Habitual travel behaviour: Evidence from a six-week travel diary

This paper introduces different methods to measure similarity of travel behaviour addressing the question of how repetitious travel behaviour actually is. It compares empirical results of the different methods based on the data from a six-week travel diary. In general, the results show that the day-to-day behaviour is more variable if measured with trip-based methods instead of methods based on time budgets. Furthermore, it is confirmed that the similarity declines if the method captures more of the complexity of the travel pattern. It is also shown that travel behaviour is neither totally repetitious nor totally variable. Even for the whole observation period, it is demonstrated that two days always have some common elements. Additionally, it is found that the different methods yield the same pattern of variability for different types of day. Travel behaviour is clearly more stable on work days. Similar results for all methods are also obtained concerning the question of how long the minimum period of observation should be. All measures show that the period should not be less than two weeks if one aims at measuring variability.     

Effects of increased awareness on choice of travel mode

This paper reports a field experiment with the purpose of studying the effects of increased awareness on travel mode choice. One hundred fifteen subjects were randomly assigned to an experimental and a control group. In the experimental group, a more deliberate choice of travel mode was induced and expected to result in a stronger relationship between attitude and behavior, a weaker relationship between habit and behavior, and a behavioral change among individuals with a strong habit. Attitude, habit, and behavior were measured in travel diaries and questionnaires. The results indicated no significant change in the relationship between attitude and behavior and no significant change in the relationship between habit and behavior. However, a temporally extended decrease in car use was observed in the experimental group. The effect was noted for individuals with a strong habit who reduced their car use but not for subjects with a weak habit.     

Incorporating the extended theory of planned behavior in a school travel mode choice model: a case study of Shaoxing,China

Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel.     

Water transit and excess travel: discrete choice modelling of bus and ferry trips in Brisbane,Australia

Recent investment in urban ferry transport has created interest in what value such systems provide in a public transport network. In some cases, ferry services are in direct competition with other land-based transport, and despite often longer travel times passengers still choose water transport. This paper seeks to identify a premium attached to urban water transit through an identification of excess travel patterns. A one-month sample of smart card transaction data for Brisbane, Australia, was used to compare bus and ferry origin–destination pairs between a selected suburban location and the central business district. Logistic regression of the data found that ferry travel tended towards longer travel times (OR?=?2.282), suggesting passengers do derive positive utility from ferry journeys. The research suggests the further need to incorporate non-traditional measures other than travel time for deciding the value of water transit systems.     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Investigating travel utility elements in association with travel time and mode choice: the case of Seoul,South Korea

In this paper, travel utility is conceptualized into the elements of disutility, or derived utility, and positive utility, which includes synergistic and intrinsic utility, and then analyzed in terms of the effects of these elements on weekly travel time according to three travel modes – the automobile, public transit, and nonmotorized modes – and on the choice of the annually most used mode. Linear regressions on mode-specific travel time and a multinomial logistic regression on mode choice show that, compared to life situation and land-use characteristics, utility elements are among the strongest travel determinants. Specifically, while some utility elements contribute exclusively to shifting the mode of travel and others to increasing nonmotorized travel, modal shift is most strongly affected by a disutility element, trip timeliness, and the increase in nonmotorized travel by a positive utility element, amenities.     

Risk aversion,regret aversion and travel choice inertia: an experimental study

This paper presents the results of an experimental study into the role of risk aversion and regret aversion as codeterminants of travel choice inertia. Theoretical results published by Chorus and Dellaert are tested empirically. More specifically, the expectation is tested that when (1) travelers are risk averse, (2) the quality of travel choices is uncertain, and (3) the quality is partially revealed upon usage, travel choice inertia emerges as a learning-based lock-in effect. In addition, this paper studies the role of regret aversion as a possible trigger of travel choice inertia. Analyses are based on data collected in an experiment, where the reward that participants obtain is a function of the outcome of choices they make. Empirical results suggest that the learning-based lock-in effect indeed plays a role in the context of our data. The evidence for the hypothesis that regret aversion triggers inertia is mixed at best.     

Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary

Abstract

Trip chaining (or tours) and mode choice are two critical factors influencing a variety of patterns of urban travel demand. This paper investigates the hierarchical relationship between these two sets of decisions including the influences of socio-demographic characteristics on them. It uses a 6-week travel diary collected in Thurgau, Switzerland, in 2003. The structural equation modeling technique is applied to identify the hierarchical relationship. Hierarchy and temporal consistency of the relationship is investigated separately for work versus non-work tours. It becomes clear that for work tours in weekdays, trip-chaining and mode choice decisions are simultaneous and remain consistent across the weeks. For non-work tours in weekdays, mode choice decisions precede trip-chaining decisions. However, for non-work tours in weekends, trip-chaining decisions precede mode choice decisions. A number of socioeconomic characteristics also play major roles in influencing the relationships. Results of the investigation challenge the traditional approach of modeling mode choice separately from activity-scheduling decisions.     

Accommodating risk in the valuation of expected travel time savings

Valuation of travel time savings is a critical measure in transport infrastructure appraisal, traffic modelling and network performance. It has been recognised for some time that the travel times associated with repeated trips are subject to variation, and hence there is risk embedded in the treatment of expected travel time. In the context of the expected utility framework, we use a nonlinear probability weighting function to accommodate choice made under risk. Although the empirical findings suggest small differences between the value of expected travel time savings (VETTS) in the presence and absence of risk, the mean estimate does make a noticeable difference to time benefits when applied to real projects. By incorporating nonlinear probability weighting, our model reveals that the probabilities associated with specific travel times that are shown to respondents in the choice experiment are transformed, resulting in overweighting of outcomes with low probabilities and underweighting of outcomes with high probabilities. Copyright © 2011 John Wiley & Sons, Ltd.     

Measuring uncertainty in discrete choice travel demand forecasting models

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.     

Door-to-door travel times in RP departure time choice models: An approximation method using GPS data

A common way to determine values of travel time and schedule delay is to estimate departure time choice models, using stated preference (SP) or revealed preference (RP) data. The latter are used less frequently, mainly because of the difficulties to collect the data required for the model estimation. One main requirement is knowledge of the (expected) travel times for both chosen and unchosen departure time alternatives. As the availability of such data is limited, most RP-based scheduling models only take into account travel times on trip segments rather than door-to-door travel times, or use very rough measures of door-to-door travel times. We show that ignoring the temporal and spatial variation of travel times, and, in particular, the correlation of travel times across links may lead to biased estimates of the value of time (VOT). To approximate door-to-door travel times for which no complete measurement is possible, we develop a method that relates travel times on links with continuous speed measurements to travel times on links where relatively infrequent GPS-based speed measurements are available. We use geographically weighted regression to estimate the location-specific relation between the speeds on these two types of links, which is then used for travel time prediction at different locations, days, and times of the day. This method is not only useful for the approximation of door-to-door travel times in departure time choice models, but is generally relevant for predicting travel times in situations where continuous speed measurements can be enriched with GPS data.     

The implications of travel profiles for transportation investment: The Bronx Center Project

Investment in transportation infrastructure is generally regarded as an effective means for inducing economic growth and employment in a region. However, the ability of such investments to achieve these objectives, to a large extent, depends on the degree to which travel results from these investments support or conflict with present travel patterns and needs in this region. Using this view as a basis, this paper analyzes travel conditions and choices in the Bronx New York, where large scale transportation and other development projects (commonly called the Bronx Center Project) are presently taking place. Using a large data base, composed of census tract information on socio-economic and travel behavior, the paper first examines the travel profile of the Bronx population, by estimating travel choice elasticities. On the basis of these elasticities it then assesses the impact of the Bronx Center Project on travel patterns and trends.     

