Design and development of a prototype mobile geographic information system for real-time collection and storage of traffic accident data

Today, Swedish police authorities nationwide collect data of traffic accidents. The information is stored in a national database managed by the Swedish Transport Agency and is an important resource in the process of analysing and improving road safety. Literature studies in this thesis, together with earlier work by the author have suggested that the data collection process is in need of an update: a digital tool for such data collection is necessary. Problems with varying quality of data and long submission times of reports have been attributed to the current method, which involves a paper form. A digital method including a handheld device is expected to improve data quality and shorten overall submission times. The aim of the thesis has thus been to design and develop a mobile GIS system for collection and management of traffic accident information for police authorities. The project has utilized mainly open source tools. The result is a system containing an Android application for data collection, a database server with a database for storage, an application server/web server to host a software server (map server), and a web server that handles requests and hosts a web service for viewing and retrieving data. The created system can collect all of the information that the currently used analogue method does as well as new media such as GPS coordinates, photographs and audio. The functionality of the web service demonstrates that data is collected and stored in suitable formats in a database schema that is flexible enough to facilitate a wide range of queries relevant to the field of road safety.The thesis describes the design and development of an Android mobile application for collecting and reporting information about traffic accidents. Additional components such as servers and a web service with a map are also included. Together with the mobile application they form a system for reporting, storage and analysis of information about traffic accidents. The tools and components that have been chosen are mainly open source, which means that they are accessible and free for everyone to use and create their own system. Swedish police authorities nationwide are currently collecting data of traffic accidents. The information, which is stored in a national database managed by the Swedish Transport Agency, is an important resource in the process of analysing and improving road safety. The data collection by the police has, since the beginning been intended to be conducted in the field on a hand held device, however this is not the case. Consequently the national database consists of information that has been collected by filling out paper forms that are later digitized. Varying data quality and long overall submission times have been noted as problems that can be attributed to this analogue step of the reporting process. It has been suggested that the data collection process is in need of an update: a new digital tool is necessary. The Android application, which is the main contribution, is intended as a suggestion to a digital replacement of the paper form. The functionality of the application is based on the requirements of the current paper form used by police. The created system can collect all of the information that the currently used analogue method does as well as new media such as GPS coordinates, photographs and audio. The functionality of the web service demonstrates that data is collected and stored in suitable formats in a database schema that is flexible enough to facilitate a wide range of queries relevant to the field of road safety

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

DESIGN AND DEVELOPMENT OF A SMART ADVISORY SYSTEM FOR HAZARDOUS MATERIALS TRANSPORTATION RISK ANALYSIS VIA QUANTITATIVE APPROACHES

Safe transportation of hazardous materials is critical as it has a high potential of catastrophic accidents depending on the amount of transported product, its hazardous characteristics and the environmental conditions. Consequently, an efficient, smart and reliable intervention is essential to enhance prediction on the impacts of transportation hazards. Although various risk assessment techniques have been used in industry and regulatory bodies, they were developed for evaluating risk of hazardous materials for fixed installation cases instead of moving risk sources. This study applies the Transportation Risk Analysis (TRA), which is an extension of a well-known Quantitative Risk Analysis (QRA) technique in developing and design a Smart Advisory Systems (SAS), to determine the safest routes for transportation of hazardous materials according to Malaysia scenario

Spatial Planning & Transport Engineering Using Children\u27s Maps to Locate Areas of Perceived Danger on Children\u27s Routes to School

Dublin faces many of the modern day transport problems associated with automobile transport. The bicycle is increasingly being viewed by Urban Planners as an interesting form of individual transportation which can form part of an integrated transportation solution to this problem. For cycling to be a sustainable mode of transport it must be all inclusive. However, there are some identifiable barriers which prevent certain groups in society from cycling. Barriers to children cycling are directly linked to safety concerns and strategies to encourage cycling to school in Ireland currently focus on promotion and cycle training with road safety engineering measures playing a minor role. This research developed a new, ethically sound methodology to locate areas of danger or perceived danger to children in an existing road network. The aim of the study was to improve the decision making process of planners and engineers when designing cycling infrastructure and road safety measures for children. This was achieved using spatial data within a Geographical Information System (GIS) and incorporated experiential data from children in three target schools in the Greater Dublin Area (GDA) and quantitative road data from the road Safety Authority (RSA). Findings from the study indicate that the two existing road safety tools currently used in Ireland, the RSA Accident Black Spot Map and the NRA Road Safety Audit, are inadequate when locating areas of perceived road danger to children. It was found that children cycling and walking to school could pinpoint locations in the road network where they experienced dangerous situations or where they did not feel safe. In both instances road types 5 (Regional Roads) and 6 (Local Roads) were identified by children as the most problematic roads. It is exactly these roads that provide the main part of the local cycle infrastructure. An important aspect of the proposed method is that the map gives children the opportunity to participate and provides valuable information which could enable Planners and Traffic Engineers to implement measures from The National Cycle Manual to help to realise the full potential of Dublin for cycling to school

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references

Bayesian Network-Based framework for cost-implication assessment of Road Traffic collisions

Abstract: Investigating the cost-implications of road traffic collision factors is an important endeavour that has a direct impact on the economy, transport policies, cities and nations around the world. A Bayesian network framework model was developed using real-life road traffic collision data and expert knowledge to assess the cost of road traffic collisions. Findings of this study suggest that the framework is a promising approach for assessing the cost-implications associated with road traffic collisions. Moreover, adopting this framework with other computational intelligence approaches would have a positive impact towards achieving the Sustainable Development Goals in terms of road safety

