Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China

TMA (track mounted attenuators) involved work zones safety analysis and modeling, using machine learning to predict crash severity and crash frequency in the state of Missouri

When talking about road maintenance safety, it is an unavoidable talking about the TMA usage, as it located in the work zone intended to reduce the damage while crash happened and at the same time protect the related construction. Many scholars focused on derived use of safety performance functions for SWZs safety level judgement and for MWZs all existing researches are still stays at focusing working TMAs related factors. This research use TMA-related crash data in Missouri from 2011 to 2016, and through those recorded crash reports, combine using MWZs working schedules, figure out which factors and under which situations are common exist through all recorded crash both in MWZs and SWZs. Differential analysis model was explored and built in this research for a detailed knowing and referring the real reasons behind existing data. (Abstract in topic1) Machine learning is been widely used in all walks of life. Unlikely the traditional mathematic models and regression models by using both math and statistical knowledge, Machine learning performs high accurate results based on a mimic of human's brain and large data experience analysis thinking by computer. Using machine learning model for crash severity prediction and crash frequency prediction is a new thought for majority existing machine learning models using for crash prediction are supervised model or with low accuracy. This paper will use unsupervised model -- LSTM to achieve "global usage" through input whole Missouri data generated by rules and shows how sever the crash will occur under specific conditions, as well as predict crash frequency in coming years under different environment conditions. (Abstract in topic2)Includes bibliographical reference

HUMAN FACTORS IN MARITIME TRANSPORTATION AND MENTAL WORKLOAD ANALYSES FOR SEAFARERS IN BRIDGE SIMULATION

Since the United States Coast Guard (USCG) reported in 1993 that human factors had essentially caused approximately 80% of maritime accidents and near misses, there has been an overwhelming understanding that human factors play a significant role in a considerable number of incidents or catastrophes by triggering chain events. The work has initially documented a literature review underlining human factors in maritime accidents, mental workload study and functional Near-Infrared Spectroscopy (fNIRS) technique to imply how it can be studied for human factors in maritime transportation. It investigates how different risk factors generate an impact on different types of human-related maritime transportation accidents using a data-driven approach, and how mental workload influences neurophysiological activation and decision- making of seafarers by conducting an experimental study in bridge simulation. The results of the developed models formalise the causal interdependencies between the risk factors with human factors perspectives and highlight the implications through scenario analyses. On the other hand, the findings of the fNIRS experimental study revealed the role of the prefrontal cortex and functional connectivity in watchkeeping and collision avoidance during maritime operations. It is concluded that the understanding of risk factors contributing to human errors will help reduce the risk level or eliminate the potential hazards of ships, and provide the clue for accident investigation and generate insights for accident prevention. Also, the experimental study supports fNIRS as a valuable neuroimaging technique in realistic situations. It examines the mental workload and functional connectivity of seafarers, which helps generate insights for human performance and seafarers’ training. Finally, the inclusion of a broader range of human factors and experimental methods shows promise by associating neurophysiological experiment in the maritime section

Development,Validation, and Integration of AI-Driven Computer Vision System and Digital-twin System for Traffic Safety Dignostics

The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive and microscopic traffic safety analysis. This research presents the development, validation, and integration of three AI-driven computer vision systems for vehicle trajectory extraction and traffic safety research: 1) A.R.C.I.S, an automated framework for safety diagnosis utilizing multi-object detection and tracking algorithm for UAV videos. 2)N.M.E.D.S., A new framework with the ability to detect and predict the key points of vehicles and provide more precise vehicle occupying locations for traffic safety analysis. 3)D.V.E.D.S applied deep learning models to extract information related to drivers\u27 visual environment from the Google Street View (GSV) images. Based on the drone video collected and processed by A.R.C.I.S at various locations, CitySim: a new drone recorded vehicle trajectory dataset that aim to facilitate safety research was introduced. CitySim has vehicle interaction trajectories extracted from 1140- minutes of video recordings, which provide a large-scale naturalistic vehicle trajectory that covers a variety of locations, including basic freeway segments, freeway weaving segments, expressway segments, signalized intersections, stop-controlled intersections, and unique intersections without sign/signal control. The advantage of CitySim over other datasets is that it contains more critical safety events in quantity and severity and provides supporting scenarios for safety-oriented research. In addition, CitySim provides digital twin features, including the 3D base maps and signal timings, which enables a more comprehensive testing environment for safety research, such as autonomous vehicle safety. Based on these digital twin features provided by CitySim, we proposed a Digital Twin framework for CV and pedestrian in-the-loop simulation, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The proposed framework is expected to guide the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development

Proposal of an adaptive infotainment system depending on driving scenario complexity

