Analysis of attrition biases and trip reporting errors for panel data

The objective of the study is to assess the nature and magnitude of reporting errors and attrition biases in the first two waves of a household panel survey data set that consists of weekly trip diaries. This paper investigates the relationship between reporting errors in a first-wave diary and the subsequent decision to participate in a second-wave survey and the relationship between the same first-wave errors and the errors in the second-wave survey for those households which chose to participate. An econometric procedure is developed which accounts for attrition bias and relates reporting errors from the two waves. The model system offers a mechanism by which relationships among mobility, trip reporting errors, and attrition behavior in a panel survey can be statistically evaluated. It consists of three elements: wave-one trip equation, attrition probability model, and wave-two trip equation. The conditional distribution of the error terms of these three components is evaluated assuming chronological dependency among the errors. The result is used to develop a correction term for attrition bias and a term which accounts for the dependency between the wave-one error and wave-two error. Results from empirical application of the model system are reported together with the use of the attrition model to develop an efficient sample weighting factor that fully utilizes the information in the survey results.     

Biases in response over time in a seven-day travel diary

Data from multi-day travel or activity diaries might be biased if recording inaccuracies and tendencies for respondents to skip certain types of trips or activities increases (or decreases) from day-to-day over the diary period. One objective of the research reported here is to test for such temporal biases in a seven-day travel diary. A second objective is to calculate correction factors which can be applied to the data in the case that biases are found. The analyses were conducted using regression and analysis-of-variance techniques. The variables investigated included total trips per day, total travel time per day, and trips per day by various modes (such as walking, car driver and car passenger). Results showed that most biases per capita statistics are due to increases over time in the percentage of respondents reporting no travel at all for an entire day. However, even after accounting for this bias by measuring statistics in terms of per mobile person, there remains a decrease over time of about 3.5 percent per day in the reporting of walking trips. This appears to be the main factor in the overall bias of about one percent per day in total trips per mobile person per day. No significant differences were found among population segments in terms of the levels of their biases.     

Extent and correlates of walking in the USA

This paper examines data about walking trips in the US Department of Transportation’s 2001 National Household Travel Survey. The paper describes and critiques the methods used in the survey to collect data on walking. Using these data, we summarize the extent of walking, the duration and distance of walk trips, and variations in walking behavior according to geographic and socio-demographic factors. The results show that most Americans do not walk at all, but those who do average close to thirty minutes of walking a day. Walk trips averaged about a half-mile, but the median trip distance was a quarter of a mile. A significant percentage of the time Americans’ walk was spent traveling to and from transit trips. Binary logit models are used for examining utility and recreational walk trips and show a positive relationship between walking and population density for both. For recreational trips, this effect shows up at the extreme low and high ends of density. For utility trips, the odds of reporting a walk trip increase with each density category, but the effect is most pronounced at the highest density categories. At the highest densities, a large portion of the effect of density occurs via the intermediary of car ownership. Educational attainment has a strong effect on propensity to take walk trips, for both for utility and recreation. Higher income was associated with fewer utility walk trips but more recreational trips. Asians, Latinos, and blacks were less likely to take utility walk trips than whites, after controlling for income, education, density, and car ownership. The ethnic differences in walking are even larger for recreational trips.     

A panel data switching regression model of mobility and car ownership

The objective of this paper is to present a panel data model of car ownership and mobility. Unobserved heterogeneity is controlled for by including correlated random effects in the equations describing car ownership and mobility. A mass-points approach is adopted to control for unobserved heterogeneity. The results show that decisions concerning the first car in the household are difficult to affect; a large number of households are inclined to keep one car. Second car ownership may be more sensitive to changes in the observed contributing factors. This suggests that in The Netherlands policies aimed at changing second car ownership will be more successful than those aimed at influencing decisions concerning the first car in households. A major part of the correlation between the unobservables in the car ownership and the mobility equations is attributable to random effects. The time-variant errors of the mobility equations are not significantly correlated to car ownership decisions. This implies that mobility can only be influenced to a small extent by policy makers without measures aimed at reducing (second) car ownership.     

A causal analysis of car ownership and transit use

The causal structure underlying household mobility is examined in this study using a sample obtained from the Dutch National Mobility Panel survey. The results indicate that car ownership is strongly associated with mode use, but that it has no influence on weekly person trip generation by household members. Characteristics of mode use are examined through a causal analysis of changes in car ownership, number of drivers, number of car trips, and number of transit trips. It is shown that observed changes in mode use cannot be adequately explained by assuming that a change in transit use influences car use. The finding suggests that the increase in car use, which is a consequence of increasing car ownership, may not be suppressed by improving public transit.     

