Declining response rates make it increasingly critical for survey designers across disciplines to utilize mechanisms that facilitate timesaving and reduce the burden on the part of respondents. In practice, this often means that questionnaires are shortened, yielding increased response rates but reduced information/ variables available for modeling purposes. Here, this challenge is addressed by using machine learning and regression algorithms, alongside passive data augmentation, to develop and apply a predictive transfer learning-based framework for enriching surveys with information from other datasets. We demonstrate the framework by applying it to supplement and enrich the U.S. National Household Travel Survey (NHTS) with psychometric data (e.g., attitudes), which have been shown in the literature to have the ability to explain and predict behavior, but which are often not captured on household travel surveys. It is, to our knowledge, among the first times such an approach has been applied: (1) in a framework-based systematic approach in transportation; and (2) to transferring attitudinal variables across survey datasets. The application shown here explains up to 25% of the variance in observed attitudes, yielding correlations of up to 0.5 between observed and predicted attitudinal variables. Future applications of the framework presented in this paper have the potential to improve travel demand forecasting and behavioral predictions; and, even more broadly, may be used to enrich other large-scale behavior-based surveys with external variables, thereby providing more diverse and robust data streams for use in an array of modeling efforts.

This research investigates current transit design practices and agency policies through the lens of gender-inclusivity, identifying the disparities between men and women. Women typically engage in shorter, more complex trips owing to caregiving and household responsibilities, and thus face additional challenges like unsafe transit settings and inadequate accommodations for strollers and belongings. This study employs a qualitative case study approach, including interviews with staff from nine transit agencies to learn about their present and future practices, and an analysis of five transit design and operations manuals to study their recommendations and standards. Key findings revealed progress in gender-sensitive data collection and design, but barriers like resource constraints and a lack of priority still remained. Agencies like Los Angeles Metro, Southeastern Pennsylvania Transportation Authority, and Washington Metropolitan Area Transit Authority have made notable advancements, with several others following suit. However, the explicit incorporation of gender-sensitive principles in widely used transit design manuals is often lacking. The study concludes that continued commitment coupled with innovative approaches could overcome existing barriers and enhance gender-inclusivity in transit systems.

Active travel (AT) data has many uses in transportation planning, engineering, public health, and recreational planning. Often, direct measures of biking and walking are not available to transportation agencies, but proxy (i.e., indirect measures) of biking and walking are, which leads to interest in using them to inform understanding of AT trends. Our work investigates two topics that can direct future use of AT proxy data in transport problems: (1) we investigate the feasibility identifying travel typologies in proxy data sets; and (2) we examine three methods of time series clustering to assess each approach's suitability for clustering AT proxy data. We apply these topics to two examples of AT data - self-reported bicycle data and pedestrian "push-button" data at intersections - and we compare the clusterings with qualitative and quantitative measures. Our work shows it is possible to extract typologies from AT proxy data, although the typologies are less distinct than they likely would be in true count data. We find that shape-based clustering results in cohesive, separated clusters that relate to socioeconomic and land use variables that are known to influence travel demand. In some cases, a simpler feature-based clustering produces high-quality clusterings on the bike data, providing practitioners with less complex options when applicable.

The megaregion is viewed as a platform from which to address a variety of issues. Despite agreement that a megaregion is a large, globally connected urban agglomeration, there is no consensus as to how to delineate its boundaries and how it differs from other urban forms. The scholarly literature is dominated by three distinct analytic approaches: interdependent systems, nodal linkages, and satellite data. We assess the utility of each approach in delineating the boundaries of four megaregions - BosWash, Greater Tokyo, the Amsterdam-Brussels-Antwerp region, and a Global South megaregion - and conclude by proposing a sequence of steps to guide future research.

We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.

