COVID-19 as a window of opportunity for cycling: Evidence from the first wave.

Across the world, the COVID-19 pandemic has forced people to reconsider their habits in terms of how they work, how they interact with each other, and of their mobility. During lockdowns, mobility was in general significantly reduced. Means of collective transportation were used much less, and people preferred means of individual transport. Evidence from some cities suggests that people turned to cycling as a resilient and reliable option with a small risk of contagiousness. This spike in demand led some governments to respond by opening additional bike lanes, reducing the fees of bike-sharing systems, banning cars on selected streets, or giving monetary incentives for the purchase of new bikes. We analyze the bike traffic in Basel and Zurich, two major Swiss cities. Throughout the pandemic, no specific measure to promote cycling was implemented in any of the two cities; we can thus see latent demand patterns exposed when conditions change. As cycling depends on the season and weather, we incorporate these data and correct the traffic counts hereby. We can identify a distinct change in cycling traffic over the course of the day. During the lockdown period, relatively more traffic is observed in the afternoon, possibly associated with leisure activities. Furthermore, there is a short-term drop in the corrected cycling traffic and a fast recovery, demonstrating cycling as a resilient transport mode. Soon bike traffic reached pre-lockdown levels, but no significant increase could be identified, possibly attributed to the absence of explicit policy measures. We furthermore survey a panel of bike policy experts to identify policy actions that could be taken in Basel and Zurich to increase bike usage. The COVID-19 pandemic disrupts life as we know it, leading people to reconsider their travel choices. Given authorities' desire to increase bike usage, it represents a window of opportunity to test new policy measures, increase bike trips of active cyclists, and attract new cyclists. As long as this window is open, people are susceptible to policy measures to reconsidering past choices. However, if no policy measures are conducted during the pandemic, as in the case study, it is likely that bike usage is not increased in the long run. Authorities are well-advised to take this opportunity to strengthen cycling and to lead to a more resilient, accessible, safe, and sustainable urban transport system.

The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area.

The COVID-19 pandemic strongly affected mobility around the world. Public transport was particularly hindered, since people may perceive it as unsafe and decide to avoid it. Moreover, in Switzerland, several restrictions were applied at the beginning of the first pandemic wave (16/03/2020), to reduce the contagion. This study observes how the pandemic affected travel behaviour of public transport users, focusing on route choice and recurrent trips. We conducted a travel survey based on GPS tracking during the first pandemic wave, following 48 users for more than 4 months. The very same users were also tracked in spring 2019, allowing a precise comparison of travel behaviour before and during the pandemic. We analyse how the pandemic affected users, in terms of travel distance, mode share and location during the day. We specifically focus on recurrent trips, commuting and non-commuting, observing how mode and route changed between the two different periods. Finally, we estimate a route choice model for public transport (Mixed Path Size Logit), based on trips during the two different years, to identify how the route choice criteria changed during the pandemic. The main differences identified in travel behaviour during the pandemic are a different perception of costs of transfers and of travel time in train, and that users no longer have a clear preferred route for a recurrent trip, but often choose different routes.

Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption.

Transport networks are becoming increasingly large and interconnected. This interconnectivity is a key enabler of accessibility; on the other hand, it results in vulnerability, i.e. reduced performance, in case any specific part is subject to disruptions. We analyse how railway systems are vulnerable to delay, and how delays propagate in railway networks, studying real-life delay propagation phenomena on empirical data, determining real-life impact and delay propagation for the uncommon case of railway disruptions. We take a unique approach by looking at the same system, in two different operating conditions, to disentangle processes and dynamics that are normally present and co-occurring in railway operations. We exploit the unique chance to observe a systematic change in railway operations conditions, without a correspondent system change of infrastructure or timetable, coming from the occurrence of the large-scale disruption at Rastatt, Germany, in 2017. We define new statistical methods able to detect weak signals in the noisy dataset of recorded punctuality for passenger traffic in Switzerland, in the disrupted and undisrupted state, along a period of 1 year. We determine how delay propagation changed, and quantify the heterogeneous, large-scale cascading effects of the Rastatt disruption towards the Swiss network, hundreds of kilometers away. Operational measures of transport performance (i.e. punctuality and delays), while globally being very decreased, had a statistically relevant positive increase (though very geographically heterogeneous) on the Swiss passenger traffic during the disruption period. We identify two factors for this: (1) the reduced delay propagation at an international scale; and (2) to a minor extent, rerouted railway freight traffic; which show to combine linearly in the observed outcomes.

Reconstruction of an informative railway wheel defect signal from wheel-rail contact signals measured by multiple wayside sensors.

Wheel impact load detectors are widespread railway systems used for measuring the wheel-rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.

Optimizing preventive maintenance policy: A data-driven application for a light rail braking system.

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.