Avoiding specification errors in estimating the value of time

Sources of specification as well as measurement error in previous estimates of the value of time are examined in this paper, and a new set of estimates avoiding these errors is presented. The sources of specification error being analyzed are: (1) failure to take account of drivers' uncertainty in making their choices between alternative routes or modes, which was found empirically to bias the value of time estimate 50 percent or more; (2) the differential in comfort and similar intangible characteristics between two modes; and (3) the inability to account for cross-time substitutions by travelers in peak-hour conditions. Ways of avoiding these sources of error are suggested and implemented. The choice between “reported” and “measured” data is re-examined, with an emphasis on random rather than systematic perception errors by travelers. Some resulting estimates are: $1.91 per hour per adult for off-peak work trips, $2.08 for off-peak social-recreational trips, and $5.17 for peak-hour work trips.     

A hazard-based approach to modelling the effects of online shopping on intershopping duration

Despite growing prevalence of online shopping, its impacts on mobility are poorly understood. This partially results from the lack of sufficiently detailed data. In this paper we address this gap using consumer panel data, a new dataset for this context. We analyse one year long longitudinal grocery shopping purchase data from London shoppers to investigate the effects of online shopping on overall shopping activity patterns and personal trips. We characterise the temporal structure of shopping demand by means of the duration between shopping episodes using hazard-based duration models. These models have been used to study inter-shopping spells for traditional shopping in the literature, however effects of online shopping were not considered. Here, we differentiate between shopping events and shopping trips. The former refers to all types of shopping activity including both online and in-store, while the latter is restricted to physical shopping trips. Separate models were estimated for each and results suggest potential substitution effects between online and in-store in the context of grocery shopping. We find that having shopped online since the last shopping trip significantly reduces the likelihood of a physical shopping trip. We do not observe the same effect for inter-event durations. Hence, shopping online does not have a significant effect on overall shopping activity frequency, yet affects shopping trip rates. This is a key finding and suggests potential substitution between online shopping and physical trips to the store. Additional insights on which factors, including basket size and demographics, affect inter-shopping durations are also drawn.     

Measurement of intention to travel: Considering the effect of telecommunications on trips

Mobility, one of the key concepts to evaluate the effect of a transportation policy such as TDM and mobility management as well as to analyze the problems such as social exclusion, must be measured by how much of an intention to make a trip can be realized, not merely by how many trips are available. The advent of new communication tools such as the Internet and mobile phones has allowed one to accomplish certain tasks that previously required a trip. This new situation has brought up a discussion over the necessity to incorporate telecommunications as an aspect of mobility. Aware of such discussions, we analyzed the relationship between the number of trips and telecommunications based on the data we collected on trips, telecommunications, and activities, and found some significant correlations. Our study which used an ordered regression model found several significant relationships between the individual attributes and the number of trips/telecommunications. We formulated a model which assumes the latent factors among the trips and telecommunications. In addition, we found that the latent factors construable as intentions to trips and telecommunications could be measured better by e-mails than by trips. These results indicate that measuring mobility requires the inclusion of information about telecommunications.     

The impact of transport pricing policy on individual energy consumption: a modeling case study in Kumamoto

To investigate the impact of traffic pricing policies on energy consumption, this study shows a microeconomic quantitative analysis scheme to simulate individual consumption behaviors from a microeconomic viewpoint. Energy consumption is estimated based on individual demand of non‐mobility goods and mobility goods under nine policy scenarios based on strategies of gasoline tax adding and mass transit fare reduction independently or combined. Results show that gasoline tax adding has strong effects on consumption behaviors. Energy consumption reduces mostly because of less consumption of non‐mobility goods and car trips. However, policy of mass transit fare reduction has limited impact on energy saving because consumption of non‐mobility goods and mass transit trips increases, but the number of car trips decline by only a small percentage. Comparing with single‐type policy, policies that combined gasoline tax adding and mass transit fare reduction show less energy consumption. Findings suggest that policies that increase cost of car trips, such as gasoline tax adding, are very helpful to reduce the consumption of non‐mobility goods and car trips, which contribute to less energy consumption. However, reducing cost of mass transit trips suggests limited effect on energy saving. Copyright © 2015 John Wiley & Sons, Ltd.     

