Regional Household Travel Survey Research Articles

Exploring the impact of walk–bike infrastructure, safety perception, and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.

Naturalistic driving cycle synthesis by Markov chain of different orders

This paper evaluates the performance of using Markov chain of different orders to synthesise real-world representative drive cycles from numerous naturalistic drive cycles. The representative drive cycles can be a valuable input into the design of powertrains, especially for plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV). Their onboard cost-sensitive electric components, such as battery, require an appropriate sizing by understanding how people drive in naturalistic settings. Applying representative drive cycles instead of federal certification drive cycles provides flexibility of drive cycle length and ensures realistic cycle aggressiveness. Even though Markov chain has been widely used to synthesise representative drive cycles, the effects of different orders have not been systematically compared. Based on a publicly accessible portion of GPS-enhanced regional household travel survey, after statistical hypothesis tests, the results show that higher degree of representativeness can be achieved with a 3-order Markov chain compared to a 2-order Markov chain. These findings help to improve the accuracy of cycle synthesis for PHEV and EV analysis.

Naturalistic driving cycle synthesis by Markov chain of different orders

This paper evaluates the performance of using Markov chain of different orders to synthesise real-world representative drive cycles from numerous naturalistic drive cycles. The representative drive cycles can be a valuable input into the design of powertrains, especially for plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV). Their onboard cost-sensitive electric components, such as battery, require an appropriate sizing by understanding how people drive in naturalistic settings. Applying representative drive cycles instead of federal certification drive cycles provides flexibility of drive cycle length and ensures realistic cycle aggressiveness. Even though Markov chain has been widely used to synthesise representative drive cycles, the effects of different orders have not been systematically compared. Based on a publicly accessible portion of GPS-enhanced regional household travel survey, after statistical hypothesis tests, the results show that higher degree of representativeness can be achieved with a 3-order Markov chain compared to a 2-order Markov chain. These findings help to improve the accuracy of cycle synthesis for PHEV and EV analysis.

Impacts of Telecommuting on Nonmandatory Activity Participation: Role of Endogeneity

Telecommuting has been regarded as an alternative work arrangement that provides higher levels of resilience and flexibility for the workforce. To provide a robust estimation of telecommuting activity, it is important to understand the extent of telecommuting and the underlying factors that contribute to telecommuters’ decisions. One line of discussion in behavioral decisions (including telecommuting) is the endogeneity issue, also referred to as simultaneity in cause and effect. The question is whether certain activity travel behaviors are the causes of telecommuting adoption or telecommuters are manifesting such behaviors because they telecommute. For instance, are people traveling more or farther because of the higher spatial–temporal freedom brought by telecommuting, or are they choosing to or having to telecommute because they already have those activity arrangements (e.g., take care of kids) in place. Exploring such interrelationships between the choice to telecommute, activity travel plans, and other personal and household decisions is the major motivation for this research. Structural equation models were developed. Results showed that adding feedback effects to the full model improved the goodness of fit of the model, and this improvement is statistically significant ( P-value = .032). Furthermore, it could be inferred that a decision to participate in nonmandatory activities has significant positive impact on propensity to telecommute, and it is more in favor of a full-day telecommuting arrangement (versus a part-day arrangement). This study used the 2010–2011 regional household travel survey data from the New York metropolitan region.

Time-dependent congestion pricing system for large networks: Integrating departure time choice, dynamic traffic assignment and regional travel surveys in the Greater Toronto Area

Congestion pricing is one of the widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a framework for large-scale variable congestion pricing policy determination and evaluation. The proposed framework integrates departure time choice and route choice models within a regional dynamic traffic assignment (DTA) simulation environment. The framework addresses the impact of tolling on: (1) road traffic congestion (supply side), and (2) travelers’ choice dimensions including departure time and route choices (demand side). The framework is applied to a simulation-based case study of tolling a major freeway in Toronto while capturing the regional effects across the Greater Toronto Area (GTA). The models are developed and calibrated using regional household travel survey data that reflect the heterogeneity of travelers’ attributes. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The case study examined two tolling scenarios: flat and variable tolling. The results indicate that: (1) more benefits are attained from variable pricing, that mirrors temporal congestion patterns, due to departure time rescheduling as opposed to predominantly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic demand are observed across the regional network in response to tolling a significant, yet relatively short, expressway serving Downtown Toronto, and (3) flat tolling causes major and counterproductive rerouting patterns during peak hours, which was observed to block access to the tolled facility itself.

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.

Examination of the Impacts of Telecommuting on the Time Use of Nonmandatory Activities

How and to what extent telecommuting engagement affects time allocation among nonmandatory activities are examined to help understand the impacts of telecommuting on daily activity–travel patterns. Five categories of nonmandatory activities are considered: shopping, maintenance, discretionary, escort, and in-home shopping. The hypothesis is that telecommuting relaxes the temporal and spatial constraints related to work activities at the regular workplace, and telecommuters may allocate some of the time budget to other nonmandatory activities, which may or may not lead to additional travel. The structural equations model approach is applied to capture the impacts of telecommuting as well as the interactions among the nonmandatory activities. The activity durations by type along with the number of total daily trips are considered as endogenous (dependent) variables. By incorporating work hours at the regular workplace and daily telecommuting hours as exogenous variables, the models can reveal how people may reallocate their time among different nonmandatory activities given different levels of telecommuting engagement (either part-day or full-day). All types of telecommuting arrangements increased nonmandatory activity durations (compared with those of nontelecommuters). Full-day telecommuters have higher durations of discretionary activities, while part-day telecommuters have higher durations of maintenance and out-of-home shopping errands. Telecommuting also increased total daily trip rates for both telecommuters and their household members. This study used data obtained from the 2010–2011 Regional Household Travel Survey in the New York metropolitan region.

Factors affecting children’s journeys to school: a joint escort-mode choice model

A child’s mode of travelling to school is influenced by, or dependent on, parental choices. Thus, an increasing proportion of car trips may reflect parental choices and constraints. Whether a parent can escort their children to school may depend on their scheduling and spatial constraints, e.g., work schedule and job location in relation to home and school locations. This research aims to understand the effect of household bundling constraints on a child’s escort-mode choice. In this study, school trip data are drawn from the 2001 SCAG (Southern California Association of Governments) Post Census Regional Household Travel Survey. The study area is the five-county Los Angeles region. Our findings show that the parents’, especially the mother’s, increased working hours and more distant job locations result in an increased likelihood of several alternative escort-mode choices. Mothers who work longer hours and further away from home are less likely to chauffeur their children. These trips have been substituted by alternative escort choices such as independent travel and being escorted by fathers, or alternative mode choices such as active commuting and busing. The effect of increased working hours may be offset by the option of flexible working hours, which allows parents to arrange more escort trips. This study elucidates an important aspect in explaining children’s changing mode choice in journeys to school and sheds light on current policy efforts in reducing children’s car dependency.

Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach

Understanding travel behavior and its relationship to urban form is vital for the sustainable planning strategies aimed at automobile dependency reduction. The objective of this study is twofold. First, this research provides additional insights to examine the effects of built environment factors measured at both home location and workplace on tour-based mode choice behavior. Second, a cross-classified multilevel probit model using Bayesian approach is employed to accommodate the spatial context in which individuals make travel decisions. Using Washington, D.C. as our study area, the home-based work (Home-work) tour in the AM peak hours is used as the analysis unit. The empirical data was gathered from the Washington-Baltimore Regional Household Travel Survey 2007–2008. For parameter estimation, Bayesian estimation method integrating Markov Chain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important role that the built environment at both home location and work ends plays in affecting commuter mode choice behavior. Meanwhile, a comparison of different model results shows that the cross-classified multilevel probit model offers significant improvements over the traditional probit model. The results are expected to give a better understanding on the relationship between the built environment and commuter mode choice behavior.

