The paper analyses the impacts of COVID-19 on daily public transport ridership in the three most populated regions of Sweden (Stockholm, Västra Götaland and Skåne) during spring 2020. The analysis breaks down the overall ridership with respect to ticket types, youths and seniors, and transport modes based on ticket validations, sales and passenger counts data. By utilizing disaggregate ticket validation data with consistent card ids we further investigate to what extent fewer people travelled, or each person travelled less, during the pandemic. The decrease in public transport ridership (40%–60% across regions) was severe compared with other transport modes. Ridership was not restricted by service levels as supply generally remained unchanged throughout the period. The ridership reduction stems primarily from a lower number of active public transport travellers. Travellers switched from monthly period tickets to single tickets and travel funds, while the use and the sales of short period tickets, used predominantly by tourists, dropped to almost zero. One-year period tickets and school tickets increased from mid-April, which could indicate that the travellers using these tickets are particularly captive to the public transport system. Collaborative effort is required to put the results in the international context.

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Introduction The COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop travelling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden, for the spring and autumn of 2020. We suggest two binomial logit models for explaining the change in travel pattern, linking socioeconomic data per area and travel data with the probability to stop travelling. Modelled variables The first model investigates the impact of the socioeconomic factors: age; income; education level; gender; housing type; population density; country of origin; and employment level. The results show that decreases in public transport use are linked to all these factors. The second model groups the investigated areas into five distinct clusters based on the socioeconomic data, showing the impacts for different socioeconomic groups. During the autumn the differences between the groups diminished, and especially Cluster 1 (with the lowest education levels, lowest income and highest share of immigrants) reduced their public transport use to a similar level as the more affluent clusters. Results The results show that socioeconomic status affect the change in behaviour during the pandemic and that exposure to the virus is determined by citizens’ socioeconomic class. Furthermore, the results can guide policy into tailoring public transport supply to where the need is, instead of assuming that e.g. crowding is equally distributed within the public transport system in the event of a pandemic.

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The paper presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map-matching and path inference, link travel time estimation, calibration of prediction model parameters, and network travel time prediction in real-time. The system design satisfies three crucial aspects: computational efficiency of prediction; internal consistency between components; and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of Probabilistic Principal Component Analysis (PPCA), which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighboring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

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