Metro Atlanta Northwest Corridor Commuter Survey Results \u2013 Assessing Express Lane Impacts on Increased Corridor Throughput

In 2022, a survey was developed to try to gain insight into why a significant increase in morning peak traffic volumes was observed on the I-75/I-575 Northwest Corridor (NWC) in the Atlanta metropolitan area after the opening of the Express Lanes. It seemed unlikely that the increase was due to induced demand (increased total vehicle miles traveled (VMT) that was suppressed due to congestion), as most morning peak trips are generally mandatory trips such as work trips, school trips, and daycare trips. The previous research team suspected that the increase may have come from a diversion of commute traffic from arterials onto the freeway corridor, or from a shift of traffic from the shoulders of the peak to the center of the peak once the Express Lanes opened and congestion declined. Where the significant increase in traffic volumes on the NWC came from is an important question, especially for transportation planners, because it helps decision-makers get a better understanding of what the effects of opening new managed lane capacity along a corridor might be on traffic patterns around that corridor

Safety and Illumination of Rural and Suburban Roundabouts (Phase II)

RP19-11This project focused on establishing the relationship between the presence/absence or levels of illumination and other geometric and traffic characteristics on nighttime safety at rural and suburban roundabouts. Eighty roundabouts from thirty-seven counties across Georgia were selected to provide a wide range of conditions in terms of illumination layout, illumination levels, number of legs, number of circulating lanes, daily entering volumes, approach speeds, etc. for field measurements of illumination levels. Urban roundabouts with significant pedestrian activity were excluded. Field data collection at each site included both direct measurements of illumination levels as well as a civil site survey to verify the geometric characteristics of the roundabout and were conducted by measurement teams from Georgia Institute of Technology and Georgia Southern University. The resulting data were processed, joined, and aggregated to the individual site level and used to establish statistical relationships between observed nighttime crash rates, severity, and crash types (e.g., single vs. multiple vehicles, impaired drivers, etc.) and underlying geometric factors and measured illuminance levels. The variation in observed crash rates were modeled against known parameters of the roundabouts to develop a predictive model as to how single vehicle nighttime crash rates were impacted by illumination and other factors. As expected, multiple vehicle crashes showed no statistically significant dependence on illumination levels as the vehicles themselves, through their head- and taillights, are important contributors to nighttime visibility at the roundabout. This was not the case for single vehicle crashes. Single vehicle crashes were shown to increase for 3-leg roundabouts for illumination values less than 5 lux. No such trend was observed in either 4 or 5-leg roundabouts and these sites showed no statistically significant variation in nighttime single vehicle crash rates at any level of illumination. An overarching conclusion of the study is that there was no observed evidence of illumination values exceeding 5 lux resulting in a statistically significant reduction in nighttime crash rates for rural and suburban roundabouts. These results suggest that for rural and suburban roundabouts with no significant pedestrian volumes, illumination values significantly lower than current standards may still prove effective as a safety treatment and that, in the absence of a need to protect pedestrians or cyclists at nighttime at a particular location, a reduction in lighting levels or the use of passive retroreflective safety treatments may be a cost-effective treatment

Latent Vehicle Type Propensity Segments: Considering the Influence of Household Vehicle Fleet Structure

69A3551747116This study applies latent class cluster analysis to a sample of 1,111 survey respondents in Georgia, identifying naturally occurring vehicle type segments based on the influence of both individual vehicle type choices and household vehicle fleet structures. The developed model identifies seven latent vehicle type propensity segments, six of which include individuals who reported being the main driver for (respectively) car, SUV/van, and truck. In three of those segments this was generally their only available vehicle, while in the other three the \u201cmain driver\u201d vehicle accompanied other available household vehicles. The seventh segment captures individuals who are main drivers of multiple vehicle types, and who also have other household vehicles available for use. We generate user profiles and discuss differences across segments regarding individual-level characteristics (e.g., gender), household-level characteristics (e.g., household income), land-use and travel-related preferences (e.g., neighborhood type, share of household-serving trips), attitudes (e.g., materialistic), and targeted marketing data variables (e.g., support for charitable causes). Selected results suggest that women choose SUVs/vans due to both personal preferences (e.g., feeling safer while driving a large vehicle) and household responsibilities; show that vehicle-owning behaviors and attitudes are generally consistent, except that strong pro-vehicle-owning attitudes exist within vehicle-deficit households; and suggest that vehicle-deficit households may be less open to alternative fuel vehicles, possibly due to reliability concerns. Overall, this study provides a new perspective on vehicle type propensity segments, and examines the association of a novel range of general and travel-related attributes with these segments, yielding nuanced insights with potential policy implications

