A data-driven approach to road accidents in the municipality of Lisbon

Traffic accidents in urban areas lead to reduced quality of life and social inequality in cities, specially in third world countries. The growth of the urban mesh and the population density is seldom accompanied by the development or sizing of the road infrastructure. It is a fact that the number and severity of road accidents in Portugal have been decreasing over the last thirty years, bringing us closer to the European average. However, despite these facts, the situation remains worrying. Despite the adoption of programs such as the European Commission Road Safety Program and the recent EU Road Safety Policy Framework 2021-2030 or, on a national basis, the PENSE 2020 - National Strategic Plan for Road Safety the number of road accidents with victims in the district of Lisbon is still higher than the European average. Thus, and for this dissertation, we conducted an exploratory data analysis (EDA) on the combined data of traffic incidents recorded in the occurrence management system of the Lisbon Fire Brigade Regiment (RSB) and the road accidents reported to ANSR by the security forces (GNR and PSP) through the Statistical Bulletin of Traffic Accidents (BEAV). Furthermore, with data from occurrences in the Municipality of Lisbon between 2010 and 2020, to identify the existence of Black Spots in Lisbon's roads and which are the most significant and contributing factors to explain their existence. The data on road accidents were also georeferenced to capitalize their spatial existence and, consequently, better understand the existing spatial patterns and risk factors. Subsequently, through the use of the ArcGIS Pro we apply the algorithms of the Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of the black spots, and that human, environmental and circumstantial factors have an influence on the severity of accidents, being the content validity guaranteed through an expert committee. This way, our research goal is to contribute to identify accident concentration areas in the city of Lisbon (hotspots), considering their influencing conditions.Os acidentes de trânsito em áreas urbanas conduzem à redução da qualidade de vida e à desigualdade social nas cidades, especialmente nos países em desenvolvimento. O crescimento da malha urbana, assim como, a densidade populacional raramente é acompanhada pelo desenvolvimento ou dimensionamento da infraestrutura rodoviária. É um facto que o número e a gravidade dos acidentes rodoviários em Portugal têm vindo a diminuir ao longo dos últimos trinta anos, o que permitiu aproximarmos da média Europeia, apesar destes factos a situação continua a ser preocupante. Apesar da adoção de programas como o Programa de Segurança Rodoviária da Comissão Europeia ou, numa base nacional, o PENSE 2020 - Plano Estratégico Nacional para a Segurança Rodoviária os números de acidentes de viação com vítimas no distrito de Lisboa continuam a ser mais elevados do que a média europeia. Desta forma e para efeitos deste trabalho realizamos uma análise de dados exploratória (AED) aos dados dos incidentes de transito registados no sistema de gestão de ocorrências do Regimento de Sapadores Bombeiros de Lisboa e a os dados de acidentes rodoviários reportados à ANSR pelas forças de segurança (GNR e PSP) através do Boletim Estatístico de Acidentes de Viação (BEAV) e ocorridos no concelho de Lisboa entre 2010 e 2020 por forma a identificar a existência de Pontos Negros nas vias de Lisboa e quais os fatores mais significantes e contribuintes que permitam explicar a sua existência. Os dados relativos aos acidentes rodoviários foram também georreferenciados para capitalizar a sua existência espacial e, consequentemente, compreender melhor os padrões espaciais existentes e os fatores de risco. Posteriormente através do recurso ArcGIS Pro aplicaram-se os algoritmos das ferramentas Densidade de Kernel e Hot Spot Analysis (Getis-Ord Gi*), identificando a existência dos pontos negros, e que fatores humanos, ambientais e circunstanciais têm influência na gravidade dos acidentes e que algumas variáveis de exposição foram consideradas importantes na explicação da ocorrência dos mesmos, sendo a validade do conteúdo garantida através de uma comissão de especialistas. Pretende-se, assim, contribuir para a identificação das zonas de concentração de acidentes da cidade de Lisboa (hotspots), tendo em conta as suas condições influenciadoras. Potenciando a segurança rodoviária no município

Route Segment Level Analysis of Bus Safety Incidents

This paper analyzes collision and non-collision incidents that occurred on TriMet’s bus system over a near two-year period. The bus route network was decomposed into stop and line haul segments, and a typology of models was estimated from segment level incident, risk exposure, and roadway feature data. The frequency of non-collision incidents – mainly slips, trips and falls – was estimated to be primarily related to associated risk exposure variables. The frequency of collision incidents was also estimated to be related to risk exposure variables, as well as a number of roadway design variables. The findings serve as an initial step in informing the safety planning process

