Household Travel Behavior Research Articles

Perturbation of Residential Preferences by COVID-19 Shocks in the Greater Toronto Area

This research investigates the immediate effects of the COVID-19 pandemic on residential preferences in the Greater Toronto Area (GTA), Canada, using a stated preference (SP) survey dataset. The study examines changes in relocation preferences and trends in the GTA after the Ontario government lifted the initial lockdown. The obtained choice data is then modeled using a mixed cross-nested logit model to find substitution patterns across regions and dwelling types, as well as explore residents’ preferences for different dwelling characteristics and the accessibility of their residence, including factors such as telecommuting options. The results reveal that the pandemic caused short-term residential dissonance, with residents tending to want to move to lower-density areas to relocate to their preferred dwelling type, emphasizing telecommuting as a key factor influencing residential relocation preferences. Housing qualities were prioritized over accessibility. The study also found heterogeneous behavior among GTA residents with regard to telecommuting as a factor in residential relocation. The study’s findings are relevant for planners and policymakers in anticipating the potential long-term pandemic-induced home relocation decisions and their impact on future household travel behavior, particularly with regard to telecommuting and accessibility.

Impact of Life Events on Incremental Travel Behavior Change

Travel demand models regenerate the travel behavior of persons or households from scratch at every model run. However, the literature suggests that travel behavior remains relatively stable over time. Change in travel behavior is triggered by life events such as change in employment, household relocation, or birth of a child. The inability of existing travel demand models to represent habitual travel behavior and change in travel behavior of a person/household becuase of life events tends to exaggerate policy sensitivity and result in longer model run times to recreate travel behavior for every agent. In this study, we examined the travel behavior of persons between two consecutive years using a mobility panel survey from Germany. The travel behavior of persons with and without a life event is compared econometrically. Here, the travel behavior is measured as the number of weekly trips by activity type and mode and the impacts of six types of life events are studied. The results show that life events affect travel behavior, but the degree of impact varies by the type of life event, the trip purpose, and the mode. In some cases the impact is found to be negligible, but for many other cases the impact is profound. Moreover, general trends (not affected by life event) in travel behavior are also found. It is concluded that such dynamics in travel behavior should be represented by travel demand models for more sensible policy testing and computationally efficient travel demand models.

Residential relocation and changes in household vehicle ownership and travel behavior: Exploring the context of Hubli-Dharwad twin-cities in India from a planning viewpoint

• Studies residential relocations and travel behaviour. • Primary data from Hubli-Dharwad twin cities in India. • Descriptive analysis and structural equations modelling. • Variations in the impact of built environment on travel behaviour. • Travel attitude is not related to self-section. • Self-selection effect due to gender varies between cities. This article simultaneously analyzes residential relocation, vehicle ownership changes, and travel demand changes while accommodating potential self-selection effects to unravel the causal impacts of built environment features on vehicle ownership and travel behavior. Earlier studies on residential mobility have not considered the causal link from objective built environment features to subjective built environment, and rarely control for attitudes and objective built environment features in the empirical analysis. Besides, the literature has not discussed how households perceive neighborhood in terms of children's/women’s travel and how that changes by residential relocation and impact mobility decisions. The paper compares built environment impacts on mobility decisions in two adjacent areas in India, Hubli-Dharwad twin cities, that vary by socio-economic and land use contexts. The study notes that Dharwad residents acquired more personal vehicles than Hubli residents, while the proportion of households who relinquished their vehicles is high in Hubli. Post movement, Dharwad residents opted for areas with higher diversity and density than their counterparts in Hubli. In Hubli, households find that their current location does not help their children use active modes to schools, and women find jobs closer to home. The statistical analysis revealed that Hubli residents prefer locations with improved travel conveniences for women and children. With improvements in the children's travel friendliness, households of both cities decreased vehicle ownership, whereas household vehicle ownership of both cities increased with improvements in women’s travel friendliness. In Dharwad, moving to residential areas characterized by high land-use diversity reduces commute travel, while an inverse association is noted in Hubli. The statistical analysis did not confirm self-selection effects due to travel attitudes. In Dharwad, the latent factor on women’s travel friendliness featured in self-selection and shows an independent effect on vehicle ownership. In Hubli, the factor did not have independent effects on vehicle ownership after accommodating travel attitudes and socio-demographics.

