Travel Behavior Variables Research Articles

The Effect of Built Environment on Carbon Emission from Travel Behavior in the City center of Surabaya

Climate change is currently demanding attention due to global warming caused by increased Greenhouse Gas (GHG) emissions from the land transportation sector. The built environment is one of the factors that can influence non-motorized travel and emissions production. The built environment is identified using the 3D component approach (Density, Diversity, Design). The City center of Surabaya area is one of the Central Business Districts (CBDs) that is not exempt from emission issues and has the potential to decrease emissions through environmental arrangement scenarios. This research investigates the correlation between the 3D components and carbon emission production using the method of correlation analysis and multiple linear regression. The analysis results show a significant correlation between carbon emission production and the travel behavior of the city center of the Surabaya area community. Travel behavior variables influencing carbon emission production are household income, vehicles ownership, and travel distance. Furthermore, the study results indicate a significant relationship between travel behavior and the 3D components, as well as a significant association between the 3D components and carbon emission production. The 3D components that affect carbon emission production are population density, land use mix index, and pedestrian pathway density.

Analysis of travel demand variability based on social media data: a case study in the city of Fortaleza

Understanding the variability in citizens' travel behavior within a city is essential for comprehending urban mobility. However, obtaining daily data on these journeys through traditional methods is expensive. Although alternative methods, such as using social media data, are widely employed in the academic community, they have contributed little due to the low representativeness of the data and the lack of efforts to understand user behavior. This study fills this gap by analyzing travel variability in Fortaleza using social media data from platforms like Instagram and Twitter. Employing empirical and parametric statistical techniques, such as box plots and statistical tests (ANOVA, Kruskal-Wallis, and Tukey), hypotheses based on a literature review analysis were validated. The results revealed patterns similar to those observed in daily life and also yielded unexpected conclusions about the phenomenon. Despite limitations regarding data representativeness, important insights were obtained for understanding daily travel patterns in Fortaleza.

Cross-analysis of the variability of travel behaviors using one-day trip diaries and longitudinal data

Many travel surveys used in transportation planning still collect information over a single weekday while mobility and activity patterns fluctuate from day to day. However, the emergence of new continuous data sources has led to the generation of large amounts of longitudinal data. Therefore, this paper aims to value these two data types to cross-analyze the temporal variability of aggregated travel behaviors. A weighting method based on large-scale single-day survey samples was first developed to extend the representativeness of the daily trip diaries available in the three most recent Origin-Destination household travel surveys of Montreal, Canada, to the entire data collection period (four months in the fall). This method was then validated using passive data streams (smart card and count data) for three modes: car, subway, and bicycle. The variations in the daily use of these modes measured independently by the surveys and passive data were compared over several months of the fall period and throughout an average week. Indicators were also proposed to evaluate whether the two data sources capture the same trend and seasonality, especially the weekly rhythms. The results show that it is possible to accurately infer day-to-day variability in travel behavior from a large-scale cross-sectional single-day survey. Furthermore, this paper demonstrates the potential for complementarity between traditional surveys and emerging data (at least over the survey period), as well as the possibility to combine them since there are large similarities in the aggregated travel patterns they allow monitoring.

The frequency use and the modal shift to ICT-based mobility services

The information and communication technology (ICT) plays an important role in improving energy consumption efficiency and reducing the emission level in the urban transport sector. ICT-based mobility services like ridesourcing provide smart tools and algorithms for matching travel demand and supply and more convenient door to door services. However, there is a concern that the convenience and competitive service fares of these new mobility modes encourage people to make more car travel or shift from more sustainable mobility modes like public transport to car travels. Therefore, it is necessary to study the frequency use and modal shift to this new mobility mode, particularly in the cities (like Cairo), where the ICT-based mobility services contain more ridesourcing of fossil fuel cars than other modes like online bikes/scooter sharing. A survey was conducted in Cairo, and logit models were developed to analyze the associations of socioeconomic, travel behavior variables with the frequency use and modal shift to ridesourcing. The results of ordinal logistic regression indicate that people who live near a metro station, with higher income, and with more non-work trips per week are more likely to be high-frequent users of ridesourcing. Moreover, women are more likely to use ridesourcing frequently than men in Cairo. The findings indicate that the most replaced mode by ridesourcing is traditional taxis (by 33 %), and the second and third shifted modes are private cars and public transport by 30% and 24 %, respectively. The results of multinomial logistic regression show that the socioeconomic parameters have significant associations with the probability of modal shift from public transport, taxis, and private cars to ridesourcing services. • Ordinal and multinominal logits were used to model the frequency use and modal shift. • Women are more likely to be high-frequent users of ridesourcing than men in Cairo. • Citizens living near metro stations, are more likely to use ridesourcing frequently. • The most replaced transport mode by ridesourcing is white taxis in Cairo. • The second and third shifted modes are private cars and public transport.

