Large-scale Travel Survey Research Articles

Time Series Comparison of Joint Mode and Destination Choice Models in Changing Transportation Environment

Following the analysis of household auto and motorcycle ownership and mode choice models presented in 2012, results are presented on joint mode and destination choice models developed on the basis of two large-scale travel surveys conducted 8 years apart. The models are compared, and implications of changes in the Jakarta, Indonesia, metropolitan area in the past decade are discussed. Interpretation of the effects of different types of variables, including basic travel, household and individual characteristics, and zonal attributes in the models estimated for 2002 and 2010, led to insights relative to changes in the transportation environment and an increase in the complexity of travel behavior in Jakarta. As long as the context of the society does not change, both models should remain unchanged with fixed parameters over a period of time. However, the models that were estimated on the basis of the surveys conducted nearly a decade apart indicated quite different parameters with different degrees of significance. As found in the comparison made in 2012, transferability of those disaggregate choice models may not always apply in urban areas of the developing world such as Jakarta, even though the model structure may remain the same. Such implications may also be worth studying for other urban areas of the developing world, although similarities may be restricted to regions that share modal and cultural norms.

Wearable Cameras: Identifying Healthy Transportation Choices

Traditionally, health researchers have used large-scale travel surveys to measure existing travel behavior and identify the determinants driving it. However, such surveys rely on self-reporting, which can be unreliable. Here, the authors discuss using wearable cameras that capture first-person point-of-view images to help objectively identify the duration, frequency, and mode of journeys and reveal potential errors inherent in self-reporting. Their approach could ultimately lead to a better understanding of the environments offering individuals opportunities to engage in more active forms of transportation. This column is part of a special issue on transit and transport.

Estimating Latent Cycling Trips in Montreal, Canada

About 1.8% of the residents of the Island of Montreal, in Quebec, Canada, ride a bicycle at least once a day during the fall season. These people make nearly 76,990 daily trips, 2.0% of the total number of commuter trips. Of these cyclists, 65% are men, although men represent only 48.1% of residents, and nearly 63.8% are employed, although the employed represent only 45.2% of the island's population. Data from a large-scale travel survey were used to confirm the influence of various factors on bike use. The study showed that men were 1.99 times more likely to make a trip by bike than were women, individuals who lived in a nonmotorized household were 2.35 times more likely to take a trip by bike than were those who lived in motorized households, people who commuted on a sunny day were 1.46 times more likely to travel by bike, and people who lived more than 9.3 mi (15 km) from the downtown area were 0.29 times less likely to travel by bike compared with people who lived nearer. The study also proposed a methodology for estimating latent bicycle trips, that is, the number of car trips that could be made by bike. When a criterion of travel range based on age cohorts and genders was applied, it appeared that about 50.7% of car trips would be made by bike. When more restrictive criteria were used (consideration of trip chains, shopping), it appeared that 18.2% of car trips would be made by bike.

A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys

The acquisition of travel data is currently based on cost- and time-intensive questionnaires and yields mostly an incomplete picture due to limited coverage and inadequate updates. There is an urgent need for technologically supported data acquisition tools. This paper introduces a novel approach to developing a large-scale travel survey by intelligently employing data from smartphones. Based on signals of the embedded accelerometers and GPS reveivers, an ensemble of probabilistic classifiers is trained for automatically reconstructing the individual trips composing a tour, including the mode choice. In the region of Vienna, Austria, 266hours of travel data were collected to train and evaluate the models. Using a set of 72 features, the best classification results are achieved for detecting walks (92%) and bike rides (98%). Railway modes were correctly identified in 80% of all cases, which is subject to further research. In case of GPS losses only accelerometer data are used, which still shows promising results. This allows the method to incorporate places where there is normally only a weak or no GPS signal. Future smartphone applications are intended to spread the tool among traffic users, while the effort for them should be kept to a minimum i.e. no manual entries or questionnaires are necessary. Due to the increasing popularity of smartphones, the tool has the potential to be used on a wide-spread basis.

Time Series Comparison of Models of Auto and Motorcycle Ownership and Mode Choice in a Changing Transportation Environment

This paper presents the results of models of household auto and motorcycle ownership and mode choice that were respectively developed on the basis of two large-scale travel surveys conducted nearly a decade apart. In the Jakarta, Indonesia, metropolitan area, unprecedented changes in the transportation environment, including growth in vehicle ownership and resultant changes in mode shares, occurred during this time. A time series comparison of the models was made, the implications of the major changes that occurred in Jakarta are discussed. Interpretation of the effects of different variables (including basic travel, household, and individual characteristics) in the models estimated for 2002 and 2010 led to insights relative to the changes in the transportation environment and an increase in the complexity of travel behavior. As long as the context of the society does not change, both models should remain unchanged with fixed parameters over a period of time. However, the models indicate quite different parameters with different degrees of significance. Transferability of disaggregate choice models may not always apply in developing urban areas such as Jakarta, even though the model structure remains the same. Such implications may also be worth studying for other urban areas, although similarities may be restricted to regions that share modal and cultural norms.

