Transportation Research Record: Journal of the Transportation Research Board

Random utility models customarily assume strict independence of individual decision makers. Evidence of crowding, peer pressure, herd behavior, and other instances of spontaneous discrete choice coordination indicates that decision makers interact and thus affect choices made by others. Socially influenced individual choices become biased toward either agreeing with or contradicting the choices made by peers. Because many social interdependencies are spatial, a basic spatial discrete choice model was obtained by extending random utility theory to discrete choices made by heterogeneous spatially dependent individuals. Although interdependencies are unobserved, the model permits the study of behavioral biases arising from spatial interdependencies. The spatial discrete choice model is shown to address the effects of behavioral biases on conditional choice probabilities, the marginal effects of exogenous variables on revealed preferences, and the spatial patterns of discrete choices. A pseudo maximum likelihood (PML) estimator for the model is developed, and closed-form expressions for conditional choice probability estimates are derived. The PML estimator is shown to be consistent and computationally feasible for large spatial data sets. Simulated data were used to illustrate the performance of the PML estimator for the spatial discrete choice model.

The spatial–temporal activity–presence approach is used to estimate and validate hourly activity populations for individual buildings. A case study modeled the spatial–temporal activity of students at North Carolina State University. For the validation of results, student registration records provide observations of class schedules and locations for dynamic, time-varying class or study activity populations at individual buildings. Results show that the spatial–temporal activity–presence approach provides reliable estimates. This empirical case study should improve acceptance of the spatial–temporal activity procedure for activity distribution and demonstrate its value for other planning applications.

Weather can influence travel demand, traffic flow, and traffic safety. A hypothesis—the type of weather determined the likelihood of a change in travel behavior, and changes in travel behavior because of weather conditions depended on trip purpose—was assayed. A stated adaptation study was conducted in Flanders (the Dutch-speaking region of Belgium). A survey, completed by 586 respondents, was administered both on the Internet and as a traditional paper-and-pencil questionnaire. To ensure optimal correspondence between the survey sample composition and the Flemish population, observations in the sample were weighted. To test the main hypotheses, Pearson chi-square independence tests were performed. Results from both the descriptive analysis and the independence tests confirm that the type of weather matters and that changes in travel behavior in response to these weather conditions are highly dependent on trip purpose. This dependence of behavioral adjustments on trip purpose provides policy makers with a deeper understanding of how weather conditions affect traffic. Further generalizations of the findings are possible by shifting the scope toward revealed travel behavior. Triangulation of both stated and revealed travel behavior on the one hand and traffic intensities on the other hand is a key challenge for further research.

The impact of public holidays on the underlying reasons for travel behavior, namely, the activities people perform and the trips made, is seldom investigated. Therefore, the effect of holidays on travel time expenditure in Flanders, differentiated by trip motive, is examined. The data used for the analysis stem from a household travel survey carried out in 2000. The zero-inflated Poisson regression approach is used; it explicitly takes into account the inherent contrast between travelers and nontravelers. The zero-inflated Poisson regression models yield findings that are harmonious with international literature: sociodemographic variables, temporal effects, and transportation preferences contribute significantly to unraveling the variability of travel behavior. In particular, it is shown that the effect of public holidays on daily travel behavior cannot be ignored. Triangulation of quantitative and qualitative techniques is a solid basis for insight into the underpinnings of travel behavior.

Research in developed countries has demonstrated a significant relationship between activity participation and travel behavior. Less work has been done in China, a typical developing country. Data from an activity–travel survey of 1,205 households in Shangyu, China, were used for a simultaneous structural equation model analysis of sociodemographics, activity participation, and trip chaining between household heads. Model estimation results show that activity participation is strongly correlated to trip chaining characteristics. Additionally, interactions exist between male and female household heads. It was found that the sociodemographics of male and female heads not only directly affected trip chaining but also indirectly influenced trip chaining behavior through activity participation. The findings of the study provide useful insight into the within-personal and cross-personal interactions of activity–travel behaviors between male and female heads in China.

