Regional Travel Survey Research Articles

The importance of recurring public transport delays for accessibility and mode choice

This paper looks at the relationship between recurring public transport delays, accessibility to jobs, and travel behaviour in the region of Scania, Sweden. The difference between potential (scheduled) accessibility, observed (actual) accessibility, and behaviour is an important part of this research. This paper contributes to the growing body of literature that uses GTFS data (for both scheduled and actual services) to provide a deeper understanding of temporal variations in accessibility with public transport. Historic public transport data were used to develop a measure for typical delays in the region. The accessibility analysis shows that, on average, recurring public transport delays result in a 4–9% reduction in accessibility to jobs in the region. The loss in accessibility varied depending on the travel time budget that was considered and the location within the region. The accessibility analysis also shows that areas with higher concentrations of households with a lower economic standard experience a smaller loss in job accessibility caused by public transport delays. However, the concentration of these effects depends on the measure that is used. The measurement of typical delays was also analysed in relation to actual trips from the regional travel survey. The statistical analysis found that recurring public transport delays were associated with a lower likelihood of using public transport compared to other motorised modes.

Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode

We examined the nonlinear associations of the built environment with travel distances by driving, transit, and active travel and compared the relative contributions of local and regional built environment characteristics through applying gradient boosting decision trees to regional travel survey data in the Twin Cities, US. We identified the common thresholds of built environment characteristics associated with the three travel distances and inform planners of the efficient allocation of limited resources to planning efforts at different scales. We found prevalent threshold associations between built environment characteristics and travel distances. The thresholds suggest the common ranges of built environment characteristics that optimize the reduction of driving and the promotion of transit and active travel. For example, job accessibility should be larger than 800 thousand jobs within 20-min of driving and distance to downtown Minneapolis should be within 10 miles. The results also showed that regional characteristics collectively have a stronger influence on all three distances than local characteristics. The findings on transit and active travel differ from the common understanding in the literature. This study suggests that planners should pay more attention to metropolitan-scale planning and deploy programs that enhance regional accessibility.

E-scooters and public transport – Complement or competition?

The rapid deployment of shared electric scooters (e-scooters) has resulted in much attention from the public and regulators. In this paper we look at what role e-scooters have in the mobility system in Oslo, Norway.Previous research suggests that e-scooters fill three main functions: first by serving areas underserved by other modes; second that they replace public transport (PT) trips where the generalised costs of PT are relatively high; and third that they can play an important role as first/last mile mode. In this paper we look at the interaction between e-scooters and PT. We ask: do shared e-scooters compete with or complement public transport?We analyse competition between e-scooters and other modes by combining four data-sources: trip data from e-scooter trips; travel planner data for alternative modes; a survey conducted among e-scooter users collected for the purpose of the study; and the regional travel survey, obtained from the PT authority in the greater Oslo area.We find that e-scooters are both competing with and complementing PT. For most e-scooter trips, the PT alternative would take twice as much time, or more. A sizable share of e-scooter trips are indeed access and egress to/from PT.

Examining motivations for owning autonomous vehicles: Implications for land use and transportation

Illustrating the associations between built environment characteristics and autonomous vehicle (AV) ownership helps policymakers understand where AVs emerge first and their impacts on society. However, few studies have addressed interest in AV ownership from the spatial perspective. Using regional travel survey data from the Twin Cities, we applied the gradient boosting decision tree method to test three hypotheses (diffusion of innovation, efficiency, and modal substitution) underlying the relationships between respondents' interest in owning AVs and its correlates. Results showed that the innovation-diffusion hypothesis dominates the motivations for owning AVs, followed by preference for efficiency and then modal substitution. However, its associations with built environment variables suggest more of preference for efficiency than of diffusion of innovation and modal substitution. Population density, road connectivity, and land use entropy are negatively associated with the interest. The results provide suggestions to address the externalities of AVs in different areas.

Exploring the factors influencing the use of public transport by commuters living in networks of small cities and towns

The use of public transport is directly associated with a reduced environmental impact for satisfying daily mobility needs. Current research has focused on identifying the factors affecting the use of public transport, elements such as age, car ownership, travel distance, or parking availability having been associated with a direct impact on an individual’s transport mode choice. However, most of these studies focused on the travel behaviour of individuals living in large cities, where the population density is high, and public transport is usually more developed than in small cities and towns. The present study provides additional insights into the impact of eleven different factors on the use of public transport by employees living and working in networks of small cities and towns in Northern Europe. The study uses ordered logistic regression to analyse the data collected in 2019 through a regional travel survey conducted in Agder, Norway. The results reveal that the choice of public transport as a daily commute mode is significantly affected by car ownership, distance to work, parking availability, and ticket prices. Additionally, the results indicate that the odds of employees using public transport increase when the respondents do not have persons in care. On the other hand, factors such as low bus frequency and long walking distances to the home bus stop show a negative impact on the use of public transport. Based on these results, regional and local policy actions are proposed.