Enhancing the Travel Survey Process and Data Using the CATI System

This paper describes an intelligent interface developed to assist in the task of collecting detailed information regarding daily travel behaviours. The computer-assisted telephone interview (CATI) system, developed by the MADITUC Research Group for the Montreal large-scale travel surveys, is structured in three main screens (household, person and trip) and assists the interaction between an interviewer and a respondent during a phone interview. This tool enhances the quality of the data collected by performing real-time validation of the spatio-temporal details related to travel behaviours. In addition to describing the design of the CATI tool, this paper proposes some empirical measurements associated with the CATI use by the interviewers owing to the processing of numerical logs saving every action taken by the user during an interview. Using these data, variables significantly influencing interview duration are identified.

Two Cities, Two Realities?

Capitalizing on large-scale origin-destination travel surveys conducted in two large Canadian urban centers, Montreal and Toronto, this paper presents a comparative analysis of the travel behavior trends in relation to variables such as demography, car accessibility, home location, and employment status. With trip rate as the dependent variable, three disaggregate multivariate regression models were estimated to observe how behaviors have evolved over time and how individual features affect the way in which people travel. These multivariate models allow the observation of the similarities and differences between explanatory factors between regions and over time. Although both cities are facing similar trends, such as the aging of the population, increasing rates of motorization, declining household sizes, and urban sprawl, they are also the sites of different trends with respect to average trip rates, differences between genders, and the impacts of car access. The geographic and cultural differences found between the populations of Toronto and Montreal include a smaller gender impact on trip generation in Montreal and a smaller age impact in Toronto. The study identified changes in the magnitude of the influence of explanatory variables on trip generation over time, including the declining importance of age and gender. The most promising policy actions that could be taken to decrease trip rates would be those that affect a decrease in household automobile ownership, as determined on the basis of the analysis in this paper.

How Many Steps Do you Have in Reserve?

The aim of this study was to estimate the benefits that people could achieve by trading their car for a nonmotorized mode of travel, such as walking, to make their short daily trips. For this purpose, detailed information on travel behavior gathered through large-scale travel surveys conducted in the greater Montreal, Quebec, Canada, area was used. The travel behavior observed in recent travel surveys was analyzed to estimate the number of short trips for various population segments. These surveys gathered travel and sociodemographic information for approximately 5% of the population. Data from the 2003 survey revealed that more than 7 million motorized trips were made during a typical weekday; 862,000 (11.7%) were shorter than 1.6 km (1 mi). With the appropriate speed and stride for each population segment, these motorized kilometers were converted into numbers of steps to appraise the potential physical activity benefits of making these short trips by foot instead of by a motorized mode. The results show that about 837,000 motorized kilometers could be converted into almost 1,156 million steps every day. Overall, 12.5% of the population had steps in reserve, an average of 2,660 steps per person. Such a shift in mode choice could help some people meet their required physical activity volumes through their daily travel patterns while helping to save energy, reduce pollution, and mitigate traffic congestion.

Developing Custom Tools for Deriving Complex Data from Travel Survey Databases

Data from large-scale travel survey databases, such as the Nationwide Personal Transportation Survey (NPTS) and the National Household Travel Survey (NHTS), are often used to derive complex parameters for transportation modeling applications not originally targeted by the surveys. When situations demand complex data manipulation tasks, publicly available tools for the purpose do not obviate the need for data users to sift through entire databases and develop custom solutions. This paper presents a flexible methodology for developing custom database tools for cost-effectively deriving application-specific information from travel survey data. The seven-step methodology accounts for assessment of needs, verification of validity of travel survey data to the application, establishment of scientific justification, development of functional requirements of the system, development of data model, definition of processes, and development and testing of tools. To demonstrate the methodology, a case study involving derivation of locality-specific parameters for emission factor models from the NPTS and NHTS databases is presented. With the use of this methodology, a custom database application called Travel-Related Inputs Model for MOBILE6.x (TRIMM) was developed for this case study. The working mechanisms of TRIMM are illustrated. The methodology outlined, though specific to travel survey databases, is sufficiently generic to be used for developing custom tools to derive various modeling parameters from large transportation databases.

Developing Custom Tools for Deriving Complex Data from Travel Survey Databases

Data from large-scale travel survey databases, such as the Nationwide Personal Transportation Survey (NPTS) and the National Household Travel Survey (NHTS), are often used to derive complex parameters for transportation modeling applications not originally targeted by the surveys. When situations demand complex data manipulation tasks, publicly available tools for the purpose do not obviate the need for data users to sift through entire databases and develop custom solutions. This paper presents a flexible methodology for developing custom database tools for cost-effectively deriving application-specific information from travel survey data. The seven-step methodology accounts for assessment of needs, verification of validity of travel survey data to the application, establishment of scientific justification, development of functional requirements of the system, development of data model, definition of processes, and development and testing of tools. To demonstrate the methodology, a case study involving derivation of locality-specific parameters for emission factor models from the NPTS and NHTS databases is presented. With the use of this methodology, a custom database application called Travel-Related Inputs Model for MOBILE6.x (TRIMM) was developed for this case study. The working mechanisms of TRIMM are illustrated. The methodology outlined, though specific to travel survey databases, is sufficiently generic to be used for developing custom tools to derive various modeling parameters from large transportation databases.

Impact of interviewing by proxy in travel survey conducted by telephone

AbstractTelephone‐interview surveys are a very efficient way of conducting large‐scale travel surveys. Recent advancements in computer technology have made it possible to improve upon the quality of data collected by telephone surveys through computerization of the entire sample‐control process, and through the direct recording of the collected data into a computer. Notwithstanding these technological advancements, potential sources of bias still exist, including the reliance on an adult member of the household to report the travel information of other household members.Travel data collected in a recent telephone interview survey in the Toronto region is used to examine this issue. The statistical tool used in the research was the Analysis of Variance (ANOVA) technique as implemented within the general linear model framework in SAS. The study‐results indicate that reliance on informants to provide travel information for non‐informant members of their respective households led to the underreporting of some categories of trips. These underreported trip categories were primarily segments of home‐based discretionary trips, and non home‐based trips. Since these latter two categories of trips are made primarily outside the morning peak period, estimated factors to adjust for their underreporting were time‐period sensitive. Further, the number of vehicles available to the household, gender, and driver license status respectively were also found to be strongly associated with the underreporting of trips and thus were important considerations in the determination of adjustment factors.Work and school trips were found not to be underreported, a not surprising result giving the almost daily repetitiveness of trips made for these purposes and hence the ability of the informant to provide relatively more precise information on them.

Nonresponse and Urban Travel Models

Considerable attention has been paid to the presence of nonresponse in large-scale travel surveys on the basis of which urban travel demand models are developed. It has been shown that the effect of nonresponse can be reduced by careful model building, with categorical trip generation models as an example. The same philosophy is extended to logit mode split models and exponential gravity models to show that the usual levels of nonresponse that one encounters in urban travel surveys have virtually no adverse effects on the parameter estimates of these models if the model has been specified correctly. Some simulation results are also presented to show the behavior of logit and exponential gravity model parameter estimates under conditions on nonresponse.

Nonresponse and Urban Travel Models

Considerable attention has been paid to the presence of nonresponse in large-scale travel surveys on the basis of which urban travel demand models are developed. It has been shown that the effect of nonresponse can be reduced by careful model building, with categorical trip generation models as an example. The same philosophy is extended to logit mode split models and exponential gravity models to show that the usual levels of nonresponse that one encounters in urban travel surveys have virtually no adverse effects on the parameter estimates of these models if the model has been specified correctly. Some simulation results are also presented to show the behavior of logit and exponential gravity model parameter estimates under conditions on nonresponse.

Designing a large-scale travel demand survey: New challenges and new opportunities

Information about travel demand, traveller behaviour and transport systems performance is an essential input to decision making on transport policy, planning and design issues. However, this information is difficult and expensive to collect and establish, so that its collection has been infrequent. Further, previous information technology and past methods for managing travel information have helped to make it expensive to access, maintain and keep current. This paper describes the findings of a research project called the Melbourne Travel Survey Research Projects (MTSRP), undertaken to determine the needs for and likely applications of new travel information for a large metropolitan area (Melbourne, Australia), and to define the desirable form for a new metropolitan travel demand survey program. The research required surveys of the present and anticipated needs for travel demand data and the available and required technology for data collection and information dissemination. The paper describes the findings of the research project, and provides an interpretation of their implications for a large-scale travel survey program.