Towards the Implementation of a Geotechnical Asset Management Program in the State of Georgia

PI# 0000240717Experiences at U.S. departments of transportation (DOTs) have demonstrated the value of geotechnical asset management (GAM) to enable a framework for informed decisions that align the DOT\u2019s objectives with investment and performance targets. However, because Georgia currently lacks a such a program, this study was performed to set the stage for developing a GAM program in the state with a primary focus on retaining walls. While walls were identified as the asset of the highest importance in Georgia, other critical infrastructure assets (i.e., slopes, embankments, and bridge foundations) were also considered. The proposed GAM system consisted of three phases: (1) inventory during design, (2) as-built inventory, and (3) maintenance inspection. Towards the development of a state-wide GAM program, a computational platform that accommodated the different proposed phases was developed and tested in metro Atlanta areas. The study also reviewed image-based and remote-sensing technologies for GAM. In particular, proof-of-concept studies that combined image-based and machine learning technologies for optimizing GAM processes for retaining walls in the metro Atlanta area were conducted, showing promising results. The study concluded by providing a road map for establishing a GAM program in the state of Georgia, considering short-term and long-term recommendations

Response Willingness in Consecutive Travel Surveys: An Investigation Based on the National Household Travel Survey Using a Sample Selection Model

69A3551747116Declining 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\u2019 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\u2019 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

Hybrid EV and Pure BEV Owners: A Comparative Analysis of Household Demographics, Travel Behavior, and Energy Use

USDOT Grant 69A3551747114Electric Vehicles (EVs) significantly reduce energy consumption and emissions from on-road operations and help create more sustainable transportation environment by reducing emissions from the entire well-to-wheel energy cycle. Differences between hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and battery electric vehicles (BEV) users is an important element in understanding potential impacts on travel demand and vehicle adoption, the fact that these vehicles may be adopted into households that undertake very different vehicle activities and energy usage patterns has not been a primary focus in the literature. This study differentiates between HEV, PHEV, and BEV users across three factors: owner household socio-demographic attributes, household daily travel patterns, and household energy usage profiles. The analyses examine factors that appear to influence users\u2019 preferences towards specific EV types and how the selection of different EV types potentially relates to household socio-demographics and daily travel patterns. The 2019 Puget Sound Regional Council travel survey data set serves as the main analytical dataset. Influential factors identified as significant through statistical approaches are employed as variables for developing a two-phase choice model for determining potential EV-purchasing households and their choice of specific EV type. As EVs continue to capture increasing market share over time, these research findings and the resulting vehicle type choice model are expected to significantly improve future travel demand model development, allowing activity-based travel demand models to assign specific vehicles to specific households and then to individual trips in planning scenario analysis

MOVES-Matrix 3.0 for High-Performance On-Road Energy and Emission Rate Modeling Applications

69A3551747114This white paper summarizes the development of MOVES-Matrix 3.0 based on EPA\u2019s latest MOVES model known as MOVES3 (version 3.0.4). The research team updated the programs to account for changes in data structures and source sub-types and applied the same conceptual design used in MOVES-Matrix 2.0. The review of the MOVES3 and MOVES 2014b databases indicated a finer definition of the regions in terms of the unique combinations of fuel supply regions vs. Inspection/Maintenance (I/M) programs, with 40 fuel scenarios and 87 I/M scenarios in MOVES3 and 22 fuel scenarios and 84 I/M scenarios in MOVES 2014b. The increased number of fuel scenarios is due to the increased number of formulation regions and the one-to-many corresponding relationship between counties vs. fuel formulation regions by year. A total of 122 regions are defined in MOVES3 compared with 109 regions in MOVES 2014b, and the team anticipates at least 10% more running time to generate matrices for MOVES3, given the larger number of regions and the more complicated source type VSP/STP variables

Economic Sustainability of Sidewalk Networks and Funding Scenario Cost Distributions in Atlanta, GA