Commuter benefits programs: Impacts on mode choice, VMT, and spillover effects

Using the 2014 Puget Sound Regional Travel Survey, this study investigates how the availability of different types of commuter benefits, such as employer-sponsored parking benefits and transit subsidies, is associated with workers' travel behavior. In addition to examining the effects of commuter benefits on the mode choice and vehicle miles traveled (VMT) of workers' commutes, this study focuses on their spillover effects at both the individual and household levels. The results show that access to commuter parking and transit benefits not only increases the likelihood of workers using the subsidized travel mode to commute but also affects workers' non-commute trips as well as their household members’ travel behavior. This study provides important policy implications by demonstrating that commuter benefits for transit use are more effective in reducing VMT than expected, after accounting for the spillover effects.

Self-employment and travel behavior: A case study of workers in central Puget Sound

Self-employment has been an important livelihood means for many U.S. workers. Self-employed workers are likely to have distinctive travel behavior because, compared to employees, they have greater autonomy over work scheduling and are less affected by imperfect information about the labor market, which increases commutes. However, very few empirical studies have addressed this subject. Using data from the 2014 Puget Sound Regional Travel Survey, I examine the multiple dimensions of work and non-work travel behavior of the self-employed in comparison to employees. The results show that the effects of self-employment on travel behavior vary by whether a worker commutes. Specifically, the self-employed who travel to work have a shorter commuting distance and time than their employee counterparts, although this seems to be offset by increased travel distance and time for other work-related and non-work purposes. In addition, self-employed commuters are more likely to drive alone for both commute and non-commute purposes, partly because they tend to travel more during off-peak hours than their employee counterparts. However, for those who work at home, being self-employed does not have significant effects on most travel behavior outcomes. This paper also elucidates the environmental sustainability of workers’ travel behavior by self-employment status, which has been missing in previous literature.

The Philadelphia story: Age, race, gender and changing travel trends

We examine changes in travel behavior in the Philadelphia region between 2000 and 2012. We use two household regional travel surveys over a decade apart, the 2000 and 2012 surveys, from the Delaware Valley Regional Planning Commission (DVRPC). Previous research, at the national scale, has highlighted changes among the Millennial cohort. We use these two regional datasets to examine the changing travel behavior among Millennials and to put these changes in context by comparing them with changes among other social groups of interest: women and minorities.We find that the travel behavior of young people did not change substantially between 2000 and 2012. Where there are changes, these pale in comparison to the changes among women and black residents. And finally, we find that the built environment factors appear to influence travel more in 2012 than in 2000. Taken together, our findings fit an overarching narrative about urban gentrification and the suburbanization of poverty, rather than a story of Millennials’ changing travel behavior.

Effect of land use and survey design on trip underreporting in Montreal and Toronto’s regional surveys

This paper contributes to the literature on travel survey methods by quantifying the relationship between land use, data collection protocol and trip under-reporting in regional travel surveys. While under-reporting more broadly is a recognized problem, the significant increase in underreporting in denser, more urban-type environments identified here has never before been demonstrated or measured. Consequences of this land use-related bias for transportation planning and modelling are explored. The work is carried out by comparing the results of two very similar household travel surveys conducted in 2011 and 2013, in Toronto and Montreal respectively. Using data on over 350,000 persons, a binary logit model for discretionary trip making is estimated and the effects of land use and data collection protocol on under-reporting are isolated. This is done by controlling for mobility tool access, household type and other key determinants of travel demand. Counterintuitive effects for urban type environments found indicate the under-reporting effect is equivalent to a reduction in the pre-existing odds of reporting discretionary trip making in more urban environments of 19 to 29%. When combined with Toronto’s data collection protocol effect, the range increases to 39 to 55%. Results should be of use to transportation planning authorities wishing to make better use of the data collected in large surveys. Recognizing some of the flaws and biases in what is reported, these authorities can complement existing sources of data or modify their approaches to demandbased infrastructure provision to better account for the large number of, largely pedestrian, unreported trips.