The Missing Link? Insights from an Innovative Feedback Exercise for Household Electricity and Travel Behaviour

The objective of this study was to explore how individuals perceive their different types of energy use and evaluate if this information could be useful for further understanding energy demand reduction. Based on a literature review, an innovative feedback exercise was developed that gave participants feedback on electricity use, travel, and then both electricity use and travel simultaneously. Through the use of an interdisciplinary mixed methods research approach, the findings indicate that electricity use and travel are viewed as being very different, there is an opportunity to improve travel-related feedback when compared to electricity, and most do not see a link between electricity and travel. A small number of participants did see a link between electricity and travel, and this was also explored. These findings, which until now have not been explored in the literature, take a first attempt at comparing how individuals perceive their different types of energy use and highlight the relevance of this type of research in order to further advance energy demand management. Implications for a low carbon energy transition and future areas of research were identified and discussed, including decreasing travel in a way that is perceived as being advantageous, improving feedback that illustrates the importance of individual choices, and the importance of examining multiple energy behaviours simultaneously.

Household Transportation Energy Affordability by Region and Socioeconomic Factors

Transportation fuel is an important component of household budgets, as 3.3% of total household expenditures are for vehicle fuel nationwide and over 50% of annual household expenditures on energy are for transportation. These average values vary geographically, and higher energy cost burdens are faced by households with lower incomes. Defining transportation energy affordability burden as the percentage of annual household income spent on vehicle fuel, this study aims to quantify affordability as a function of household characteristics and geography. Through analysis at the census tract level, this study (i) projects annual household vehicle miles traveled (VMT) based on demographic factors using machine-learning techniques, (ii) estimates local differences in vehicle fuel economy and fuel price, and (iii) quantifies resulting transportation energy affordability by census tract. This study found that the average burden by tract varies from 0.15% to 8%. The variation in affordability can be largely explained by income level and vehicle fuel efficiency. Suburban and rural households spend more on transportation energy compared with urban households because of the usage of less fuel-efficient vehicle technologies and higher annual VMT. Lower-income groups have a wide distribution of the percentage of income spent on transportation energy, 1.2% to 8%, whereas the range for the highest income group ($125,000+) is from 0.15% to 3.9%. This detailed transportation energy affordability analysis provides a better understanding of regional variations in household travel behavior, helps to determine where fuel-efficient vehicle technologies are more likely to be used, and improves estimates of vehicle ownership costs.

Exploring Travel Behavior of Households with Pre-School Aged Children

Child-care centers are major trip generators for households with pre-school aged children (< 5 years). This paper presents a framework to analyze the factors affecting daily travel choices and challenges of child-care going households. Through rigorous literature review, this study first identifies important variables affecting mode choice, child-care center choice, transit use, and intention to drive. Next, it develops and distributes an online survey questionnaire to the registered child-care centers in Halifax Regional Municipality, Canada, to get data on individual households’ travel choices, challenges, factors affecting those choices, and importance level of those factors, as well as socio-demographic information. The study collects data from over 200 households, which is then analyzed using structural equation modeling to examine the effects of the identified factors such as safety, cost, distance, location, affordability, and convenience on day-to-day child-care trip decisions. The findings suggest that most of the families (73%) rely on private vehicles to make child-care trips as a result of family time, travel freedom, and parking availability. Other variables that motivate them to drive are travel safety, convenience, and opportunity to do trip chaining. Results show that 57% of the households group their child-care trip with a work trip. Distance to nearest bus stop, carrying child belongings, and length of trip to child-care are considered major barriers for transit use. The results of this study are expected to assist policy makers to understand travel patterns and barriers of households with pre-school aged children and help in designing communities to promote sustainable travel behavior.

Scenarios for transitioning cars from ICEV to BEVs and PHEVs using household level GPS travel data

There has been a continuous effort to quantify the expected greenhouse gas reductions from plug- in electric vehicles (PEVs). Most existing studies are based on standardized driving cycles to compare the performance of PEVs and conventional internal combustion engine (ICE) vehicles. This study is based on assigning the most efficient plug-in hybrid or full electric vehicle to a household based on the household travel behavior as collected by GPS tracking data of thousands of vehicles’ real trips in California, and it found that PEV adoption can reduce greenhouse gas emission by up to 65%. We further explore the market share difference in assigning optimal vehicle models by considering vehicle-based and household-based travel demand, and we found that, given currently available technology, in many cases adoption of Plug-in Hybrid Electric vehicles (PHEVs) will not necessarily result in significantly higher greenhouse gas (GHG) emissions (2.8% higher) compared to battery electric vehicles (BEVs), while PHEVs have advantages to satisfy household’s long-distance travel demand. However, if PHEV drivers never charge their vehicles, the achievable GHG emission reduction will be decreased by nearly 2/3.

Structural Equation Modelling of Household Long-Distance Flexible Travel Behavior

Structural Equation Modelling of Household Long-Distance Flexible Travel Behavior

Do high income households reduce driving more when living near rail transit?

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 impact of curbside parking regulations on car ownership

Car ownership is a major driver of household travel behavior, and has implications on transport demand, energy consumption, emission levels and land use. However, how curbside parking regulations (i.e. paid parking) affect car ownership remains unclear. Here, we employ a two-way fixed effect model using panel data and difference-in-differences estimations to determine the causal impact of changes in parking regulations and the impact of an extension of a city-wide parking policy in Barcelona. Our results show that the introduction of paid curbside parking to reduce non-resident/visitor demand had a positive impact on resident car ownership levels (who enjoy parking permits for free or at a very low cost – 1€/week). Our calculation of the net social cost of reallocating curbside parking to residents indicates that the costs derived from the residents’ parking subsidy and their likely increase in car usage can easily offset the benefits derived from visitor-trip deterrence, showing the relevance of the tradeoff between efficiency and acceptability.

Scissors Difference of Socioeconomics, Travel and Space Consumption Behavior of Rural and Urban Households and Its Impact on Modeling Accuracy and Data Requirements

It is believed that the “scissors difference” of socioeconomics between rural and urban households in typical municipalities of China is significant. This may result in differences in their behavior and has important implications for urban land use and transportation planning policies, as well as related modeling accuracy and data requirements. However, detailed analyses regarding such “scissors differences” between rural and urban groups in China have not been done before. In this study, travel survey data collected from the City of Wuhan in 2008 is used to study if rural and urban households are statistically different in terms of household income, household size, space consumption, highest household mobility and travel distance. A set of statistical tests, such as the Kolmogorov–Smirnov test, Mann–Whitney U test and Kruskal–Wallis H test, are applied to the study data. The study results show that the “scissors difference” is found to be statistically significant in terms of household size (HS), household income (HI), building area (BA) consumed and household mobility (except for travel distance) between rural and urban households. Conversely, analyses applied to travel distance of urban and rural household subgroups (categorized by HS and HI) reveal that the urban and rural counterparts show almost exactly opposite behavior. The study results also suggest that such differences should be explicitly considered in relevant modeling exercises by separately setting up urban and rural household groups, but the number of household groups used should be determined based on a balance between modeling accuracy and data required/modeling workload.

How do they get by without cars? An analysis of travel characteristics of carless households in California

In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. We perform parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a CHTS question that inquires why a carless household does not own any motor vehicle. We find substantial differences between our different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.

