As an increasing number of people work in office buildings and new sophisticated sensor technologies become available there is, both, a need and potential, to develop more complex building service controls that increase the energy efficiency of buildings as well as the well-being of employees. The data required for the testing and evaluation of such control systems is usually in the form of movements and locations of office building occupants collected over long periods of time. However, such data is generally difficult to obtain for reasons ranging from the need to evaluate un-commissioned buildings to privacy concerns related to data sharing. Therefore, this study develops a pedestrian behaviour model that can simulate office occupants’ movements and locations thereby acting as a research platform that produces data for external applications. The model is integrated as it simulates not only the movements of occupants between different locations in the building but also decisions that drive the movements such as which activities occupants want to carry out throughout the day, and when and where they want to perform these activities. Furthermore, the model is based on the guidelines of (i) flexibility – the model is able to simulate movements in different building plans and represent movement patterns of different organizations; (ii) extensibility – the model uses a modular framework that enables easy adoption of more complex components and integration with other workplace related studies as and when required; and (iii) data parsimony – the model has low and simple data requirements itself.

Unreliable waiting times may cause frustration and anxiety amongst public transport travellers. Although the effect of travel time reliability has been studied extensively, most studies have used stated preferences which have disadvantages, such as an inherent hypothetical bias, or have analysed revealed preferences for road traffic. Here, we derive revealed preferences from passively collected smart card data to analyse the role of waiting time reliability in public transport route choice. We study waiting time reliability as regular and irregular deviations from scheduled values, examining a number of indicators for the latter. Behaviour in morning peak and off-peak hours is contrasted and differences in reliability coefficients for different modes in the network, and for origin and transfer stops are reported. Results from The Hague indicate relatively low reliability ratios with travellers perceiving a 5-minute standard deviation in realised waiting times as an extra 1–5.6 min of planned waiting time.@en

Introduction Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers' confidence it is critical to assess their risk determinants and trade-offs. Methods To this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers' COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively. Results We find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact-nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: Reduced crowding, mask mandates, and increased sanitization. Conclusion Since we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk. @en