Characterizing co-modality in urban transit systems from a passengers’ perspective

Co-modality is a concept based on a unified network system which will ensure the effective and sustainable utilization of all transportation modes. However, the application of co-modality as a measure of evaluating public transit system performance is recent and has been predominantly used in freight transport systems. This study proposes a novel approach by using co-modality as a key performance indicator to characterize public transit systems for passengers. This paper examines a new data set to evaluate transit systems from a user perspective. The data is gathered from an Application Programming Interface (API) which pulls from the real-time General Transit Feed Specification (GTFS). Data was collected over 24 h to explore 4320 transit trips and 69,120 attributes for a single origin–destination pair. Co-modality is used to understand how dozens of transit routes and schedules will best serve transit users. A detailed analysis of trips involving multiple transit segments is conducted to understand how varying headways influence the overall trip travel time. The main conclusion for this paper is that a user perspective is necessary to understand co-modality across public transit systems. Some of the metrics identified in this paper, such as percent of trip spent walking, will be useful in assessing last-mile portions of travel across multiple trips. A better understanding of transit service to travelers by the transit system as a whole will help to improve transportation in dense urban areas

Indiana Traffic Signal Hi Resolution Data Logger Enumerations

Assessing the operational performance of traffic signals required detailed information regarding sensors inputs and controller actions. In 2012, a series of enumerations used to encode traffic signal events at a 100 millisecond was developed and published at https://doi.org/10.4231/K4RN35SH. Techniques for using that data are published in https://doi.org/10.5703/1288284315333 and https://doi.org/10.5703/1288284316063 As part of a multi-state Federal Highway Administration pooled fund study, TPF-5(377), a series of stakeholder engagements and panel meetings were conducted in 2018 and 2019 to update these enumerations. In 2020, updates to the enumerations included minor changes to event codes 14, 17, and 54

Foot-and-Mouth Disease Space-Time Clusters and Risk Factors in Cattle and Buffalo in Bangladesh

Foot-and-mouth disease (FMD) is highly endemic in Bangladesh. Using passive surveillance data (case records from all 64 districts of Bangladesh, 2014–2017) and district domestic ruminant population estimates, we calculated FMD cumulative incidence per 10,000 animals at risk per district, conducted cluster (Moran’s spatial autocorrelation and scan statistics) and hotspot analysis (local indicator of spatial association statistic), created predictive maps and identified risk factors using a geographically weighted regression model. A total of 548,817 FMD cases in cattle and buffalo were reported during the four-year study period. The highest proportion (31.5%) of cases were reported during the post-monsoon season, and from Chattogram (29.2%) division. Five space-time clusters, 9 local clusters, and 14 hotspots were identified. Overall, higher cumulative incidences of FMD were consistently predicted in eastern parts of Bangladesh. The precipitation in the pre-monsoon season (p = 0.0008) was positively associated with FMD in Bangladesh. Results suggest climate plays an important role in the epidemiology of FMD in Bangladesh, and high risk zones exist. In a resource limited-setting, hotspots and clusters should be prioritized for vaccination coverage, and surveillance for FMD should be targeted in eastern areas of Bangladesh and during the post-monsoon season

Antiviral Peptides as Promising Therapeutics against SARS-CoV-2

Over 50 peptides, which were known to inhibit SARS-CoV-1, were computationally screened against the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. Based on the binding affinity and interaction, 15 peptides were selected, which showed higher affinity compared to the α-helix of the human ACE2 receptor. Molecular dynamics simulation demonstrated that two peptides, S2P25 and S2P26, were the most promising candidates, which could potentially block the entry of SARS-CoV-2. Tyr489 and Tyr505 residues present in the "finger-like" projections of the RBD were found to be critical for peptide interaction. Hydrogen bonding and hydrophobic interactions played important roles in prompting peptide-protein binding and interaction. Structure-activity relationship indicated that peptides containing aromatic (Tyr and Phe), nonpolar (Pro, Gly, Leu, and Ala), and polar (Asn, Gln, and Cys) residues were the most significant contributors. These findings can facilitate the rational design of selective peptide inhibitors targeting the spike protein of SARS-CoV-2