Travel Behavior Research Research Articles

Transport mode choice in South East Asia: Investigating the relationship between transport users’ perception and travel behaviour in Johor Bahru, Malaysia

The worldwide increase in private car dependency poses a set of significant environmental, economic and social sustainability challenges that continue to undermine the urban quality of life. Rapid motorisation, particularly in South East Asia (SEA), has emerged as a global concern given the region’s cumulative population, rate of industrialisation, and large-scale urbanisation. Thus, there is a compelling need to enhance our understanding of the underlying dynamics of how people perceive and use transportation such that transport planning is better placed to address the current, unsustainable travel patterns in SEA. Despite this need, there has been relatively limited SEA-based research that has endeavoured to examine travel perceptions and transport mode choice from a non-instrumental perspective. This research redresses this deficit by investigating the relationship between transport users’ perceptions and travel behaviours within SEA, with a particular focus on psychosocial drivers of transport mode choice interfaced with more traditional instrumental measures.Spatially stratified survey data have been collected in a case study area, Johor Bahru, Malaysia, comprising users from different transport user groups. Employing regression modelling, drivers of individual’s travel behaviour are examined. Results highlight the merit in recognising the role of non-instrumental motives alongside instrumental motives to explain transport mode choice. We conclude by highlighting that transport mode choices are motivated by a range of locational, socio-demographic, psychological and cultural determinants. The current research has contributed to a better understanding of transport mode choice in Johor Bahru and provides a foundation for future SEA-based travel behaviour research. Studies in this area can inform more sustainable travel behaviour in the SEA region.

The importance of neighborhood type dissonance in understanding the effect of the built environment on travel behavior

For many years, researchers have struggled to separate the effects of personal tastes—including residential choices—from built environment and transport related factors when attempting to understand and model travel behavior. This paper will briefly describe how issues related to self-selection, if not controlled for in a travel behavior analysis, can lead to over- and under-estimation of the effect of the built environment on travel behavior. A theoretical model is presented, which is followed by an empirical analysis based on survey data capturing residential choice factors to test our theory. Our analysis shows that by separating people that have chosen their current home location based primarily on transport-related concerns from people who have located based primarily on housing and neighborhood characteristics, we are able to gain a nuanced understanding of how various “costs” associated with using public transit (access time, waiting time, and transfers) affect the likelihood of taking transit. We find a strong aversion to transfers as well as different responses to these factors based on reasons for living in a given location. We demonstrate how model predictions vary greatly especially when self-selection factors are included in the analysis. Findings from this research shed light on the importance of self-selection in travel behavior research, giving transport planners and engineers clear examples how ignoring these factors can lead to misleading findings.

Analysis of consumer response to fuel price fluctuations applying sample selection model to GPS panel data: Dynamics in individuals’ car use

The effects of fuel price increases on people’s car use have been widely discussed during the last few decades in travel behavior research. It is well recognized that fuel price has significant effects on driving distance and driving efficiency. However, most of this research assumed that these effects are invariant across individuals and weather conditions. Moreover, intrinsic variability in people’s preferences has not been given much attention due to the difficulty of collecting the necessary data. In this paper, we collected detailed travel behavior data of 276 respondents in the Netherlands, spanning a time period between one week and three months using GPS logs. These GPS data were fused with weather data, allowing us to estimate both exogenous (such as weather and fuel price) and endogenous effects (inertia and activity plans) on individual’s car use behavior. To further understand the effects of fuel price on the environment, we estimated the effects of fuel price fluctuation on CO2 emissions by car. The results show a significant degree of inertia in car use behavior in response to increased fuel prices. Weather and fuel price showed significant effects on individual’s car using behavior. Moreover, fuel price shows two-week lagged effects on individual’s travel duration by car.

Factors influencing social satisfaction and loneliness: a path analysis

Social networks have increasingly received attention in transportation research. However, more subjective aspects of the social network, such as social satisfaction and loneliness hardly received any attention in travel behavior research, whereas these aspects are very important in shaping quality of life. In this study, a path analysis is used to analyze the relationships between personal characteristics, neighborhood characteristics, travel behavior, social interactions, social satisfaction and loneliness. The analyses are based on data collected in Eindhoven and surrounding towns in the Netherlands in a survey among 177 respondents. Results show relationships between mobility and social aspects. They indicate that frequency of walking affects social satisfaction, frequency of cycling affects the number of social interactions and car ownership affects loneliness. Regarding the neighborhood characteristics, interesting results are found as well. The percentage of non-western ethnic minorities in the neighborhood is found to negatively affect social satisfaction, social cohesion is found to affect the number of social interactions and place attachment is found to affect loneliness.