Car fuel-type choice under travel demand management and economic incentives

Future levels of vehicle air pollution in urban areas will depend on the proportion of new car buyers who opt for less polluting vehicles, as these appear on the market. This paper examines the factors likely to influence the demand for lower emission and zero emission vehicles. Using a discrete choice experiment, suburban driver commuters choose between three types of vehicle, one conventional, one fuel-efficient and one electric. Each is characterized by varying vehicle cost and performance measures, range and refueling rates, and commuting costs and times. The latter are manipulated to determine how their use as economic instruments might influence vehicle choice. All cost and time variables are expressed as ratios of the respondent’s current situation. Parameters of a multinomial discrete choice model are used in a choice simulator to estimate the average choice probability of each type of vehicle under different scenarios reflecting possible future relative vehicle prices and performance levels as well as differential commuting costs and times based on policies aimed at encouraging the purchase of cleaner vehicles. The evidence is that the latter economic instruments will have modest effects on vehicle choice. By contrast there would be a large shift of demand to cleaner and zero-emission vehicles provided their cost and performance came within an acceptable range of conventional vehicles.     

Travel,transport and energy implications of university-related student travel: A case study approach

This study highlighted significant cultural differences and complexity in travel behaviour associated with travel to university across the UK and Ireland. This paper examines university travel behaviours and the implications for emissions, across the 2012–2013 academic year, based on responses from 1049 students across 17 universities in Ireland and the UK. Surveys were analysed to examine the trips of students both during term time and when accessing the universities each year. The data analysis in this paper examines three aspects of the transport implications of travel to and from university. Firstly the journey between university and term time address (or permanent address if the respondent does not have a separate term time address), secondly the journey between the university area and a separate permanent address where relevant; and thirdly implications for emissions resulting from university-related travel.The study found that student car users were more likely to be female, older students, or studying part time; male students were more likely to use active modes. The study indicated interesting differences between students living in different parts of the UK and Ireland. For example, it was found that there was a higher level of car dependence amongst Northern Irish students compared to other areas; and a greater variability in travel distances in Scotland and Northern Ireland. In England, car use was more pronounced when students travelled from their permanent address to term time address, and, as in Ireland, there was evidence of more car sharing on such trips. Public transport usage was more pronounced amongst Scottish students. The effect of these transport choices on emissions is significant and demonstrates the importance of education related trips to the development of a transport policy response. The analysis shows that annual emissions are highest for regular travel to and from university when a student has a permanent address rather than a separate term time and permanent address.     

An exploration of shoppers travel mode choice in visiting convenience stores in the UK

Using data from over 2000 convenience store customers within and outside London, this paper explores how individuals access their convenience stores and how significant the influence of their socio-demographics, shopping types and trip chaining is to their mode choice in visiting the stores. Trip chaining is found to be crucial in influencing customers' mode choice and their visit frequency. The application of logit models also shows that frequent shoppers are the ones most likely to visit the stores on foot. Interestingly, the estimation results also show that the location's density, shopping types and the day of the week are not significant in influencing travel modes. Customers who live in the most deprived areas are less likely to use a private car in visiting the stores.     

Why do urban travelers select multimodal travel options: A repertory grid analysis

The increasing number of travelers in urban areas has led to new opportunities for local government and private mobility providers to offer new travel modes besides and in addition to traditional ones. Multimodal travel provides an especially promising opportunity. However, until now the underlying reasons why consumers choose specific alternatives have not been fully understood. Hence, the design of new travel modes is mainly driven by obvious criteria such as environmental friendliness and convenience but might not consider consumers’ real or latent needs. To close this research gap, sixty in-depth interviews with urban travelers were conducted. To identify the perceptual differences of customers among different travel modes, the repertory grid technique as an innovative, structured interview method was applied. Our data show that urban travelers distinguish and select travel alternatives based on 28 perceptual determinants. While some determinants associated with private cars such as privacy, flexibility and autonomy are key indicators of travel mode choice, costs and time efficiency also play a major role. Furthermore, by comparing travel modes to an ideal category, we reveal that some perceptual determinants do not need to be maximized in order to fulfill customer needs optimally. A comparison of consumers’ perceptual assessments of alternative travel modes identifies specific advantages and disadvantages of all alternatives, and provides fruitful implications for government and private mobility providers.     