Tesi en modalitat Doctorat industrialPla de Doctorat industrial de la Generalitat de CatalunyaThe PhD research project is framed within the plan of industrial doctorates of the “Generalitat de Catalunya”. During the investigation, most of the work was carried out at the facilities of the vehicle manufacturer SEAT, specifically at the information and entertainment (infotainment) department. In the same way, there was a continuous cooperation with the telematics department of the UPC. The main objective of the project consisted in the design and validation of an adaptive infotainment system dependent on the driving complexity. The system was created with the purpose of increasing driver’ experience while guaranteeing a proper level of road safety. Given the increasing number of application and services available in current infotainment systems, it becomes necessary to devise a system capable of balancing these two counterparts. The most relevant parameters that can be used for balancing these metrics while driving are: type of services offered, interfaces available for interacting with the services, the complexity of driving and the profile of the driver. The present study can be divided into two main development phases, each phase had as outcome a real physical block that came to be part of the final system. The final system was integrated in a vehicle and validated in real driving conditions. The first phase consisted in the creation of a model capable of estimating the driving complexity based on a set of variables related to driving. The model was built by employing machine learning methods and the dataset necessary to create it was collected from several driving routes carried out by different participants. This phase allowed to create a model capable of estimating, with a satisfactory accuracy, the complexity of the road using easily extractable variables in any modern vehicle. This approach simplify the implementation of this algorithm in current vehicles. The second phase consisted in the classification of a set of principles that allow the design of the adaptive infotainment system based on the complexity of the road. These principles are defined based on previous researches undertaken in the field of usability and user experience of graphical interfaces. According to these of principles, a real adaptive infotainment system with the most commonly used functionalities; navigation, radio and media was designed and integrated in a real vehicle. The developed system was able to adapt the presentation of the content according to the estimation of the driving complexity given by the block developed in phase one. The adaptive system was validated in real driving scenarios by several participants and results showed a high level of acceptance and satisfaction towards this adaptive infotainment. As a starting point for future research, a proof of concept was carried out to integrate new interfaces into a vehicle. The interface used as reference was a Head Mounted screen that offered redundant information in relation to the instrument cluster. Tests with participants served to understand how users perceive the introduction of new technologies and how objective benefits could be blurred by initial biases.El proyecto de investigación de doctorado se enmarca dentro del plan de doctorados industriales de la Generalitat de Catalunya. Durante la investigación, la mayor parte del trabajo se llevó a cabo en las instalaciones del fabricante de vehículos SEAT, específicamente en el departamento de información y entretenimiento (infotainment). Del mismo modo, hubo una cooperación continua con el departamento de telemática de la UPC. El objetivo principal del proyecto consistió en el diseño y la validación de un sistema de información y entretenimiento adaptativo que se ajustaba de acuerdo a la complejidad de la conducción. El sistema fue creado con el propósito de aumentar la experiencia del conductor y garantizar un nivel adecuado en la seguridad vial. El proyecto surge dado el número creciente de aplicaciones y servicios disponibles en los sistemas actuales de información y entretenimiento; es por ello que se hace necesario contar con un sistema capaz de equilibrar estas dos contrapartes. Los parámetros más relevantes que se pueden usar para equilibrar estas métricas durante la conducción son: el tipo de servicios ofrecidos, las interfaces disponibles para interactuar con los servicios, la complejidad de la conducción y el perfil del conductor. El presente estudio se puede dividir en dos fases principales de desarrollo, cada fase tuvo como resultado un componente que se convirtió en parte del sistema final. El sistema final fue integrado en un vehículo y validado en condiciones reales de conducción. La primera fase consistió en la creación de un modelo capaz de estimar la complejidad de la conducción en base a un conjunto de variables relacionadas con la conducción. El modelo se construyó empleando "Machine Learning Methods" y el conjunto de datos necesario para crearlo se recopiló a partir de varias rutas de conducción realizadas por diferentes participantes. Esta fase permitió crear un modelo capaz de estimar, con una precisión satisfactoria, la complejidad de la carretera utilizando variables fácilmente extraíbles en cualquier vehículo moderno. Este enfoque simplifica la implementación de este algoritmo en los vehículos actuales. La segunda fase consistió en la clasificación de un conjunto de principios que permiten el diseño del sistema de información y entretenimiento adaptativo basado en la complejidad de la carretera. Estos principios se definen en base a investigaciones anteriores realizadas en el campo de usabilidad y experiencia del usuario con interfaces gráficas. De acuerdo con estos principios, un sistema de entretenimiento y entretenimiento real integrando las funcionalidades más utilizadas; navegación, radio y audio fue diseñado e integrado en un vehículo real. El sistema desarrollado pudo adaptar la presentación del contenido según la estimación de la complejidad de conducción dada por el bloque desarrollado en la primera fase. El sistema adaptativo fue validado en escenarios de conducción reales por varios participantes y los resultados mostraron un alto nivel de aceptación y satisfacción hacia este entretenimiento informativo adaptativo. Como punto de partida para futuras investigaciones, se llevó a cabo una prueba de concepto para integrar nuevas interfaces en un vehículo. La interfaz utilizada como referencia era una pantalla a la altura de los ojos (Head Mounted Display) que ofrecía información redundante en relación con el grupo de instrumentos. Las pruebas con los participantes sirvieron para comprender cómo perciben los usuarios la introducción de nuevas tecnologías y cómo los sesgos iniciales podrían difuminar los beneficios.Postprint (published version