Segmenting travellers based on day-to-day variability in work-related travel behaviour

Travel needs for commute and business trips are complex and choices are not made based on the characteristics of individual trips, but instead based on the needs over weeks and months. For example, the cost per trip of commuting by bus varies depending upon the frequency of travel, and the cost of a monthly subway pass depends upon the number of zones visited during that period. Intrapersonal variability, namely the variation in an individual's travel behaviour from day to day, therefore shapes our transport choices and should influence service provision. Changes in working patterns such as increases in part time working, self-employment and tele-commuting challenge the traditionally held assumptions that work activities are fixed in time and space, thus making intrapersonal variability increasingly relevant. This research uses a data-driven approach to segment workers based on their work-related travel behaviour, including frequency of travel and both spatial and time of day intrapersonal variability. The analysis uses survey and seven day travel diary data for over 110,000 people collected over a 19 year period in England. Four groups of workers were identified: infrequent, spatially variable, temporally variable and regular travellers. These groups do not align closely with self-reported working arrangements such as self-employment or part time working. The group of regular travellers has decreased in size between 1998 and 2016 but remains the largest group, containing just under 60% of workers in 2016. Both the infrequent and spatially variable groups have grown over the same period. For a small but growing group of workers, a seven day diary is insufficient to understand their work-related transport needs as little or no work travel is recorded. These findings have implications for the design of public transport ticketing, the design of mobility as a service packages and the appraisal of congestion charging schemes.

Identification of neighborhood typology for potential transit-oriented development

While a good number of studies have defined neighborhood typologies based on transit-oriented development (TOD) factors, the literature offers limited clues for the identification of neighborhood types that are likely to be designated as TODs. The present study fills this gap through the use of a cluster-multilevel modeling approach for examining the underlying effects of urban structure on travel behavior among different neighborhood types. A two-step cluster analysis on spatial data from 47 neighborhoods of the Delhi metropolitan region has resulted in six neighborhood types. The study verifies the developed typology through the analysis of significant differences in terms of socio-demographic, and travel behavior variables, and found that the typology is valid. Besides, individual multilevel regression (MLR) models were developed using 5553 individual responses from all neighborhoods of typology. The MLR models were evident that the urban structure has an apparent effect and explains about one-third of the total variation in the distance traveled, assuming that other factors such as residential self-selection, land values, and environment may explain the remaining effects. This study has identified that the ‘transit’ type has shown consistent relationships between urban structure and vehicle kilometers traveled (VKT), thereby replicate TOD as a concept. The findings from the study are useful as a prescription tool for planners, policymakers, and government authorities to compare the performance of TOD characteristics within existing and planned neighborhoods. The modeling results are easy to be interpreted and are transferable to other Indian cities.

