Assessing the effects of a mixed-mode design in a longitudinal household travel survey

The German Mobility Panel (MOP) is a national household travel survey, which has been collecting data on travel behavior in Germany since 1994. One of the MOP’s central assets is its ability to provide time-series data on travel behavior. Thus, the comparability of survey results from different years is a major objective of the survey method used. Declining survey participation rates in the last decade in various socio-demographic groups resulted in the implementation of a mixed-mode design for the MOP in 2013, both for the sampling stage (landline and mobile phone recruitment) and the data collection stage (paper and web). In this study, we analyze whether the adaptations in the survey mode do indeed improve the results and, if so, why and to what degree. Ideally, the survey mode adaptions have increased the representativeness of the MOP. However, measurement biases due to the mixed-mode design are also conceivable. To decompose survey mode effects, we applied the propensity score weighting method. This method imputes the hypothetical responses participants would have given in different survey modes; disparities between actual responses and hypothetical responses under another mode are then traced back to the mixed-mode design. Our analysis indicates that trip-rate biases on shopping, leisure, and short trips are partly caused by the mixed-mode design; in contrast, quantities of time spent in the transportation system, trips made by car and public transportation, and commuting trips are hardly biased.

     

Noise pollution associated with the operation of the Dar es Salaam International Airport

The operation of airports results in environmental impacts associated with high levels of noises and vibrations. These may have severe negative effects to both workers and surrounding residents and their properties. Here we look at the noise impacts associated with the operation of the Dar es Salaam International Airport (DIA) in Tanzania. Field measurements were carried out to determine noise levels at various positions at the airport during aircraft landing and take-off. The surrounding residents' perceptions on the noises were also investigated using semi-structured interviews. Workers on the apron (marshellers), house keepers, security workers, and mechanics are exposed to noise levels that could affect their health. The noise levels in the surrounding settlements of Kipawa and Kiwalani were higher than the WHO recommended limits and causing annoyance to residents. The use of appropriate ear protectors by the workers was found to reduce the noises to harmless levels. Periodical audiometric tests of the workers will help to monitor the noise impacts. Enforcement of appropriate environmental regulations on the airliners can also reduce noise pollution at the airport.     

Heterogeneity in marginal value of urban mobility: evidence from a large-scale household travel survey in the Greater Toronto and Hamilton Area

Transportation - The value of mobility is an unresolved question in transportation economics literature. The advent of ride-hailing services and the emergence of mobility as a service (MaaS) place...     

Investigating the capacity of continuous household travel surveys in capturing the temporal rhythms of travel demand

Transportation - Continuous household travel surveys have been identified as a potential replacement for traditional one-off cross-sectional surveys. Many regions around the world have either...     

Characteristics of household activity and travel patterns in the Mumbai metropolitan region

Activity-based modelling approaches require a typical survey instrument which can collect the finer details of activities of each individual over both time and space. This paper focuses on the design of a new survey instrument called an activity-travel diary; examines its method of administration; and analyses activity-travel behaviour in the context of developing countries. The Mumbai Metropolitan Region in India is selected as the study area. With the aim of understanding the activities of each individual over a period of time, a pilot survey was conducted in a continuous time frame for a period of 15 days, followed by a main survey. The analysis of data collected by the instrument reveals some interesting facts regarding the relationships between socioeconomic attributes, activities and trip making behaviour. Identification of interactions among households and other members were also facilitated by the newly designed diary, which is not a well-versed topic for research in the context of a developing economy like Mumbai's.     

Measuring the impact of efficient household travel decisions on potential travel time savings and accessibility gains

Using the conceptual framework of time–space geography, this paper incorporates both spatio-temporal constraints and household interaction effects into a meaningful measure of the potential of a household to interact with the built environment. Within this context, personal accessibility is described as a measure of the potential ability of individuals within a household not only to reach activity opportunities, but to do so with sufficient time available for participation in those activities, subject to the spatio-temporal constraints imposed by their daily obligations and transportation supply environment. The incorporation of activity-based concepts in the measurement of accessibility as a product of travel time savings not only explicitly acknowledges a temporal dimension in assessing the potential for spatial interaction but also expands the applicability of accessibility consideration to such real-world policy options as the promotion of ride-sharing and trip chaining behaviors. An empirical application of the model system provides an indication of the potential of activity-based modeling approaches to assess the bounds on achievable improvements in accessibility and travel time based on daily household activity patterns. It also provides an assessment of roles for trip chaining and ride-sharing as potentially effective methods to facilitate transportation policy objectives.     