The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications

The empirical valuation of travel time savings is a derivative of the ratio of parameter estimates in a discrete choice model. The most common formulation (multinomial logit) imposes strong restrictions on the profile of the unobserved influences on choice as represented by the random component of a preference function. As we progress our ability to relax these restrictions we open up opportunities to benchmark the values derived from simple (albeit relatively restrictive) models. In this paper we contrast the values of travel time savings derived from multinomial logit and alternative specifications of mixed (or random parameter) logit models. The empirical setting is urban car commuting in six locations in New Zealand. The evidence suggests that less restrictive choice model specifications tend to produce higher estimates of values of time savings compared to the multinomial logit model; however the degree of under-estimation of multinomial logit remains quite variable, depending on the context.     

A tour-based model of travel mode choice

This paper presents a new tour-based mode choice model. The model is agent-based: both households and individuals are modelled within an object-oriented, microsimulation framework. The model is household-based in that inter-personal household constraints on vehicle usage are modelled, and the auto passenger mode is modelled as a joint decision between the driver and the passenger(s) to ride-share. Decisions are modelled using a random utility framework. Utility signals are used to communicate preferences among the agents and to make trade-offs among competing demands. Each person is assumed to choose the best combination of modes available to execute each tour, subject to auto availability constraints that are determined at the household level. The households allocations of resources (i.e., cars to drivers and drivers to ride-sharing passengers) are based on maximizing overall household utility, subject to current household resource levels. The model is activity-based: it is designed for integration within a household-based activity scheduling microsimulator. The model is both chain-based and trip-based. It is trip-based in that the ultimate output of the model is a chosen, feasible travel mode for each trip in the simulation. These trip modes are, however, determined through a chain-based analysis. A key organizing principle in the model is that if a car is to be used on a tour, it must be used for the entire chain, since the car must be returned home at the end of the tour. No such constraint, however, exists with respect to other modes such as walk and transit. The paper presents the full conceptual model and estimation results for an initial empirical prototype. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression.     

Stated adaptation surveys and choice process: Some methodological issues

The "Stated Adaptation" survey is an interactive technique which allows us to obtain a clearer picture of the attitudes and behaviours of individuals when confronted with hypothetical situations, in particular inexperienced travel conditions. This method makes use of a simulation game whose purpose is to explore on small samples individuals' choice processes when selecting between the different transport alternatives which are available to them. This paper describes how gaming-simulation is designed, with reference to the issues tackled by two surveys which have recently been carried out in France (reactions to urban road pricing and perception of electric vehicles). It describes the benefits of this experimental approach which allows stated behaviours to be checked to a considerable degree. The limits and potential developments of this survey technique are also discussed.     

Development of destination choice models for pedestrian travel

Most research on walking behavior has focused on mode choice or walk trip frequency. In contrast, this study is one of the first to analyze and model the destination choice behaviors of pedestrians within an entire region. Using about 4500 walk trips from a 2011 household travel survey in the Portland, Oregon, region, we estimated multinomial logit pedestrian destination choice models for six trip purposes. Independent variables included terms for impedance (walk trip distance), size (employment by type, households), supportive pedestrian environments (parks, a pedestrian index of the environment variable called PIE), barriers to walking (terrain, industrial-type employment), and traveler characteristics. Unique to this study was the use of small-scale destination zone alternatives. Distance was a significant deterrent to pedestrian destination choice, and people in carless or childless households were less sensitive to distance for some purposes. Employment (especially retail) was a strong attractor: doubling the number of jobs nearly doubled the odds of choosing a destination for home-based shopping walk trips. More attractive pedestrian environments were also positively associated with pedestrian destination choice after controlling for other factors. These results shed light on determinants of pedestrian destination choice behaviors, and sensitivities in the models highlight potential policy-levers to increase walking activity. In addition, the destination choice models can be applied in practice within existing regional travel demand models or as pedestrian planning tools to evaluate land use and transportation policy and investment scenarios.