A structural equations analysis of commuters' activity and travel patterns

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, DC metropolitan area to investigate and estimate relationships among socio-demographics, activity participation, and travel behavior. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information. Three types of structural equations model systems were estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third that models the generation of complex work trip chains. The model estimation results show that strong relationships do exist among commuters' socio-demographic characteristics, activity engagement information, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. Virtually all of the results obtained in this paper corroborate earlier findings reported in the literature regarding relationships among time use, activity participation, and travel. This revised version was published online in June 2006 with corrections to the Cover Date.     

A time-use activity-pattern recognition model for activity-based travel demand modeling

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).

     

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

Urban form,travel time,and cost relationships with tour complexity and mode choice

The primary purpose of this study was to investigate how relative associations between travel time, costs, and land use patterns where people live and work impact modal choice and trip chaining patterns in the Central Puget Sound (Seattle) region. By using a tour-based modeling framework and highly detailed land use and travel data, this study attempts to add detail on the specific land use changes necessary to address different types of travel, and to develop a comparative framework by which the relative impact of travel time and urban form changes can be assessed. A discrete choice modeling framework adjusted for demographic factors and assessed the relative effect of travel time, costs, and urban form on mode choice and trip chaining characteristics for the three tour types. The tour based modeling approach increased the ability to understand the relative contribution of urban form, time, and costs in explaining mode choice and tour complexity for home and work related travel. Urban form at residential and employment locations, and travel time and cost were significant predictors of travel choice. Travel time was the strongest predictor of mode choice while urban form the strongest predictor of the number of stops within a tour. Results show that reductions in highway travel time are associated with less transit use and walking. Land use patterns where respondents work predicted mode choice for mid day and journey to work travel.

A theoretical and empirical model of trip chaining behavior

This paper addresses the theoretical and empirical issues involved in modeling complex travel patterns. Existing models have the shortcoming of not representing the interdependencies among trip links in trip chains with multiple non-home stops. A theoretical model based on utility theory and explicitly accounting for the trade-offs involved in the choice of multiple-stop chains is developed. Using this theoretical model, utility maximizing conditions for a household's choice of a daily travel pattern are derived. The optimum travel pattern is described in terms of the number of chairs (tours) traveled on a given day and in terms of the number of stops (sojourns) made on each of those chains. For a given household, the form of the optimum pattern is a function of the transportation expenditures (time, cost) required to reach potential destinations. Constraints on the conditions of optimality due to the limited and discrete nature of travel pattern alternatives are also considered. Parameters of the general utility function were estimated empirically using actual travel data derived from a home interview survey taken in Washington, D.C. The multinomial logit model is used to relate utility scores for the alternative travel patterns to choice probabilities. The resulting parameter estimates agree with theoretical expectations and with empirical results obtained in other studies. In order to demonstrate the empirical and theoretical implications of the model, forecasts for various transportation policies (e.g., gasoline price increases, transit fare reductions), as made by this model and by other less complex models, are compared. The results of these comparisons indicate the need for expanding the scope of existing travel forecasting models to explicit considerations of trip chaining behavior.     

Household travel attributes transferability analysis: application of a hierarchical rule based approach

This paper presents a system of hierarchical rule-based models of trip generation and modal split. Travel attributes, like trip counts for different transportation modes and commute distance, are among the modeled variables. The proposed framework could be considered as an alternative for several modules of the traditional travel demand modeling approach, while providing travel attributes at the highly disaggregate level that can be also used in activity-based micro-simulation modeling systems. Nonetheless, the modeling framework of this study is not considered as a substitute for activity-based models. The explanatory variables set ranges from socio-economic and demographic attributes of the household to the built environment characteristics of the household residential location. Another important contribution of the study is a framework in which travel attributes are modeled in conjunction with each other and the interdependencies among them are postulated through a hierarchical system of models. All the models are developed using rule-based decision tree method. Moreover, the models developed in this study present a useful improvement in increasing the practicality and accuracy of the rule-based travel data simulation models.     

