated in the year-two project as expanded and refined in this study in response to the recommendations of local interest groups and the results of the year-one report. Finally, the evaluation of the scenarios is expanded to include total benefit and equity measures. Travel demand management; urban development; urban planning; vehicle miles of travel; vehicle monitoring

In this study, the authors apply an integrated land use and transportation model, the Sacramento MEPLAN model, to evaluate transit investment alternatives combines with supportive land use policies and pricing policies in the Sacramento region. The current study builds upon the year-one study (Johnson et. al., 2000) in two important respects. First, the study employs a second version of the Sacramento MEPLAN model that explicitly represents floorspace consumption in the land use component of the model. Second, the transit, land use and pricing policies evaluated in the year-two project as expanded and refined in this study in response to the recommendations of local interest groups and the results of the year-one report. Finally, the evaluation of the scenarios is expanded to include total benefit and equity measures. Travel demand management; urban development; urban planning; vehicle miles of travel; vehicle monitoring

The relationship between travel growth, increased congestion and effectiveness of traffic management measures can be better understood by examination of change in people's travel patterns due to congestion and its mitigation policies. The studies suggested that combined models ...

The relationship between travel growth, increased congestion and effectiveness of traffic management measures can be better understood by examination of change in people's travel patterns due to congestion and its mitigation policies. The studies suggested that combined models ...

The interest in activity-travel patterns has gained huge momentum lately. Most studies focus on analysis of particular aspects of such patterns and examine the relationships between aspects of activity patterns and sociodemographic variables. To complement this work, a study was conducted to examine whether and how activity-travel patterns vary across spatial settings. A set of descriptors was developed, and tests were conducted to determine whether these descriptors are significantly different across 8 cities and 17 neighborhoods. The results indicate that the organization of activity-travel patterns is relatively constant across urban settings. Characteristics of urban settings have a modest impact at best on descriptors of activity-travel patterns.

The development of sustainable transport and mobility systems for the future will not only need more efficient, less contaminating, and technologically enhanced systems, information, and infrastructures; it will also require a transition to new forms of living and modification of contemporary forms of mobility and immobility. This challenge will undoubtedly require an understanding of past and present modes of living in order to disentangle the complexity of contemporary life and pinpoint the implications of new forms of sustainable mobility. Given that new systems, information, materialities, and infrastructures affect people differently, it is vital that preparations be made for the potentially uneven implications of introducing new mobility assemblages, particularly for countries in the global South where sustainability in transport and mobility systems are crucial to overcoming persistent inequalities. An important step in this direction is to understand the current mobility str...

Past studies show that those living near train stations tend to rail-commute far more often than the typical resident of rail-served cities. Some contend this is largely due to selfselection, marked by those with an affinity to transit riding consciously moving into neighborhoods that are well-served by transit. This article explores the self-selection question by constructing a nested logit model that jointly estimates the probability someone will reside near a rail stop and in turn commute by rail transit, using year-2000 travel data from the San Francisco Bay Area. A multinomial logit model is also used to predict car ownership levels. The research reveals that residential location and commute choice are jointly related decisions among station-area residents. A comparison of odds ratios among those living near and away from transit, controlling for the influences of other factors, suggests that residential self-selection accounts for approximately 40 percent of the rail-commute decision. These findings suggest that supportive zoning should be introduced and barriers to residential mobility should be eliminated to allow the self-selection process to occur naturally through the marketplace.

Transportation planning today requires an understanding of how income and near-rail residence jointly influence household travel behavior. This article fills a gap in the literature by showing how vehicle miles traveled (VMT) and transit trips taken (TT) vary with income and rail transit access by neighborhood type. Results indicate that, when comparing households with similar incomes and examining how the "near-rail" versus "far from rail" VMT and TT gap varies by income, the cross-sectional reduction in nominal VMT and the increase in TT on a percentage basis is generally larger for higher-income households (>$50,000), and particularly so in neighborhoods dense with both jobs and population. These findings offer support for the notion that near-transit housing targeting higher-income households can have both sustainability and transit use benefits. We note, though, that equity considerations are a strong reason to include low-income housing near rail transit, and argue that policies focusing overly singly on either low-income or high-income housing near rail transit will not be as impactful as a robust focus on mixed-income housing developments in rail transit-oriented developments (TODs).

The prediction of travel demand is a key step in transport planning and is a topic of intense discussion of the literature. This paper adds to the debate about the accuracy of travel demand prediction by addressing the 'technical' problem of spatial autocorrelation. This paper aims to systematically assess the predictive performance of spatially explicit models that take spatial autocorrelation into account vis-à-vis more conventional models. We compare the performance of both types of models in predicting the transit passenger flows for alternative transit network designs in the region of Arnhem-Nijmegen, the Netherlands. We find that models taking spatial dependence into account outperform the conventional models in nearly all respects: model fit, parameters of variables, and the quality and stability of the predictions. Results show that taking spatial autocorrelation into account is not only important for the analysis of spatial interactions, but also result in different and more accurate predictions of the impact of interventions. We conclude that travel demand models should account for spatial dependence in order to avoid overprediction of the impact of transport system changes. We end with a discussion about the relevance of our findings for the debate about the causes for the observed systematic overestimation of travel demand in the practice of transport planning.