The Burden of Choice? Assessing the Impact of School Choice on Student Travel and Household Activity Philadelphia

The debate over school choice generally neglects the effects of school choice on student and household travel, and additional work is needed to understand how transportation policies foster educational equity or exacerbate inequity in large urban school districts. This study examines how school choice affects household travel behavior in Philadelphia. School choice creates access to better neighborhoods and schools, but at a cost of increased and inefficient travel. We offer direction for how education administrators and transportation planners can use disparate sources of data to estimate school policy effects and call for improved data collection by local and regional planners.

On target? Examining the effects of information displays on household energy and travel behaviour in Oxford, United Kingdom

The energy literature is rich with information on the targeted effects of a behavioural intervention – numerous studies have examined if/how a particular energy behaviour changes over time. There has, however, been much less research on the untargeted effects of a behavioural intervention – limited studies have examined if/how a particular energy behaviour changes untargeted behaviour. More specifically, the relevant literature does not explicitly examine simultaneously how targeted and untargeted effects occur. The purpose of this paper is to examine the targeted and untargeted effects of an electricity display intervention on electricity, gas, and travel through a mixed methods (quantitative and qualitative) approach. The findings indicate that in a sample of 19 participants, 2 had no behavioural or attitudinal changes, 2 had changes in electricity only, 11 had changes in electricity and gas, and 4 had changes in electricity, gas, and travel. Reasons for this are explored through illustrative case studies. This is followed by a discussion and conclusion, where implications and areas of future research are identified.

The impacts of built environment characteristics of rail station areas on household travel behavior

Transit-oriented development (TOD) has gained popularity worldwide as a sustainable form of urbanism by concentrating developments near a transit station so as to minimize auto-dependency and maximize ridership. Existing TOD studies, however, have limits in terms of small sample size and aggregate-level analysis. This study examines various travel outcomes – VMT, auto trips, transit trips, and walk trips – in rail-based station areas in eight U.S. metropolitan areas in order to understand the role of neighborhood built environment characteristics. Two-stage hurdle models handle excess zero values in trip count variables and multi-level models deal with three-level data structure – household within station areas within regions. The final models show that automobile use is associated with land-use diversity and street network design of a station area; transit use is strongly related to transit availability and land-use diversity; and walking is related to transit availability, land-use diversity, and street network design. The weakest influence among station-area environment factors is density. In sum, a TOD, a station area having a dense, mixed-use, walkable, and transit-friendly environment, motivates residents to walk more and take transit more while driving less.

Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared to many existing classification methods (e.g., hot-spot detection, sequential alignment method), the network method substantiates the social meanings underlying the interconnectedness and similarities of people's activity trajectories and better integrates spatial interaction (colocation or distance-decay) and temporal connections (concurrence or sequence) of daily lives in the measure of similarity. This approach enables us to detect variant community structures, with individuals in the same community interacting relatively more than individuals belonging to different communities, by decomposing the complex trajectories into different meaningful events (e.g., activities, trips, tours, and subsequences). We also demonstrate the practicality and scientific merit of the network analysis approach in a case study of household travel behavior in Charlotte, North Carolina. Results derived from disaggregated survey data establish the effectiveness and flexibility of the network methods to detect cohesive communities of individuals and ATPs by different narratives of everyday-life events. This study also suggests that the network analysis approach has great potential to classify large datasets of other space-time trajectories and to discover policy-sensitive activity, trip, and tour patterns that help us develop policy and planning alternatives for sustainable communities and mobility.

The research on prediction models for urban family member trip generation

This paper validates the hypothesis that head of household's attributes and travel behavior having effects on other family members' trip frequency in the same household. With using Multinomial Logit Model (MNL) and data source obtained from Resident Trip Survey in Nanjing, 2010, it shows that other family members' trip frequency are much more relative to head of household's. Therefore, we propose an idea that investigates the potential possibility of using Multinomial Logit model (MNL) and Support Vector Machine (SVM) to predict other family members' trip frequency with household's information. By comparing the prediction accuracy, it is found that the SVM only slightly performs better on overall average accuracy. Then a modified SVM model with significant variables estimated by MNL model is given. The results report that, modified SVM model not only performs better on overall average accuracy than general SVM model and MNL model, but significantly improves partial average accuracy compared with general SVM model. However, the modified SVM still expectedly has a poor performance on predicting for small proportion samples. In the end, some possible improvements are discussed as well as expectation for further studies.