Effects of household structure and accessibility on travel

The concept of accessibility has been widely used in the transportation field, commonly to evaluate transportation planning options. The fundamental hypothesis of many studies related to accessibility could be “greater accessibility leads to more travel”. However, several studies have shown inconsistent results given this common hypothesis, finding instead that accessibility is independent of the trip/tour frequency. In addition, empirical aggregate urban modeling applications commonly produce either non-significant or negative (wrong sign) relationships between accessibility and the trip/tour frequency. For this reason, many practitioners rarely incorporate a measure of accessibility into trip/tour generation models out of consideration of the induced demand. In this context, this study examined the effect of accessibility in urban and suburban residences on the maintenance and discretionary activity tour frequencies of the elderly and the non-elderly using household travel survey data collected in the Seoul Metropolitan Area of Korea. The major finding of this study is that a higher density of land use and better quality of transportation service do not always lead to more tours due to the presence of intra-household interactions, trip chaining, and different travel needs by activity type. This finding implies that accessibility-related studies should not unquestioningly accept the common hypothesis when they apply accessibility measures to evaluate their transportation planning options or incorporate them into their trip/tour generation models.     

Effects of household structure and selected travel characteristics on trip chaining

This paper analyzes trip chaining, focusing on how households organize non-work travel. A trip chaining typology is developed using household survey data from Portland, Oregon. Households are organized according to demographic structure, allowing analysis of trip chaining differences among household types. A logit model of the propensity to link non-work trips to the work commute is estimated. A more general model of household allocation of non-work travel among three alternative chain types — work commutes, multi-stop non-work journeys, and unlinked trips — is also developed and estimated. Empirical results indicate that the likelihood of linking work and non-work travel, and the more general organization of non-work travel, varies with respect to household structure and other factors which previous studies have found to be important. The effects of two congestion indicators on trip chaining were mixed: workers who commuted in peak periods were found to have lower propensity to form work/non-work chains, while a more general congestion indicator had no effect on the allocation of non-work trips among alternative chains.     

Defining and understanding trip chaining behaviour

Trip chaining is a phenomenon that we know exists but rarely investigate. This could be attributed to either the difficulty in defining trip chains, extracting such information from travel diary surveys, the difficulty in analysing all the possible trip chain types, or all of the above. Household travel diary surveys provide a wealth of information on the travel patterns of individuals and households. Since such surveys collect all information related to travel undertaken, in theory it should be possible to extract trip-chaining characteristics of travel from them. Due to the difficulty in establishing and analysing all of the possible trip chain types, the majority of research on trip chaining has appeared to focus on work travel only. However, work related travel in many cities does not represent the majority of activities undertaken and, for some age groups, does not represent any travel at all. This paper begins by reviewing existing research in the field of trip chaining. In particular, investigations into the definitions of trip chaining, the defined typologies of trip chains and the research questions that have been addressed are explored. This review of previous research into trip chaining facilitates the following tasks: the identification of the most useful questions to be addressed by this research; defining trip chaining and associated typologies and defining data structures to extract trip chaining information from the household travel surveys conducted in metropolitan Adelaide, South Australia. The definition and typology developed in our research was then used to extract trip-chaining information from the household travel diary survey (MAHTS99) conducted in Adelaide in 1999. The extracted trip chaining information was then used to investigate trip-chaining behaviour by households. The paper reports the results of this analysis and concludes with a summary of the findings and recommendations for further investigations.     

Activity–travel behaviour of non-workers from Bangalore City in India

This paper presents exploratory and statistical analyses of the activity–travel behaviour of non-workers in Bangalore city in India. The study summarises the socio-demographic characteristics as well as the activity–travel behaviour of non-workers using a primary activity–travel survey data collected by the authors. Where possible, the research also compares the analysis findings with the case studies on activity–travel behaviour of non-workers, carried out in developed and developing countries. This gives an opportunity to understand the differences/similarities in the activity–travel behaviour of non-workers across diverse socio-cultural settings. The preliminary exploratory analysis shed light on the differences in activity participation, trip chaining, time-of-day preference for trip departure, and mode use behaviour of non-workers in Bangalore city. Statistical models were developed for investigating the effects of individual and household socio-demographics, land use parameters, and travel context attributes on activity participation, trip chaining, time-of-day choice, and mode choice decisions of non-workers. A few important results of the analysis are the influence of viewing television at home on out-of-home activity participation and trip-chaining behaviour, and the impact of in-home maintenance activity duration on time-of-day choice. Further, based on the findings of the initial analyses, an attempt has been made in this study to develop an integrated model that links time allocation, time-of-day choice, and trip chaining behaviour of non-workers. The study also discusses the implications of the research findings for transportation planning and policy for Bangalore city.     

