This study investigates the use of gender-sensitive planning practices in public transit systems, addressing the disparities in travel behaviors between men and women. Women typically engage in shorter, more complex trips due to caregiving and household responsibilities and often face additional challenges such as safety concerns and inadequate infrastructure for strollers and belongings. This research investigates current transit design practices and agency policies through the lens of gender-inclusivity. To achieve this goal, this research employs a qualitative case study approach, including interviews with staff from nine transit agencies to learn their present and future practices, and an analysis of five transit design and operations manuals to study their recommendations and standards. Key findings reveal progress in gender-sensitive data collection and design initiatives, even as significant barriers, including resource limitations, resistance to change, and infrastructural constraints, remain. While agencies like LA Metro, SEPTA, and WMATA have made notable advancements, the explicit incorporation of gender-sensitive principles in widely used transit design manuals is often lacking. The study concludes that continued commitment and innovation are essential to overcoming existing barriers and enhancing gender-inclusivity in transit systems, offering valuable insights for agencies aiming to create equitable and inclusive transportation environments.

Planners and engineers need to know how to assess the impacts of proposed cycling infrastructure projects, so that projects that have the greatest potential impact on the actual and perceived cycling safety are selected over those that would be less effective. Planners also need to be able to communicate these impacts to decision-makers and the public. This research addresses these problems using the BikewaySim cycling shortest path model. BikewaySim uses link impedance functions to account for link attributes (e.g., presence of a bike lane, steep gradients, the number of lanes) and find the least impedance path for any origin-destination pair. In this project, BikewaySim was used to assess the impacts of using time-only and time with attribute impedances, as well as two proposed cycling infrastructure projects, on 28,392 potential trips for a study area in Atlanta, Georgia. These impacts were visualized through bikesheds, individual routing, and betweenness centrality. Two metrics, percent detour and change in impedance, were also calculated. Results demonstrate that BikewaySim can effectively visualize potential improvements of cycling infrastructure and has additional applications for trip planning. An expanded study area was also used to demonstrate bike + transit mode routing for four study area locations. Visualizations examine the accessibility to TAZs, travel time, and the utilized transit modes for each location. Compared to the walk + transit mode, the bike + transit mode provided greater access to other TAZs and reached them in a shorter amount of time. The locations near the center of the transit network where many routes converge offered the greatest accessibility for both the bike + transit and walk + transit modes. The difference in accessibility was greatest for locations near fewer transit routes. This research demonstrated how BikewaySim can be used to both examine the current cycling network and show changes in accessibility likely to result from new infrastructure. Both BikewaySim and TransitSim are open-source Python based tools that will be made available for practitioners to use in bicycle network planning. Future research will focus on calibrating link impedance functions with revealed preference data (cycling GPS traces) and survey response data (surveys on user preference for cycling infrastructure).

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Many cities are focused on increasing bicycle use through development of infrastructure such as bicycle lanes and multi-use paths. Traditionally, travel demand models (TDMs) are used to evaluate the demand for (and impact of) proposed transportation projects. However, the vast majority of TDMs cannot be used to evaluate the impact of bicycle projects. Improved TDMs are needed to help estimate the impacts of new bicycle projects on cycling activity and prioritize the construction of the most beneficial bicycle projects with limited transportation department resources.

To more accurately model bicycle travel, preference-based route assignments are needed. Researchers at the Georgia Institute of Technology created a semi-automated process for developing an all-streets network to be used in TDM applications. The researchers combined detailed roadway characteristic information from three different transportation networks in GIS shapefile format and used BikewaySim, Georgia Tech’s newly developed shortest-path calculator for cycling trips, to compare shortest-path routing on the newly created all-streets network versus the simplified TDM network for a 12-square-mile study area in Atlanta. 

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Bicycle transportation is often excluded from travel demand and route choice models. Even when bicycle modes are incorporated, models may use a simplified network that does not contain all streets and bicycle paths that a cyclist could feasibly take. These models may also only use trip distance and travel time when modelling a cycling trip; research on revealed route choice preferences of cyclists has shown that cyclist routing is influenced by other factors, such as the presence of a bicycle facility or road elevation gain. The City of Atlanta plans to triple its mileage of protected bicycle infrastructure in the next two years, and needs a tool to be able to plan and prioritize these projects based on the estimated effects on bicycle accessibility, bicycle mode share, energy usage, and emissions, to make the best use of the limited funding. The objective of this project is to develop this analytical tool and an associated network that includes all possible bicycle paths (i.e., roads, bicycle paths, cut-through paths, etc.) for a 12 square mile study area in the City of Atlanta that can be expanded later to the Atlanta Metro area. The tool, BikewaySim, is a shortest path calculator that uses Dijkstra’s shortest path algorithm to find both the preferred route from any origin to any destination within the study area using lowest travel time and lowest total impedance cost. The BikewaySim network was created by conflating network data from the Atlanta Regional Commission (ARC), OpenStreetMap (OSM), and HERE into a whole road and pathway for BikewaySim and future use in the ARC’s activity-based travel demand model. The methods for conflating networks and developing the shortest path model are publicly available resources. The final model is destined to include all viable pathways and incorporate cyclist preferences for use in planning and modelling bicycle travel for research, planning, and design. The framework allows other organizations and researchers to contribute to the project over time.

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Many 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.

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Background

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Conclusions