Exploring the Potentials of Using Crowdsourced Waze Data in Traffic Management: Characteristics and Reliability

Real-time traffic information is essential to a variety of practical applications. To obtain traffic data, various traffic monitoring devices, such as loop detectors, infrastructure-mounted sensors, and cameras, have been installed on road networks. However, transportation agencies have sought alternative data sources to monitor traffic, due to the high installation and maintenance cost of conventional data collecting methods. Recently, crowdsourced traffic data has become available and is widely considered to have great potential in intelligent transportation systems. Waze is a crowdsourcing traffic application that enables users to share real-time traffic information. Waze data, including passively collected speed data and actively reported user reports, is valuable for traffic management but has not been explored or evaluated extensively. This dissertation evaluated and explored the potential of Waze data in traffic management from different perspectives. First, this dissertation evaluated and explored Waze traffic speed to understand the characteristics and reliability of Waze traffic speed data. Second, a calibration-free incident detection algorithm with traffic speed data on freeways was proposed, and the results were compared with other commonly used algorithms. Third, a spatial and temporal quality analysis of Waze accident reports to better understand their quality and accuracy was performed. Last, the dissertation proposed a network-based clustering algorithm to identify secondary crashes with Waze user reports, and a case study was performed to demonstrate the applicability of our method and the potential of crowdsourced Waze user reports

Real-time Traffic State Assessment using Multi-source Data

The normal flow of traffic is impeded by abnormal events and the impacts of the events extend over time and space. In recent years, with the rapid growth of multi-source data, traffic researchers seek to leverage those data to identify the spatial-temporal dynamics of traffic flow and proactively manage abnormal traffic conditions. However, the characteristics of data collected by different techniques have not been fully understood. To this end, this study presents a series of studies to provide insight to data from different sources and to dynamically detect real-time traffic states utilizing those data. Speed is one of the three traffic fundamental parameters in traffic flow theory that describe traffic flow states. While the speed collection techniques evolve over the past decades, the average speed calculation method has not been updated. The first section of this study pointed out the traditional harmonic mean-based average speed calculation method can produce erroneous results for probe-based data. A new speed calculation method based on the fundamental definition was proposed instead. The second section evaluated the spatial-temporal accuracy of a different type of crowdsourced data - crowdsourced user reports and revealed Waze user behavior. Based on the evaluation results, a traffic detection system was developed to support the dynamic detection of incidents and traffic queues. A critical problem with current automatic incident detection algorithms (AIDs) which limits their application in practice is their heavy calibration requirements. The third section solved this problem by proposing a selfevaluation module that determines the occurrence of traffic incidents and serves as an autocalibration procedure. Following the incident detection, the fourth section proposed a clustering algorithm to detect the spatial-temporal movements of congestion by clustering crowdsource reports. This study contributes to the understanding of fundamental parameters and expands the knowledge of multi-source data. It has implications for future speed, flow, and density calculation with data collection technique advancements. Additionally, the proposed dynamic algorithms allow the system to run automatically with minimum human intervention thus promote the intelligence of the traffic operation system. The algorithms not only apply to incident and queue detection but also apply to a variety of detection systems

Detection of traffic events from Finnish social media data

Social media has gained significant popularity and importance during the past few years and has become an essential part of many people s everyday lives. As social media users write about a broad range of topics, popular social networking sites can serve as a perfect base for various data mining and information extraction applications. One possibility among these could be the real-time detection of unexpected traffic events or anomalies, which could be used to help traffic managers to discover and mitigate problematic spots in a timely manner or to assist passengers with making informed decisions about their travel route. The purpose of this study is to develop a Finnish traffic information system that relies on social media data. The potential of using social network streams in traffic information extraction has been demonstrated in several big cities, but no study has so far investigated the possible use in smaller communities such as towns in Finland. The complexity of Finnish language also poses further challenges. The aim of the research is to investigate what methods would be the most suitable to analyse and extract information from Finnish social media messages and to incorporate these into the implementation of a practical application. In order to determine the most effective methods for the purposes of this study, an extensive literature research was performed in the fields of social media mining and textual and linguistic analysis with a special focus on frameworks and methods designed for Finnish language. In addition, a website and a mobile application were developed for data collection, analysis and demonstration. The implemented traffic event detection system is able to detect and classify incidents from the public Twitter stream. Tests of the analysis methods have determined high accuracy both in terms of textual and cluster analysis. Although certain limitations and possible improvements should be considered in the future, the ready traffic information system has already demonstrated satisfactory performance and lay the foundation for further studies and research

Detection of traffic events from Finnish social media data

Social media has gained significant popularity and importance during the past few years and has become an essential part of many people s everyday lives. As social media users write about a broad range of topics, popular social networking sites can serve as a perfect base for various data mining and information extraction applications. One possibility among these could be the real-time detection of unexpected traffic events or anomalies, which could be used to help traffic managers to discover and mitigate problematic spots in a timely manner or to assist passengers with making informed decisions about their travel route. The purpose of this study is to develop a Finnish traffic information system that relies on social media data. The potential of using social network streams in traffic information extraction has been demonstrated in several big cities, but no study has so far investigated the possible use in smaller communities such as towns in Finland. The complexity of Finnish language also poses further challenges. The aim of the research is to investigate what methods would be the most suitable to analyse and extract information from Finnish social media messages and to incorporate these into the implementation of a practical application. In order to determine the most effective methods for the purposes of this study, an extensive literature research was performed in the fields of social media mining and textual and linguistic analysis with a special focus on frameworks and methods designed for Finnish language. In addition, a website and a mobile application were developed for data collection, analysis and demonstration. The implemented traffic event detection system is able to detect and classify incidents from the public Twitter stream. Tests of the analysis methods have determined high accuracy both in terms of textual and cluster analysis. Although certain limitations and possible improvements should be considered in the future, the ready traffic information system has already demonstrated satisfactory performance and lay the foundation for further studies and research

Automated Approach for Computer Vision-based Vehicle Movement Classification at Traffic Intersections

Movement specific vehicle classification and counting at traffic intersections is a crucial component for various traffic management activities. In this context, with recent advancements in computer-vision based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on manual specification of several regions of interest. In this study, we proposed an automated classification method for movement specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique Thereafter a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention.This is a pre-print of the article Jana, Udita, Jyoti Prakash Das Karmakar, Pranamesh Chakraborty, Tingting Huang, Dave Ness, Duane Ritcher, and Anuj Sharma. "Automated Approach for Computer Vision-based Vehicle Movement Classification at Traffic Intersections." arXiv preprint arXiv:2111.09171 (2021). DOI: 10.48550/arXiv.2111.09171. Copyright 2021 The Authors. Posted with permission

Utilização da velocidade de objetos na deteção e rastreio de veículos autónomos em 3D

Currently, a wide range of research and development activities are taking place in the field of autonomous vehicles, and many companies and organizations are working on developing and testing vehicles with various levels of automation. Autonomous vehicles require multiple sensors and advanced control systems to perceive the surrounding environment and make decisions without resorting to a human driver. LiDAR (Light Detection and Ranging), highly utilized in the development of autonomous vehicles, stands out for its potential to provide long-range highresolution 3D vision. Utilizing lasers and photodetectors to measure the time it takes for light pulses to bounce back, LiDAR produces precise and accurate 3D point clouds of a vehicle’s surrounding environment. The development of a secondgeneration LiDAR, coherent LiDAR, promises better reliability and the capability to directly measure relative radial velocity alongside distance for each point. This direct velocity measurement has the potential to revolutionize perception tasks in autonomous vehicles, including Multiple Object Tracking (MOT), paving the way for significant advancements in 3D perception. The fundamental motivation of this thesis is to investigate whether LiDAR-based 3D MOT can be improved by taking advantage of the radial velocity in each point of the point cloud provided by a coherent LiDAR. Due to the novelty of this sensor and the lack of a publicly-available MOT dataset with velocity information, a synthetic dataset was generated with a modified version of PreSIL, displaying 19 training and 20 testing sequences, with over 16k frames and 80k annotations. By adapting a state-of-the-art MOT model, it is was possible to conclude that, with velocity information, this model yields more accurate object proposals, at the cost of worse trajectory associations. These results were influenced by limitations of the data generation code, such as the lack of inertial data, problematic car models and poor performance in high inclination driving scenarios. When only taking into account sequences where these problems are absent, the proposed solution obtains improved performance when compared to the original implementation, showing that the additional velocity information has a positive effect on MOT tasks. This work thus points out that a second-generation LiDAR may indeed improve the perception systems, that are critical to the development of safe autonomous vehicles.Atualmente, existe um vasto leque de investigação e desenvolvimento no domínio dos veículos autónomos, e muitas empresas e organizações estão a trabalhar no desenvolvimento e teste de veículos com vários níveis de automatização. Estes veículos requerem a utilização de múltiplos sensores e sistemas de controlo avançados para percecionar o ambiente circundante e tomar decisões sem recorrer às capacidades de um condutor humano. O LiDAR (Light Detection and Ranging), muito utilizado no desenvolvimento de veículos autónomos, destaca-se pelo seu potencial para fornecer uma visão 3D de alta resolução e de longo alcance. Utilizando lasers e fotodetectores para medir o tempo que os impulsos de luz demoram a ser refletidos, o LiDAR produz nuvens de pontos 3D precisas e exatas do ambiente que rodeia um veículo. O desenvolvimento de um LiDAR de segunda geração, o LiDAR coerente, oferece maior fiabilidade e a capacidade de medir diretamente a velocidade radial relativa juntamente com a distância de cada ponto. Esta medição direta da velocidade tem o potencial de revolucionar as tarefas de perceção em veículos autónomos, incluindo o rastreio de múltiplos objetos (RMO), abrindo caminho para avanços significativos na indústria. A motivação fundamental desta tese é investigar se o RMO 3D baseado em LiDAR pode ser melhorado tirando partido da velocidade radial em cada ponto da nuvem de pontos fornecida por um LiDAR coerente. Devido à novidade deste sensor e à falta de um conjunto de dados de RMO publicamente disponível com informação sobre a velocidade, foi gerado um conjunto de dados sintético com uma versão modificada do PreSIL, apresentando 19 sequências de treino e 20 sequências de teste, com mais de 16 mil fotogramas e 80 mil anotações. Ao adaptar um modelo de MOT de última geração, foi possível concluir que, com informação sobre a velocidade, este modelo produz propostas de objetos mais precisas, à custa de uma pior associação de trajetórias. Estes resultados foram influenciados por limitações do código de geração de dados, tais como a falta de dados inerciais, modelos de automóveis problemáticos e fraco desempenho em cenários com elevada inclinação. Se considerarmos apenas as sequências em que estes problemas estão ausentes, a solução proposta apresenta melhorias de desempenho em relação à implementação original, mostrando que a informação adicional sobre a velocidade tem um efeito positivo nas tarefas de RMO. Este trabalho aponta, assim, que um LiDAR de segunda geração pode efetivamente melhorar os sistemas de perceção, que são cruciais para o desenvolvimento de veículos autónomos seguros.Mestrado em Engenharia Informátic

Incorporation of micro-level analysis in strategic urban transport modelling: with a case study of the Greater Beijing

Many developing countries and regions are suffering from severe urban transport problems arising from accidents, congestion, air pollution, rising carbon intensity, and chronic under-funding of infrastructure and services. The problems make those cities the most polluted and often the least liveable. Strategic transport modelling has been recognised as an effective approach for developing and testing policy options, especially where it is integrated with land use planning and urban design. However, in most developing-country cities strategic transport modelling has been out of reach for practical policy use because of its sophisticated data and skill requirements, which currently imply unaffordable high costs and long durations for model development. This means that strategic urban transport modelling is the least available where it is needed most urgently. Meanwhile, the spread of smart data in mapping and urban activity monitoring has often been just as rapid in developing countries as in the developed. This has triggered new approaches in micro-level analyses of transport networks, personal movements and vehicles. In the most advanced cases, the new analyses have started to influence strategic modelling. The main hypothesis of this dissertation is that an incorporation of the micro-level smart data and analyses in strategic urban transport modelling will make it feasible to establish a sufficiently robust strategic transport model for evidence-based policy analysis with cost, time and skill thresholds that are close to being affordable in developing country cities. In order to test this main hypothesis, a number of novel model development tasks have been carried out which contribute to the field of applied urban modelling. This new approach aims to contribute to the transformation of the prevailing modus operandi where model development could not start in earnest until extensive data collection and skills training have been completed to a situation where a sufficiently robust model can be established cheaply and quickly to support on-going and incremental refinements. More specifically, new modelling tools have been developed as part of this dissertation using sparse GPS taxi traces to identify slow-moving and stopping traffic hotspots using an extended density-based spatial clustering algorithm that is tolerant of significant data noise, and to estimate congested road speeds (which used to be very costly and time-consuming to obtain if at all). The micro-level network, congested speeds and insights into the nature of the congested traffic have been incorporated into a MEPLAN-based strategic transport model interacting with a MEPLAN-based land use and travel demand model. This means that the strategic economic, social and environmental impacts of transport interventions can be tested in a robust way through accounting for the interactions among transport, land-use and background social-technical trends. A new approach to establish the medium to long term visions for alternative travel demand management and transport investment scenarios has been tested using this model. The methods and algorithms have been tested in a case study of the Greater Beijing region, which consists of the municipalities of Beijing and Tianjin together with the surrounding areas in the province of Hebei. The government’s data regulations of restricting overseas studies to using only publicly available data sources have made the case study ideal for testing the new approach. The potential of the new strategic urban transport model has been tested through a wide range of policy scenarios. The results suggest that the new approach developed in this dissertation has made it not only cheaper and faster to develop a robust model, but could also potentially fill a gap in the lack of medium to long term perspectives regarding major road and metro investments over the next two decades. Such analyses could be of critical importance in improving the performance of the transport system in terms of safety, economic efficiency, air quality and carbon reduction given the long lead times to plan and deliver transport infrastructure investments

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies