An examination of the determinants of day-to-day variability in individuals' urban travel behavior

Day-to-day variability in individuals' travel behavior (intrapersonal variability) has been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual's travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodernographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical results support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

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.     

Weekly mobility of life cycle groups

This paper describes parts of a study of travel by urban residents on seven consecutive days. The conceptual structure of the research understands travel as a demand derived by a dynamic process of individuals and households allocating their budgets to activities within the framework posed by societal regimens. Societal rules have a time dimension which is repetitive with a weekly cycle. Thus, distinctive basic patterns of weekly mobility are expected for segments of the society and differences in the obligation to adhere to those basic patterns. The data used for testing these hypotheses is described and the issue of increasing reporting bias over the diary period is addressed. Sixteen life cycle groups of persons are selected here for the presentation of some findings.The profiles of trip rates for the groups over the seven days of the week (estimated with analysis of covariance) and the decomposition of the variances of trip rates into interindividual, intraindividual and systematic parts (by repeated measurement analysis) are reported. Characteristic differences in the volume of mobility, the shape of profiles and the variance components reflect different patterns of tripmaking for segments of the population over the week. It is concluded that even for well-defined person categories, interpersonal variety of mobility behavior is large but has to be seen in relation to even greater intrapersonal variability. Both components can best be understood within the period of one week which individuals use to organize their mobility.     

Statistical and activity-based modeling of university student travel behavior

Abstract

This paper conducts a statistical analysis of student travel behavior at Virginia Commonwealth University (VCU). The data source is the ‘University NHTS’ project launched by the Virginia Department of Transportation (VDOT) in 2009. Through this empirical study, it has been found that university student travel behavior is different from that of the general population; urban universities have lower percentages of nonmotorized trips than college-town universities; undergraduate students are likely to make more daily trips than graduate students – similarly, on-campus students make more frequent trips than off-campus students; the most frequent student activities are home and academic activities; and student group categories have virtually no impact on daily activity profiles, though activity types do have a dramatic impact on daily activity profiles. Based on these research findings, the paper makes a series of recommendations regarding trip generation, trip distribution, mode choice, and activity-based modeling.     

Exploring the trade-off between greenhouse gas emissions and travel time in daily travel decisions: Route and departure time choices

This study analyzes the problem of conflicting travel time and emissions minimization in context of daily travel decisions. The conflict occurs because the least travel time option does not always lead to least emissions for the trip. Experiments are designed and conducted to collect data on daily trips. Random parameter (mixed) logit models accounting for correlations among repeated observations are estimated to find the trade-off between emissions and travel time. Our results show that the trade-off values vary with contexts such as route and departure time choice scenarios. Further, we find that the trade-off values are different for population groups representing male, female, individuals from high income households, and individuals who prefer bike for daily commute. Based on the findings, several policies are proposed that can help to lower greenhouse gas (GHG) emissions from transportation networks. This is one of the first exploratory studies that analyzes travel decisions and the corresponding trade-off when emissions related information are provided to the road users.     

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.     

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.     

Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility

In the past decade, many studies have explored the relationship between travelers’ travel mode and their trip satisfaction. Various characteristics of the chosen travel modes have been found to influence trip experiences; however, apart from the chosen modes, travelers’ variability in mode use and their ability to vary have not been investigated in the trip satisfaction literature. This current paper presents an analysis of commuting trip satisfaction in Beijing with a particular focus on the influence of commuters’ multimodal behavior on multiple workdays and their modal flexibility for each commuting trip. Consistent with previous studies, we find that commuting trips by active modes are the most satisfying, followed by trips by car and public transport. In Beijing, public transport dominates. Urban residents increasingly acquire automobiles, but a strict vehicle policy has been implemented to restrict the use of private cars on workdays. In this comparatively constrained context for transport mode choice, we find a significant portion of commuters showing multimodal behavior. We also find that multimodal commuters tend to feel less satisfied with trips by alternative modes compared with monomodal commuters, which is probably related to their undesirable deviation from habitual transport modes. Furthermore, the relationship between modal flexibility and trip satisfaction is not linear, but U-shaped. Commuters with high flexibility are generally most satisfied because there is a higher possibility for them to choose their mode of transport out of preference. Very inflexible commuters can also reach a relatively high satisfaction level, however, which is probably caused by their lower expectations beforehand and the fact that they did not have an alternative to regret in trip satisfaction assessments.     

