The impact of COVID-19 on mobility choices in Switzerland.

We study the effect of the COVID-19 pandemic and the associated government measures on individual mobility choices in Switzerland. Our data is based on over 1,600 people for which we observe all trips during eight weeks before the pandemic and until May 2021. We find an overall reduction of travel distances by 60 percent, followed by a gradual recovery during the subsequent re-opening of the economy. Whereas driving distances have almost completely recovered, public transport re-mains under-used. The introduction of a requirement to wear a mask in public transport had no measurable impact on ridership. The individual travel response to the pandemic varies along socio-economic dimensions such as education and house-hold size, with mobility tool ownership, and with personal values and lifestyles. We find no evidence for a significant substitution of leisure travel to compensate for the reduction in work-related travel.

Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel.

In Switzerland, strict measures as a response to the Covid-19 pandemic were imposed on March 16, 2020, before being gradually relaxed from May 11 onwards. We report the impact of these measures on mobility behaviour based on a GPS tracking panel of 1439 Swiss residents. The participants were also exposed to online questionnaires. The impact of both the lockdown and the relaxation of the measures up until the middle of August 2020 are presented. Reductions of around 60% in the average daily distance were observed, with decreases of over 90% for public transport. Cycling increased in mode share drastically. Behavioural shifts can even be observed in response to the announcement of the measures and relaxation, a week before they came in to place. Long-term implications for policy are discussed, in particular the increased preference for cycling as a result of the pandemic.

Understanding metropolitan patterns of daily encounters.

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters' bounded nature. An individual's encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of "familiar strangers" in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or "structure of co-presence" across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and--particularly--disclosing the impact of human behavior on various diffusion/spreading processes.

A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective.

This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

Travel behaviour changes under Work-from-home (WFH) arrangements during COVID-19.

Life, including working style and travel behaviour, has been severely disrupted by the COVID-19 pandemic. The unprecedented number of work-from-home (WFH) employees after the outbreak of COVID-19 has attracted much scholarly attention. As it is generally believed that WFH arrangements are not ephemeral, it is imperative to study the impacts of WFH on travel behaviour and its impact on sustainable transport in the post-pandemic era. In relation, this study uses a set of longitudinal GPS tracking data in Switzerland to examine changes in trip characteristics (i.e. travel distance, travel time), travel behaviours (i.e. travel frequency, peak hour departure, trip destination, travel mode), and activities (i.e. trip pattern diversity, trip purpose, and time spent at home). Two groups of participants (WFH and Non-WFH) are identified and compared through three periods (pre-COVID, during lockdown, and post lockdown) from September 2019 to October 2020. Results show that more significant reductions of trip distance, travel time, travel frequency, morning peak hours trips, trips to the CBD are observed among the WFH group. These changes helped to mitigate negative transport externalities. Meanwhile, active transport trips, trip pattern diversity, leisure trips, and time spent at home also increased more significantly for the WFH group when compared to their counterparts. Hence, promoting WFH may not only be beneficial to teleworkers but also to the wider community through more sustainable transport. Future research direction and policy implications are also discussed.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

Introduction: This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Methods: Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results: Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Discussion: Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

The MOBIS dataset: a large GPS dataset of mobility behaviour in Switzerland.

This article presents the MOBIS dataset and underlying survey methods used in its collection. The MOBIS study was a nation-wide randomised controlled trial (RCT) of transport pricing in Switzerland, utilising a combination of postal recruitment, online surveys, and GPS tracking. 21,571 persons completed the first online survey, and 3680 persons completed 8 weeks of GPS tracking. Many continued tracking for over a year after the study was completed. In the field experiment, participants participated through the use of a GPS tracking app, Catch-my-Day, which logged their daily travel on different transport modes and imputed the trip segments and modes. The experiment lasted 8 weeks, bookended by two online surveys. After the first 4-week control phase, participants were split into two different treatment groups and a continued control group. An analysis of the survey participation shows that the technology is capable of supporting such an experiment on both Android and iOS, the two main mobile platforms. Significant differences in the engagement and attrition were observed between iOS and Android participants over the 8-week period. Finally, the attrition rate did not vary between treatment groups. This paper also reports on the wealth of data that are being made available for further research, which includes over 3 million trip stages and activities, labelled with transport mode and purpose respectively. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-022-10299-4.

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model.

UNLABELLED: world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread. METHODS: We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns. RESULTS: The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east. CONCLUSIONS: We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.

How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics.

Cities worldwide adopted lockdown policies in response to the outbreak of coronavirus disease 2019 (COVID-19), significantly influencing people's travel behavior. In particular, micro-mobility, an emerging mode of urban transport, is profoundly shaped by this crisis. However, there is limited research devoted to understanding the rapidly evolving trip patterns of micro-mobility in response to COVID-19. To fill this gap, we analyze the changes in micro-mobility usage before and during the lockdown period exploiting high-resolution micro-mobility trip data collected in Zurich, Switzerland. Specifically, docked bike, docked e-bike, and dockless e-bike are evaluated and compared from the perspective of space, time and semantics. First, the spatial and temporal analysis results uncover that the number of trips decreased remarkably during the lockdown period. The striking difference between the normal and lockdown period is the decline in the peak hours of workdays. Second, the origin-destination flows are used to construct spatially embedded networks. The results suggest that the origin-destination pairs remain similar during the lockdown period, while the numbers of trips between each origin-destination pair is reduced due to COVID-19 pandemic. Finally, the semantic analysis is conducted to uncover the changes in trip purpose. It is revealed that the proportions of Home, Park, and Grocery activities increase, while the proportions of Leisure and Shopping activities decrease during the lockdown period. The above results can help planners and policymakers better make evidence-based policies regarding micro-mobility in the post-pandemic society.

Surveying and analysing mode and route choices in Switzerland 2010-2015.

Every five years, the Mobility and Transport Microcensus (MTMC), a one-day CATI diary survey representative of the Swiss population in terms of socio-economics and trip characteristics, is carried out. In the year 2015, for the second time after 2010, an additional stated preference (SP) survey on respondents' mode and route choices was linked to the MTMC. The combination of revealed preferences (RP) from the MTMC interview and stated preferences from the follow-up survey provides a valid set of parameters for a new generation of regional and national transport demand models in Switzerland that are sensitive in terms of trip purposes, target groups and spatial patterns. These models, in turn, are needed for reliable transport forecasts and thus build the foundation of future transport policy in Switzerland. Willingness-to-pay indicators savings are found to be rather stable over time, which bodes well for their use in cost-benefit analyses.

Studying bicyclists' perceived level of safety using a bicycle simulator combined with immersive virtual reality.

There is a need for methods that provide a better understanding of bicyclists' perceived safety and preferences on currently unavailable and/or unknown bicycle facilities. Different survey methods have been used to study bicyclists' behavior, experiences, and preferences; ranging from verbally described facilities to surveys including images and videos. Virtual Reality (VR) experiments blur the boundaries between stated preference (SP) surveys and revealed preference (RP) surveys and provide a realistic sense of design. This research introduces a novel research method in bicycling research and discusses the results of an experiment using a bicycle simulator combined with immersive VR. In total, 150 participants participated in this experiment and were asked about demographics and perceptions and preferences after bicycling in five different environments with an instrumented bicycle in VR. A 5 × 2 mixed design was used with bicycling environment as within-subject factor and pedestrian / traffic volume as between-subject factor. ANOVA tests revealed how each environment and ambient pedestrian / traffic volume affected perceived level of safety (PLOS) and willingness to bicycle (WTB). Pairwise comparison showed that participants felt safer bicycling on the segregated bicycle path compared to bicycling on the painted bicycle path on the road and roadside. There was no meaningful difference between WTB for less than 10 min and WTB for more than 10 min between bicycling on a painted bicycle path on the sidewalk and painted bicycle path on the road. PLOS and WTB ratings of men and women were not significantly different from each other. The older segment of the sample was more worried about roadside bicycling and bicycle commuters were more confident to ride on the roadside. Despite having several limitations, immersive 360-degree VR was found a powerful presentation tool to evaluate future street designs which can inform transport and urban planning.