Cross-Nested Joint Model of Travel Mode and Departure Time Choice for Urban Commuting Trips: Case Study in Maryland–Washington, DC Region

The aim of this paper is to contribute to describing the simultaneous choice of travel mode and departure time by making use of a cross-nested logit structure that allows for the joint representation of interalternative correlation along the both choice dimensions. Traditional multinomial logit model and nested logit model are formulated respectively. The analysis uses the revealed preference data collected from Maryland-Washington, DC, regional household travel survey during 2007–2008 for commuting trips, considering more work-related characteristics than previous studies. A comparison of the different model results shows that the presented cross-nested logit structure offers significant improvements over multinomial logit and nested logit models. The empirical results of the analysis reveal significant influences on commuter joint choice behavior of travel mode and departure time. Moreover, a Monte Carlo simulation for two groups of scenarios arising from transportation policies, congestion pricing, and improvements to transit service during peak period is undertaken respectively to examine the impact of a change in car travel cost and transit travel time on the travel mode and departure time switching. The simulation results show that US$5 increase in car travel cost during peak period has a similar effect on reducing drive alone in peak hours as 30% saving in transit travel time but only half of the latter policy in the transit ridership increase.

The Impact of Employer Attitude to Green Commuting Plans on Reducing Car Driving: A Mixed Method Analysis

Reducing car trips and promoting green commuting modes are generally considered important solutions to reduce the increase of energy consumption and transportation CO2 emissions. One potential solution for alleviating transportation CO2 emissions has been to identify a role for the employer through green commuter programs. This paper offers an approach to assess the effects of employer attitudes towards green commuting plans on commuter mode choice and the intermediary role car ownership plays in the mode choice decision process. A mixed method which extends the traditional discrete choice model by incorporating latent variables and mediating variables with a structure equation model was used to better understand the commuter mode choice behaviour. The empirical data were selected from Washington-Baltimore Regional Household Travel Survey in 2007-2008, including all the trips from home to workplace during the morning hours. The model parameters were estimated using the simultaneous estimation approach and the integrated model turns out to be superior to the traditional multinomial logit (MNL) model accounting for the impact of employer attitudes towards green commuting. The direct and indirect effects of socio-demographic attributes and employer attitudes towards green commuting were estimated. Through the structural equation modelling with mediating variable, this approach confirmed the intermediary nature of car ownership in the choice process. The results found in this paper provide helpful information for transportation and planning policymakers to test the transportation and planning policies effects and encourage green commuting reducing transportation CO2 emissions.

Will you escort your child to school? The effect of spatial and temporal constraints of parental employment

Children's independent mobility in many urban areas in the United States has been in decline. This implies that children's out-of-home travel involves the company of adults more often than before. This need requires parents to seek ways to balance work-family life. This study examines the factors that influence a parent-child joint trip to school in dual-earner households, with special consideration given to parental work arrangements and location, which act as intra-household temporal and spatial constraints. The study is carried out in the five-county Los Angeles region, drawing from household travel diaries from the 2001 Southern California Association of Governments (SCAG) Post Census Regional Household Travel Survey. The results show that parental employment, especially the mother's, is a very important factor influencing the probability of a joint trip to school. In terms of temporal constraints, longer working hours will reduce the likelihood that a child will be escorted by its parents. However, the option of flexible work hours offsets the negative effect of long working hours by offering a higher degree of scheduling flexibility. In terms of spatial constraints, the closer the mother's workplace is to the school, the more likely it is that the child will be escorted by the parents. This research provides important evidence that a child's joint trip is heavily influenced by the parents' work schedule and location. The findings shed light on how the provision of certain employment policies and programs may affect joint trips to school.

Injury incidence rates of cyclists compared to pedestrians, car occupants and powered two-wheeler riders, using a medical registry and mobility data, Rhône County, France

PurposeIn France, the bicycle's modal share is stabilizing after a decline; in some of France's major cities, it has even increased since the 1990s. It is hence relevant to improve the knowledge of the injury risk associated with cycling, compared with other means of transport such as car, walking and powered two-wheeler (PTW) riding. MethodsThe injury incidence rates were estimated by the ratio between accident data and mobility (exposure) data. Two accident data sources were used: police data and hospital-based data (outpatients and inpatients) from the Rhône road trauma Registry. This provides four injury categories: all-injury, hospitalization, serious-injury and fatal-injury. Exposure data were estimated from a regional household travel survey (RTS), using three measures of mobility: number of trips, distance traveled and time spent traveling. The survey was carried out from November 2005 to April 2006, on weekdays, outside school and public holidays; this seasonality was corrected using the 2007–2008 national household travel survey (NTS) that covered an entire year. Only information involving accidents and trips in, and residents of, the Rhône County (1.6 million inhabitants, including the city Lyon) were included in our study. Trends of injury rates were also evaluated in Greater Lyon, using previous travel surveys. ResultsThe PTW riders had the highest all-injury, hospitalization, serious-injury and fatal-injury rates, followed by cyclists, and lastly by pedestrians and car occupants. The rates between men and women seemed similar among pedestrians and among car occupants. For car occupants, pedestrians and cyclists, the age group 18–25 years had higher all-injury rate compared with the age group 25–65 years. On the contrary, the age group ≥65 years seemed to have higher hospitalization and serious-injury rates, compared with the age group 25–65 years. For cyclists, the injury rates seemed higher in non-dense areas than in dense areas. Between 1996–1997 and 2005–2006 and with regards to time spent traveling, the all-injury, serious-injury and fatal-injury rates seemed to have decreased for car occupants and cyclists. ConclusionThe higher risk for PTW riders is confirmed and quantified; it is very high. Decrease in injury rates seems more marked for cyclists; this may indicate the “safety in numbers” effect. Countermeasures for improving road safety could be implemented, especially for vulnerable road user types. However, they will not be sufficient to fill in the gap between the much higher risk for PTW riders and that of car occupants. Exposure-based injury rates can be a tool for monitoring and evaluating the effectiveness of policies and programs, and for comparisons between countries.

A household-level activity pattern generation model with an application for Southern California

This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.

Are Bikeshare Users Different from Regular Cyclists?

In recent years, bikeshare systems have spread throughout the United States. Despite the increase in systems and users, little is known about how bikeshare member demographics and travel patterns compare with those of traditional bicyclists. To bridge the gap, this study investigated bikeshare system user travel behavior and developed a profile of user demographics through the comparison of short-term (1 day) users and annual members of Capital Bikeshare (CaBi) in Washington, D.C., with profiles of area bicyclists. Data originated from a Washington, D.C., regional household travel survey of 2007–2008, an intercept survey of short-term CaBi users, and an online survey of annual CaBi members. The analysis compared gender, race and ethnicity, age, and student status, as well as socioeconomic status across the surveys. The comparison included trip purpose, helmet use, and travel modes for trips that were replaced by bikesharing. Profiles of CaBi and area cyclists differed with respect to many demographic and socioeconomic characteristics. Compared with area cyclists, CaBi short-term users and annual members were more likely to be female and younger, to have lower household incomes, and to own fewer cars and fewer bicycles and were more likely to cycle for utilitarian purposes. CaBi trips mainly replaced trips made by public transport and by walking. CaBi short-term users and members shared similar characteristics, but short-term users were more likely to ride for recreational purposes and were less likely to wear a helmet. The study results indicated that bikesharing can encourage new segments of society to cycle and can help increase the bicycling mode share overall.

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.

