Data-Driven Operational and Safety Analysis of Emerging Shared Electric Scooter Systems

The rapid rise of shared electric scooter (E-Scooter) systems offers many urban areas a new micro-mobility solution. The portable and flexible characteristics have made E-Scooters a competitive mode for short-distance trips. Compared to other modes such as bikes, E-Scooters allow riders to freely ride on different facilities such as streets, sidewalks, and bike lanes. However, sharing lanes with vehicles and other users tends to cause safety issues for riding E-Scooters. Conventional methods are often not applicable for analyzing such safety issues because well-archived historical crash records are not commonly available for emerging E-Scooters. Perceiving the growth of such a micro-mobility mode, this study aimed to investigate E-Scooter operations and safety by collecting, processing, and mining various unconventional data sources. First, origin-destination (OD) data were collected for E-Scooters to analyze how E-Scooters have been used in urban areas. The key factors that drive users to choose E-Scooters over other options (i.e., shared bikes and taxis) were identified. Concerning user safety tied to the growing usage, we further assessed E-Scooter user guidelines in urban areas in the U.S. Scoring models have been developed for evaluating the adopted guidelines. It was found that the areas with E-Scooter systems have notable disparities in terms of the safety factors considered in the guidelines. Built upon the usage and policy analyses, this study also creatively collected news reports as an alternative data source for E-Scooter safety analysis. Three-year news reports were collected for E-Scooter-involved crashes in the U.S. The identified reports are typical crash events with great media impact. Many detailed variables such as location, time, riders’ information, and crash type were mined. This offers a lens to highlight the macro-level crash issues confronted with E-Scooters. Besides the macro-level safety analysis, we also conducted micro-level analysis of E-Scooter riding risk. An all-in-one mobile sensing system has been developed using the Raspberry Pi platform with multiple sensors including GPS, LiDAR, and motion trackers. Naturalistic riding data such as vibration, speed, and location were collected simultaneously when riding E-Scooters. Such mobile sensing technologies have been shown as an innovative way to help gather valuable data for quantifying riding risk. A demonstration on expanding the mobile sensing technologies was conducted to analyze the impact of wheel size and riding infrastructure on E-Scooter riding experience. The quantitative analysis framework proposed in this study can be further extended for evaluating the quality of road infrastructure, which will be helpful for understanding the readiness of infrastructure for supporting the safe use of micro-mobility systems. To sum up, this study contributes to the literature in several distinct ways. First, it has developed mode choice models for revealing the use of E-Scooters among other existing competitive modes for connecting urban metro systems. Second, it has systematically assessed existing E-Scooter user guidelines in the U.S. Moreover, it demonstrated the use of surrogate data sources (e.g., news reports) to assist safety studies in cases where there is no available crash data. Last but not least, it developed the mobile sensing system and evaluation framework for enabling naturalistic riding data collection and risk assessment, which helps evaluate riding behavior and infrastructure performance for supporting micro-mobility systems

Scalable Methods to Collect and Visualize Sidewalk Accessibility Data for People with Mobility Impairments

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. To address this problem, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C. As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used. Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers. My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments

Modeling Backscattering Behavior of Vulnerable Road Users Based on High-Resolution Radar Measurements

Bei der Weiterentwicklung der Technologie des autonomen Fahrens (AD) ist die Beschaffung zuverlässiger dreidimensionaler Umgebungsinformationen eine unverzichtbare Aufgabe, um ein sicheres Fahren zu ermöglichen. Diese Herausforderung kann durch den Einsatz von Fahrzeugradaren zusammen mit optischen Sensoren, z. B. Kameras oder Lidars, bewältigt werden, sei es in der Simulation oder in konventionellen Tests auf der Straße. Das Betriebsverhalten von Fahrzeugradaren kann in einer Over-the-Air (OTA) Vehicle-in-the-Loop (ViL) Umgebung genau bewertet werden. Für eine umfassende experimentelle Verifizierung der Fahrzeugradare muss jedoch die Umgebung, insbesondere die gefährdeten Verkehrsteilnehmer (VRUs), möglichst realistisch modelliert werden. Moderne Radarsensoren sind in der Lage, hochaufgelöste Erkennungsinformationen von komplexen Verkehrszielen zu liefern, um diese zu verfolgen. Diese hochauflösenden Erkennungsdaten, die die reflektierten Signale von den Streupunkten (SPs) der VRUs enthalten, können zur Erzeugung von Rückstreumodelle genutzt werden. Darüber hinaus kann ein realistischeres Rückstreumodell der VRUs, insbesondere von Menschen als Fußgänger oder Radfahrer, durch die Modellierung der Bewegung ihrer Extremitäten in Verkehrsszenarien erreicht werden. Die Voraussetzung für die Erstellung eines solchen detaillierten Modells in verschiedenen Situationen sind der Radarquerschnitt (RCS) und die Doppler-Signaturen, die sich aus den menschlichen Extremitäten in einer bewegten Situation ergeben. Diese Daten können durch die gesammelten Radardaten aus hochauflösenden RCS-Messungen im Radial- und Winkelbereich gewonnen werden, was durch die Analyse der Range-Doppler-Spezifikation der menschlichen Extremitäten in verschiedenen Bewegungen möglich ist. Die entwickelten realistischen Radarmodelle können bei der Wellenausbreitung im Radarkanal, bei der Zielerkennung und -klassifizierung sowie bei Datentrainingsalgorithmen zur Validierung und Verifizierung der Kfz-Radarfunktionen eingesetzt werden. Anschließend kann mit dieser Bewertung die Sicherheit von fortschrittlichen Fahrerassistenzsystemen (ADAS) beurteilt werden. Daher wird in dieser Arbeit ein hochauflösendes RCS-Messverfahren vorgeschlagen, um die relevanten SPs verschiedener VRUs mit hoher radialer und winkelmäßiger Auflösung zu bestimmen. Eine Gruppe unterschiedliche VRUs wird in statischen Situationen gemessen, und die notwendigen Signalverarbeitungsschritte, um die relevanten SPs mit den entsprechenden RCS-Werten zu extrahieren, werden im Detail beschrieben. Während der Analyse der gemessenen Daten wird ein Algorithmus entwickelt, um die physischen Größen der gemessenen Testpersonen aus dem extrahierten Rückstreumodell zu schätzen und sie anhand ihrer Größe und Statur zu klassifizieren. Zusätzlich wird ein Dummy-Mensch vermessen, der eine vergleichbare Größe wie die vermessenen Probanden hat. Das extrahierte Rückstreuverhalten einer beispielhaften VRU-Gruppe wird für ihre verschiedenen Typen ausgewertet, um die Übereinstimmung zwischen virtuellen Validierungen und der Realität aufzuzeigen und den Genauigkeitsgrad der Modelle sicherzustellen. In einem weiteren Schritt wird diese hochauflösende RCS-Messtechnik mit der Motion Capture Technologie kombiniert, um die Reflektivität der SPs von den menschlichen Körperregionen in verschiedenen Bewegungen zu erfassen und die Radarsignaturen der menschlichen Extremitäten genau zu schätzen. Spezielle Signalverarbeitungsschritte werden eingesetzt, um die Radarsignaturen aus den Messergebnissen des sich bewegenden Menschen zu extrahieren. Diese nachbearbeiteten Daten ermöglichen es der Technik, die zeitlich variierenden SPs an den Extremitäten des menschlichen Körpers mit den entsprechenden RCS-Werten und Dopplersignaturen einzuführen. Das extrahierte Rückstreumodell der VRUs enthält eine Vielzahl von SPs. Daher wird ein Clustering-Algorithmus entwickelt, um die Berechnungskomplexität bei Radarkanalsimulationen durch die Einführung einiger virtueller Streuzentren (SCs) zu minimieren. Jedes entwickelte virtuelle SCs hat seine eigene spezifische Streueigenschaft