Estimating a mixed-profile MDCEV: case of daily activity type and duration

Multiple Discrete Continuous Extreme Value (MDCEV) has become popular in the past years. Yet, the model suffers from an ‘empirical identification’ issue that is mainly due to inter-relations between two of its parameters, α and γ. This paper presents a hybrid optimization paradigm (named HELPME) to address this issue in a basic MDCEV formulation and take full advantage of the model by estimating a ‘mixed-profile.’ HELPME benefits from a coarse-to-fine search strategy, in which a customized Electromagnetism-like meta-heuristic precedes a gradient-based approach. The Atlanta Regional Travel Survey (2011) is used to empirically analyze performance of HELPME as well as significance of the accuracy gap between the mixed-profile, and α and γ profiles. As part of the results, it is observed that in-sample fit is significantly improved, percentage error of out-of-sample prediction is reduced up to 97% in a 90% confidence level, and bias of out-of-sample predictions are reduced up to 67%.

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.

A Multiscale, Transit-Oriented Development Definition Based on Context-Sensitive Paradigm

This study investigates the connection between transit-oriented development (TOD) input and output and proposes a multiscale TOD definition to address the underutilization of the definition in binary TOD–non-TOD. Data about neighborhood characteristics, a regional travel survey, and two experts’ surveys are used to select and rate variables that impact communities in a study of 33 neighborhoods in Baltimore City, Maryland. Defining scaled-TOD is still nascent and very subjective at this moment because what affects TOD—infrastructure and policies—is controlled and managed by agencies that do not use TOD performance data (outputs) that depend on the users’ personal choice. TOD outputs such as reduced vehicle miles traveled (VMT) or increased transit ridership depend on the users’ choice based on their perception of the effective quality and accessibility of the infrastructure (inputs). Thus, a scaled definition can explain, with a range, the relationship between various components of a context-sensitive paradigm using neighborhood-level characteristics (inputs) and the quality of the neighborhood, VMT, and the performance data (output). The multiscale TOD definition is a step toward developing a much-needed standard metric to evaluate neighborhood sustainability and use as a tool to assess needs and allocate resources in urban transportation planning. The overall analysis of the data indicated that the quality and the condition of the infrastructure influence the neighborhood output in various degrees that can be flagged by its defined level of TODness.

Geographically Weighted Regression Approach to Investigate Spatial Variations in Activity Space

This study examined individual activity space focusing on whether and how the effects of the built environment vary spatially across the study region. With the use of the 2012–2013 Northeast Ohio Regional Travel Survey data, a global regression model and a geographically weighted regression (GWR) model were estimated to analyze these effects after controlling for sociodemographics and travel time. The global results indicate that people living in areas characterized by high densities, a variety of jobs and populations, and high access and connectivity generally have smaller activity spaces. The GWR results report better fit than the global model and present spatial variations in the relationships between the six built environment variables and individuals’ activity spaces. Relevant transportation and land use policies, such as transit-oriented development and smart growth, may directly affect the built environment and therefore change individual access and sizes of activity spaces. The spatial variations suggest that the effects of these policies may vary. These findings are of great assistance to policy makers as they point out the target areas for effective policy interventions and infrastructure provisions.

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.

How do socio-demographics and built environment affect individual accessibility based on activity space? Evidence from Greater Cleveland, Ohio

Since the early 2000s, accessibility-based planning has been increasingly used to mitigate urban problems (e.g., traffic congestion and spatial mismatch) from a sustainable perspective. In particular, the concept of accessibility has been applied to investigate transport exclusion in many studies. However, few of them shed light on the effects of socio-demographics (e.g., income and gender) and the built environment (e.g., density) on accessibility at the individual level as a measure of transport exclusion. This study measures individual accessibility as the opportunities available per square mile within individual daily activity space for evaluating transport exclusion status based on the Capability Approach. Using data from the 2012 Northeast Ohio Regional Travel Survey and two opportunity sets (land uses and jobs), we calculate individual accessibility and compare them across three income groups. The comparisons report that low-income people are not disadvantaged in our study region. Path models are estimated to examine the relationships between socio-demographics, built environment, trip characteristics, and individual accessibility. We apply K-means cluster analysis to construct seven neighborhood types for the built environment. The results indicate that the effect of income on accessibility varies by opportunity types and living in urbanized neighborhoods increases people’s accessibility after controlling for other characteristics.

