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Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers. [ABSTRACT FROM AUTHOR]

The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space–time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedule-based and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning. [ABSTRACT FROM AUTHOR]

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods. [ABSTRACT FROM AUTHOR]

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually. • PySWMM is an open-source Python wrapper for EPA SWMM. • Embedding SWMM into Python's scientific computing ecosystem expands its capabilities. • Two utility CSO management applications leveraging PySWMM for real-time control and decision support are described. • Application results demonstrate how intelligent control can help reduce CSOs by tens of millions of gallons annually. [ABSTRACT FROM AUTHOR]

Libraries can improve their ebook collections by weeding them to remove outdated and irrelevant content. This paper reports on an ebook weeding project conducted by the Franklin University Library in Columbus, Ohio. It discusses the library's reasons for weeding its ebooks, how the library weeded its demand driven acquisitions ("DDA") pool, and the challenges the library encountered weeding ebooks. The library ultimately conducted a more limited weeding of its ebook collection than initially planned. This paper explains the reasons for this limited weeding project, the decisions the library made throughout the project and the method used to conduct the ebook weeding project. [ABSTRACT FROM AUTHOR]

In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9–11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing. [ABSTRACT FROM AUTHOR]

Continuous developments in automotive technology brought about the reality of having an increasing number of autonomous vehicles (AV) on roadways soon. However, this advancement comes with the uncertainty of how the interaction between the autonomous and non-autonomous (non-AV) vehicles will play out. Using traffic simulators to model both AVs and non-AVs, and studying the effect of AVs on roadway mobility can help researchers in learning what to expect in real life. In this paper, an open-source simulator called Simulation of Urban Mobility (SUMO) was used to develop AVs by modifying Intelligent Driver Model (IDM) car following and LC2013 lane changing models. Then, three actual roadways in Columbus, Ohio were modeled, and 40 simulations were run for each traffic network. The first set of simulations focused on the impacts that AVs have on vehicle roadway mobility with non-AVs when all the AVs have the same autonomy level. The second set of simulations had mixed AV autonomy levels (SAE Level 2, 3 and 4) with mixed AV and non-AV traffic. The analysis revealed that mobility of AVs and non-AVs increased generally as autonomy level increased. When the AV autonomy levels were mixed, the non-AVs experienced lower average speeds and decreased roadway mobility. [ABSTRACT FROM AUTHOR]

Public transit vehicles such as buses operate within shared transportation networks subject to dynamic conditions and disruptions such as traffic congestion. The operational delays caused by these conditions can propagate downstream through scheduled transit routes, affecting system performance beyond the initial delay. This paper develops an approach to measuring and assessing vehicle delay propagation in public transit systems. We fuse data on scheduled bus service with real-time vehicle location data to measure the originating, cascading and recovery locations of delay events across space with respect to time. We integrate the resulting patterns to construct stop-specific delay propagation networks. We also analyze the spatiotemporal patterns of propagating delays using parameters such as 1) transit line-based network distance, 2) total propagating delay size, and 3) distance decay. We apply our methodology using publicly available schedule and real-time location data from the Central Ohio Transit Authority (COTA) public bus system in Columbus, Ohio, USA. We find that delay initiation is spatially and temporally uneven, concentrating on specific stops in downtown and specific suburban locations. Core stops play a critical role in propagating delays to a wide range of connected stops, eventually having a disproportional impact on the on-time performance of the bus system. [ABSTRACT FROM AUTHOR]

Academic libraries across the United States provide student engagement events for a diverse set of reasons. Like many academic libraries, the University Library of Columbus (ULC) strives to provide student engagement opportunities to increase the library's visibility and student involvement on campus. In the Fall 2017 semester, a TED Talks series was developed and implemented. The chosen format for the series was to present a TED talk each Tuesday, followed by a brief discussion and a short survey of attendees. This paper shares an overview of the series implementation, promotion, evaluation, and progression during the 2017-2018 academic year, including attendees' feedback and author's recommendations. It suggests that a weekly series such as TED Talks provide a unique venue for academic libraries to engage their academic community on an ongoing basis and build relationships with new and existing patrons. The series can enhance existing library programming or serve as a standalone engagement and outreach initiative. [ABSTRACT FROM AUTHOR]

Public transit systems are facing higher risk of system degradation from external disruptions, affecting their ability to deliver reliable accessibility to transit users. Therefore, resilience, the ability to maintain functions during a disruption, becomes a crucial assessment of public transit systems. In this paper, we calculate two space-time prism-based measures with General Transit Feed Specification real-time (GTFS-RT) data: realizable real-time accessibility, a conservative real-time accessibility measure that can be achieved by users subject to delays, and scheduled accessibility, accessibility based on schedule. We also define accessibility unreliability, the deviation between realizable accessibility and scheduled accessibility, to measure the reliability of delivered accessibility. We use the two measures to gauge the resilience of public transit systems and conduct two case studies of short- and long-term disruptions, namely Ohio State football games and the COVID-19 pandemic, on the Central Ohio Transit Authority (COTA) bus system in Columbus, Ohio. We find there are two peaks of high unreliability before and after each football games, with the stadium as the geographic center of the disruption. The after-game peaks are shorter and more intense than the before-game. We also find COVID-19 had persistent negative impacts on accessibility and reliability: Realizable accessibility universally declined during the pandemic, but only part of cities experienced unreliability increase, primarily in urban perimeters and suburbs. Improved traffic conditions during the pandemic may help to reduce unreliability, but the later service cuts increased unreliability. The two case studies prove the effectiveness of the method to detect system disturbances and provide important guidance for public transit system operation and planning. [ABSTRACT FROM AUTHOR]

Dockless micromobility services have potential as a fast and flexible solution to short-distance trips and public transit's first-mile/last-mile (FM/LM) access problem; however, these services also have limitations, including uneven spatial distribution, low capacity, and user out of pocket expense. This can impact on the ability of micromobility to enhance public transit accessibility. We introduce accessibility increment measures – the amount by which public transit accessibility improves due to micromobility services. We apply these measures to hypothetical trips using public transit and micromobility data from Columbus, Ohio, USA. We find dockless scooters can increase accessibility by multimodal public transit trips, with increments in the first mile significantly outweighing last mile accessibility increments. Accessibility increments are highly concentrated in the city center due to the distributions of scooters and bus stops. We also find that scooters' accessibility increment contribution is highly unequal: a small number of scooters contribute most of the accessibility increments. Monetary cost simulations show that the first-mile accessibility increment will rapidly decrease and last-mile increment slightly increase with lower willingness to pay. Capacity simulations show a group of users' accessibility increment will rapidly decrease as the group size increases, but this depends on whether they are competing or collaborating for scooters. Our results show that despite showing promising potentials, vendors and policymakers still need to address these issues to make collaboration between public transit and dockless micromobility sustainable and equitable. The paper provides measures and evidence for future transit and micromobility planning for scooter vendors and transit authorities. • Micromobility can incrementally improve public accessibility. • Accessibility increments are greater for first mile public transit access than last mile. • Accessibility improvements are very unequal across space and mobile devices. • Ability to pay and capacity constraints limit accessibility improvements. • Collaboration between public transit and micromobility is promising but faces challenges. [ABSTRACT FROM AUTHOR]