Proposal of an adaptive infotainment system depending on driving scenario complexity

The PhD research project is framed within the plan of industrial doctorates of the “Generalitat de Catalunya”. During the investigation, most of the work was carried out at the facilities of the vehicle manufacturer SEAT, specifically at the information and entertainment (infotainment) department. In the same way, there was a continuous cooperation with the telematics department of the UPC. The main objective of the project consisted in the design and validation of an adaptive infotainment system dependent on the driving complexity. The system was created with the purpose of increasing driver’ experience while guaranteeing a proper level of road safety. Given the increasing number of application and services available in current infotainment systems, it becomes necessary to devise a system capable of balancing these two counterparts. The most relevant parameters that can be used for balancing these metrics while driving are: type of services offered, interfaces available for interacting with the services, the complexity of driving and the profile of the driver. The present study can be divided into two main development phases, each phase had as outcome a real physical block that came to be part of the final system. The final system was integrated in a vehicle and validated in real driving conditions. The first phase consisted in the creation of a model capable of estimating the driving complexity based on a set of variables related to driving. The model was built by employing machine learning methods and the dataset necessary to create it was collected from several driving routes carried out by different participants. This phase allowed to create a model capable of estimating, with a satisfactory accuracy, the complexity of the road using easily extractable variables in any modern vehicle. This approach simplify the implementation of this algorithm in current vehicles. The second phase consisted in the classification of a set of principles that allow the design of the adaptive infotainment system based on the complexity of the road. These principles are defined based on previous researches undertaken in the field of usability and user experience of graphical interfaces. According to these of principles, a real adaptive infotainment system with the most commonly used functionalities; navigation, radio and media was designed and integrated in a real vehicle. The developed system was able to adapt the presentation of the content according to the estimation of the driving complexity given by the block developed in phase one. The adaptive system was validated in real driving scenarios by several participants and results showed a high level of acceptance and satisfaction towards this adaptive infotainment. As a starting point for future research, a proof of concept was carried out to integrate new interfaces into a vehicle. The interface used as reference was a Head Mounted screen that offered redundant information in relation to the instrument cluster. Tests with participants served to understand how users perceive the introduction of new technologies and how objective benefits could be blurred by initial biases.El proyecto de investigación de doctorado se enmarca dentro del plan de doctorados industriales de la Generalitat de Catalunya. Durante la investigación, la mayor parte del trabajo se llevó a cabo en las instalaciones del fabricante de vehículos SEAT, específicamente en el departamento de información y entretenimiento (infotainment). Del mismo modo, hubo una cooperación continua con el departamento de telemática de la UPC. El objetivo principal del proyecto consistió en el diseño y la validación de un sistema de información y entretenimiento adaptativo que se ajustaba de acuerdo a la complejidad de la conducción. El sistema fue creado con el propósito de aumentar la experiencia del conductor y garantizar un nivel adecuado en la seguridad vial. El proyecto surge dado el número creciente de aplicaciones y servicios disponibles en los sistemas actuales de información y entretenimiento; es por ello que se hace necesario contar con un sistema capaz de equilibrar estas dos contrapartes. Los parámetros más relevantes que se pueden usar para equilibrar estas métricas durante la conducción son: el tipo de servicios ofrecidos, las interfaces disponibles para interactuar con los servicios, la complejidad de la conducción y el perfil del conductor. El presente estudio se puede dividir en dos fases principales de desarrollo, cada fase tuvo como resultado un componente que se convirtió en parte del sistema final. El sistema final fue integrado en un vehículo y validado en condiciones reales de conducción. La primera fase consistió en la creación de un modelo capaz de estimar la complejidad de la conducción en base a un conjunto de variables relacionadas con la conducción. El modelo se construyó empleando "Machine Learning Methods" y el conjunto de datos necesario para crearlo se recopiló a partir de varias rutas de conducción realizadas por diferentes participantes. Esta fase permitió crear un modelo capaz de estimar, con una precisión satisfactoria, la complejidad de la carretera utilizando variables fácilmente extraíbles en cualquier vehículo moderno. Este enfoque simplifica la implementación de este algoritmo en los vehículos actuales. La segunda fase consistió en la clasificación de un conjunto de principios que permiten el diseño del sistema de información y entretenimiento adaptativo basado en la complejidad de la carretera. Estos principios se definen en base a investigaciones anteriores realizadas en el campo de usabilidad y experiencia del usuario con interfaces gráficas. De acuerdo con estos principios, un sistema de entretenimiento y entretenimiento real integrando las funcionalidades más utilizadas; navegación, radio y audio fue diseñado e integrado en un vehículo real. El sistema desarrollado pudo adaptar la presentación del contenido según la estimación de la complejidad de conducción dada por el bloque desarrollado en la primera fase. El sistema adaptativo fue validado en escenarios de conducción reales por varios participantes y los resultados mostraron un alto nivel de aceptación y satisfacción hacia este entretenimiento informativo adaptativo. Como punto de partida para futuras investigaciones, se llevó a cabo una prueba de concepto para integrar nuevas interfaces en un vehículo. La interfaz utilizada como referencia era una pantalla a la altura de los ojos (Head Mounted Display) que ofrecía información redundante en relación con el grupo de instrumentos. Las pruebas con los participantes sirvieron para comprender cómo perciben los usuarios la introducción de nuevas tecnologías y cómo los sesgos iniciales podrían difuminar los beneficios