Sustainability and automobility among the elderly: An international assessment

In the next three decades there will a huge increase in both the absolute number of older people and in their percentage of the populationin almost all Western European countries, North America, and Australia. Most older people will have active lifestyles in which mobility and access play a major role and almost all older men and a majority of older women will be car drivers, used to the convenience and flexibility which the car provides.Using data from the US, Australia, Germany, New Zealand, Norway, and the United Kingdom, the paper shows that, in spite of cultural and policy differences, older people around the world are more likely to have a license, to take more trips, and to do so more often as the driver of a car than older people just a decade ago; they are also less likely to use public transit. These trends have a number of sustainability implications – the most obvious one is increased environmental pollution. For example, even though older people may travel less than younger drivers they may be polluting proportionately more because a) they are less likely to make as great a proportion of trips in public transit as younger people and b) the trips they do make may create more pollutants. In addition, older drivers may incur more wasted miles due to wayfinding errors and trip-scouting behavior. And when older people curtail their driving, younger family members may have to increase (or lengthen) their trip-making to provide needed services or additional transportation.While this paper stresses the environmental problems posed by an aging population, effective strategies arise from a focus on a broader definition of sustainability. The most important approach is to accept the inevitable and work to make the private car "greener" and safer. New transit service concepts and strategic community and neighborhood design and service elements can complement the development of cleaner cars.Although many of the potential strategies are not new, or can be expensive to implement, the convergence of environmental concerns with other problems arising from the automobility of the elderly – including increasing crash rates and serious loss of mobility among those unable to drive – may make these policies more politically viable than in the past.     

A comprehensive study of life course,cohort, and period effects on changes in travel mode use

This paper studies changes in people’s travel mode use from one year to the next. It is informed by three distinct discourses: travel behaviour change, the mobility biographies approach, and cohort analysis. The data used is the German Mobility Panel (GMP) 1994–2008 in which households and their members are asked three times in three subsequent years to report the trips they made over a week. The changes reported are regressed to key events over the life course, cohort effects and period effects, while various sociodemographic and spatial attributes are controlled. Due to the non-independent nature of panel observations, a cluster-robust regression approach is used. The findings suggest that behind the aggregate stability in travel mode use over time there is much change ‘under the surface’, induced by life course changes, individual and household sociodemographic, and spatial context. The changes found induced by life course related key events favour the notion of mobility biographies. However, taken over all key events seem to be relatively loosely associated with mode use changes. Nonetheless, various significant effects of baseline variables suggest that mode use may change even in the absence of a key event.     

Demand and welfare effects in recreational travel models: Accounting for substitution between number of trips and days to stay

In this paper we present a non-linear demand system for households’ joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the effects of an increased CO₂ tax. In particular, we focus on the effect of including substitution between households choice of the number of trips and days to spend at a destination in the welfare analysis. The analysis reveals that the equivalent variation (EV) measure, for the count data demand system, can be seen as an upper bound for the households welfare loss. Approximating the welfare loss by the change in consumer surplus, accounting for the positive effect from longer stays, imposes a lower bound on the households welfare loss. The difference in the estimated loss measures, from the considered CO₂ tax reform, is about 20%. This emphasizes the importance of accounting for substitutions toward longer stays in travel demand policy evaluations.     

The impact of transit station areas on the travel behaviors of workers in Denver,Colorado

Transit development is one planning strategy that seeks to partially overcome limitations of low-density single use car oriented development styles. While many studies focus on how residential proximity to transit influences the travel behaviors of individuals, the effect of workplace proximity to transit is less understood. This paper asks, does working near a light rail transit station influence the travel behaviors of workers differently than workers living near a station? We begin by examining workers’ commute mode based on their residential and workplace proximity to transit station areas. Next, we analyze the ways in which personal travel behaviors differ between those who drive to work and those who do not. The data came from a 2009 travel behavior survey in the Denver, Colorado metropolitan area, which contains 8000 households, 16,000 individuals, and nearly 80,000 trips. We measure sustainable travel behaviors as reduced mileage, reduced number of trips, and increased use of non-car transportation. The results of this study indicate that living near a transit station area by itself does not increase the likelihood of using non-car modes for work commutes. But if the destination (work) is near a transit station area, persons are less likely to drive a car to work. People who both live and work in a transit station area are less likely to use a car and more likely to take non-car modes for both work and non-work (personal) trips. Especially for persons who work near a transit station area, the measures of personal trips and distances show a higher level of mobility for non-car commuters than car commuters – that is, more trips and more distant trips. The use of non-car modes for personal trips is most likely to occur by non-car commuters, regardless of their transit station area relationship.     

