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To understand the route choices of public transport users, it is important to know the information available to them, and the context present at that moment. In fact, each choice situation in a transport network has different characteristics and possibilities, also depending on the current status of the transport network. In this regard, travel diaries based on tracking technologies can capture precise observations for a long term. In this work, we exploit a large-scale tracking dataset, collected through a mode detection algorithm, to understand route choices of public transport users. We propose a choice set generation algorithm, able to cover more than 94% of the collected trips without any computational constraint. We compare the users’ paths in the public transport network with different choice sets, under multiple performance indicators, including coverage, size, and fit. This latter is computed by the estimation of a Path Size Logit model. The use of Automatic Vehicle Location (AVL) data allows comparing the available paths in terms of public transport vehicles used. We also consider different information provisions of network conditions and disturbances (full knowledge, no knowledge and current knowledge), and study which information provision best represents the choice set inferred by the observed users’ behaviour. Estimating a Mixed Path Size Logit model, we identified high heterogeneity among the users in only a few aspects. Overall, a condition of no knowledge results as the best fit, i.e. users seem to take into account in a minor way the realized delays in the alternatives considered when deciding their public transport route.

Public transport networks (PTN) are affected daily by different types of disturbances. In fact, between a single delay and a long service interruption, there is a range of disruptions with different impacts, depending on their characteristics. Despite this, in literature, the common definition of disruption is a link closure for a certain amount of time. Low interest is given to different types of disruptions or to the connection between delays and disruptions. In addition, in multimodal PTN a physical link closure is not always observable, but rather people experience delays or cancelled stops on different lines. The aim of this work is to explore the relationship between delays and disruptions, analyzing different degrees of disruptions, in relation to duration, delay, size, and network characteristics. Real disturbances of the PTNs in Zürich and Bern, Switzerland, are analyzed to identify disruptions with different characteristics. Therefore, the disruption impact is computed on simulated origin–destinations (ODs), based on the sets of possible paths with and without the disruption. For this purpose, a choice set generation algorithm is used. Finally, relationships between the disruption characteristics and the impact are analyzed to identify the main features of a disruption.

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., Transport Policy, 116, ISSN:0967-070X

Visibilities for the HOM peak in the far field and the near field with a certain ROI radius. Detected spatial correlations in the far field.

Development of resistance to chemo- and immunotherapies often occurs following treatment of melanoma brain metastasis (MBM). The brain microenvironment (BME), particularly astrocytes, cooperate toward MBM progression by upregulating secreted factors, among which we found that monocyte chemoattractant protein-1 (MCP-1) and its receptors, CCR2 and CCR4, were overexpressed in MBM compared with primary lesions. Among other sources of MCP-1 in the brain, we show that melanoma cells altered astrocyte secretome and evoked MCP-1 expression and secretion, which in turn induced CCR2 expression in melanoma cells, enhancing in vitro tumorigenic properties, such as proliferation, migration, and invasion of melanoma cells. In vivo pharmacological blockade of MCP-1 or molecular knockout of CCR2/CCR4 increased the infiltration of cytotoxic CD8+ T cells and attenuated the immunosuppressive phenotype of the BME as shown by decreased infiltration of Tregs and tumor-associated macrophages/microglia in several models of intracranially injected MBM. These in vivo strategies led to decreased MBM outgrowth and prolonged the overall survival of the mice. Our findings highlight the therapeutic potential of inhibiting interactions between BME and melanoma cells for the treatment of this disease.

This work was developed within the Horizon 2020 D-NOSES project, with the purpose of validating a new methodology for odour pollution management based on citizen science. The work was carried out in the area of Castellanza, in the Region of Lombardy (Italy), and involved 4 plants that have been identified as potential causes for odour emissions. In this complex situation, it was proposed to combine the D-NOSES methodology with the “traditional” way involving olfactometric measurements and dispersion modelling, in order to identify the causes of the odour problem. The results of the olfactometric surveys allowed to identify the major odour emissions of the plants under investigation. Moreover, the preliminary analysis of the 358 odour observations collected in the period between May 14th and September 30th 2020 by a group of trained citizens, resulted in the disprove of the common belief that a particular product of the chemical plant caused the release of odorous chemical substances generating malodours at the municipal WWTP.

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