Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

Calming New York: An Examination of Neighborhood Slow Zones

Road traffic crashes are a leading cause of death and injury worldwide and in the US. In New York City, there are about 60,000 annual traffic casualties, including over 200 deaths. Area-wide traffic calming can improve traffic safety, pedestrian and cyclist comfort, and quality of life in neighborhoods (Elvik, 2009). This dissertation examines an area-wide traffic calming program, New York City’s “Neighborhood Slow Zones” (NSZs), in terms of environmental justice, traffic safety, and street design. The dissertation consists of three distinct but interrelated empirical studies. The first one asks if the NSZ program furthers environmental justice in New York City. It examines the locations of the 28 zones in terms of minority and low-income areas, as well as the inclusion of these populations in the process that led to the siting of the zones. This chapter concludes that the NSZ program improves environmental justice in New York City, both because the zones are equitably distributed in poor and minority areas, and because the planning process that led to the siting of the zones was inclusive of these populations. The second study examines the effectiveness of the zones at reducing traffic casualties. This analysis uses a quasi-experimental, before and after research design, with a treatment group (the Neighborhood Slow Zones) and a comparison group (similar zones that did not receive the treatment). The analysis does not detect statistically significant reductions in traffic casualties associated with the NSZs. The final empirical uses a policy transfer approach to compare street design in New York City’s 20-mph zones to similar zones in London. London’s “Slow Zones” were found to be effective at preventing traffic casualties (Grundy et al., 2009), and were the inspiration for New York’s Neighborhood Slow Zones. This study analyzes the traffic calming devices transferred from the zones in London to those in New York. While street designs in London’s 20-mph zones included a robust implementation of traffic calming devices, New York’s NSZs had a much more skeletal implementation of these devices. This suggests that the nature of the transfer of street design from London to New York City contributed to the disappointing results of 20-mph zones in the latter city. Despite these findings, I argue that the NSZ program has had partial success

Green Cities Artificial Intelligence

119 pagesIn an era defined by rapid urbanization, the effective planning and management of cities have become paramount to ensure sustainable development, efficient resource allocation, and enhanced quality of life for residents. Traditional methods of urban planning and management are grappling with the complexities and challenges presented by modern cities. Enter Artificial Intelligence (AI), a disruptive technology that holds immense potential to revolutionize the way cities are planned, designed, and operated. The primary aim of this report is to provide an in-depth exploration of the multifaceted role that Artificial Intelligence plays in modern city planning and management. Through a comprehensive analysis of key AI applications, case studies, challenges, and ethical considerations, the report aims to provide resources for urban planners, City staff, and elected officials responsible for community planning and development. These include a model City policy, draft informational public meeting format, AI software and applications, implementation actions, AI timeline, glossary, and research references. This report represents the cumulative efforts of many participants and is sponsored by the City of Salem and Sustainable City Year Program. The Green Cities AI project website is at: https://blogs.uoregon.edu/artificialintelligence/. As cities continue to evolve into complex ecosystems, the integration of Artificial Intelligence stands as a pivotal force in shaping their trajectories. Through this report, we aim to provide a comprehensive understanding of how AI is transforming the way cities are planned, operated, and experienced. By analyzing the tools, applications, and ethical considerations, we hope to equip policymakers, urban planners, and stakeholders with the insights needed to navigate the AI-driven urban landscape effectively and create cities that are not only smart but also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues

A Citizen-Science Approach for Urban Flood Risk Analysis Using Data Science and Machine Learning