A joint time-assignment and expenditure-allocation model: value of leisure and value of time assigned to travel for specific population segments.

Based on a time-use model with a sound theoretical basis and carefully collected data for Austria, the value of leisure (VoL) for different population segments has been estimated. Through the combination of these results with mode-specific values of travel time savings from a related study based on the same data, the first mode-specific values of time assigned to travel (VTAT) were calculated. Data was collected using a Mobility-Activity-Expenditure Diary, a novel survey format which gathers all activities, expenditures, and travel decisions from the same individuals for 1 week in a diary-based format. The average VoL is 8.17 €/h, which is below the mean wage of 12.14 €/h, indicating that the value of work is, on average, negative. Regarding the reliability of the VoL, we show its sensitivity to the variance of working time in a sample, something that has been ignored in previous studies and could be used to avoid inadequate segmentation. We controlled this effect in the analysis of the heterogeneity of the VoL across the population by estimating the parameters from the total (unsegmented) dataset with single interaction terms. We find that the VTAT is strictly negative for walking, predominantly negative for cycling and car, and predominantly positive for public transport with 0.27 €/h on average. The positive VTAT for public transport is a strong indication for the importance of travel conditions, in turn suggesting that improvements in travel conditions of public transport might be as important as investing in shorter travel times.

Understanding traffic capacity of urban networks.

Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network's critical number of vehicles and the network's traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.

Combining urban scaling and polycentricity to explain socio-economic status of urban regions.

The fast pace of urbanisation may benefit or be detrimental to the socio-economic status of urban areas. Understanding how the configuration of urban areas influences the socio-economic status of their inhabitants is of crucial importance for urban planning. In theory, urban scaling laws and polycentric development are two well-known concepts developed to increase our understanding of urbanisation and its socio-economic effects. In practice, however, they fall short to explain the socio-economic status of urban regions. The urban scaling concept is constructed from a theoretical perspective, but functional relationships between urban centres are not taken into account in scaling models. In contrast, the concept of polycentricity is developed from a practical perspective and incorporates the socio-economic effect of relationships between urban centres in the process of urban development. However, polycentricity lacks a theoretical foundation, which would explain the socio-economic status of urban regions. In this study, we assess whether combining both concepts improves the ability to explain personal incomes in metropolitan areas in Switzerland. We first delineated metropolitan areas by implementing a modularity maximisation algorithm on the settlement network. Nodes in this network are Swiss municipalities and links are inter-municipal commuter flows. We found a strong relationship between the hierarchical organisation of functional connections within metropolitan areas and the socio-economic status of these areas. Both concepts were complementary and combining them proved to enhance the ability to explain socio-economic status. The combined model is a theoretical progress, which complements the traditional approaches and increases our understanding of cities and urbanisation processes.

Quantifying long-term evolution of intra-urban spatial interactions.

Understanding the long-term impact that changes in a city's transportation infrastructure have on its spatial interactions remains a challenge. The difficulty arises from the fact that the real impact may not be revealed in static or aggregated mobility measures, as these are remarkably robust to perturbations. More generally, the lack of longitudinal, cross-sectional data demonstrating the evolution of spatial interactions at a meaningful urban scale also hinders us from evaluating the sensitivity of movement indicators, limiting our capacity to understand the evolution of urban mobility in depth. Using very large mobility records distributed over 3 years, we quantify the impact of the completion of a metro line extension: the Circle Line (CCL) in Singapore. We find that the commonly used movement indicators are almost identical before and after the project was completed. However, in comparing the temporal community structure across years, we do observe significant differences in the spatial reorganization of the affected geographical areas. The completion of CCL enables travellers to re-identify their desired destinations collectively with lower transport cost, making the community structure more consistent. These changes in locality are dynamic and characterized over short timescales, offering us a different approach to identify and analyse the long-term impact of new infrastructures on cities and their evolution dynamics.

Efficient detection of contagious outbreaks in massive metropolitan encounter networks.

Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme--a simple, but universal strategy requiring only local information--and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.