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Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socio-economic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crash occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011–2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction.

Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5862 crashes of different severities were recorded over an 8-year period (2011–2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the north-west and south of city centre. We analyse the modifiable areal unit problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify ‘hotspots’ on the road network and to inform effective local interventions.

The monitoring of production processes on a planar surface typically involves sampling network to gather information about the status of the process. In order to save time and money, when the process goes into a stable status it might be appropriate to reduce the dimension of the sampling grid. In some cases, the allocation of a new network of smaller dimension is not free of constraints and it might be necessary the selection of a subgrid extracted from the original network. Discussion is focused on some recent methods used to achieve this aim. Possible extensions to consider jointly tabu search algorithm and co-kriging models is reported.

Negli ultimi anni è nato un interesse sempre crescente verso l’analisi statistica di dati spaziali aventi supporto di network. Gli esempi più classici di questa tipologia di eventi sono, ad esempio, gli incidenti stradali, i furti di auto, i crimini, e gli interventi delle ambulanze, mentre le linee che compongono la network rappresentano tipicamente le strade, i fiumi, i binari della ferrovia, oppure le terminazioni nervose. L’analisi di questi fenomeni è interessante sotto diversi punti di vista. Innanzitutto, i modelli statistici presentano diverse problematiche legate al supporto spaziale. Per questo motivo, negli ultimi anni sono stati pubblicati diversi paper che mostrano le difficoltà principali legate alla natura stessa della network. Inoltre, il recente sviluppo di database spaziali open source (quali Open Street Map) ha permesso il download e la creazione di dataset che coprono le reti stradali di quasi tutto il mondo. L’enorme mole di dati e gli (inevitabili) errori geometrici presenti nei database di Open Street Map rappresentano due problematiche ulteriori. Infine, dato che al momento la maggior parte dei pacchetti R per l’analisi di dati su network sono ancora in fase di sviluppo, esistono anche diverse difficoltà computazionali e problemi nell’implementazione di metodologie nuove. Questo lavoro di tesi riassume quattro articoli che presentano strutture dati e metodologie statistiche per l’analisi di dati spaziali aventi supporto di network, considerando sia un approccio di tipo network-lattice che un approccio di tipo point-pattern. Il primo paper presenta una revisione bibliografica dei pacchetti R che implementano classi e funzioni per l’analisi di network stradali, concentrandosi in particolare su stplanr e dodgr. Vengono introdotte le principali routines legate al calcolo di shortest paths e centrality measures utilizzando dataset via via più complessi. Il secondo lavoro presenta un modello di Poisson Dinamico Zero Inflated per la stima di d, In the last years, we observed a surge of interest in the statistical analysis of spatial data lying on or alongside networks. Car crashes, vehicle thefts, bicycle incidents, roadside kiosks, neuroanatomical features, and ambulance interventions are just a few of the most typical examples, whereas the edges of the network represent an abstraction of roads, rivers, railways, cargo-ship routes or nerve fibers. This type of data is interesting for several reasons. First, the statistical analysis of the events presents several challenges because of the complex and non-homogeneous nature of the network, which creates unique methodological problems. Several authors discussed and illustrated the common pitfalls of re-adapting classical planar spatial models to network data. Second, the rapid development of open-source spatial databases (such as Open Street Map) provides the starting point for creating road networks at a wide range of spatial scales. The size and volume of the data raise complex computational problems, while common geometrical errors in the network’s software representations create another source of complexity. Third, at the time of writing, the most important software routines and functions (mainly implemented in R) are still in the process of being re-written and readapted for the new spatial support. This manuscript collects four articles presenting data structures and statistical models to analyse spatial data lying on road networks using point-pattern and network-lattice approaches. The first paper reviews classes, vital pre-processing steps and software representations to manipulate road network data. In particular, it focuses on the R packages stplanr and dodgr, highlighting their main functionalities, such as shortest paths or centrality measures, using a range of datasets, from a roundabout to a complete network covering an urban city. The second paper proposes the adoption of two indices for assessing the risk of car crashes on the street network

Worldwide thousands of people die annually in highway-related crashes and millions are injured. By 2030 highway-related crashes will be the 5-th leading cause of death in the world according to the WHO (Mannering and Bhat, 2014). The statistical analysis of accident data has historically been fundamental to develop road-safety policies aiming at saving lives and reducing injuries severity. In the absence of detailed driving-related data (such as acceleration and braking) and crash-related data (such as those made available from vehicle black-boxes) that would help improve the identification of cause and effect relationships, a large majority of research has addressed the problem in terms of understanding the factors that affect the frequency of crashes, i.e. the number of crashes occurring in some geographical space over some specified time periods.[...]

Worldwide, thousands of people die annually in highway-related crashes and millions are injured. Hence, car wrecks have very high direct social costs but also relevant indirect economic effects such as an adverse impact on the burden of hospitalization and an increased health expenditure. The analysis of car crash data has long been used as a basis for influencing highway and vehicle designs but also, and perhaps more importantly, to support local authorities in allocating resources aimed at improving road safety and making political decisions to mitigate road risks in the most exposed areas. In this paper, we show how a range of information collected from open data sources concerning the structure of the road network (road typology, traffic lights, pedestrian crossings, etc.), socio-demographical dimensions and crash history can be proficiently used for this aim. We adopt a dynamic Zero Inflated Poisson (ZIP) regression model to define two indexes. The first index, derived from the counting component of the ZIP model, measures how prone to crash risk a segment is. The other, derived by the zero component of the ZIP model, represents a measure of the likelihood of segments to not be exposed to crashes. Focussing on the city of Milan (Northern Italy), we found that the most relevant determinant of road risk proneness is crash history and that structural characteristics of the road are much more relevant than demographic information. Finally, we show how this information can be spatialized to produce maps of crash proneness and predict future spatial risk indexes.

This contribution deals with a quite common but relevant issue in insurance. Suppose to be interested in covering a risk (e.g. motor accident, life/health risks, risk of disability, adverse weather events, etc...) and that you want to stipulate a contract to protect yourself against the loss of economic resources in case that the event occurs. In such circumstances any insurer will ask you to pay an amount (called premium) that approximately equals the pure premium (i.e. the amount that corresponds to the expected loss times the odds of the event) plus the so called loadings (i.e. the amount of money that consider both the gain of the contractor and administrative/process costs). The challenging point is: how to compute the odds? In particular, how to compute the probability of an adverse event given covariates that should be considered explanatory of the risk? [...]

In this paper we propose a methodology to estimate the probability that a car accident occurs in urban roads. Our approach is based on logistic regression and takes into account the particular nature of the data which conforms to a spatial point pattern on a network. Using the open data on street networks provided within the OpenStreetMap project, we estimate the probability of car accidents for every street in the municipality of Milan.

L’organizzazione di un servizio di emergenza sul territorio risulta essere un compito complesso nelle aree metropolitane come Milano dato l’enorme numero di persone che vi transitano quotidianamente. In questo lavoro si adotta un modello spazio temporale per rappresentare la dinamica delle chiamate di emergenza sul territorio del capologuo Lombardo. Il metodo adottato si ´e dimostrato essere efficace nel prevedere gli eventi sul territorio comunale nel periodo di tempo considerato e computazionalmente efficiente nonostante la consistente mole di dati da elaborare., To organize the emergency medical system in a big city is an extremely difficult task given the huge number of people that everyday pass through the city area. In this paper we employed a spatio-temporal process to model the emergency event occurrences in Milan. The proposed approach has been found effective in predicting events through the city area and computationally efficient despite the big amount of data to be processed.