Geographically weighted logistic regression approach to explore the spatial variability in travel behaviour and built environment interactions: Accounting simultaneously for demographic and socioeconomic characteristics

The relationship between built environment and travel behaviour has been a topical subject of academic debate over the last two decades. This has given rise to a plethora of empirical literature in this area of study. Ultimately, these studies were conducted using statistical models that fail to explain spatial non-stationarity of the processes in their dataset. To improve understanding concerning built environment and travel behaviour interactions, local model against global model is suggested. The aim of this study is to analyze the spatial variation in travel behaviour and built environment interactions using Geographically Weighted Logistic Regression (GWLR) and at the same time accounting for the individual attributes of commuters (e.g., demographic and socioeconomic characteristics). Based on valid responses from 1028 survey points carried out in Benin metropolitan region, a GWLR of travel mode choice was estimated. The result shows that unlike global statistics, local model revealed a significant spatial variation in the association between travel mode choice and the factor scores of demographic and socioeconomic variables across neighbourhoods. GWLR model also revealed the occurrence of spatial mismatch between demographic and socioeconomic characteristics, and this created a dichotomy by demarcating the neighbourhoods into two levels of influence. The result further showed that built environment variables are weak predictors of mode choice in the region. Local model proved to be most suitable for exploring this relationship since it accounted for local variation which is often lost when using global models.

Sociodemographic, Psychographic, and Behavioral Correlates of Fun in the Destination Context

Over the past decade, experiential consumption has received a growing attention from tourism and hospitality scholars. Even though fun is one of the most used concepts in daily rhetoric, it was mostly mentioned as a related concept in studying other variables in tourism, hospitality, and leisure contexts. The current study investigated fun as a construct and its sociodemographic, psychographic, and behavioral correlates. On an online research platform, 412 respondents living in the US answered questions about dimensions of fun related to destinations, sociodemographic, psychographic, and several travel behavior variables. Data revealed that both gender and the emotion/logic-based decision-making trait explain the importance of destination fun dimensions. Expectation of destination fun was further found to be significantly correlated with the preferences for travel types, travel risk concerns, and preferences for travel information sources. The theoretical and practical implications are discussed.

Cycling environmental perception in Beijing – A study of residents' attitudes towards future cycling and car purchasing

This study focuses on three groups: cyclists, non-cyclists, and non-car owners and examines the significance of the perceived cycling environment, current travel behavior, urban form and socio-demographic variables for the respondents' attitudes towards future cycling and car purchasing. The paper uses survey data (N = 1427) collected in eight Beijing neighborhoods. The analysis is carried out by means of principal component analysis and multinomial logistic regression analysis. The respondents were generally more positive towards continuing cycling or cycling more in the future than towards car purchasing. The perceived cycling environment was found to be associated with respondents' attitude towards future cycling and car purchasing. The higher the level of satisfaction with the clarity of cycling space allocation and the higher the agreement with pro-cycling policies, the higher the probability that the respondents will cycle in the future and the lower probability that they will buy a car. Associations with current travel behavior indicate that long everyday travel distances (e.g. 10 km to work or longer) negatively affects the respondents attitude towards their future cycling, whereas short everyday travel distances up to 2 km are positively linked to future cycling prospects. Non-car owners' attitude to future car ownership is strongly linked to socio-demographic status - low education and low income level groups seems to be most unlikely to take up driving in the future. To encourage people to cycle more and drive less, policy should direct efforts to promoting the clarity of cycling space on the street and strengthen pro-cycling policies. Attention should also be given to stabilizing the current travel modes of non-car users, including promoting the image of cycling, improving the service of walking, cycling, public transport and generally by introducing attractive alternatives to private car ownership.

An analysis of first-time and repeat visitors to Korean national parks from 2007 and 2013

This paper examined visitors to Bukhansan National Park in Korea based on first-time and repeat visitation. Bukhansan National Park is Korea’s most heavily visited national park with annual visitation of six million visitors in 2015. Data used in this article were collected on-site in the park in 2007 and 2013 by the Korea National Park Service (KNPS). The study variables included socio-demographics, travel behavior, motivation, and satisfaction with park attributes, services and facilities. Data were analyzed by each study year for first-time and repeat visitors. Results showed significant differences for both first-time and repeat visitors on several socio-demographic, travel behavior, and motivation variables within each study year. For satisfaction with park attributes, facilities and services, first-time visitors were significantly different from repeat visitors on seven variables for both years. Implications are given regarding programs and services to improve visitor satisfaction and sustain the natural, cultural and historical resources at Bukhansan National Park and other national parks in Korea. Future research suggestions are provided to learn more about visitor characteristics, standards of quality and preferences in the national parks.

Hitting the sweet spot: Variability in commute lengths and vehicle emissions across a diverse state

In this paper we consider travel across Virginia and identify sustainability “sweet spots” where commute lengths and vehicle emissions per mile combine to maximize green travel in terms of total CO2 emissions associated with commuting. The analysis is conducted across local voter precincts (N=2373 in the state) because they are a useful proxy for neighborhoods and well-sized for implementing policy designed to encourage sustainable travel behavior. Virginia is especially appropriate for an examination of variability in sustainable travel behavior and technologies because the state’s transportation, demographic, and political patterns are particularly diverse and have been changing rapidly. We identify four Virginia precinct-based sustainability clusters: Sweet Spots, Emerging Sweet Spots, Neutral and Non-sustaining. A model of demographic differences among the clusters shows that sustainability outcomes, understood in terms of both local commute behavior and vehicle emissions, are significantly associated with the diverse demography and politics of the state. We also look at changes in transportation sustainability and socio-demographic trends within the clusters over the past half-decade, showing that differences in sustainability and demographic metrics are actually accelerating within the state over time. We conclude with a discussion of the implications of the differences among the clusters for developing and implementing effective transportation sustainability policies across the state.

Modelling correlation patterns in mode choice models estimated on multiday travel data

Understanding individual choices over time and measuring day-to-day variability in travel behaviour is important to capture the full range of travel behaviour. Although not very common, to date several multi-day travel surveys have been conducted and panel data is available to model different transport choices. However, determining the length of a panel that allows revealing variability in travel behaviour remains an open question. Also, no final agreement has been reached about modelling the various dimensions of correlation over the repeated observations. In this paper, we use the six-week panel data from the Mobidrive survey to estimate a mode choice model that accounts for correlation across individual observations over two time periods: all days of a single week and different days of the week (e.g. all Mondays) in the wave. We first analyse these effects separately, estimating different models for each type of correlation; then we try to disentangle the relative effects of each type of correlation, estimating both types jointly. We found that both types of correlation appeared highly significant when estimated alone, while only the correlation across a given day over the six-week period remained significant, when both types were estimated jointly. This implies that for the Mobidrive panel there is much less variability in mode choice across weeks than across the days of each week. It also suggests that one week could be an appropriate length for a panel to estimate modal choice and to correctly reveal day-to-day variability.

Travel and the Built Environment: Insights Using Activity Densities, the Sprawl Index, and Neighborhood Types

There are many ways to evaluate the built environment, including measures of observable individual characteristics (such as activity density), continuous composite measures (such as the sprawl index), and categorically measured variables (such as neighborhood types). However, a systematic comparison of how well each of these three measurement types captures the influence of the built environment on travel behavior has not yet been undertaken. This lack presents a quandary for both researchers and practitioners who seek to quantify and describe the effects of the built environment on travel behavior. This paper assesses whether continuous, composite, or categorical measures provide more information and better-fitting models compared with measures of observable individual characteristics across four travel behaviors: vehicle miles traveled, walk trips, transit trips, and trip length. For each travel variable, four multivariate regression models were estimated with various measures of the built environment: activity density, sprawl index, neighborhood type, and combined sprawl index and neighborhood type. Both the sprawl index and the neighborhood-type models outperformed the activity density model. Moreover, a combined model with both the sprawl index and neighborhood types provided the best fit for all four travel behavior variables. These results suggest that both continuous and categorical composite variables provide unique and complementary information about how the built environment influences travel behavior. These findings underscore the importance of researchers’ decisions on how to represent the built environment quantitatively in models, because measurement decisions influence the understanding of how the built environment affects travel behavior.