Comparing travel mode and trip chain choices between holidays and weekdays

Choices of travel mode and trip chain as well as their interplays have long drawn the interests of researchers. However, few studies have examined the differences in the travel behaviors between holidays and weekdays. This paper compares the choice of travel mode and trip chain between holidays and weekdays tours using travel survey data from Beijing, China. Nested Logit (NL) models with alternative nesting structures are estimated to analyze the decision process of travelers. Results show that there are at least three differences between commuting-based tours on weekdays and non-commuting tours on holidays. First, the decision structures in weekday and holiday tours are opposite. In weekday tours people prefer to decide on trip chain pattern prior to choosing travel mode, whereas in holiday tours travel mode is chosen first. Second, holiday tours show stronger dependency on cars than weekday tours. Third, travelers on holidays are more sensitive to changes in tour time than to the changes in tour cost, while commuters on weekdays are more sensitive to tour cost. Findings are helpful for improving travel activity modeling and designing differential transportation system management strategies for weekdays and holidays.     

Examination of relationships between urban form,household activities,and time allocation in the Atlanta Metropolitan Region

A substantial body of research is focused on understanding the relationships between socio-demographics, land-use characteristics, and mode specific attributes on travel mode choice and time-use patterns. Residential and commercial densities, inter-mixing of land uses, and route directness in conjunction with transportation performance characteristics interact to influence accessibility to destinations as well as time spent traveling and engaging in activities. This study uniquely examines the activity durations undertaken for out-of-home subsistence; maintenance, and discretionary activities. Also examined are total tour durations (summing all activity categories within a tour). Cross-sectional activities are obtained from household activity travel survey data from the Atlanta Metropolitan Region. Time durations allocated to weekdays and weekends are compared. The censoring and endogeneity between activity categories and within individuals are captured using multiple equations Tobit models.The analysis and modeling reveal that land-use characteristics such as net residential density and the number of commercial parcels within a kilometer of a residence are associated with differences in weekday and weekend time-use allocations. Household type and structure are significant predictors across the three activity categories, but not for overall travel times. Tour characteristics such as time-of-day and primary travel mode of the tours also affect traveler’s out-of-home activity-tour time-use patterns.     

How much can trip chaining reduce VMT? A simplified method

Trip chaining represents a way to reduce vehicle miles traveled (VMT) that does not require people to shift away from driving private automobiles. While the existing literature on trip chaining acknowledges this potential, little has been done by way of quantifying this. This research seeks to fill this gap by using a large travel survey from the San Francisco Bay area to model the VMT generated by automobile tours as a function of tour composition (i.e., the number and type of destinations on that tour). The model results indicate that many tours involving trips chains (i.e., those tours with more than one destination) generate significantly less VMT than would occur if the destinations in these tours were split into multiple tours with single destinations. Tours that combine a work and non-work destination (which are the most common types of trip chains) particularly demonstrate potential for VMT reduction. Adding a non-work destination to a work tour is usually (depending on the specific type of destination) predicted to result in a reduction of 6–11 VMT, or about 20–30 %. Adding two non-work destinations to a work tour is usually predicted to result in a reduction of 10–22 VMT, or about 25–50 %.     

Trip chaining as a barrier to the propensity to use public transport

Trip chaining is a growing phenomenon in travel and activity behaviour. Individuals increasingly seek out opportunities to minimise the amount of travel required as part of activity fulfilment, given the competing demands on time budgets and their valuation of travel time savings. This search for ways of fulfilling (more) activities with less travel input has produced a number of responses, one of which is trip chaining. A particularly important policy implication of trip chaining is the potential barrier it creates in attracting car users to switch to public transport. This paper seeks to improve our understanding of trip chaining as a barrier to public transport use. A series of discrete choice models are estimated to identify the role that socio-economic and demographic characteristics of households have on the propensity to undertake trip chains of varying degrees of simplicity/complexity that involve use of the car or public transport with an embedded commuting or non-commuting primary purpose. Multinomial logit, nested logit and random parameter logit models are developed and contrasted to establish the gains in relaxing the strict conditions of the multinomial logit model.     