Bicycles are a potential first-last mile mode that can augment the service area of public transit, yet it is difficult to fully account for bike-to-transit trips in planning and travel demand modeling processes. This paper presents a methodology for assessing bicycle first-last mile trips from one area to many possible areas using three visualizations on accessibility, travel times, and transit mode(s) utilized. Two configurations of bicycle first-last mile travel are considered: bringing the bicycle aboard transit to have the bicycle for biking at both ends of the trip (bike-transit-bike) and leaving the bike at the first stop (bike-transit-walk). Three locations in and near Atlanta, GA, U.S., are selected for analysis, and the optimal routes to all possible destinations in the transit service area are calculated for walk-transit-walk, bike-transit-walk, and bike-transit-bike. The walking and biking portions of trips are modeled using Dijkstra's algorithm, and the transit portion is modeled using the round-based public transit optimized routing (RAPTOR) algorithm. Results indicate that bike-transit-bike and bike-transit-walk decrease travel and wait times for transit, and in many cases reduce the number of transfers required compared with walk-transit-walk. Transit services with higher travel speeds or frequencies, such as heavy rail, greatly increased the number of accessible destinations and reduced travel times. Thus, an origin's distance to rail service had a major impact on the number of accessible TAZs. Planners and engineers can use this research to examine how public transit service changes and new cycling infrastructure can affect the accessibility of bike-transit trips.

With the advent of new mobility modes and technologies, we have seen meaningful changes in travel behavior. One such new mobility mode is on-demand transit. The Metropolitan Atlanta Rapid Transit Authority deployed its own on-demand transit system, dubbed MARTA Reach, in March of 2022. This paper provides an evaluation of the characteristics of two groups of people related to MARTA Reach: those who were interested in it and used it and those who were interested in it but did not use it. In addition, this paper explores the factors that influence membership in each of those two groups using a binary logit model, revealing the underlying characteristics that are linked with the decision to use or not use the service given prior interest. The findings show that simply providing more service has the strongest effect on adoption. Among 561 survey respondents, 426 expressed that the service area for MARTA Reach was too limited for their needs. Modeling results support this finding, in addition to the following strong predictors of on-demand transit adoption: 1) being a frequent transit user, 2) being satisfied with the current state of fixed-route transit service, 3) being part of a low-income household, 4) living within an on-demand transit service area, and 5) being younger. Understanding these group characteristics and underlying factors can help guide future efforts to provide on-demand transit service, such as by targeting the market segments that share features with the underlying factors that are shown herein to be linked with on-demand transit adoption.

The lack of cycling infrastructure is a major deterrent to cycling for transportation in the U.S., yet planners and engineers may lack the tools to assess and communicate the potential network impacts of proposed cycling infrastructure. Without these tools, cycling infrastructure may be built ad hoc or where it is politically convenient, instead of where it would be most effective at improving mobility, accessibility, and the actual and perceived safety of cycling. To address these problems, this research outlines a framework for assessing new and existing infrastructure through BikewaySim. BikewaySim is an open-source shortest path model that uses link impedance functions to account for cyclists' preferences for link attributes (e.g., presence of a bike lane, flat terrain, traffic volumes, traffic speeds) and Dijkstra's algorithm to find the least impedance path between any origin-destination pair. To demonstrate the framework, the shortest path is found between 28,392 potential origin-destination pairs for a small study area in Atlanta, GA to assess the impacts of two infrastructure projects. Two impedance functions are demonstrated. The first is based on travel time, while the second uses travel time and example attribute impedances. From the solved shortest paths, percent detour, change in impedance, and link betweenness centrality are calculated, and bikesheds, selected routes, and link betweenness centrality are visualized. Results demonstrate that BikewaySim can effectively visualize potential network improvements from cycling infrastructure and has additional applications for trip planning. Future research will focus on calibrating link impedance functions with GPS trace data showing revealed preferences and surveyed response data on user preferences for cycling infrastructure.

This research identified current challenges, constraints and interest in the implementation and management of new mobility services (on-demand and micromobility) for United States transit agencies. Twenty-four semi-structured interviews with employees of transit agencies were conducted, and NVivo, a qualitative analysis software, was used to classify agency involvement, constraints, and challenges with services. The most common constraints and challenges for on-demand services (cited by more than five agencies) were labor shortages, software performance, fare integration, vehicle shortages, and funding. For agencies not offering on-demand services, developing strong backbone networks with fixed-routes was a priority, and cost-effectiveness, service area design, and funding were additional concerns. The top constraints and challenges among transit agencies with respect to micromobility (cited by more than four agencies) included fare integration, obstruction of public space, funding, adequate supporting infrastructure, and development of equitable service. For integrating new mobility services, most transit agencies (14 of 20, 70%) expressed interest in becoming mobility managers, but larger agencies (those with more than 10 million unlinked passenger trips) were split between mobility manager and partner roles. This research suggests that transit agencies have evolved in the new mobility space, but some constraints and challenges still stand in their way. Policy makers and decision makers can help transit agencies by encouraging open-source software development, improving labor contracts, increasing funding opportunities, and coordinating land-use, transportation, and transit planning. Future research should continue investigating barriers to entry for new mobility services and identify how transit agencies can increase coordination with technology and micromobility companies.