Variability in daily activity-travel patterns: the case of a one-week travel diary

Understanding temporal rhythms in travel and activity patterns has been recognized as an important issue for the effective management of urban congestion. Research issues related to this topic concern the degree to which travel behaviour varies from one day to another, the differences between weekday and weekend travel, and the determinants of variability. Thanks to a seven-day travel diary collected for 707 individuals in the city of Ghent (Belgium) in 2008, this study goes further by studying this variability according to various time periods within the week and by analysing interpersonal and intrapersonal variations according to the varying attributes of activity-travel patterns. Different variance indicators and the sequential alignment method are applied for the measurement of variability of travel-activity behaviour. Moreover, the influence of individual characteristics on these variations is examined. The overall picture of a large intrinsic variability in travel behaviour (i.e. trip or home-based tour generation) is confirmed. There is more difference in the number of trips per day for a given individual depending on the various days of week than there is between individuals per se, not including the weekend period, and this aspect is reinforced when considering home-based tours. Unlike the case of trip generation, there is greater difference between persons in their daily time allocation to various activities than between days for a given person in general, either during working days or during the weekend. This is also the case for daily activity sequence. Finally, the influence of socio-demographic characteristics on intrapersonal variability is weak, whether for daily trips, tours, time use or activity sequence. The large level of intrapersonal variability in daily trip numbers already demonstrated in the literature is confirmed. Systematic day-to-day variability is shown to have an extremely low share in intrapersonal variability. The global picture is that intrapersonal variability is large while systematic day-to-day variability is marginal. Moreover, a striking result is that socio-demographic characteristics are mostly unable to explain the level of intrapersonal variability. The results reveal that individual behaviour is neither completely habitual nor completely random. On the one hand, intrapersonal variability is more important than the interpersonal one as regards daily trip numbers for the realization of mobility needs. On the other hand, activity time allocation and sequencing show an inverse trend, which can be linked with the habitual part of behaviour and the social role of the individual (through e.g. work, childcare and other activities).

Variability in baseline travel behaviour as a predictor of changes in commuting by active travel, car and public transport: a natural experimental study.

PurposeTo strengthen our understanding of the impact of baseline variability in mode choice on the likelihood of travel behaviour change.MethodsQuasi-experimental analyses in a cohort study of 450 commuters exposed to a new guided busway with a path for walking and cycling in Cambridge, UK. Exposure to the intervention was defined using the shortest network distance from each participant’s home to the busway. Variability in commuter travel behaviour at baseline was defined using the Herfindahl–Hirschman Index, the number of different modes of transport used over a week, and the proportion of trips made by the main (combination of) mode(s). The outcomes were changes in the share of commute trips (i) involving any active travel, (ii) involving any public transport, and (iii) made entirely by car. Variability and change data were derived from a self-reported seven-day record collected before (2009) and after (2012) the intervention. Separate multinomial regression models were estimated to assess the influence of baseline variability on behaviour change, both independently and as an interaction effect with exposure to the intervention.ResultsAll three measures of variability predicted changes in mode share in most models. The effect size for the intervention was slightly strengthened after including variability. Commuters with higher baseline variability were more likely to increase their active mode share (e.g. for HHI: relative risk ratio [RRR] for interaction 3.34, 95% CI 1.41, 7.89) and decrease their car mode share in response to the intervention (e.g. for HHI: RRR 7.50, 95% CI 2.52, 22.34).ConclusionsPeople reporting a higher level of variability in mode choice were more likely to change their travel behaviour following an intervention. Future research should consider such variability as a potential predictor and effect modifier of travel and physical activity behaviour change, and its significance for the design and targeting of interventions.

Length of Stay: A Case of Northern Norway

Length of stay (LOS) is one of the most commonly used variables in visitor surveys. The economic theory of consumer behavior postulates that a rational consumer maximizes utility subject to his /her budgetary restrictions. The objective of this study was to combine socio-demographic and travel behavior variables in order to examine the determinants of LOS for northern Norway as a vacation destination. Using data generated from a cross-sectional survey of visitors, the study employed truncated negative binomial regression analysis to examine the relationship between LOS and selected determinants of LOS. The results indicated that not all demographic variables but gender of visitors and income influenced LOS. Motivation, perception of the destination (brand personality) and perceived experience value also exerted varying degrees of influence on LOS. Visitors perceiving northern Norway as wholesome, cheerful, charming and sporty/outdoorsy stayed longer in the destination. Further, those with strong social motivations and experiences also stayed longer. Subsequently, it is recommended that managers carry out goal-directed product development and promotion strategies for attractions in northern Norway.