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, manhole issues, and sewer back-ups) and climatic (precipitation) contributions toward street flooding are investigated by using Stage IV radar precipitation data and crowd-sourced sewer reports (NYC 311 complaints), spanning a 10-year period. By applying a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with an embedded Zero-Inflation (ZI) model, the variables statistically significant as predictors, specific to each zip code, are detected. Second, with an intent to understand how factors affect the spatial variability of street flooding, the Random Forest regression machine learning algorithm is employed, where the 311 street flooding reports serve as the response, while the explanatory variables include topographic and land feature, physical and population dynamics, locational, infrastructural, and climatic influences. This model also analyzes socio-economic variables as predictors, as to allow for better insight into potential reporting biases within the NYC 311 crowdsourced platform. Third, utilizing the machine learning method of hierarchical clustering, the NYC zip codes are further analyzed for flood susceptibilities. The three variables are street flooding reports, catch basin blockages reports and radar precipitation data. Aggregated to the zip code level, the severe days of precipitation and street flood occurrence, over a ten-year period, are examined. Then, by the application of the algorithm, the zip codes with similar joint behavior (rainfall, street flooding and catch basin complaints) are clustered. Therefore, using crowdsourced data, three data driven models have been created, revealing the significant flood factors of NYC, the causes of variability among neighborhoods, and areas prone to urban flooding. Localized urban flood forecasting proves to be a difficult undertaking in major U.S. metropolitan areas. In these cities, the drainage information may be incomplete, or the access to the underground system may be restricted. Subsequently, with the capacity of the urban system unknown, traditional rainfall-runoff calculations are unrealistic. This research advances our knowledge of the variables associated with urban flooding, and, by various data analytic techniques, determine the extent of their effects within the study area of NYC. The research further builds upon this understanding of the factors to develop an urban risk zones map, pinpointing the localized areas (zip codes) of which street flooding will likely occur when there is a forecasted rain event. Utilizing regression and machine learning methodologies, with a unique investigation into infrastructural elements from crowd-sourced data, invaluable information towards advancements in urban flooding detection and prevention is provided

Space Systems: Emerging Technologies and Operations

SPACE SYSTEMS: EMERGING TECHNOLOGIES AND OPERATIONS is our seventh textbook in a series covering the world of UASs / CUAS/ UUVs. Other textbooks in our series are Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA\u27s Advanced Air Assets, 1st edition. Our previous six titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols et al., 2021) (Nichols R. K. et al., 2020) (Nichols R. et al., 2020) (Nichols R. et al., 2019) (Nichols R. K., 2018) Our seventh title takes on a new purview of Space. Let\u27s think of Space as divided into four regions. These are Planets, solar systems, the great dark void (which fall into the purview of astronomers and astrophysics), and the Dreamer Region. The earth, from a measurement standpoint, is the baseline of Space. It is the purview of geographers, engineers, scientists, politicians, and romantics. Flying high above the earth are Satellites. Military and commercial organizations govern their purview. The lowest altitude at which air resistance is low enough to permit a single complete, unpowered orbit is approximately 80 miles (125 km) above the earth\u27s surface. Normal Low Earth Orbit (LEO) satellite launches range between 99 miles (160 km) to 155 miles (250 km). Satellites in higher orbits experience less drag and can remain in Space longer in service. Geosynchronous orbit is around 22,000 miles (35,000 km). However, orbits can be even higher. UASs (Drones) have a maximum altitude of about 33,000 ft (10 km) because rotating rotors become physically limiting. (Nichols R. et al., 2019) Recreational drones fly at or below 400 ft in controlled airspace (Class B, C, D, E) and are permitted with prior authorization by using a LAANC or DroneZone. Recreational drones are permitted to fly at or below 400 ft in Class G (uncontrolled) airspace. (FAA, 2022) However, between 400 ft and 33,000 ft is in the purview of DREAMERS. In the DREAMERS region, Space has its most interesting technological emergence. We see emerging technologies and operations that may have profound effects on humanity. This is the mission our book addresses. We look at the Dreamer Region from three perspectives:1) a Military view where intelligence, jamming, spoofing, advanced materials, and hypersonics are in play; 2) the Operational Dreamer Region; whichincludes Space-based platform vulnerabilities, trash, disaster recovery management, A.I., manufacturing, and extended reality; and 3) the Humanitarian Use of Space technologies; which includes precision agriculture wildlife tracking, fire risk zone identification, and improving the global food supply and cattle management. Here’s our book’s breakdown: SECTION 1 C4ISR and Emerging Space Technologies. C4ISR stands for Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance. Four chapters address the military: Current State of Space Operations; Satellite Killers and Hypersonic Drones; Space Electronic Warfare, Jamming, Spoofing, and ECD; and the challenges of Manufacturing in Space. SECTION 2: Space Challenges and Operations covers in five chapters a wide purview of challenges that result from operations in Space, such as Exploration of Key Infrastructure Vulnerabilities from Space-Based Platforms; Trash Collection and Tracking in Space; Leveraging Space for Disaster Risk Reduction and Management; Bio-threats to Agriculture and Solutions From Space; and rounding out the lineup is a chapter on Modelling, Simulation, and Extended Reality. SECTION 3: Humanitarian Use of Space Technologies is our DREAMERS section. It introduces effective use of Drones and Precision Agriculture; and Civilian Use of Space for Environmental, Wildlife Tracking, and Fire Risk Zone Identification. SECTION 3 is our Hope for Humanity and Positive Global Change. Just think if the technologies we discuss, when put into responsible hands, could increase food production by 1-2%. How many more millions of families could have food on their tables? State-of-the-Art research by a team of fifteen SMEs is incorporated into our book. We trust you will enjoy reading it as much as we have in its writing. There is hope for the future.https://newprairiepress.org/ebooks/1047/thumbnail.jp