USDOT Grant 69A3551747114Sidewalk infrastructure presence is a key indicator of pedestrian safety and walkability for neighborhoods in cities throughout the United States. The existence and condition of sidewalk infrastructure, however, is not prioritized as much as motor vehicle infrastructure. Many cities lack sustained maintenance and operations programs for sidewalk infrastructure and comprehensive datasets covering the locations and distributions of sidewalk infrastructure, limiting the ability to develop such programs. This work refines prior sidewalk infrastructure network generation techniques, contributing new methods to identify sidewalk infrastructure presence. QA/QC efforts were conducted for Atlanta\u2019s sidewalk network by correcting errors identified in input data. Error identification and correction times were comprehensively tracked and used to estimate future labor costs. A custom application with online access to Bing Maps Streetside and aerial imagery was developed to allow technicians to verify sidewalk presence data, which were joined to the structural sidewalk network and associated with adjacent parcels. Cost of ownership of Atlanta\u2019s sidewalk infrastructure over an 80-year management period is then broken down by asset type and allocated in part to property owners directly adjacent to the applicable infrastructure, while remaining costs are recovered through a proportional increase in property tax millage rates. Sidewalk network estimates developed in previous Atlanta research efforts decreased sidewalk network mileage by 12% (386 miles), post-QA/QC. Regression analysis of error correction activity and labor data indicates gaps between tax parcels and misplacement of intersection centroids significantly increased QA/QC labor costs. Overall, 46% of Atlanta\u2019s potential sidewalk links were present (i.e., along property superblock boundaries), with significant clustering in the city\u2019s oldest neighborhoods. Hence, sidewalk repair and maintenance costs accrue disproportionately to these areas. Sidewalk infrastructure costs across neighborhoods also differ considerably, depending on whether estimates account for existing sidewalk infrastructure. The annual cost burden on property owners to implement a program to fund sustainable sidewalks (lifecycle assessment) by increasing property tax millage rates varies significantly across household income and ethnicity. The research suggests that sustainable sidewalk infrastructure assessments should consider spatial and demographic disparities in cost allocation (i.e., equity) for any proposed pedestrian infrastructure asset management program

Combined Effect of Changes in Transit Service and Changes in Occupancy on Per-Passenger Energy Consumption

69A3551747114Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in MARTA service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates. MARTA automated passenger count (APC) data were appended to the routes, and the energy use per passenger mile was compared across routes for the three periods. The results showed that the coupled effect of shift in transit frequency and decrease in ridership from 2019 to 2020 increased route-level energy use for more than 87% of the routes and per-passenger mile energy use for more than 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about the pre-pandemic level, but per-passenger energy use remained higher than pre-pandemic for more than 91% of the routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, per-passenger energy use depends on both trip service and ridership. The results also indicated a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service

A Tool to Predict Fleet-Wide Heavy-Duty Vehicle Fuel-Saving Benefits from Low Rolling Resistance Tires

The cost of fuel represents a major portion of the costs of operating on-road heavy-duty vehicles (HDV). According to the American Transportation Research Institute, fuel costs alone amounted to about 25 percent of truck operating costs in 2015. Within the U.S. on-road transportation sector HDVs consume a disproportionately high amount of the total refined petroleum-based fuel and carbon dioxide emissions from consumption of this fuel were estimated to be equivalent to over 400 million metric tons. HDVs also contributed a disproportionately high 2.5 million short tons of Oxides of Nitrogen (NOx) emissions, emitted as a by-product of fuel combustion in on-road vehicle engines. NOx is a precursor of ozone, which is an air pollutant harmful to humans, plants, and animals. Over the next couple of decades, the total energy demand from the HDV sector will likely increase due to forecasted growth in freight demand in many global markets, including the United States, and much of this energy will continue to be provided by fossil fuels. Therefore, carbon dioxide emissions from the HDV sector are also expected to increase in the absence of effective mitigating measures to reduce the sectors reliance on fossil fuels. In this study, the authors develop a tool to predict the fleet-wide fuel-saving benefits from low rolling resistance tires. Unlike previous studies, the developed tool is applicable to both stabilized speed operations and transient speed operations. The tool is based on empirical models that estimate the fuel consumption contribution from tires as a function of vehicle payload, aerodynamic drag, road grade, duration of acceleration, duration of deceleration and, and road facility type (freeway, major arterial, and minor arterial/local road). The authors limited the scope of the developed tool to tractor-trailers in the U.S. heavy-duty vehicle market, because the United States has the second largest HDV market in the world and tractor-trailers account for the largest share of the market. The tool was developed with data generated by simulating real-world heavy-duty vehicle operating cycles with Autonomie\uae, the state-of-the-art model for automotive control-system design, and simulating vehicle energy consumption and performance. Autonomie\uae is a preferred vehicle simulation tool of the United States Department of Energy