Modeling Social Network Influence on Joint Trip Frequency for Regular Activity Travel Decisions

Interest in activity-based travel demand modeling has recently increased significantly because of the level of accuracy offered by this type of modeling and its applicability in travel behavior research. Understanding the linkage between social influence and travel behavior enables efficient characterization of discretionary activities that account for a major fraction of total urban trips. In particular, some recent studies looked at the social network structure of individuals and measured the influence that social network members have on the performance of social activities. However, it is expected that the process of taking joint trips (trips that individuals take with their network members) has an intrinsic social context for different activities in general (not only social activities). In this regard, this study used an egocentric (i.e., personal) network approach to explore empirically the influence of personal network characteristics on the frequency of weekly trips that individuals (egos) took part in with their personal network members (alters). With the help of zero-inflated Poisson models and egocentric social network data, the results of this study present a framework for predicting the number of joint trips for six types of activities: work, eating out, shopping, recreation, study, and extra curricular activities. Estimation findings suggest that personal network measures such as network density, homophily, heterogeneity, and ego–alter tie attributes have a significant impact on the joint trip-making process, that is, the number of weekly shared trips in which an individual participates. The findings of this study should help practitioners implement targeted policies such as car sharing for various user groups.

Defining, Measuring, and Using the Lifestyle Concept in Modal Choice Research

Although there is not a formally stated, agreed-upon definition of “lifestyle,” interest in this concept has been growing in travel behavior research. Some studies analyze what might be called lifestyles, but in fact various objective socioeconomic characteristics are combined and referred to as stage of life or household composition. This paper, therefore, provides a structured overview of the lifestyle concept (definitions, measurement methods) and illustrates the usefulness of three lifestyle approaches: (a) the socioeconomic and demographic lifestyle approach, based on socioeconomics and demographics; (b) the socio-graphic approach, based on attitudes toward the family–work balance and leisure time; and (c) the mechanistic approach, based on holiday and leisure activities. Data from an Internet survey organized in 2007 in Flanders, Belgium, were used in this analysis to indicate how lifestyles were associated with car use and active travel for active leisure activities. Adding lifestyles to the analysis increased the explained variances in modal choices, but other predictors—having a driver's license, car ownership, and the built environment—were more important. With respect to car use, a mechanistic approach that used behavioral data seemed more appropriate. With respect to active travel, no striking differences were found between the three lifestyle approaches.

A Review of Latent Variable on Urban Travel Behavior

Latent variable method is an effective way to improve the prediction accuracy of travel behavior. And a new perspective to travel behavior research is provided by latent variable. In order to solve the problems about lack of cognition and application of latent variable of travel behavior, the characteristics of existing travel behavior modeling method are analyzed based on social psychology. And the necessity of research on the latent variable is proposed. The connotation, application field and model building of latent variable on urban travel behavior are systemized. Three core problems are defined, which are identification, validation and calculation. It is reviewed and studied the opportunity, difficulty and challenge of travel behavior integrated model covering the latent variables. The relevance between measure method of latent variable and SP/RP survey is revealed. And then the model test standard is summarized. Finally the development trend of latent variable research and the direction of our efforts are proposed.

How real is a reported desire to travel for its own sake? Exploring the ‘teleportation’ concept in travel behaviour research

The ‘teleportation test’ has been used in travel behaviour research for more than a decade, as a means of assessing whether an individual views travel purely as a disutility. The teleportation concept has been used successfully in qualitative research to elicit responses and clarify attitudes to travel time. However, survey-based studies have revealed an apparent inconsistency, in that many people who report an ideal travel time greater than zero, and/or do not consider their travel time as wasted, also report wanting to teleport. This note reviews a range of studies involving the teleportation concept, highlights the inconsistency, suggests reasons for it, and proposes a research approach for testing the validity of those reasons.

Special section: Innovations in location choice modeling underlying activity-travel behavior

This special section of the Journal of Transport and Land Use focuses on location choice modeling underlying activity-travel behavior and includes five manuscripts that were originally presented in Toronto, Canada at the Thirteenth International Conference on Travel Behaviour Research, organized by the International Association for Travel Behaviour Research (IATBR).

The SMS–GPS-Trip method: A new method for collecting trip information in travel behavior research

This article presents a new method for collecting travel behavior data, based on a combination of GPS tracking and SMS technology, coined the SMS–GPS-Trip method. The state-of-the-art method for collecting data for activity based traffic models is a combination of travel diaries and GPS tracking data, an approach which is not well suited for capturing data on experiences surrounding trips. Currently increasing research is being done on how to incorporate such data in traffic models, and there is therefore a need for a method, which is suited to collect such data. The new method presented in this article builds on ideas from experience sampling methods (ESM) and it is well suited specifically for collecting such experience data. Given the use of SMS technology, this method makes it possible to reach a wide range of respondents. The usefulness of the new method is proven on a theoretical level and illustrated in practice through a case study.

TRAVEL BEHAVIOUR IN SAUDI ARABIA AND THE ROLE OF SOCIAL FACTORS

Travel behaviour research indicates that travel decisions are usually influenced by accessibility as well as characteristics of the transport systems. Factors such as travel times, travel costs, waiting times, walking times have the most significant contributions in mode choice and travel decisions. In the case of developing countries however, the most influencing factors for travel behaviour and decisions are the social factors. This is very important for transport modellers and decision makers to realise in order to achieve appropriate design and implementations of various transport policies. The influence of social and economic factors on travel behaviour are discussed and investigated in this paper. In Saudi Arabia, a randomly selected sample of 1220 households was interviewed in the Tabuk city of the Saudi Arabia and data on their socio-economic and trip-making behaviour was obtained. The relative impact of socioeconomic variables on household travel behaviour was discussed and discrete choice models were calibrated. These types of studies can be useful in the development of plans, programs and policies for the improvement of transportation systems in urban areas of the Saudi Arabia and other similar countries in the region. The findings show that the social factors have the most important impact on travel behaviour in Saudi Arabia.

Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research

The overwhelming majority of models in travel behavior research assume implicitly or explicitly that individuals choose between alternatives under conditions of certainty. The validity of this assumption can be questioned as in reality urban and transportation networks are in a constant state of flux. It is important therefore that transportation researchers develop relevant approaches and models to analyze and predict decision-making under conditions of uncertainty. Transportation researchers have tended to adopt theories and models, originally developed in social psychology and decision sciences, and explore their applicability to travel choice behavior. This invited review paper summarizes the most important of these theories and models, and illustrates their application using examples of empirical research in travel behavior research. Arguing that some basic assumptions underlying these theories may not mimic the quintessence of day-to-day activity–travel behavior, the paper is completed by reflecting on the limitations of these models and identifying avenues of future research.

Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies

Understanding travel behaviour is significant in travel demand management as well as in urban and transport planning. Over the past decade, with the advancement of data collection techniques, such as GPS, transit smart cards, and mobile phones, various types of travel trajectory data are increasingly complementing or replacing conventional travel diaries and stated preference data. Other location-aware data are used in studying human movement patterns, such as social network check-in data and banknote dispersal data. Abundance of the emerging trajectory data has driven a new wave of travel behaviour research, and introduced new research problems. This paper provides a state-of-the-art review of the travel behaviour studies categorised by trajectory data types. Based on the literature review, research challenges are discussed and promising research topics in this field are proposed for future studies.

Towards a Multi-Agent Based Software Framework for Fine-Scale Pedestrian Movement Modelling

Pedestrians are important to cities. However, pedestrians have traditionally received limited attention in travel behaviour research. One reason for this is the lack of simulation tools that can effectively describe and predict pedestrian traffic. In particular, there is a great need to model the fine-scale movement of urban pedestrian especially in the context of high-density urban living. This paper presents a multi-agent-based simulation system that models the fine-scale movement behaviour of pedestrians. A parametric test is carried out to demonstrate the performance of the system. The system is proved to be able to generate reasonable pedestrian flow compared with empirical data in the literature. The system has the potential to be applied in real-world cases and serves as a spatial decision support tool in pedestrian planning because of its support for geographical information system (GIS).

Characterizing Household Vehicle Fleet Composition and Count by Type in Integrated Modeling Framework

There has been considerable interest, and consequent progress, in the modeling of household vehicle fleet composition and utilization in the travel behavior research domain. The multiple discrete-continuous extreme value (MDCEV) model is a modeling approach that has been applied frequently to characterize this choice behavior. One key drawback of the MDCEV modeling methodology is that it does not provide an estimate of the count of vehicles in each vehicle type alternative represented in the MDCEV model. Moreover, the classic limitations of the multinomial logit model, such as violations of the independence of irrelevant alternatives property in the presence of correlated alternatives and the inability to account for random taste variations, apply to the MDCEV model as well. A new methodological approach, developed to overcome these limitations, is applied in this paper to model vehicle fleet composition and count in each body type. The modeling methodology involves tying together a multiple discrete-continuous probit (MDCP) model and a multivariate count (MC) model capable of estimating vehicle counts in vehicle type categories considered by the MDCP model. The joint MDCP-MC model system was estimated by using a Greater Phoenix, Arizona, travel survey data set. The joint model system was found to offer behaviorally intuitive results and to provide superior goodness of fit in comparison with an independent model system that ignores the jointness between the MDCP component and the MC component.

Analysis of China Tourism Research Literature in 2009 ~ 2013 Based on the Theory of Literature “Tourism Science”

This paper, taking the literature published in "tourism science" as the sample in recent five years, through the analysis of theses statistical data in EXCEL of 2009 ~ 2013, focuses on the analysis of the hot field in the research of domestic tourism and the future research trend. Research shows that tourism research focuses on travel behavior and psychological research, tourism management and decision-making research, the effect of tourism research, tourism enterprises, tourism science study and cultivation of talents; leisure tourism, adventure tourism research and tourism safety research, tourism planning. Mice tourism has become the new trend of its concern.

Measuring the impact of unfamiliar transit travel using a university access survey

A number of campaigns, policy initiatives, and research studies target promoting travel behaviour change in an effort to reduce automobile dependency. However there is scant, if any, research about the actual experience of new travel behaviours and their potential importance. Yet research in psychology has shown first impressions to be integral to all attitude development due to a phenomenon referred to as the ‘primacy effect’. However the ‘primacy effect’ concept has never been explored in the context of urban transit, one of the key modes targeted for individuals to use in place of cars.This paper explores the experience of unfamiliar travel and its potential importance by comparing first trip experiences (which in this study context refers to individuals' first time using public transport to travel to a university campus) with perceptions of overall trip experiences through a university access survey. The results show that unfamiliar travel by transit tended to be more negative experience than familiar travel. ‘Ease of navigation’ (wayfinding), ‘emotional state’, ‘ease of navigating transfer’, and ‘ease of ticketing’ were particularly negative aspects of first trips. Unfamiliar travel was found to be significantly correlated with overall ratings of transit suggesting a strong basis for the ‘primacy effect’ in public transport. Results also suggest that first trip experiences are significantly correlated with subsequent transit usage but only for ‘choice travellers’ i.e. those with access to a car and not for ‘captive’ transit users.This is a novel research area with important implications for travel behaviour and user attitude research. Suggestions are offered for future research and practical applications for transport practitioners and organisations with an interest in reducing automobile reliance.

An examination of the relationship between social interactions and travel uncertainty

Recent advances in travel behaviour research hypothesise that travellers, in particular under uncertain conditions, take a number of decisions not in total independence but as members of a social network. The travel decisions could relate to a range of choices including transport mode choice and time of departure. This paper seeks to provide an answer to the following question: Do travellers, both prior and during travelling, refer to their social network when taking travel decisions in uncertain conditions?An internet-based survey was conducted with over 2000 respondents in the two United Kingdom cities of London and Glasgow. Respondents were asked to name those within their social network and to provide information on their contacts including age, gender, relationship length, car availability, and the type and frequency of social interaction.Insights are also provided from the analysis of relationships between an individual’s socio–demographic characteristics, their ego-centric social network, their social interactions and the location in which they live, through the use of clusters analysis, and how this links to two key travel behaviour aspects: who respondents would turn to in particular for advice on travel decisions, and who (and why) they would contact, if they were experiencing an uncertain situation while travelling. It is shown that the first named member of the social network member is a key person for individuals facing travel uncertainty, and that individuals will turn to others, often within their social network, for emotional as well as decision-making support. In addition, older people, those with a lower number of contacts, and those living in smaller households are more likely to decide by themselves in uncertain travel situations.

A Process Model for Route Choice in Risky Traffic Networks

The accurate modeling of travelers’ route choice decision making when faced with unreliable (risky) travel times is necessary for the assessment of policies aimed at improving travel time reliability. Compared with econometric models, process models have not been investigated in travel decision making under risk. A process model aims to describe the actual decision making procedure and could potentially provide a better explanation to route choice behavior. A process model, the priority heuristic (Brandstatter et al. , 2006), is introduced to the travel choice context and its probabilistic version, the probabilistic priority heuristic (PPH) model, is developed in this study. With data collected from a stated preference survey, a rank-dependent expected utility (RDEU) model and two other alternative models are compared with the PPH model through cross validation. Results showed that the PPH model outperforms the RDEU model in both data- fitting and predictive performances. This suggests that the process modeling paradigm could be a promising new area in travel behavior research. Major drawbacks of the PPH model include the discontinuity of the choice probability with respect to outcomes and associated probabilities, the limited applicability in situations where one alternative dominates or almost dominates the other, and the non-trivial extension to multiple-alternative situations.

Transportation mode-based segmentation and classification of movement trajectories

The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the Global Positioning System have replaced traditional survey methods (paper diaries, telephone) because they are more accurate and problems such as under reporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this article a new method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, that is membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (including between tram, bus and car) and propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point data set collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6%.