With a focus on the multitasking behavior of public transportation users during travel, an examination is made of factors affecting activity participation along the axis of travel time. The probability of participation in an activity is represented by using a scobit (or a skewed logit) model, within which the widely used logit model is nested with the help of a skewness parameter. With this skewness parameter, it is not necessary to assume that individuals with a probability of 0.5 for performing an activity are most sensitive to changes in travel time or other influential factors. This analysis is the first attempt to apply the scobit model to transportation issues. An empirical analysis was conducted by using data (523 individuals) collected in Hiroshima City, Japan, in December 2008. Because multitasking behavior along the axis of travel time may be interrelated, the scobit model is extended to simultaneously incorporate the influences of state dependency and the remaining travel time as well as the other influential factors, by dealing with the data as panel data. As a result, a scobit-based panel model was developed. Model estimation results confirm the effectiveness of the scobit model. It was further revealed that introduction of a heterogeneous skewness parameter is more effective for representing activity participation than assumption of a homogeneous skewness parameter. Calculation results of travel time elasticity show that activity participation is sensitive to change in the travel time up to the first half of travel time and becomes less sensitive to travel time afterward.

The modeling of dynamic activity generation is high on the research agenda in activity-based transport demand modeling. The concept of dynamic needs has been put forward as such a mechanism. Needs that underlie the generation of such discretionary activities as social, recreational, and sports activities is investigated. Three surveys were conducted to elicit, establish, and analyze the needs. Qualitative face-to-face interviews were carried out with a laddering technique to reveal need dimensions through an exhaustive classification of discretionary activities. Quantitative approaches were then used to determine which needs are equivalent in their effects on activities and, hence, can be merged. Finally, a questionnaire-based survey involving a large sample of individuals was used to measure personal levels of the needs identified and to correlate these measures with socioeconomic and behavioral characteristics. Six independent needs emerged from this research: physical exercise, social contact, relaxation, fresh air and outdoors, new experiences, and entertainment. Many-to-many relationships between activities and needs support the hypothesis that substitution relationships may play a significant role in activity generation. This observation implies that current practice in activity-based modeling of focusing on activities may produce biased results in development of dynamic models of transport demand. Furthermore, the results show that personal levels of these needs correlate with various socioeconomic as well as behavioral variables.

Results of a study on the planning of social activities are reported. Data collected in the Netherlands from social interaction diaries were used to estimate a multinomial logistic regression model to analyze whether a social activity is prearranged, routine, or spontaneous as a function of personal and household characteristics, social activity characteristics, and characteristics of the contacted person. The results show that the planning of social activities is significantly influenced by gender, presence of children, education level, income, and time spent on work and school. Social activity characteristics were also found to have a significant effect. Social activities scheduled later in the day are less likely to be routine. In contrast, social activities of longer duration and taking place on the weekend are more likely to be routine or planned. The location, the main purpose of the social interaction, and detailed characteristics of with whom the social interaction took place were also found to affect the scheduling process significantly.

Recent literature suggests a need for a more realistic representation of driver behavior. In an effort to integrate prospect theory as a potential descriptive method into traveler behavior, it is important to determine the validity of previous estimated parameters obtained from empirical studies and interviews of people who are not in the same fast-paced, dynamic, travel time disutility setting as real drivers. A genetic algorithm is used to simultaneously estimate the parameters of the cumulated prospect theory (CPT) value and weight functions as well as the coefficients of the random utility model; this procedure leads to estimates that have a higher likelihood value and statistical significance than an equivalent expected utility–based logit model or a CPT-based logit model using the empirical values developed earlier. The value function parameters generally conform to conclusions from previous literature. The weight function parameters, however, suggest that drivers in a fast-paced changing environment with multiple subjects for prospect evaluation may become overwhelmed by the certainty effect.