Modeling travel behavior by the structural relationships between lifestyle,built environment and non-working trips

In the context of sustainable urban transport in developing countries, individuals’ travel behavior faces multiple factors which influence their mobility patterns. Recognizing these factors could be a favorable method to organize more regular and sustainable trip patterns. This study aims to identify the less well-known lifestyle along with more popular built environment as the main factors which shape travel behaviors. Employing data from 900 respondents of 22 urban areas in city of Shiraz, Iran, this paper explores travel behaviors as non-working trip frequencies by different modes. Results of structural equation model indicate a strong significant effect of individual’s lifestyle patterns on their non-working trips. However, built environment impact on travel behavior is small compared to lifestyle. Besides, other variables such as travel attitudes and socio-economic factors stay crucial in the mode choice selection. These findings indicate the necessity of regarding lifestyle orientations in travel studies as well as objective factors such as land use attributes.     

Modeling household activity–travel interactions as parallel constrained choices

The daily activity-travel patterns of individuals often include interactions with other household members, which we observe in the form of joint activity participation and shared rides. Explicit representation of joint activity patterns is a widespread deficiency in extant travel forecasting models and remains a relatively under-developed area of travel behavior research. In this paper, we identify several spatially defined tour patterns found in weekday household survey data that describe this form of interpersonal decision-making. Using pairs of household decision makers as our subjects, we develop a structural discrete choice model that predicts the separate, parallel choices of full-day tour patterns by both persons, subject to the higher level constraint imposed by their joint selection of one of several spatial interaction patterns, one of which may be no interaction. We apply this model to the household survey data, drawing inferences from the household and person attributes that prove to be significant predictors of pattern choices, such as commitment to work schedules, auto availability, commuting distance and the presence of children in the household. Parameterization of an importance function in the models shows that in making joint activity-travel decisions significantly greater emphasis is placed on the individual utilities of workers relative to non-workers and on the utilities of women in households with very young children. The model and methods are prototypes for tour-based travel forecasting systems that seek to represent the complex interaction between household members in an integrated model structure.     

Moving from trip-based to activity-based measures of accessibility

This paper studies the properties and performance of a new measure of accessibility, called the activity-based accessibility (ABA) measure, and compares it to traditional measures of accessibility, including isochrone, gravity and utility-based measures. The novel aspect of the ABA is that it measures accessibility to all activities in which an individual engages, incorporating constraints such as scheduling, and travel characteristics such as trip chaining. The ABA is generated from the day activity schedule (DAS) model system, an integrated system based on the concept of an activity pattern, which identifies the sequence and tour structure among all the activities and trips taken by an individual during a day. A byproduct is an individual’s expected maximum utility over the choices of all available activity patterns, and from this the ABA is derived. The ABA is related to the logsum accessibility measures frequently derived from destination and mode discrete choice models. The key difference is that it is generated not by examining a particular trip, but by examining all trips and activities throughout the day.A case study using data from Portland, Oregon, demonstrates the rich picture of accessibility made available by use of the ABA, and highlights differences between the ABA and more traditional measures of accessibility. The ABA is successful in (a) capturing taste heterogeneity across individuals (not possible with aggregate accessibility measures), (b) combining different types of trips into a unified measure of accessibility (not possible with trip-based measures), (c) reflecting the impact of scheduling and trip chaining on accessibility (not possible with trip-based measures), and (d) quantifying differing accessibility impacts on important segments of the population such as unemployed and zero auto households (not possible with aggregate measures, and limited with trip-based measures).     

Determinants of ground transport modal choice in long-distance trips in Spain

In transport economics, modeling modal choice is a fundamental key for policy makers trying to improve the sustainability of transportation systems. However, existing empirical literature has focused on short-distance travel within urban systems. This paper contributes to the limited number of investigations on mode choice in medium- and long-distance travel. The main objective of this research is to study the impacts of socio-demographic and economic variables, land-use features and trip attributes on long-distance travel mode choice. Using data from 2007 Spanish National Mobility Survey we apply a multilevel multinomial logit model that accounts for the potential problem of spatial heterogeneity in order to explain long-distance travel mode choice. This approach permits us to compute how the probability of choosing among private car, bus and train varies depending on the traveler spatial location at regional level. Results indicate that travelers characteristics, trip features, cost of usage of transport modes and geographical variables have significant impacts on long-distance mode choice.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.