Urban household travel behavior in a time of economic crisis: Changes in trip making and transit importance

This research explores changes in trip frequency and importance of bus transit in an urban environment affected by the financial crisis of 2008. The survey was conducted in 2009 in the Reykjavik capital region of Iceland and yielded 1,160 responses.Descriptive analyses and multivariate statistics are applied. The results demonstrate the economic crisis influenced people's travel behavior. About 30% of the participants make fewer trips since the crisis, due in part to reduced income and/or unemployment, suggesting increased risk of transport-related social exclusion. About 20% of the participants perceive bus transit as more important than before, to some extent at the expense of personal automobiles.Important variables associated with reduced trip frequency and increased importance of bus transit were: working more at home and more than 40% household income reduction. Those using their own vehicle less also reported greater bus transit importance.The residents in the suburbs of Reykjavik and in the nearby communities were more likely than the residents in central parts of Reykjavik to reduce their number of trips, suggesting that central city residents were less sensitive to the economic crisis in terms of transportation. The importance of the bus system was found more based on personal and household characteristics than neighborhood type (urban, suburban, nearby communities).

Application of Hazard Based Model for Choice Set Formation of Housing Location

The problem of choice set formation for decision makers is an important subject in discrete choice modeling especially when the choice set contains a large number of elemental alternatives. In general, the choice set of an individual could be a randomly sampled choice set; however, this is claimed to be a behaviorally inacceptable practice due to the fallacious assumption of full knowledge of individuals about potential random choices. This brings up the need to come up with methods to logically allocate credible choice alternatives for individuals. This article attempts to provide a detailed study of this screening and filtering practice to develop a modeling framework that can replicate the choice set formation process. In order to show the potential of the method, two decision criteria — affordability and maximum distance to work — are considered the potential attribute that the household evaluates for feasible housing alternatives. It is postulated that alternatives will only be included in the choice set if the maximum work distance and price satisfy the household thresholds. This article explores the application of proportional hazard models in the housing search process. Some of the specifications of hazard-based models that are typically used on temporal data are examined on work distance and price. The study has used the household travel behavior survey conducted by Chicago Metropolitan Agency for Planning (CMAP). Furthermore, several extensive land use and transportation related data sources are incorporated to complement the scope of the modeling results.

Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Previous urban sprawl studies have typically taken a cross-sectional approach without examining how sprawling urban areas are performing over time. Longitudinal studies of individual or household travel behavior and built-environment preference have made some progress in this direction, but very few studies have explored the longitudinal interaction of urban form with transportation and environmental outcomes. This study begins to fill this gap by evaluating the transportation and environmental impact dynamics of several key dimensions of urban sprawl: density, land use mix, centrality, and street connectivity. The central hypothesis is that while the built environment is durable and development patterns change slowly, the impacts of urban sprawl are dynamic rather than static and are accelerated in more sprawling cities relative to less sprawling cities. To test this hypothesis, a panel dataset of 7 years (2000–2007) was developed for 60 Metropolitan Statistical Areas of the US and a hybrid modeling framework that incorporates fixed and random effects is applied to evaluate different transportation and environmental outcomes over time. We found that the influence of urban centrality or the strength of urban sub-centers on the travel pattern of commuters and transit users is dynamic. This suggests that people living in cities with stronger centers drive less and use public transportation more over time. For environmental outcomes, we found that cities with higher density have experienced a significant decrease in ambient ozone and PM2.5 concentrations after controlling city-specific variables.