Integration and Evaluation of Automated Pavement Distress Data in INDOT’s Pavement Management System

This study was in two parts. The first part established and demonstrated a framework for pavement data integration. This is critical for fulfilling QC/QA needs of INDOT’s pavement management system, because the precision of the physical location references is a prerequisite for the reliable collection and interpretation of pavement data. Such consistency is often jeopardized because the data are collected at different years, and are affected by changes in the vendor, inventory, or referencing system or reference points. This study therefore developed a “lining-up” methodology to address this issue. The applicability of the developed methodology was demonstrated using 2012-2014 data from Indiana’s highway network. The results showed that the errors in the unlined up data are significant as they mischaracterize the true pavement condition. This could lead to the reporting of unreliable information of road network condition to the decision makers, ultimately leading to inappropriate condition assessments and prescriptions. Benefits of the methodology reverberate throughout the management functions and processes associated with highway pavements in Indiana, including pavement performance modeling, optimal timing of maintenance, rehabilitation, and reconstruction (MRR), and assessment of the effectiveness of MRR treatments and schedules. The second part of the study developed correlations for the different types of pavement distresses using machine learning algorithms. That way, the severity of any one type of distress can be estimated based on known severity of other distresses at that location. The 2012-2014 data were from I-70, US-41, and US-52, and the distress types considered are cracking, rutting, faulting, and roughness. Models were developed to relate surface roughness (IRI) to pavement cracks, and between the different crack types, with resulting degrees of confidence that varied across the different crack types and road functional classes. In addition, for each functional class and for each crack type, models were built to relate crack depth to crack width. The concept can be applied to other distress types, such as spalling, bleeding, raveling, depression, shoving, stripping, potholes, and joint distresses, when appropriate data are available

Decommissioning strategy to reduce the cost and risk-driving factors in the offshore wind industry.

With the increasing number of wind turbines approaching their end of life, there has to be a decommissioning strategy in place as the removal of these assets is not as direct as reverse installation. Offshore asset decommissioning involves technical, financial, operational, safety, policy, and environmental considerations on handling offshore marine assets at their end-of-life, with phases from the planning to site clean-up and monitoring. Offshore decommissioning activities cost significantly more than onshore; thus, adequate financial and safety provisions are essential, and more research required in this area. Decommissioning projects have hitherto been performed on a small scale, but with large-scale aging structures, they must be optimised for lowered costs and risks. In terms of planning, execution and costs, there have been significant cost overruns on decommissioning projects, which are not profit-generating projects. These forecasted large-scale decommissioning activities also have associated risks. Although risk management is a well-researched area, there is limited literature on offshore wind decommissioning risk management. This research thus, applies risk management methods and strategies to develop a robust decommissioning risk framework. In addition, to improve decommissioning processes and technologies, there is a need to develop new protocols for decommissioning. This research identifies potentials for computational simulations and automations that need to be developed to identify and manage the highest cost and risk-drivers. This study seeks to close the research gap in understanding how to decrease decommissioning costs and risks. This research addresses potential opportunities in cost and risk estimation research, impact analysis and reduction frameworks that can be adapted to decommissioning activities specific to the offshore wind industry.Shafiee, Mahmood (Associate)PhD in Energy and Powe