The COVID-19 pandemic has dramatically impacted the global community in almost every aspect of society. These impacts have led to continued struggles for organizations and industries to adapt to the new reality imposed on them by the pandemic. A common symptom among industries like transit, is a downturn in demand for their services often resulting from a lack of rider confidence. The reduction in transit ridership during the pandemic can be attributed to many factors including increased prevalence of working from home (WFH), social distancing, rider apprehension and various other governmental directives. Rider safety as it relates to virus spread has become more important with the prevalence of the COVID-19 and has further increased the need for designs that mitigate the spread of the virus. The purpose of this project was to retrofit or redesign various transit elements specifically within train stations to improve the level of social distancing in these areas as well as quantify the levels of disease transmission risk within stations. This was done using a simulated model of Marlborough station using the software Vissim with various designs implemented within, the outputs of this design contained certain variables such as pedestrian density and travel time which were then used in a risk function to assess the benefit of these designs in mitigating the risk of COVID-19 disease transmission and compared to the cost of these designs. Final designs were selected after exploring various alternatives using this model and analysis. A critical aspect of this methodology is that the models could be used to monitor further changes to the transit system as well as test other potential designs by simply changing design elements and altering the inputs which allows for greater flexibility and reproducibility.
With the advent of autonomous vehicles, rideshare apps, and electric vehicles, a new system of transportation is emerging from the nexus of these three technologies, called Autonomous Mobility on Demand (AMoD). Autonomous electric vehicles will be integrated as rideshares into the transportation system of cities, overhauling the dominance of individual owner-driver internal combustion vehicles in cities today. This process will be led by an alliance of different companies such as Tesla (AV and EV), Uber (rideshare), and Google (AV) with municipalities worldwide. For cities to integrate this new and disruptive technology into transportation networks, their effects must be analyzed and changes to current networks should be made to optimise the transition. This report outlines the result of quantitatively and qualitatively assessing the effect of AMoD for inner Calgary and makes infrastructure and policy recommendations to develop a future transportation network that enhances quality of life. We developed three pick-up drop-off (PUDO) designs by utilizing researched literature with each design having a unique configuration and layout depending on traffic parameters. We examined potential risks around implementing a project of such a large magnitude, created curbside designs for PUDO points and assessed their effectiveness, and mapped out optimized network locations for the various curbside designs provided. A simulation using MATSim was developed to compare the numerical data of a base network and followed up by three different models which were utilized in an iterative process to plot variations of the PUDO locations and street design on a map of the entire network until optimal solutions for both vehicles and pedestrians converged in the indicators of the software output. The final optimized network ensures increased walkability and active mode alternatives, reclaimed green space, improved environmental quality, and increased safety in the network as analyzed by the aforementioned simulation outputs, cost estimation comparisons, and reclaimed area measurements.