Modelling the Impacts of Land Use on Transit Utilization in the Central Okanagan 1 2 3 4 5 6 Md. Nobinur Rahman* 7 MASc. Candidate 8 School of Engineering 9 Faculty of Applied Science, University of British Columbia 10 EME 4222 3333 University Way, Kelowna, BC V1V 1V7, Canada 11 Email: nobinur.rahman@alumni.ubc.ca 12 13 14 Cameron Taylor-Noonan 15 M.A. Candidate 16 School of Community and Regional Planning 17 Faculty of Applied Science, University of British Columbia 18 433 6333 Memorial Road, Vancouver, BC, V6T 1Z2 19 Email: ctaylornoonan@gmail.com 20 21 22 Ahmed O. Idris, Ph.D., P.Eng. 23 Assistant Professor 24 School of Engineering, University of British Columbia 25 EME 4242 3333 University Way, Kelowna, BC V1V 1V7, Canada 26 Tel: (250) 807-9809 27 Fax: (250) 807-9850 28 Email: ahmed.idris@ubc.ca 29 30 31 Rafael Villarreal, P.Eng., MCIP, RPP 32 Regional Planning Manager 33 City of Kelowna and Sustainable Transportation Partnership of the Central Okanagan 34 1435 Water Street, Kel...

This paper uses data from the 1995 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey to examine trip-chaining trends in the United States. The research focuses on trip chaining related to the work trip and contrasts travel characteristics of workers who trip chain with those who do not, including their distance from work, current levels of trip making, and the purposes of stops made within chains. Trends examined include changes in the purpose of stops and in trip-chaining behavior by gender and life cycle. A robust growth in trip chaining occurred between 1995 and 2001, nearly all in the direction of home to work. Men increased their trip chaining more than women, and a large part of the increase was to stop for coffee (the Starbucks effect). It was found that workers who trip chain live farther from their workplaces than workers who do not. It was also found that, in two-parent, two-worker households that drop off children at school, women are...

The influence of residential relocation, as a life event, on travel behavior has become the focus of research on determinants of travel behavior in recent years. Although several studies have investigated the influence of changes in the built environment of the residential environment, the complex relationships between the built environment, travel attitudes and travel behavior has remained controversial. One crucial research gap in this area is the paucity of longitudinal and semi-longitudinal research designs that could capture the influence of changes in the built environment, activity space dispersion, car and bike ownership, latent attitudes towards travel, as well as travel behavior, and the interrelationships between these factors. This study attempts to fill this gap by collecting retrospective travel behavior and attitudinal data in Helsinki Metropolitan Area using an online map-based survey tool. In total, 1321 residents who had relocated to a new residential location between three to eleven months prior to the survey date participated in the study. The study collected data related to common home-based trips during a typical week in September 2017 and September 2018, stated change in the use of different modes of transport after the move, as well as socio-demographic, and attitudinal data before and after the move. Based on residential location and the visited destinations during a typical week, respondents' activity space dispersion was measured. Structural equation modelling was then used to investigate the interrelationships between changes in the built environment, activity space dispersion, car and bike ownership, travel attitudes, and travel behavior. Results indicate the existence of reciprocal influences between changes in car and bike ownership, travel attitudes, and travel behavior. It is also found that the built environment can modify and change travel related attitudes and influence activity space dispersion, which in turn affects travel behavior. The results of this study support the effectiveness of nudging approaches rather than marketing activities in changing travel attitudes and encouraging sustainable travel behavior.

The relationship between travel growth, increased congestion and effectiveness of traffic management measures can be better understood by examination of change in people's travel patterns due to congestion and its mitigation policies. The studies suggested that combined models ...

In recent years, many countries have tried to adapt traffic demand management measures to tackle social problems such as reducing traffic congestion and lowering vehicle emissions. When trying to consider these traffic demand management measures, it is necessary to consider people's travel behavior in detail. It was understood that the implementation of traffic management measures such as road pricing and teleworking has not just affected people's modal choice and route choice, these measures have a high likelihood to change activity patterns of all the people affected either directly or indirectly. The current method of the conventional trip-based traffic demand forecasting (four-step model) focuses only on the change of travel behaviors; however, this method could not represent the change of activity patterns when the traffic demand management measures were implemented. Therefore, it is necessary to apply a much more robust Activity-Based Model (ABM) that could explicitly incorporate the change of activity patterns and estimate the effectiveness of implementing these measures. Thus, this study focuses on evaluating traffic demand management measures based on the change of activity patterns. By applying the ABM, road pricing and teleworking as traffic demand management measures were evaluated using the typical performance indices and activity patterns in the case of the Jakarta metropolitan area, Indonesia. Several key findings were found, population synthesis plays an integral part in ABM calculation. By applying ERP policy in the CBD area, some people shift mode, increase work-related activity patterns, and reduce the generation of secondary activities in order to avoid extra expenses for the toll. Teleworking policy increases other activities such as shopping and leisure, while it reduces work-related activities Hence, it was concluded that the analysis of activity patterns can provide additional information for decision-makers to consider in implementing a policy, by understanding how people change their activity patterns, much more efficient planning can be organized in the future.

The question of whether population density affects the amount of household automobile travel in the United States is revisited. Controls for income and demographics are included in a multivariate regression model of vehicle travel that includes vehicle ownership as an intermediate factor and that treats a household's pick of neighborhood density and the amount of travel as a simultaneous relationship. The data come from the 1990 Nationwide Personal Transportation Survey. It is found that density matters, but not much. A 10 percent increase in density leads to only a 0.7 percent reduction in household automobile travel. By comparison, a 10 percent increase in household income leads to a 3 percent increase in automobile travel. The results are similar when vehicle trips are used as the dependent variable. The effect of density is so small that even a relatively large-scale shift to urban densities would have a negligible impact on total vehicle travel.