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

Context-based Information Fusion: A survey and discussion

This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of \u201ccontext\u201d. It shows how its fortune in the distributed computing world eventually permeated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploitation dynamics and architectural aspects peculiar to the fusion domain are presented and discussed

Methodological and empirical challenges in modelling residential location choices

The modelling of residential locations is a key element in land use and transport planning. There are significant empirical and methodological challenges inherent in such modelling, however, despite recent advances both in the availability of spatial datasets and in computational and choice modelling techniques. One of the most important of these challenges concerns spatial aggregation. The housing market is characterised by the fact that it offers spatially and functionally heterogeneous products; as a result, if residential alternatives are represented as aggregated spatial units (as in conventional residential location models), the variability of dwelling attributes is lost, which may limit the predictive ability and policy sensitivity of the model. This thesis presents a modelling framework for residential location choice that addresses three key challenges: (i) the development of models at the dwelling-unit level, (ii) the treatment of spatial structure effects in such dwelling-unit level models, and (iii) problems associated with estimation in such modelling frameworks in the absence of disaggregated dwelling unit supply data. The proposed framework is applied to the residential location choice context in London. Another important challenge in the modelling of residential locations is the choice set formation problem. Most models of residential location choices have been developed based on the assumption that households consider all available alternatives when they are making location choices. Due the high search costs associated with the housing market, however, and the limited capacity of households to process information, the validity of this assumption has been an on-going debate among researchers. There have been some attempts in the literature to incorporate the cognitive capacities of households within discrete choice models of residential location: for instance, by modelling households’ choice sets exogenously based on simplifying assumptions regarding their spatial search behaviour (e.g., an anchor-based search strategy) and their characteristics. By undertaking an empirical comparison of alternative models within the context of residential location choice in the Greater London area this thesis investigates the feasibility and practicality of applying deterministic choice set formation approaches to capture the underlying search process of households. The thesis also investigates the uncertainty of choice sets in residential location choice modelling and proposes a simplified probabilistic choice set formation approach to model choice sets and choices simultaneously. The dwelling-level modelling framework proposed in this research is practice-ready and can be used to estimate residential location choice models at the level of dwelling units without requiring independent and disaggregated dwelling supply data. The empirical comparison of alternative exogenous choice set formation approaches provides a guideline for modellers and land use planners to avoid inappropriate choice set formation approaches in practice. Finally, the proposed simplified choice set formation model can be applied to model the behaviour of households in online real estate environments.Open Acces

Data-Driven Optimization for Bicycle Station Location in a Small to Medium-Sized City: The Case Study of Cuenca, Ecuador

Bike sharing systems (BSSs) are an important transportation alternative, and station distribution is a key component of these that is driven by user demand and resource constraints. Designing an effective BSS with appropriate station distribution requires a method that consists of steps structured in a flexible, parameterizable, repeatable, and organized way, based on and aligned with proven or accepted standards---particularly in resource-limited environments. This includes data-driven analysis of information relevant to BSS station design from various sources and in different formats. Models and algorithms are used to organize and examine the data, reduce redundant data, standardize factors, and find patterns that can inform the efficient design and implementation of a BSS. The algorithms and models used in the present study provide a data-driven approach to determining effective BSS station distribution in a city. Factor analysis and principal component analysis (PCA) were used as the various sources of data involved in the design of a BSS (i.e., data on traffic, demographic, and land use) can often overlap and/or have redundant data and these techniques allow minimizing superfluous data without losing relevant information. Econometric models were also used to identify the costs of pollutants, with the aim of locating stations in areas where pollution is a problem, and an emission-free BSS might be of greatest benefit. Patterns of potential users and mobility are derived from unsupervised learning algorithms. Finally, the set covering model (SCP), an optimization model for the distribution of stations, is used to define the number of stations in the city and their locations. This model\u27s objective is to minimize costs while still satisfying user demand. Using this data-driven approach can help guide the strategic design and planning of a BSS. A case study using this method was carried out using data from the city of Cuenca, Ecuador, the third most populous city in this developing country. Cuenca is considered a mid-sized city and is a UNESCO World Heritage Site. When compared to the costly Spanish--Ecuadorian consortium that implemented the currently BSS running in Cuenca, applying the proposed data-driven approach to this real-life practical case study resulted in a 70--90\% match in the locations of stations. The consortium had to study the place of implementation in a great amount of depth and obtained a similar design to that obtained in this case study. This demonstrates the potential of the proposed method as a simple, effective, and low-cost method for the strategic planning of BSSs in small and mid-sized cities. The present study provides an affordable solution to the design of BSS station distribution for cities without many resources. Using this method, cities can take advantage of a standardized platform to define a network of stations through an established step-by-step process. The method of BSS design proposed here demonstrates three significant advantages: 1) in-depth knowledge of the area in which a BSS is to be implemented location is not required, as the design can be driven by existing data and can even be adapted to new data sources; 2) implementation is economical as this reduces the need to hire expensive expert personnel with knowledge and experience in implementing BSSs; 3) the method is versatile since it can accept input data of various kinds, which enables the adaptation of the solution to any small or mid-sized city. This method, therefore, provides small and mid-sized resource-limited cities with a simple and cost-effective method to design a BSS that can be tailored to particular contexts and can be adapted to the specific goals of BSS implementation in a given city

Characterising and Modelling Urban Freight in Developing Economies

Urban freight systems in developing countries present significant challenges due to their complexity. Authorities often have inadequate institutional structures, making it difficult to identify and implement relevant initiatives. This thesis aims to characterise the systems in developing economies and model freight demand using innovative approaches by considering new attributes, dimensions and alternatives. As a first modelling step, freight (trip) generation was improved by considering spatial and locational determinants, as freight activities are strongly related to spatial and locational characteristics of establishments. Spatial models were developed using a combined spatial autoregressive model (SAR) and geographically weighted regression (GWR) or multiscale GWR (MGWR) (GWR/MGWR-SAR model). This model accounted for non-linearity, spatial heterogeneity and spatial dependency and demonstrated significant improvements (R2 0.29-0.71, RMSE reduced by 71% and AIC value by 56%). Shipment size decisions related to the choice of truck type were strongly timedependent, with commodity type, activities at the trip end, truck body type and industry sector affecting the preferences. Freight demand, including shipment size choices, was influenced by economic fluctuations, with shipment size declining after an economic slowdown. In freight demand modelling, it is imperative to consider economic conditions, especially those in developing countries, which are often susceptible to strong economic fluctuations. The models were applied in ex ante testing of a policy restricting large trucks from entering a city centre, as commonly considered in many developing countries. In tests, the truck restriction was accompanied by single-tier and two-tier distribution systems. The results showed that the two-tier system had a slight advantage over the single-tier system regarding operational expenditure and emission levels. Truck restriction was generally counterproductive, even when accompanied by distribution systems with greater speed and efficiency. We conclude that the models enhance the accurate prediction of freight demand patterns. The ex ante evaluation of policy alternatives supports the decision-making process for urban freight systems of large cities in developing economies. The models allow considering relevant practical, local contextual conditions

Physical-geographic factors of terrain trafficability of military vehicles according to Western World methodologies

U ovom se radu analizira postojeće stanje, ograničenja i mogućnosti istraživanja fizičko-geografskih čimbenika u kontekstu terenske prohodnosti vojnih vozila u svrhu potpore procesa donošenja vojnih odluka prilikom planiranja pokreta vojnih snaga. Rad prikazuje do sada korištene tipove modela, metodologije i načine prikaza rezultata utjecaja fizičko-geografskih čimbenika na pokretljivost vozila. Provedena istraživanja pokazala su da je u dosadašnjim istraživanjima najčešće korišten parametar nagib padina, potom fizičke osobine tla, hrapavost površine vodotoci, tipovi vegetacije i klimatsko-meteorološki uvjeti. Kvaliteta rezultata dosadašnjih istraživanja određena je kvalitetom i točnošću ulaznih podataka te primjenjivanim metodama korištenima u modelima. U novijim istraživanjima geografski informacijski sustav (GIS) autorima omogućuje sjedinjavanje cjelokupne problematike utvrđivanja terenske prohodnosti vozila jer objedinjuje sve mogućnosti na jedinstvenoj platformi.This paper analyses the existing state, limitations, and possibilities for research of physical-geographic factors in the context of terrain trafficability of military vehicles in order to support the military decision-making process regarding planning movements of military forces. This paper shows which models and research methodologies were used to ascertain how physical-geographical factors influence vehicle cross-country mobility. Research has shown that slope has been used most frequently as a parameter, followed by soil properties, surface roughness, watercourses, vegetation types, and climatic-meteorological conditions. The quality of the results achieved so far has been largely determined by the quality and accuracy of the input data and by the various methods used in the models. Recent studies have shown that the Geographic Information System (GIS) unifies the issues determining vehicle terrain trafficability, as it displays all possibilities in a single platform

Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit