GETTING ACTIVE WITH PASSIVE CROSSINGS: INVESTIGATING THE EFFICACY OF IN-VEHICLE AUDITORY ALERTS FOR RAIL ROAD CROSSINGS

Train-vehicle collisions at highway-rail grade crossings continue to be a major issue in the US and across the world. Installing additional hardware at individual crossings is expensive, time consuming, and potentially ineffective. To prevent recent trends in safety improvement from plateauing, experts are turning towards novel warning devices that can be applied to all crossings with minimal cost. In-vehicle auditory alerts (IVAAs) could potentially remedy many of the human factor issues related to crossing safety in a cost effective manner. This thesis presents a series of experiments designing and testing an IVAA system for grade level railroad (RR) crossings. Study 1 collected subjective data on a pool of potential in-vehicle auditory alerts from 31 undergraduate participants. The type of IVAAs was varied along a number of dimensions (pitch, repetition, wave shape, wording, voice, etc.). Results from study 1 were used to design a prototype IVAA crossing notification system. A pilot study was conducted to calibrate the simulated driving scenario featuring multiple RR crossings and a compliance behavior coding procedure. Compliance behavior was operationalized as an amount of visual scanning and pedal depression. Study 2 recruited 20 undergraduate participants to drive in a medium fidelity driving simulator featuring four types of RR crossings with and without IVAAs. Results suggest that IVAAs not only inform and remind drivers of how to comply at RR crossings, but also have a lasting effect on driver behavior after the IVAA is no longer presented. Compliance scores were highest among novel RR crossing visual warnings such as crossbucks featuring STOP or YIELD signs. Compliance was lowest for crossbucks alone and active gates in the off position. IVAAs had the largest impact on compliance scores at crossbucks and gates. The discussion includes implications for designing IVAA systems for RR crossings, and the potential implementation of prototype systems as a smartphone application

The Assessment of Soundscape Quality in Urban Parks - A Case Study in Penn Park

The sonic environment is an invisible but crucial part of the urban environment. Increasing density of population and diversification of social functions driven by urbanization lead to a more complex sound environment in our daily life. As an important multifunctional service area, the urban park is usually regarded as a buffer for urban noise pollution. The assessment of the sonic environment in urban parks can help park-users and park-designers get a better understanding of the health of the park environment. This study approached the urban noise pollution in urban parks with a soundscape quality assessment, from both acoustical and psychological perspectives. An urban park on the campus of the University of Pennsylvania named Penn Park was selected as a case study for soundscape quality assessment. Sound Pressure Level (SPL) was measured at ten sampled positions in Penn Park and processed in ArcMap to make the sound maps, which clearly shown the uneven distribution of the average sound energy in the park: inner part of the park with trees surrounded was the “quietest” and the part along the edge with areas of grass was the “loudest.” In three months (May, June, July) when sound pressure level was recorded by the sound pressure meter, park-users’ subjective responses to the sonic environment of Penn Park were investigated by randomly recruiting park visitors to complete a questionnaire about the soundscape quality. In total, 90 questionnaires were collected and analyzed on SPSS. Results demonstrated that there was a significant positive correlation between overall landscape quality, overall soundscape quality, and overall impression. Compared to mechanical sounds and human-made sounds, visitors preferred more natural sounds (birds, insects, wind) to be increased in Penn Park. Overall, the sonic environment of Penn Park was perceived as pleasant, quiet, smooth, varied, calming, directional, natural, and steady. The results of this study may have implications for the enhancement of soundscape design in other urban parks that are similar to Penn Park

Use of Machine Learning and Natural Language Processing to Enhance Traffic Safety Analysis

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies within and outside traffic safety domain.The primary source for traffic safety analysis is the structure (also called tabular) data collected from crash reports. However, structure data may be insufficient because of missing information, incomplete sequence of events, misclassified crash types, among many issues. Crash narratives, a form of free text recorded by police officers to describe the unique aspects and circumstances of a crash, are commonly used by safety professionals to supplement structure data fields. Due to its unstructured nature, engineers have to manually review every crash narrative. Thanks to the rapid development in natural language processing (NLP) and machine learning (ML) techniques, text mining and analytics has become a popular tool to accelerate information extraction and analysis for unstructured text data. The primary objective of this dissertation is to discover and develop necessary tools, techniques, and algorithms to facilitate traffic safety analysis using crash narratives. The objectives are accomplished in three areas: enhancing data quality by recovering missed crashes through text classification, uncovering complex characteristics of collision generation through information extraction and pattern recognition, and facilitating crash narrative analysis by developing a web-based tool. At first, a variety of NoisyOR classifiers were developed to identify and investigate work zone (WZ), distracted (DD), and inattentive (ID) crashes. In addition, various machine learning (ML) models, including multinomial naive bayes (MNB), logistic regression (LGR), support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and gated recurrent unit (GRU), were developed and compared with NoisyOR. The comparison shows that NoisyOR is simple, computationally efficient, theoretically sound, and has one of the best model performances. Furthermore, a novel neural network architecture named Sentence-based Hierarchical Attention Network (SHAN) was developed to classify crashes and its performance exceeds that of NoisyOR, GRU, Hierarchical Attention Network (HAN), and other ML models. SHAN handled noisy or irrelevant parts of narratives effectively and the model results can be visualized by attention weight. Because a crash often comprises a series of actions and events, breaking the chain of events could prevent a crash from reaching its most dangerous stage. With the objectives of creating crash sequences, discovering pattern of crash events, and finding missing events, the Part-of-Speech tagging (PT), Pattern Matching with POS Tagging (PMPT), Dependency Parser (DP), and Hybrid Generalized (HGEN) algorithms were developed and thoroughly tested using crash narratives. The top performer, HGEN, uses predefined events and event-related action words from crash narratives to find new events not captured in the data fields. Besides, the association analysis unravels the complex interrelations between events within a crash. Finally, the crash information extraction, analysis, and classification tool (CIEACT), a simple and flexible online web tool, was developed to analyze crash narratives using text mining techniques. The tool uses a Python-based Django Web Framework, HTML, and a relational database (PostgreSQL) that enables concurrent model development and analysis. The tool has built-in classifiers by default or can train a model in real time given the data. The interface is user friendly and the results can be displayed in a tabular format or on an interactive map. The tool also provides an option for users to download the word with their probability scores and the results in csv files. The advantages and limitations of each proposed methodology were discussed, and several future research directions were outlined. In summary, the methodologies and tools developed as part of the dissertation can assist transportation engineers and safety professionals in extracting valuable information from narratives, recovering missed crashes, classifying a new crash, and expediting their review process on a large scale. Thus, this research can be used by transportation agencies to analyze crash records, identify appropriate safety solutions, and inform policy making to improve highway safety of our transportation system