Day-to-day variability in travellers’ activity-travel patterns in the Jakarta metropolitan area

Using four consecutive days of SITRAMP 2004 data from the Jakarta metropolitan area (JMA), Indonesia, this study examines the interactions between individuals’ activity-travel parameters, given the variability in their daily constraints, resources, land use and road network conditions. While there have been a significant number of studies into day-to-day variability in travel behaviour in developed countries, this issue is rarely examined in developing countries. The results show that some activity-travel parameter interactions are similar to those produced by travellers from developed countries, while others differ. Household and individual characteristics are the most significant variables influencing the interactions between activity-travel parameters. Different groups of travellers exhibit different trade-off mechanisms. Further analyses of the stability of activity-travel patterns across different days are also provided. Daily commuting time and regular work and study commitments heavily shape workers’ and students’ flexibility in arranging their travel time and out-of-home time budget, leading to more stable daily activity-travel patterns than non-workers.

Changes in Variability and Flexibility of Individual Travel in Germany

Are daily life and daily travel behavior in Germany more flexible today than it was 10 to 15 years ago? Against the background of an increasing use of information and communication technologies (ICT) and the question of that impact on daily life, this paper analyzes changes in the variability of travel behavior. The paper examines the data from individual travel diaries of a whole week covering a longitudinal perspective over 15 years. These two dimensions of information allow for more detailed evaluation of variability profiles. The analyses were based on traditional key values of daily travel as well as new customized measures of individual variability in travel behavior in the course of a whole week. The paper investigated the temporal development by sociodemographic group. The results show that travel behavior patterns of young men and woman in Germany are becoming more similar. The young age group drives several trends, including multimodality. However, at the same time, young men are shifting their leisure habits toward more home-located activities; this effect is possibly partly induced by the influence of ICT.

An examination of recent trends in multimodal travel behavior among American motorists

ABSTRACTAccording to national statistics, 87% of all trips in the United States are by automobile and 90% of commuters typically get to work by car. Statistics for individual trips or the main mode of commuting do not capture variability in individual travel behavior over time. This article uses the 2001 and 2009 National Household Travel Surveys to analyze recent trends in the share of multimodal motorists who use a car and also walk, bicycle, or ride public transport during a day or week. This article identifies trends of multimodal behavior among car users in the United States and provides profiles of these multimodal motorists.During a typical day about 14% of American car users make at least two trips by foot, bicycle, or public transport, while during a typical week about 25% of motorists make at least seven trips by means of transport other than the car. Results from a bivariate analysis and logistic regressions suggest significant shifts toward more multimodal behavior among motorists between 2001 and 2009. Multimodal motorists tend to be younger, educated beyond high school, in households without cars, and live in high-density neighborhoods with access to a rail system. Results suggest that planning for walking, cycling, and public transport benefits a larger proportion of the U.S. population than suggested by traditional trip-based analysis.

Spatial self-selection in land-use–travel behavior interactions: accounting simultaneously for attitudes and socioeconomic characteristics

Spatial self-selection can be ascribed to two main factors: socioeconomic characteristics or attitudinal aspects towards travel and location choices. Several studies have investigated the influence of self-selection on the relations between travel behavior and land-use patterns. So far the results could be considered mixed. 
 The model proposed herein uses data collected in 2009 for the Lisbon Metropolitan Area. A structural equations model was built to study the effects of spatial self-selection due to both socioeconomic and attitudinal effects. Three model specifications were considered: one treating attitudes toward travel as exogenous and two others considering them as endogenous. The preferred specification considered attitudes as exogenous. The land-use patterns are described by factors both at the residence and employment zones of each individual. The travel behavior variables included here are multidimensional and include commuting distance, car ownership, the number of trips by mode, and the total amount of time between the first and last trips. On account of the data characteristics and sample size, Bayesian estimation, as implemented in AMOSTM software, was used. The results obtained show that although the attitudinal variables significantly influence travel behavior, they do not annul the effects of land-use patterns in also contributing to shape the same behavior.