Quantifying the relative contribution of factors to household vehicle miles of travel

Household vehicle miles of travel (VMT) has been exhibiting a steady growth in post-recession years in the United States and has reached record levels in 2017. With transportation accounting for 27 percent of greenhouse gas emissions, planning professionals are increasingly seeking ways to curb vehicular travel to advance sustainable, vibrant, and healthy communities. Although there is considerable understanding of the various factors that influence household vehicular travel, there is little knowledge of their relative contribution to explaining variance in household VMT. This paper presents a holistic analysis to identify the relative contribution of socio-economic and demographic characteristics, built environment attributes, residential self-selection effects, and social and spatial dependency effects in explaining household VMT production. The modeling framework employs a simultaneous equations model of residential location (density) choice and household VMT generation. The analysis is performed using household travel survey data from the New York metropolitan region. Model results showed insignificant spatial dependency effects, with socio-demographic variables explaining 33 percent, density (as a key measure of built environment attributes) explaining 12 percent, and self-selection effects explaining 11 percent of the total variance in the logarithm of household VMT. The remaining 44 percent remains unexplained and attributable to omitted variables and unobserved idiosyncratic factors, calling for further research in this domain to better understand the relative contribution of various drivers of household VMT.     

Simulating the impacts of household travel on greenhouse gas emissions,urban air quality,and population exposure

This paper establishes a link between an activity-based model for the Greater Toronto Area (GTA), dynamic traffic assignment, emission modelling, and air quality simulation. This provides agent-based output that allows vehicle emissions to be tracked back to individuals and households who are producing them. In addition, roadway emissions are dispersed and the resulting ambient air concentrations are linked with individual time-activity patterns in order to assess population exposure to air pollution. This framework is applied to evaluate the effects of a range of policy interventions and 2031 scenarios on the generation of vehicle emissions and greenhouse gases in the GTA. Results show that the predicted increase of approximately 2.6 million people and 1.3 million jobs in the region by 2031 compared to 2001 levels poses a major challenge in achieving meaningful reductions in GHGs and air pollution.     

Assessing the impact of the built environment on travel behavior: a case study of Buffalo,New York

Assessing the impact of characteristics of the built environment on travel behavior can yield valuable tools for land use and transportation planning. Of particular interest are planning models that can estimate the effects of ‘smart growth’ planning. In this paper, a post-processor method of quantifying and searching for relationships among many aspects of travel behavior and the built environment is developed and applied to the Buffalo, NY area. A wide scope of travel behavior is examined, and over 50 variables, many of which are based on high-detail data sources, are examined for potentially quantifying the built environment. Linear modeling is then used to relate travel behavior and the built environment, and the resulting models may be applied in a post-processor fashion to travel models to provide some measure of sensitivity to built environment modifications. The study’s findings demonstrate that mode choice is highly correlated to measures of the built environment, and that many of the principles of smart growth appear to be a valid way to encourage non-vehicle travel. Home-based VHT and VMT appear to be affected by the built environment to a lesser degree.     

Inferring proxy response in household travel surveys with unknown completer using a group-based choice model

Transportation - A proxy response is often accepted for household travel surveys to reduce the survey cost and increase the sample size, but proxy-response biases may be introduced into the sample...     

Assessing social equity in distance based transit fares using a model of travel behavior

The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal/continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare distance travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe.     

Understanding individual travel decisions: results from a commuters survey in Geneva

In this paper, we present the results of an extended traveler behavior survey conducted in Geneva (Switzerland) in March 1994. In this survey, commuters were asked about the impact of various factors, related to individual or household characteristics and situational, contextual and environmental constraints on their basic travel decisions. In particular, the issues of mode, route and departure time choice were investigated, together with the diversion from normal patterns in response to adverse weather conditions. Some cross cultural comparisons are discussed with respect to the results of a similar survey conducted in Brussels (Belgium) in 1992 (Khattak & de Palma 1995, 1997).     

Urban form correlates of pedestrian travel in youth: Differences by gender,race-ethnicity and household attributes

This paper looks at pedestrian travel in Atlanta by US youths aged 5–18 years. Relationships between five urban form variables and walking in specific demographic subgroups are assessed using stratified logistic models and controlling for participant demographics. All five urban form and recreation measures were related to walking among whites, but only land use mix and access to recreation spaces were significantly related to walking in non-whites. There were more significant urban form physical activity associations in high-income than in low-income households. More urban form variables were related to walking in households with 3 or more cars than in households with no cars. Living in mixed use-areas and having access to recreational space were related to youth walking for transport in 11 of 13 population subgroups studied.     

Assessing the effects of stochastic perception error under travel time variability

Perceived mean-excess travel time is a new risk-averse route choice criterion recently proposed to simultaneously consider both stochastic perception error and travel time variability when making route choice decisions under uncertainty. The stochastic perception error is conditionally dependent on the actual travel time distribution, which is different from the deterministic perception error used in the traditional logit model. In this paper, we investigate the effects of stochastic perception error at three levels: (1) individual perceived travel time distribution and its connection to the classification by types of travelers and trip purposes, (2) route choice decisions (in terms of equilibrium flows and perceived mean-excess travel times), and (3) network performance measure (in terms of the total travel time distribution and its statistics). In all three levels, a curve fitting method is adopted to estimate the whole distribution of interest. Numerical examples are also provided to illustrate and visualize the above analyses. The graphical illustrations allow for intuitive interpretation of the effects of stochastic perception error at different levels. The analysis results could enhance the understanding of route choice behaviors under both (subjective) stochastic perception error and (objective) travel time uncertainty. Some suggestions are also provided for behavior data collection and behavioral modeling.     

Car-deficit households: determinants and implications for household travel in the U.S.

Transportation - In the U.S., households with less than one car per driver (auto-deficit households) are more than twice as common as zero-vehicle households. Yet we know very little about these...     

Trip misreporting forecast using count data model in a GPS enhanced travel survey

As Global Positioning System (GPS) technology advances, it has been increasingly used to supplement traditional self-reported travel surveys due to its promising features in capturing travel data with better accuracy and reliability. Realizing the limitations of diary-based surveys, this paper presents a study that directly accounts for trip misreporting behavior in trip generation models. Travel data were obtained from prompted-recall assisted GPS survey along with a diary-based survey. Negative Binomial models for count data were developed to accommodate misreporting behavior by introducing interaction effects of the sample-indicator variable with various personal and household variables. The interaction effects indicate how the impacts of the socioeconomic and demographic variables on trip-making vary across the two samples. Assuming that the GPS sample represents the ground truth, the interaction effects actually capture the likelihood and the extent of trip misreporting by detailed personal and household characteristics. The model results reveal significant interaction effects of several personal and household variables, indicating misreporting behavior associated with these attributes. The addition of interaction coefficients to the main effect model represents the real impacts of the independent variables, after compensating for trip misreporting behavior, if any.     

Influence of survey methods on the results of representative travel surveys

Several recent basic investigations have pointed out that the choice, specific format and content of the survey method employed for an empirical study already constitute an important determinant for the type and scale of answers to be expected. In the context of transportation research this implies, for example, that the type and level of measured mobility also depends on the method by which these mobilities were determined. Such basic investigations provide important indications of the effects to be expected, but generally they are not able to quantify the true level of such influences. For that purpose studies are necessary that actually investigate the same behavioral phenomenon by means of different survey methods. Such a comparison of the trip purpose “vacation travel” was possible for the Federal Republic of Germany on the basis of three different surveys. Surveys (1) and (2) were similar in their design, namely in the “classical” form of the personal interview. By contrast, the third survey employed mail-back questionnaires. In addition, in survey (3) an attempt was made to include factors other than the commonly used sociodemographic weightings (applied in all three surveys) that bias the results. These other factors are especially the non-response effect, the order of magnitude for non-reported trips and other influences that can be attributed to the survey design. Substantial differences between the three surveys became evident when the key measures of vacation travel were subjected to a quantitative analysis. In general, it could be observed that the corresponding values for surveys (1) and (2) were substantially (up to 30%) below those generated by survey (3). However, these deviations could be attributed to the survey method selected, both in terms of their direction and in their order of magnitude. This means that the reported variances of the random sampling errors do not permit statements about the validity of the data, since the systematic bias due to design is at least ten times the size of the random error. It was shown also that the reliability of the survey data is affected by the survey design. The paper concludes with a number of suggestions derived from these insights. They should be taken into account in future surveys of vacation travel in order to improve the accuracy of the reported results.     

Post-Car World: data collection methods and response behavior in a multi-stage travel survey

Transportation - The main research question addressed by this study is to what degree individuals would change travel modes, time allocation and activity patterns after experiencing large changes...     

Impact of interviewing by proxy in travel survey conducted by telephone

Telephone‐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.