Continuous Improvement in Regional Household Travel Surveys

Funding constraints limit the ability of local and state agencies to fund quality data collection efforts. At the same time, public agencies that oversee transportation programs must support planning and policy making with empirical data. As a result, transportation data users and suppliers are consistently pressed to find better, faster, and cheaper ways of collecting data. Thus, continuous improvement in travel survey data methods, procedures, and tools is an imperative, not a luxury. This paper presents the implementation and results of a continuous improvement process directed by the New York Metropolitan Transportation Council (NYMTC) with regard to its regional household travel survey. The paper reports on how lessons learned from NYMTC's 1997–1998 household travel survey along with feedback from respondents were used to plan and design the 2010 household travel survey. New methods and tools that were implemented in a 2010 survey pretest are identified, and conclusions concerning the recommended survey design are provided.

Effect of School Quality and Residential Environment on Mode Choice of School Trips

Trips to school are increasingly taken in automobiles. Smart growth and health advocates suggest that a better-designed built environment in a neighborhood can promote walking and biking to school and that children's walking and biking improve the environment, lower obesity, and increase physical activity. The effects of school variables on travel behavior, however, have rarely been documented. The objective of this research is to examine the impact of school location and characteristics on students’ choice of travel mode. The argument is that if the local school is good, then a child is more likely to attend it. The chance increases that the child will commute actively to a school within a short distance, all else equal. School trips from the 2001 Post Census Regional Household Travel Survey of the Los Angeles, California, region were analyzed in relation to school quality in the area of the traveler's origin (residence) and destination. Results revealed that for young travelers who attended kindergarten to sixth grade, school quality and residential environment had no significant effect on nonmotorized modes. In addition, school quality had little impact on mode choice. Nonetheless, the distance from home to the nearest high school significantly increased the probability that students who attended seventh to 12th grades would choose the bus over a private vehicle. A 10% increase in the distance from home to the nearest high school raised the probability of taking the bus by 2.86%, while an identical increase in residential density increased the probability of walking or biking by 1.09%.

How Will We Get There?

Access to essential goods and services is increasingly recognized as a key factor influencing household socioeconomic disadvantage within cities. Socioeconomic status (SES) and spatial location partly determine differential accessibility. Spatial variation of low-SES groups across cities and the travel patterns of these groups have been mostly ignored by strategic transport models, which have been concerned more with traffic volumes on the road network and peak hour travel. This study expanded on a method that used cluster analysis techniques to identify low-SES groups on the Gold Coast of Australia from a large regional household travel survey. This method allowed for the identification of the actual travel behavior of low-SES groups. With this information, the study advanced a new origin–destination-based land use and transport accessibility model. The model used the output from the cluster analysis, in conjunction with 2006 Australian census data, to highlight accessibility to goods and service needs for a set of low-SES groups on the Gold Coast. The method developed provided unique opportunities for research into spatial disadvantage and accessibility in Australian cities. The conceptualization of the transport network with outputs provided at the census collection district level (approximately 200 households) ensured that the model could be run concurrently with conventional transport models.

Use of Walk Opportunities Index to Quantify Local Accessibility

A research effort was undertaken by the Baltimore Metropolitan Council, Maryland, to explore further the relationship between land use and transportation to support regional planning analysis. Particular attention was given to quantifying the role of key land use factors such as density, diversity, and design (3Ds) and regional accessibility in determining rates of vehicle ownership and vehicle miles traveled (VMT). The research was facilitated by a recent regional household travel survey and significant untapped geographic information system (GIS) resources. What began as an effort simply to replicate the approach of an earlier published study that incorporated the 3Ds took an unexpected turn. The result was an entirely new measure, a walk opportunities index, which combines information on the number, character, and desirability of key activities located within walking distance of a household. GIS procedures were used to trace walk paths to each destination by using the street grid and to compute travel times while adjusting for topography and difficult street crossings. By using the gravity model paradigm, each opportunity was then discounted by its travel time and the results summed into a single measure of walk opportunities. When used in combination with measures of entropy, regional accessibility, and socioeconomic characteristics, this new index had important statistical value in regression models of household auto ownership and VMT. The ability of these models to address the travel effects of alternative transportation and land use development plans or policies makes them a potentially powerful tool in regional planning activities.