Systematic variability in repetitious travel

The focus of this paper is the degree to which day-to-day variability in the individual's travel pattern has a systematic, or nonrandom, component. We first review the different sources of variability in travel, emphasizing the difference between between-individual and within-individual variation and the implications of this difference for travel analysis. After discussing the impact of measurement (i.e. the way in which travel behavior is measured) on the study of repetition and variability, we use the Uppsala data to examine the level of systematic variability in an individual's longitudinal travel record. The analysis focuses on two questions:

- How well does observation over one week capture longer-term (five-week) travel behavior; in other words, is behavior highly repetitive from week to week?

- How systematic is within-individual variability; in other words, are certain stops distributed over the five-week record in a nonrandom, that is either regular or clustered, fashion?

Using measures of travel that include more than one stop attribute (e.g. activity, mode, time of day, and location), we found that:

- A seven-day record of travel does not capture most of the separate behaviors exhibited by the individual over a five-week period, but it does capture, for most people, a good sampling of the person's different typical daily travel patterns.

- Whereas a considerable portion of intraindividual variability is systematic (nonrandom), clustering is a more important source of nonrandom variation than is regularity.

The results suggest that behavior does not follow a weekly cycle closely enough for a one-week travel record to measure the longer-term frequency with which the individual makes certain stops or to assess the level of day-to-day variation present in the individual's record. Because these results are likely to reflect the particular measures of behavior we used, one conclusion of this study is the need for other studies that replicate the aims of this one but use a variety of other travel measures. Only through such additional work can we truly assess the sensitivity of our findings to measurement techniques.     

Understanding adolescents’ intentions to commute by car or bicycle as adults

This study focuses on the intentions of adolescents to commute by car or bicycle as adults. The behavioral model is based on intrapersonal and interpersonal constructs from the theory of planned behavior extended to include constructs from the institutional, community and policy domains. Data from a survey among Danish adolescents is analyzed. It is found that car use intentions are related to positive car passenger experience, general interest in cars, and car ownership norms, and are negatively related to willingness to accept car restrictions and perceived lack of behavioral control. Cycling intentions are related to positive cycling experience, willingness to accept car restrictions, negative attitudes towards cars, and bicycle-oriented future vision, and are negatively related to car ownership norms. Attitudinal constructs are related to individual characteristics, such as gender, residential location, current mode choice to daily activities, and parental travel patterns.     

Travel time estimation for urban road networks using low frequency probe vehicle data

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey

According to US Census Bureau, the number of individuals in the age group above 65 years is expected to increase by more than 100% from the year 2000 to 2030. It is anticipated that increasing elderly population will put unforeseen demands on the transportation infrastructure due to the atypical mobility and travel needs of the elderly. Consequently, transportation professionals have attempted to understand the travel behavior of the elderly including the trip frequency, trip distance and mode choice decisions. Majority of the research on elderly travel behavior have focused on the mobility outcomes with limited research into understanding the tradeoffs made by this population segment in terms of their in-home and out-of-home activity engagement choices. The goal of the current research is to contribute to this line of inquiry by simultaneously exploring the daily activity engagement choices of the elderly Americans including their in-home and out-of-home activity participation (what activities to pursue) and time alloocation (duration of each activity) decisions while accounting for the temporal constraints. Further, the study attempts to explore the relationship between physical and subjective well-being and daily activity engagement decisions of the elderly; where subjective well-being is derived from reported needs satisfaction with life and different domains of it. To this end, data from the Disabilities and Use of Time survey of Panel Study of Income Dynamics was used to estimate a panel version of MDCEV model. In addition to person- and household-level demographic variables, activity participation and time use choices of elderly were found to vary across different levels of reported physical and subjective well-being measures. The model estimation results were plausible and provide interesting insights into the activity engagement choices of the elderly with implications for transportation policy development. Among other socio-demographic variables, living arrangements (living with family versus in elderly homes) were found to have significant influence on how people participate into different in-home versus out-of-home activities. For example, elderly living in the elderly home were found to participate more into out-of-home activities compared to people living with families. Elderly with disabilities were found to compensate lower participation into out-of-home activities with more participation into in-home activities. Considerable heterogeneity was observed in time engagement behavior of the elderly across reported levels of satisfaction with finance, job and cognitive needs. For example, elderly expressing high satisfaction with job was found to spend less time in in-home social activities. Elderly reporting higher satisfaction with finance were found to spend more time into OH social and shopping activities.     

Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city

Suburban development patterns, flexible work hours, and increasing participation in out-of-home activities are making the travel patterns of individuals more complex, and complex trip chaining could be a major barrier to the shift from drive-alone to public transport. This study introduces a cohort-based approach to analyse trip tour behaviors, in order to better understand and model their relationships to socio-demographics, trip attributes, and land use patterns. Specifically, it employs worker population cohorts with homogenous activity patterns to explore differences and similarities in tour frequency, trip chaining, and tour mode choices, all of which are required for travel demand modeling. The paper shows how modeling of these important tour variables may be improved, for integration into an activity-based modeling framework. Using data from the Space–Time Activity Research (STAR) survey for Halifax, Canada, five clusters of workers were identified from their activity travel patterns. These were labeled as extended workers, 8 to 4 workers, shorter work-day workers, 7 to 3 workers, and 9 to 5 workers. The number of home-based tours per day for all clusters were modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model, and tour mode choice was modeled using a Multinomial logit (MNL) model. Statistical analysis showed that socio-demographic characteristics and tour attributes are significant predictors of travel behavior, consistent with existing literature. Urban form characteristics also have a significant influence on non-workers’ travel behavior and tour complexity. The findings of this study will assist in the future evaluation of transportation projects, and in land-use policymaking.

     

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.     

Rural mobility and mode choice: Evidence from the 2001 National Household Travel Survey

This article uses data from the 2001 National Household Travel Survey (NHTS) to compare travel behavior in rural and urban areas of the U.S. As expected, the car is the overwhelmingly dominant mode of travel. Over 97% of rural households own at least one car vs. 92% of urban households; 91% of trips are made by car in rural areas vs. 86% in urban areas. Regardless of age, income, and race, almost everyone in rural areas relies on the private car for most travel needs. Mobility levels in rural areas are generally higher than in urban areas. That results from the more dispersed residences and activity sites in rural areas, which increase trip distances and force reliance on the car. Somewhat surprisingly, the rural elderly and poor are considerably more mobile than their urban counterparts, and their mobility deficit compared to the rural population average is strikingly less than for the urban elderly and poor compared to the urban average. Data limitations prevented a measurement of accessibility, however, and it seems likely that rural areas, by their very nature, are less accessible than urban areas, especially for the small percentage of car-less poor and elderly households.     

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.     

Personal travel management: the adoption and consideration of travel‐related strategies

Traveler behavior plays a role in the effectiveness of travel demand management (TDM) policies. Personal travel management is explored in this paper by analyzing individuals' adoption and consideration of 17 travel‐related alternatives in relation to socio‐demographic, mobility, travel‐related attitude, personality and lifestyle preference variables. The sample comprises 1282 commuters living in urban and suburban neighborhoods of the San Francisco Bay Area. Among the findings: females were more likely to have adopted/considered the more ‘costly’ strategies; those with higher mobility were more likely to have adopted/considered travel‐maintaining as well as travel‐reducing strategies; and those who like travel and want to do more are less likely to consider travel‐reducing strategies. These findings, when combined with those of earlier work on this subject, present a compelling argument for the need to further understand traveler behavior – particularly in response to congestion and TDM policies.     

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.     

Multi-day activity-travel pattern sampling based on single-day data

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.