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

Promoting Intermodal Connectivity at California’s High Speed Rail Stations

High-speed rail (HSR) has emerged as one of the most revolutionary and transformative transportation technologies, having a profound impact on urban-regional accessibility and inter-city travel across Europe, Japan, and more recently China and other Asian countries. One of HSR’s biggest advantages over air travel is that it offers passengers a one-seat ride into the center of major cities, eliminating time-consuming airport transfers and wait times, and providing ample opportunities for intermodal transfers at these locales. Thus, HSR passengers are typically able to arrive at stations that are only a short walk away from central business districts and major tourist attractions, without experiencing any of the stress that car drivers often experience in negotiating such highly congested environments. Such an approach requires a high level of coordination and planning of the infrastructural and spatial aspects of the HSR service, and a high degree of intermodal connectivity. But what key elements can help the US high-speed rail system blend successfully with other existing rail and transit services? That question is critically important now that high-speed rail is under construction in California. The study seeks to understand the requirements for high levels of connectivity and spatial and operational integration of HSR stations and offer recommendations for seamless, and convenient integrated service in California intercity rail/HSR stations. The study draws data from a review of the literature on the connectivity, intermodality, and spatial and operational integration of transit systems; a survey of 26 high-speed rail experts from six different European countries; and an in-depth look of the German and Spanish HSR systems and some of their stations, which are deemed as exemplary models of station connectivity. The study offers recommendations on how to enhance both the spatial and the operational connectivity of high-speed rail systems giving emphasis on four spatial zones: the station, the station neighborhood, the municipality at large, and the region

Design and Development of Assistive Robots for Close Interaction with People with Disabilities

People with mobility and manipulation impairments wish to live and perform tasks as independently as possible; however, for many tasks, compensatory technology does not exist, to do so. Assistive robots have the potential to address this need. This work describes various aspects of the development of three novel assistive robots: the Personal Mobility and Manipulation Appliance (PerMMA), the Robotic Assisted Transfer Device (RATD), and the Mobility Enhancement Robotic Wheelchair (MEBot). PerMMA integrates mobility with advanced bi-manual manipulation to assist people with both upper and lower extremity impairments. The RATD is a wheelchair mounted robotic arm that can lift higher payloads and its primary aim is to assist caregivers of people who cannot independently transfer from their electric powered wheelchair to other surfaces such as a shower bench or toilet. MEBot is a wheeled robot that has highly reconfigurable kinematics, which allow it to negotiate challenging terrain, such as steep ramps, gravel, or stairs. A risk analysis was performed on all three robots which included a Fault Tree Analysis (FTA) and a Failure Mode Effect Analysis (FMEA) to identify potential risks and inform strategies to mitigate them. Identified risks or PerMMA include dropping sharp or hot objects. Critical risks identified for RATD included tip over, crush hazard, and getting stranded mid-transfer, and risks for MEBot include getting stranded on obstacles and tip over. Lastly, several critical factors, such as early involvement of people with disabilities, to guide future assistive robot design are presented

Mobility Design

Climate change and the scarcity of resources, but also the steadily increasing amount of traffic, make it indispensable to develop new solutions for environmentally friendly and people-friendly mobility. With the expansion of digital information systems, we will in future be able to easily combine different modes of transport according to our needs. These developments are a great challenge for the design of different mobility spaces. While the focus in Volume 1 was on practice, Volume 2 now brings together research from the fields of design, architecture, urban planning, geography, social science, transport planning, psychology and communication technology. The current discussion about the traffic turnaround is expanded to include the perspective of user-centred mobility design

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described