Trade-offs between mobility and equity maximization under environmental capacity constraints: A case study of an integrated multi-objective model

This paper investigates the performance of a policy decision tool proposed for multi-objective decision under different policy interventions. This tool deals with the trade-off between mobility and equity maximization under environmental capacity constraints. Two system objectives, maximization of mobility and equity, are formulated in terms of the sum of total car ownership and number of trips, and the differences in accessibility between zones. Environmental capacities are based on production efficiency theory in which the frontier emission under maximum system efficiency is taken as environmental capacity. To examine the performance of the proposed model, three types of hypothetical policies (network improvement, population increase and urban sprawl) are formulated. Effects are simulated using data pertaining to Dalian City, China. Results show that the proposed model is capable of representing the trade-offs between mobility and equity based on different policy interventions. Compared with two extreme cases with the single objective of mobility maximization or equity maximization, the Pareto-optimal solutions provide more interesting practical options for decision makers. Taking the solution based on the maximum equity as an example, the policy of urban sprawl yields the most significant improvement in both emission and accessibility of the three scenarios.     

A GPS-aided survey for assessing trip reporting accuracy and travel of students without telephone land lines

A geo-positioning satellite (GPS)-based survey, using a web-based prompted recall tool, was conducted on a sample of 94 students at the University of Toronto from November 2008 to April 2009. The sample included students with and without telephone land lines, allowing for a statistical comparison of demographic and travel behaviour attributes. The same subjects simultaneously completed a traditional trip reporting survey, modelled on the household travel survey in Toronto, allowing for a comparison between the travel behaviour information obtained from the GPS and that reported by the participants in the traditional survey. Students with a land line are more likely to live in houses, with parents, and to live in suburban areas than students without a land line. They also make fewer trips in total, fewer discretionary trips, more transit and auto trips and fewer active trips than students without a land line. By comparing questionnaire-based data and GPS data, we found that most participants reported in the questionnaire either the same number of GPS-based trips or fewer. On average, the GPS survey captured 1.29 more daily trips per participant than the corresponding trips reported in the questionnaire.     

Transportation impacts of center-based telecommuting: Interim findings from the Neighborhood Telecenters Project

The transportation impacts of center-based telecommuting for 24 participants (representing 69 person-days of travel and 295 trips) in the California Neighborhood Telecenters Project are analyzed. Comparing non-telecommuting (NTC) day to telecommuting (TC) day travel shows that person-trips did not change significantly, whereas vehicle-trips increased significantly (by about one trip) on TC days. Both PMT and VMT decline significantly on TC days: by an average of 68 miles (74%) and 38 miles (65%), respectively. When these savings are weighted by the frequency of telecommuting, overall reductions in PMT and VMT come to 19% and 17%, respectively, of total weekday travel. Commute trips increase slightly (by 0.5 trips) but significantly, mainly due to lunch-time trips made home from the telecenter. Total non-commute travel does not increase, but there is a significant shift from other modes to driving alone on TC days. Commute mode split on NTC days is not affected by telecommuting. Travel on TC days tends to be compressed into fewer hours. Higher numbers of return home, eat meal, shopping, and social/recreational trips are made on TC days, in exchange for a reduction (to zero) in the number of change mode trips.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

The effects of ozone action day public advisories on train ridership in Chicago

Ground level ozone is a criteria pollutant that is significantly affected by transportation patterns. Ozone action day advisories represent one type of voluntary ozone-abating program operating in urban areas where ozone pollution is concentrated. When forecasts predict that ground level ozone will exceed healthy levels, public advisories urge citizens to voluntarily choose public transportation as a means of eliminating automobile trips and reducing mobile emissions. To obtain credit for emission reductions spurred by voluntary programs, states must provide verifiable reduction estimates. This paper applies a fixed effects regression model to a panel of hourly Chicago Transit Authority train ridership data to evaluate the potential effects of Ozone Action Day advisories in Chicago from 2002 to 2003. Findings show that while the overall effect of ozone action days on ridership is not significant, there are statistically significant changes in hourly ridership patterns that indicate a more complex relationship between the public advisories and travel behavior.