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

Exploring cyclists’ and pedestrians’ personal exposure, wellbeing and protective practices on-the-move

In dieser Doktorarbeit wurde untersucht, welche Faktoren Wohlbefinden, wahrgenommene Gesundheit und Mobilitätspraktiken von Radfahrenden und Fußgänger:innen während des Unterwegsseins beeinflussen. Ziel war es, die persönliche Exposition gegenüber Feinstaub und Lärm unterwegs zu messen und diese der individuell wahrgenommenen Belastung gegenüberzustellen. Zudem wurden weitere Faktoren, die das Wohlbefinden beeinflussen, untersucht. Die Arbeit beleuchtet überdies, wie über gesunde und angenehme Mobilität informiert werden könnte. Zuerst wurden mobile qualitative Interviews (Go-/Ride-Alongs) durchgeführt und mit tragbaren Sensoren zur Messung von Feinstaub und Lärm ergänzt. Der situative Kontext, die sensorische Wahrnehmung und soziale Aspekte beeinflussen, ob das Unterwegsseins in der Stadt als gesund und angenehm empfunden wird. Diese Faktoren können in vergleichsweise als hoch belastend gemessenen Situationen ausgleichend wirken. Weiterhin wurden Informationsmöglichkeiten für eine gesunde Mobilität in der Stadt exploriert. Ein Literaturreview hat aufgezeigt, dass Gesundheitsthemen wenig Berücksichtigung in Forschung zu Mobilitäts-Apps finden. Daran anschließend wurden Fokusgruppen durchgeführt. Es wurde ermittelt, wie gesunde und angenehme Routen kommuniziert werden können. Hier könnendas Vorhandensein von Routenalternativen und Bewältigungsstrategien ein Gefühl von Selbstwirksamkeit geben. Es wurde eine „pleasant routing app“ vorgeschlagen, die angenehme und gesunde Routenaspekte integriert. Um die Attraktivität des Fahrradfahrens und zu Fuß Gehens zu steigern, sollten Erfahrungen, Wahrnehmungen und Praktiken von Radfahrenden und Fußgänger:innen berücksichtigt werden. Letztendlich kann somit aktive Mobilität ihr Potenzial entfalten und zu einer lebenswerten, gesunden und umweltfreundlichen Stadt beitragen.This thesis investigates factors influencing cyclists’ and pedestrians’ health and wellbeing on-the-move. Moreover, the possibilities of smartphone apps for supporting a healthy and pleasant trip are investigated. The scope of this thesis is to combine the topic healthy and pleasant mobility with possibilities of mobility apps. First, the thesis explores how cyclists and pedestrians perceive their personal exposure towards air pollution and noise as well as other factors influencing commuting experience and wellbeing on-the-move. This is contrasted to actual measured particulate matter and noise. Qualitative interviews on-the-move (‘go-/ride-alongs’) are complemented by wearable sensors measuring particulate matter and noise. The results show discrepancies as well as coherences between perceived and measured exposure. The situational context, sensory awareness (e.g. water views) and social cues (e.g. seeing other people) are important for a perceived pleasant commute, even in polluted areas. Second, this thesis identifies how far health impacting factors are considered in research using mobility apps to identify their possibilities for supporting a healthy commute. A literature review reveals that research applying mobility apps is lacking the consideration of health topics and it is proposed to integrate health topics in mobility app development. Following these findings, the thesis investigates communication options to inform about a healthy and pleasant commute. Focus groups were applied showing that information should include feasible coping strategies and increase self-efficacy. Pleasant trip characteristics could be included in a healthy mobility app. If active mode users’ experiences, perceptions and practices are considered, cycling and walking can become more attractive and more people are encouraged to cycle or walk. Hence, active modes can unfold their potential for supporting the transformation towards liveable, healthy and environmentally friendly cities

Safe and Sound: Proceedings of the 27th Annual International Conference on Auditory Display

Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022), June 24-27. Online virtual conference