With ridership declining nationally and transit agencies looking for innovative ways to maintain and attract riders, a more complex understanding of transit riders and their satisfaction could provide additional insight and guidance to benefit the future of transit. This study challenged the traditional captive versus choice rider dichotomy and indicates the need for a more nuanced breakdown of transit riders based on the attributes most important to them. To conduct the analysis, the authors obtained rider survey data from nine agencies across the United States from varying geographic regions and representing various agency sizes. Agencies were selected based on their intentional use of demographic classifications and questions about satisfaction with various aspects of transit service. The authors then applied ordered logit regression across the 18,544 rider survey responses to predict the relative importance of service attributes on overall satisfaction. The findings suggested that different classifications of riders by gender, race, and income yielded diverse priorities, although certain service aspects such as reliability were important across demographics. In addition to the findings from the regression analysis, this study also offers a series of recommendations to facilitate future investigations by using more consistent, standardized data to further the breadth and depth of national transit rider analyses.

As ride-sharing services increasingly redefine how people move within urban areas, the curb environment (the public space between roadway and sidewalk) will have to be able to accommodate new uses and new users. This study seeks to understand how formalizing a space for curbside pick-up and drop-off (PUDO) activity typical of new transportation modes such as ride-sharing will impact traffic flow and curb use. Microscopic simulation models calibrated using data collected in Atlanta, GA, were devised, and performance metrics such as delay and occupancy rate were collected. By varying traffic flow conditions and changing the percentage of PUDO parking events, an analysis of different curb configurations was conducted, and results were compared with those from a traditional curb design. The introduction of dedicated PUDO zones created significant reductions in delay. With high utilization, these zones have the potential to reduce double-parking, increase curb utilization, and positively affect through traffic.

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis-initial post-disruption mobility index, changepoint on restoration of a "new normal," and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes.

Fleet control operation tools for high frequency bus service seek to maintain headway regularity between consecutive vehicles. Almost always, these tools suggest a series of control actions that must be executed by drivers that are assumed to be identical in their driving behavior and that this behavior is identical in all driving contexts. However, not all bus drivers drive in the same way and their behavior can be affected by different factors related to the environment in which they carry out their work. In this paper the behavioral difference is characterized by the average speed at which each of them drives along a given route. This work focuses on determining the impact of this heterogeneity on headway variability, and on how (erroneously) considering drivers to be homogeneous impacts the performance of headway regularity control tools based on holding decisions. The results show that the reduction in waiting times when the holding control strategy is applied compared to the case without control increases when speed variability across drivers also increases. We show that an easy way of improving headway regularity is to stratify drivers by line according to historical driving speed, regardless of whether or not a control action is applied.

Declining survey response rates have increased the costs of travel survey recruitment. Recruiting respondents based on their expressed willingness to participate in future surveys, obtained from a preceding survey, is a potential solution but may exacerbate sample biases. In this study, we analyze the self-selection biases of survey respondents recruited from the 2017 U.S. National Household Travel Survey (NHTS), who had agreed to be contacted again for follow-up surveys. We apply a probit with sample selection (PSS) model to analyze (1) respondents’ willingness to participate in a follow-up survey (the selection model) and (2) their actual response behavior once contacted (the outcome model). Results verify the existence of self-selection biases, which are related to survey burden, sociodemographic characteristics, travel behavior, and item non-response to sensitive variables. We find that age, homeownership, and medical conditions have opposing effects on respondents’ willingness to participate and their actual survey participation. The PSS model is then validated using a hold-out sample and applied to the NHTS samples from various geographic regions to predict follow-up survey participation. Effect size indicators for differences between predicted and actual (population) distributions of select sociodemographic and travel-related variables suggest that the resulting samples may be most biased along age and education dimensions. Further, we summarized six model performance measures based on the PSS model structure. Overall, this study provides insight into self-selection biases in respondents recruited from preceding travel surveys. Model results can help researchers better understand and address such biases, while the nuanced application of various model measures lays a foundation for appropriate comparison across sample selection models.

