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Response measures to contain COVID-19 spread varied from country to country, some imposed a complete lockdown while some imposed partial restrictions. This paper compares the transport impacts of the COVID-19 pandemic for two countries having dissimilar characteristics, Germany and State of Qatar, based on the rates of infection and response measures. Secondary data, obtained from Google mobility reports, and primary data, collected from local agencies, were used for comparison purposes. The secondary data comparison from February 2020 to July 2020 indicated an overall decline in mobility for all commercial activities and an increase was noted for parks and residential locations for Saxony, Germany. For State of Qatar, the mobility was decreased to all places except residential locations. Further, the comparison for traffic volumes and the number of crashes during the first wave of the pandemic indicated that the reduction in traffic volumes, major, and minor crashes was coupled with restrictive measures rather than COVID-19 incidences for both countries. Further, the traffic volumes showed a statistically significant inverse linear relationship with the stringency index for both countries during weekdays as well as weekends. These results suggest that the policy measures are key in governing movement restrictions and containing the spread of pandemic rather than the number of COVID-19 incidences. Further, the authorities should monitor the traffic trends during the pandemic and enforce the traffic rules and regulations as soon as the movement restrictions are lifted.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

There is increasing interest in automating train operations of mainline services, e.g., to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but is still not implemented on a large scale. Functional, interoperability and performance tests are necessary before ATO can be introduced generally. Virtual preliminary analysis will contribute to the validation process to ensure a safe and successful implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators commonly used in the railway sector, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. They are refined by adding sub-indicators specific to the performance evaluation of ATO algorithms. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. For demonstration purposes, a simple case study is conducted. Thereby we exemplarily show-cased the approach for ATO performance testing using a microscopic train simulator in combination with an ATO algorithm.