CONNECTED AND AUTONOMOUS VEHICLES EFFECTS ON EMERGENCY RESPONSE TIMES

Emergency response times have been shown to be directly correlated with mortality rates of out-of-hospital patients. Studies have been conducted to show the relationship between time and mortality rates until patients receive the proper treatment. With more cardiac arrests and other life threatening illnesses occurring in the United States, more emergency calls will be required as well. As of today, technological advancements have been made to reduce response times, but human factors still require certain procedures, causing delays in the run time and increasing the rate of mortality. Here we show the results of emergency response times with the market penetration of connected and autonomous vehicles. With connected and autonomous vehicles, the average time emergency vehicles spend on the roadways can be significantly decreased. Safety procedures with human drivers can be eliminated, giving the emergency vehicle a proper right-of-way through virtual emergency lanes and removing the need to slow down and avoid vehicles at intersections or during periods of heavy congestion. Our results show a three minute decrease in response time under full market penetration of the technology, reducing the mortality rate and increasing the potential to save lives

Sidestepping Equity? A Case Study on the Provision and Quality of Sidewalks in Fremont, California

An overview of the history and scope of pedestrian policy at the federal, state, regional, and local levels followed by an investigation into existing conditions in the San Francisco Bay Area suburb of Fremont, California. Findings and possible implications for the Metropolitan Transportation Commission and the City of Fremont are discussed and several modest policy recommendations are made

Mapping Crisis: Participation, Datafication, and Humanitarianism in the Age of Digital Mapping

This book brings together critical perspectives on the role that mapping people, knowledges and data now plays in humanitarian work, both in cartographic terms and through data visualisations. Since the rise of Google Earth in 2005, there has been an explosion in the use of mapping tools to quantify and assess the needs of the poor, including those affected by climate change and the wider neo-liberal agenda. Yet, while there has been a huge upsurge in the data produced around these issues, the representation of people remains questionable. Some have argued that representation has diminished in humanitarian crises as people are increasingly reduced to data points. In turn, this data becomes ever more difficult to analyse without vast computing power, leading to a dependency on the old colonial powers to refine the data of the poor, before selling it back to them. These issues are not entirely new, and questions around representation, participation and humanitarianism can be traced back beyond the speeches of Truman, but the digital age throws these issues back to the fore, as machine learning, algorithms and big data centres take over the process of mapping the subjugated and subaltern. This book questions whether, as we map crises, it is the map itself that is in crisis

A data science approach to portuguese road accidents’ data

Dissertação de mestrado integrado em Informatics EngineeringWe frequently hear about accidents and traffic news on television, radio and even social networks. Even though we have witnessed a decrease in mortality rate in Portuguese roads, the number of road victims have been increasing recently so we should be more aware of this problem, study it and come up with solutions to decrease the mortality rate and the number of victims in Portuguese roads. One possible solution to this problem is the identification of blackspots (areas with a high number of accidents or an abnormal number of fatalities) associated with temporal and spatial analysis, and relations between them. By doing this, we will be closer to decreasing accidents as well as the mortality rate on Portuguese roads. This dissertation is going to focus on these concerns using the information present on ANSR (Autoridade Nacional de Segurança Rodoviária) reports as well as other data gathered by the research team regarding road traffic incidents in Portuguese cities. After researching about the state of the art, we realize that, on one hand, there’s a big problem which is traffic accidents and resultant victims that are still to this day very concerning to society, on the other hand, many techniques and methods have been developed and improved to help mitigate this problem. The data have shown that Portugal still has work to do on decreasing the number of accidents and victims according to those evolution curves, data collected in ANSR reports and the comparison between traffic numbers in EU countries. This dissertation focused on understanding, processing and exploring data in-depth, developing models to analyze data, preventing accidents and enhancing road safety and coming up with useful insights about the road network and publishing them in a dashboard platform open to the community.Frequentemente, ouvimos falar de acidentes e notícias sobre trânsito na televisão, rádio e redes sociais. Apesar de estarmos a testemunhar um decréscimo da taxa de mortalidade em estradas portuguesas, o número de vítimas resultantes de acidentes têm vindo a aumentar recentemente, por isso, devemos estar mais atentos a este problema, estudá-lo e arranjar soluções para diminuir a taxa de mortalidade e o número de pessoas vítimas de acidentes em estradas portuguesas. Uma possível solução para este problema é a identificação de zonas negras (zonas com um número elevado de acidentes ou um número anormal de óbitos) associado a uma análise temporal e espacial, juntamente com as relações entre eles. Ao fazer isto, estaremos mais perto de diminuir o número de acidentes, bem como a taxa de mortalidade nas estradas portuguesas. Esta dissertação irá focar-se nestes aspetos, utilizando a informação presente no relatórios da ANSR (Autoridade Nacional de Segurança Rodoviária) e também outros dados recolhidos pela equipa de investigação relativamente a incidentes rodoviários em estradas portuguesas. Depois de recolher dados sobre o estado de arte, percebemos que, por um lado, existe um grande problema com os acidentes rodoviários e vítimas dos mesmos que são até ao dia de hoje muito preocupantes para a sociedade, por outro lado, muitas técnicas e métodos que têm vindo a ser desenvolvidos e melhorados para ajudar a mitigar este problema. Os dados mostram que Portugal ainda tem trabalho a fazer para diminuir os números de acidentes e de vítimas tendo em consideração as curvas de evolução destes indicadores, dados recolhidos em relatórios da ANSR e a comparação entre dados rodoviários entre países da UE. Esta dissertação focou-se em perceber, processar e explorar os dados a fundo, desenvolver modelos para analisar os dados, prevenir acidentes e aumentar a segurança rodoviária e encontrar perceções sobre a rede rodoviária e publicá-las numa plataforma com painéis de informação disponíveis para a comunidade