Impact of COVID-19 on Waiting Passenger Distribution on a Bus Rapid Transit Station Platform in Brisbane, Australia.

The operation of a bus rapid transit (BRT) station platform is a key factor that affects BRT system performance. As waiting passengers occupy more platform space than circulating passengers, evaluation of their distribution across the platform is important. Public transport systems have been affected by the global pandemic Coronavirus disease 2019 (COVID-19). This may have affected the waiting passenger distribution on BRT platform space. Therefore, this study aimed to identify the impact of COVID-19 on waiting passenger distribution on a platform during the peak period at an important station on the BRT system in Brisbane, Australia. Manual data collection was carried out before COVID-19 and during COVID-19. Waiting passenger counts in each case were evaluated separately to identify variation across the platform. The total waiting passenger count on the platform at a given time reduced significantly during COVID-19. To compare the two cases, data sets were normalized, and statistical analysis performed. The test results indicated that the distribution of waiting passengers during COVID-19 has significantly changed, bringing more waiting passengers into the platform center than the two ends, whereas before COVID-19, more waiting passengers were observed at the upstream half of the platform. There was also greater temporal variation across the whole platform during COVID-19. These findings were used to postulate the reasons behind the changes resulting from COVID-19, which affected platform operation.

Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes.

The capability of 'demand-responsive transport', particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users' needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public's adoption intention of 'autonomous shuttle buses' as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers' attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions.

Time-space analysis to evaluate cell-based quality of service in bus rapid transit station platforms through passenger-specific area.

It is important to evaluate the quality of service (QoS) of bus rapid transit (BRT) station platform operation. Passenger-specific area (PSA) is used as a QoS measure which is determined by considering passenger activities separately. As passengers perform various activities on the same platform space, there is a need to evaluate BRT platform QoS by considering the activities collectively. When evaluating transit station platforms, many researchers calculated PSA for the whole platform area, while very few researchers highlighted the importance of evaluating the platform as small, partitioned areas. By considering these findings and gaps in the literature, this study evaluates QoS of the platform on a cell by cell basis using PSA. We use time-space analysis and passenger-minutes of each activity to develop a methodology to determine PSA, by considering stationary passengers, circulating passengers, and passengers overall. To evaluate platform QoS, we define threshold service levels using passenger-minutes of activities and Fruin's QoS criteria. For the case study BRT station, we find that PSA varies significantly between platform cells. It is evident from the results that it is important to identify highly congested areas in the platform and apply measures to improve platform QoS.