Effects of neighborhood types & socio-demographics on activity space

This paper examines the relationships between socio-demographic characteristics, travel time, the built environment and resulting average activity spaces for all activities and non-work activities separately using data from the 2012 Northeast Ohio Regional Travel Survey. Multiple regression models are developed to analyze these relationships at individual level. First K-means cluster analysis is conducted to create seven neighborhood types based on five built environment variables. These new neighborhood types are used as discrete explanatory variables to explain average activity spaces, while controlling for travel time, individual and household features, access to transit facilities and the job-population balance. The modeling results indicate that residential location characteristics have significant influences on activity spaces. People living in places away from suburban and rural areas and with a high mix of population and employment tend to have smaller activity spaces. Moreover, this study finds out that while the effects of some explanatory variables (such as age and gender) vary for all activities and non-work activities, socially disadvantaged people (such as the elderly and low income households) generally experience smaller activity spaces.

Understanding Travel Plan Effectiveness for New Residential Developments

Travel plans can be required for new or expanded buildings as part of the process of land use planning and approval. The purpose of these plans is to manage car use of future building occupants and support access by more sustainable forms of transport. Although the effectiveness of travel plans is generally well established at preexisting sites, there is very little evidence of the impacts when they are applied to new developments, particularly residential sites. In response to this situation, a series of multimodal person trip counts and parking surveys was undertaken at four case sites (residential developments with travel plans) and four control sites (similar residential developments without travel plans) in Melbourne, Australia. Results indicated that the average weekday (7:00 a.m. to 9:00 a.m.) mode share for car driver trips was 14 percentage points lower at the case sites than at the control sites. The findings also highlighted issues with relying solely on comparisons with secondary data sources, such as published vehicle trip generation rates and regional travel survey data, to assess travel plan effectiveness. Such reliance was shown to lead to an overestimation of travel plan impacts because of inconsistencies in the geographical locations and data collection periods of the secondary data sources. This finding highlights the importance of using suitable control sites to account for such factors. Further work is needed to strengthen the evidence base through evaluating travel plans at other residential developments and also accounting for residential self-selection effects.

Brother can you Spare a Ride? Carpooling in Immigrant Neighbourhoods

Immigrants are more likely to travel by carpool than the US-born. Strong ethnic ties within immigrant communities may contribute to immigrants’ propensity to carpool, enabling residents to find carpool partners more easily and increasing the likelihood that residents will travel to and from common destinations. Drawing on data from the 2000 US census and a 2001 regional travel survey, this paper examines whether residents of ethnic neighbourhoods in Southern California are more likely to carpool than other residents. A strong positive relationship is found between the percentage foreign-born in a census tract and carpooling rates. Analysis of individual data shows that this relationship is strongest for immigrants who live in immigrant neighbourhoods; immigrants living in non-immigrant neighbourhoods are less likely to carpool. These findings suggest an important role for social networks in travel behaviour and the potential benefits of linking land use to the specific needs of local residents.

Transferring Telephone-Based National Household Travel Survey to the Internet

Transfer of the National Household Travel Survey (NHTS) and regional travel surveys to the Internet is inevitable, partly because online surveys offer an efficient means of collecting data. Behavioral surveys are increasingly being offered in multiple media and giving respondents the choice of filling out the survey by using the Internet, the telephone, or other means. This study reports experience and lessons learned from transforming the computer-assisted telephone interview methodology used for the NHTS to an Internet-based method for university students, who have ubiquitous access to the Internet. The study reflects innovations in survey research methodology in the context of surveying students at large universities in Virginia. Comparisons of two rounds of behavioral surveys conducted in 2009 and 2010 are provided, and statistical models that quantify trip underreporting are developed. In an attempt to mimic closely the NHTS instrument, the Internet instrument had a relatively high survey response burden. On the basis of analysis of trip frequencies, trip underreporting was suspected in the first round of surveys. Documented here are improvements in survey design that were intended to lower the survey response burden and reduce trip underreporting. Statistical models are estimated to quantify how changes in the instrument captured more trips. Results show that survey design improvements can encourage students to recall and report 15% to 20% more trips. The study also found that, consistent with earlier studies, discretionary trips and shorter trips were more likely to be underreported. The results from this study are valuable for future regional and national survey implementations.

Can Built and Social Environmental Factors Encourage Walking among Individuals with Negative Walking Attitudes?

We investigate whether the design of the built environment encourages walking above and beyond individuals’ attitudes toward walking. With data from a regional travel survey, we use regression analyses to examine differences in neighborhood walking trips among residents with positive and negative attitudes toward walking. The results show that built and social environment factors have a differential impact on walking trips depending on a person’s walking attitudes. Therefore, strategies to promote positive walking attitudes should be pursued in tandem with land use policies to encourage neighborhood walking.