Drivers have available an increasing number of options for informing themselves about the current traffic state and to subsequently make route choice decisions based on this information. Governments are investing heavily to build and operate variable message signs (VMS), drivers are buying navigation systems that show the current traffic state, and third-party providers are setting up systems that give information about the traffic state via Internet or phone. To evaluate public investments particularly, it is necessary to know how this information affects route choice behavior. Analyses were made of route choices in two study areas equipped with VMS that give recommendations for routes. The influences of VMS, broadcast traffic state, and other factors are evaluated by using maximum likelihood estimations. A unique feature of the analyses is the large database of observed route choice decisions, which are extracted from mobile phone trajectories. The total number of observations analyzed for this paper amounts to 1.4 million.

A recent project addressed how travelers would react to fuel prices rising above the high levels that were reached in mid 2008. Study participants were recruited during phone interviews, in the course of which trips made on a specified day were recorded. On the basis of one of those trips and the respondents’ possession of mobility tools, stated preference (SP) experiments were constructed. The first part consisted of a mode choice situation under modified price (and travel time) settings (tactical decisions). The second part focused on long-term (strategic) choices between the current and an alternative fleet, including a redistribution of yearly mileage. From the SP data, multinomial logit models for mode and fleet choice were estimated. The mode choice models were estimated by using income- and distance-dependent nonlinear utility functions and separately for the various trip purposes (as was the practice in earlier Swiss studies on similar topics) and controlled for all relevant trip characteristics. The models for mobility tool ownership, which were formulated by using a new approach, aimed to yield trade-offs between the various attributes of the offered fleets and to forecast the distribution of annual transit passes under modified settings. The findings suggest that inertia is present in both mode choice and mobility tool ownership. Elasticities do not change much from previous studies, where more-conservative price increases were assumed. Transit pass ownership is expected to grow only when increasing fuel prices coincide with stable public transport fares.

Researchers in travel behavior have explored attitudes as important determinants since the 1980s and are now broadening the use of attitudinal data in models to explain behavior. Much of the application, however, has focused on attitudes about entities such as lifestyles and attributes of different transport modes. Little attention has been given to attitudes related to places and attributes of human–place interaction. There has been much theorizing in the past 40 years about this human–place interaction, which has been formalized into a multivoiced theory of sense of place. Quantification of this theory and implementation of this attitudinal information into travel behavior modeling are discussed. A survey was conducted in Santa Barbara, California, to measure sense of place and to estimate models of travel behavior. Patrons of two outdoor shopping malls in Santa Barbara were interviewed about place attitudes, sociodemographic information, and details regarding their activity and travel on that day. Regression models were then used with their data to examine differences between the two study locations, the use of mode for arrival, and the timing of the activity, showing the value of the sense-of-place construct as a determinant of travel behavior.

Because of the increasing number of vehicles in Bhutan, vehicle emissions have become a major cause of air pollution in the city of Thimphu. Although the Bhutanese government has implemented many strategies to control vehicle emissions, the strategies appear inadequate; there is still a need to explore alternative strategies for alleviating the problem. Although vehicle emission control strategies can be easily proposed, they are difficult to implement because they depend on public acceptance and support. A questionnaire survey of public opinion on vehicle emission control strategies was carried out in Thimphu City. Data from the survey have been used to analyze individual preference for vehicle emission control strategies and whether they can be statistically linked to socioeconomic characteristics of survey respondents. The results from this study could be helpful to policy makers for evaluating long-term plans to reduce vehicle emissions in Thimphu City.

The capacity for suburban households to respond to a changing global energy context by changing their motor vehicle technology is examined. Transforming transport systems will make up a crucial element in policy and planning responses to energy and climate challenges. Government policy appears focused on a transition to more-efficient vehicle types or alternative fuel and engine types. Yet such policies have failed to account for the considerable social differences in household exposure to the costs of transport energy and the adaptability of households in altering their use of modes and vehicle types. Nor do such policies recognize how urban social structure, household social status, and automobile type intersect spatially within Australian cities. The links between urban social structure and composition of the motor vehicle fleet are examined to test whether the households that are most reliant on motor vehicles for transport have the financial capacity to rapidly alter their vehicle technology in response to changing energy prices and supply conditions. Australian Bureau of Statistics census data and motor vehicle registration data at the postcode level are used to compare socioeconomic status with the age, fuel consumption, and value of the suburban vehicle fleet for the Brisbane and South East Queensland regions of Australia. This spatial deployment of census and vehicle registration data is novel in the Australian context. It is argued that policies that focus on vehicle technology alone face a number of social equity hurdles as measures to overcome urban transport fuel security problems.

Consumers’ marginal willingness to pay for a unit change of automotive fuel economy was estimated through development of a hedonic regression of new automobiles sales. The research combined national data on vehicle attributes with a unique data set that contains demographic information on all new vehicles registered in Maine in 2007. The research estimates the impact of demographic factors on consumer demands for fuel economy by generating a function for fuel economy demand in a second-stage hedonic model. Results show that consumers undervalue the long-run fuel savings of vehicle ownership, but they significantly value short-run fuel savings. Age and education are positively correlated with fuel economy demand, whereas income is statistically insignificant. Car consumers’ net benefits from an increase in fuel economy from 25 to 35 mpg are computed from the fuel economy demand curve and are approximately $2,232. Strengthening corporate average fuel economy standards is reasonable because consumers can receive significant net benefits from increasing fuel economy.

The online session behavior of users in a multiplayer online role playing game is examined. Three dimensions of online sessions are studied: frequency of sessions, distribution of start and stop times, and duration. These measures are modeled and examined econometrically to understand their relationships with attributes of the users and their avatars. Frequency is analyzed by using a negative binomial regression to uncover relationships with contributing factors, such as the experience level of players and the number of avatars per user account. Start and stop time distributions are examined aggregately with discrete choice models that capture the probability of starting a session in relation to the sociodemographics of users. Duration models are estimated by using data on the characteristics of events that occur within the duration and other user sociodemographics and player attributes. Since sessions are not explicitly indicated within the data set, sessions were stitched together on the basis of an assumed threshold for offline duration. Overall, these models and results provide insight into temporal characteristics of online sessions, including their aggregate traffic characteristics, such as frequency, headways, and duration length. These results also provide information on the nature of online game playing from a user perspective.

A study was done to shed light on the determinants of working from home beyond the traditional office-based work hours. The frequency of work participation from home was examined for individuals who also have a traditional work pattern of traveling to an out-of-home workplace and a fixed number of work hours at the out-of-home workplace. The sample for the empirical analysis was drawn from the 2002 to 2003 Turin, Italy, survey of time use, which was designed and administered by the Italian National Institute of Statistics. The methodology recognizes both spatial and social clustering effects by using a cross-clustered ordered response structure to analyze the frequency of work participation from home during off-work periods. The model is estimated through the use of the inference technique of composite marginal likelihood, which represents a conceptually, pedagogically, and implementationally simpler procedure relative to traditional frequentist and Bayesian simulation techniques.

Research was done to reveal the travel impact of e-shopping in the Minneapolis–St. Paul (Twin Cities) metropolitan area of Minnesota. A sample of Internet users drawn from urban, suburban, and exurban neighborhoods was used to identify the relationship between e-shopping and in-store shopping. An online survey composed of direct and attitudinal questions was used to obtain the data. Ordered probit models were developed to account for the influences of a variety of confounding factors, such as shopping attitudes, shopping accessibility, shopping responsibility, and sociodemographics. The preliminary results, controlled for the confounding factors, show that e-shopping behavior (for online searching and online buying) tends to have a complementarity effect on in-store shopping.