Introduction: Bicycling has individual and collective health benefits. Safety concerns are a deterrent to bicycling. Incomplete data on bicycling volumes has limited epidemiologic research investigating safety impacts of bicycle infrastructure, such as protected bike lanes. Methods: In this case-control study, set in Atlanta, Georgia, USA between 2016-10-01 and 201808-31, we estimated the incidence rate of police-reported crashes between bicyclists and motor vehicles (n = 124) on several types of infrastructure (off-street paved trails, protected bike lanes, buffered bike lanes, conventional bike lanes, and sharrows) per distance ridden and per intersection entered. To estimate underlying bicycling (the control series), we used a sample of highresolution bicycling data from Strava, an app, combined with data from 15 on-the-ground bicycle counters to adjust for possible selection bias in the Strava data. We used model-based standardization to estimate effects of treatment on the treated. Results: After adjustment for selection bias and confounding, estimated ratio effects on segments (excluding intersections) with protected bike lanes (incidence rate ratio [IRR] = 0.5 [95% confidence interval: 0.0, 2.5]) and buffered bike lanes (IRR = 0 [0,0]) were below 1, but were above 1 on conventional bike lanes (IRR = 2.8 [1.2, 6.0]) and near null on sharrows (IRR = 1.1 [0.2, 2.9]). Per intersection entry, estimated ratio effects were above 1 for entries originating from protected bike lanes (incidence proportion ratio [IPR] = 3.0 [0.0, 10.8]), buffered bike lanes (IPR = 16.2 [0.0, 53.1]), and conventional bike lanes (IPR = 3.2 [1.8, 6.0]), and were near 1 and below 1, respectively, for those originating from sharrows (IPR = 0.9 [0.2, 2.1]) and off-street paved trails (IPR = 0.7 [0.0, 2.9]). Conclusions: Protected bike lanes and buffered bike lanes had estimated protective effects on segments between intersections but estimated harmful effects at intersections. Conventional bike lanes had estimated harmful effects along segments and at intersections.

This paper reports on the results of the six-month pilot MARTA Reach, which aimed to demonstrate the potential value of On-Demand Multimodal Transit Systems (ODMTS) in the city of Atlanta, Georgia. ODMTS take a transit-centric view by integrating on-demand services and traditional fixed routes in order to address the first/last mile problem. ODMTS combine fixed routes and on-demand shuttle services by design (not as an after-thought) into a transit system that offers a door-to-door multimodal service with fully integrated operations and fare structure. The paper fills a knowledge gap, i.e., the understanding of the impact, benefits, and challenges of deploying ODMTS in a city as complex as Atlanta, Georgia. The pilot was deployed in four different zones with limited transit options, and used on-demand shuttles integrated with the overall transit system to address the first/last mile problem. The paper describes the design and operations of the pilot, and presents the results in terms of ridership, quality of service, trip purposes, alternative modes of transportation, multimodal nature of trips, challenges encountered, and cost estimates. The main findings of the pilot are that Reach offered a highly valued service that performed a large number of trips that would have otherwise been served by ride-hailing companies, taxis, or personal cars. Moreover, the wide majority of Reach trips were multimodal, with connections to rail being most prominent.

Introduction: Civil engineers play an outsize role in shaping the built environment, which plays an outsize role in health, especially in transportation safety. While there is growing interest in integrating public health and transportation engineering and planning to improve safety outcomes, existing efforts fall short.Method: We review prior efforts to integrate public health into transportation safety, and frameworks from injury prevention and control and risk management.Result: Based on the Hierarchy of Controls and the Health Impact Pyramid, we present a framework for prioritizing policies and interventions, known as the Safe Systems Pyramid, that contains five ascending levels - Socioeconomic Factors, Built Environment, Latent Safety Measures, Active Measures, and Education. The levels of the framework prioritize increased population health impact and decreased individual effort.Conclusions: Frameworks like "The 3 E's" emphasize collaboration rather than a change in thinking and action among transportation safety professionals, and do not prioritize specific actions. We argue that Vision Zero and other "Safe Systems" prioritize implementation of policies, programs, and infrastructure to increase population health impact by considering the individual effort necessary to obtain a protective effect.Practical applications: This framework is designed to shift the thinking of engineers, planners, and policy makers that shape the transportation system. We conclude this work by applying the Safe Systems Pyramid to a hypothetical Vision Zero program, highlighting how the framework can be used to prioritize efforts using a Safe Systems approach.