Integrating COVID-19 health risks into crowding costs for transit schedule planning.

The public transport sector worldwide experienced the worst impact in recent history, in terms of ridership loss, due to the COVID-19 pandemic. The pandemic negatively affected passengers' perceptions of public transport and is likely to make a lasting impact on ridership, trip patterns, and modal share. Without any supportive changes to transit operations, ridership is likely to decline. This study explores the setting of frequencies in transit lines and proposes a two-part methodology that addresses the changing perceptions of users, especially in a health-related context. The first part develops a mathematical model that expresses the pre-COVID-19 cost of passenger crowding as an integral part of user costs to determine the optimal headway that considers the trade-offs between user and operator costs. A continuum approximation for the demand of the bus line has been used in the derivation. The second part extends the developed model to include both the costs of the health risks associated with the COVID-19 pandemic and crowding. The developed models will help transit planners and operators to plan and adapt operations to changing health risks during the pandemic and post-pandemic. Several numerical examples are provided to describe the uses and applications of the analytical models using information obtained from the literature.

Social distancing in airplane seat assignments.

This paper addresses the airplane passengers' seat assignment problem while practicing social distancing among passengers. We proposed a mixed integer programming model to assign passengers to seats on an airplane in a manner that will respect two types of social distancing. One type of social distancing refers to passengers being seated far enough away from each other. The metric for this type of social distancing is how many passengers are seated so close to each other as to increase the risk of infection. The other type of social distancing refers to the distance between seat assignments and the aisle. That distance influences the health risk involved in passengers and crew members walking down the aisle. Corresponding metrics for both health risks are included in the objective function. To conduct simulation experiments, we define different scenarios distinguishing between the relative level of significance of each type of social distancing. The results suggest the seating assignments that best serve the intention of the scenarios. We also reformulate the initial model to determine seat assignments that maximize the number of passengers boarding an airplane while practicing social distancing among passengers. In the last part of this study, we compare the proposed scenarios with the recommended middle-seat blocking policy presently used by some airlines to keep social distancing among passengers. The results show that the proposed scenarios can provide social distancing among seated passengers similar to the middle-seat blocking policy, while reducing the number of passengers seated close to the aisle of an airplane.

Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada.

Public transportation agencies are one of the industries that generate a large volume of data on a high frequency and velocity basis. The General Transit Feed Specification (GTFS) is one of the datasets these agencies generate and share openly with the public. GTFS feeds contain data for scheduled transit service including stop and route locations, and schedules information. This paper aims to demonstrate the potential of GTFS data, specifically, the paper describes the development of a GTFS data visualization tool that displays spatial and temporal patterns of transit services from which qualitative information and insights can be gained. In this paper, GTFS data from Calgary Transit was used as a case study. Previous studies focused on the development of visualization tools that display transit movement, or static graphical representation of transit operation. However, there is still a need for a dynamic interactive visualization tool that can also measures and displays transit system operation geographically and statistically. This study builds on the previous investigations and further develops a new public transit system operation visualization tool (called PubtraVis) with six visualization modules that reflect on different transit system operational characteristics; mobility, speed, flow, density, headway, and analysis. The user can evaluate two modules side by side for comparative analysis. The analysis module provides an insightful statistical summary and similarity measure and clustering results based on the transit operation characteristics. PubtraVis was tested with real-world users through a user experience study from which it was found to be useful and easy to start using. PubtraVis can be a useful tool to demonstrate the dynamism of transit vehicles from the entire transit network at a glance, and can be used to facilitate communication between transit operators, city authorities, and the general public regarding the public transit planning and operation.

Managing speed at school and playground zones.

Since speeding is one of the major causes of frequent and severe traffic accidents around school and playground areas, many jurisdictions have reduced the speed limits in these areas to protect children who may be at risk. This paper investigated the speed compliance, mean speed and 85th percentile speed at selected school and playground zones in the City of Calgary in Alberta. Our results showed that the mean speed was lower and the rate of compliance was higher in the school zone compared to the playground zone, 2 lane roads relative to 4 lane roads, roads with fencing, traffic control devices and the presence of speed display device or children, and zones that were longer (> 200 m). Accordingly, this study provided recommendations to improve the effectiveness of school and playground zone speed limits.

Factors contributing to hit-and-run in fatal crashes.

Hit-and-run in a collision is a punishable offence as it delays crash notification thereby delaying emergency response which increases the likelihood of traffic fatality. To obtain a better understanding of hit-and-run behavior, a logistic regression model has been applied in this study to identify the factors that might affect the occurrence of hit-and-run in fatal crashes in California, USA. Our results show that roadway functional class, routes, traffic flow, types of roadway section, speed limit, traffic control device, functioning of traffic control device, lighting condition, roadway alignment and roadway profile are important determinants that engineers can target to reduce hit-and-run in fatal crashes. In addition, targeted traffic enforcement should be performed on weekends and nighttime.