AN INTEGRATED SCORE-BASED TRAFFIC LAW ENFORCEMENT AND NETWORK MANAGEMENT IN CONNECTED VEHICLE ENVIRONMENT

The increasing number of traffic accidents and the associated traffic congestion have prompted the development of innovative technologies to curb such problems. This dissertation introduces a novel Score-Based Traffic Law Enforcement and Network Management System (SLEM), which leverages connected vehicle (CV) and telematics technologies. SLEM assigns a score to each driver which reflects her/his driving performance and compliance with traffic laws over a predefined period of time. The proposed system adopts a rewarding mechanism that rewards high-performance drivers and penalizes low-performance drivers who fail to obey traffic laws. The reward mechanism is in the form of a route guidance strategy that restricts low-score drivers from accessing certain roadway sections and time periods that are strategically selected in order to shift the network traffic distribution pattern from the undesirable user equilibrium (UE) pattern to the system optimal (SO) pattern. Hence, it not only incentivizes drivers to improve their driving performance, but it also provides a mechanism to manage network congestion in which high-score drivers experience less congestion and a higher level of safety at the expense of low-performing drivers. This dissertation is divided into twofold. iv First, a nationwide survey study was conducted to measure public acceptance of the SLEM system. Another survey targeted a focused group of traffic operation and safety professionals. Based on the results of these surveys, a set of logistic regression models was developed to examine the sensitivity of public acceptance to policy and behavioral variables. The results showed that about 65 percent of the public and about 60.0 percent of professionals who participated in this study support the real-world implementation of SLEM. Second, we present a modeling framework for the optimal design of SLEM’s routing strategy, which is described in the form of a score threshold for each route. Under SLEM’s routing strategy, drivers are allowed to use a particular route only if their driving scores satisfy the score threshold assigned to that route. The problem is formulated as a bi-level mathematical program in which the upper-level problem minimizes total network travel time, while the lower-level problem captures drivers’ route choice behavior under SLEM. An efficient solution methodology developed for the problem is presented. The solution methodology adopts a heuristic-based approach that determines the score thresholds that minimize the difference between the traffic distribution pattern under SLEM’s routing strategy and the SO pattern. The framework was applied to the network of the US-75 Corridor in Dallas, Texas, and a set of simulation-based experiments was conducted to evaluate the network performance given different driver populations, score class aggregation levels, recurrent and non-recurrent congestion scenarios, and driver compliance rates

Networking Transportation

Networking Transportation looks at how the digital revolution is changing Greater Philadelphia's transportation system. It recognizes several key digital transportation technologies: Artificial Intelligence, Big Data, connected and automated vehicles, digital mapping, Intelligent Transportation Systems, the Internet of Things, smart cities, real-time information, transportation network companies (TNCs), unmanned aerial systems, and virtual communications. It focuses particularly on key issues surrounding TNCs. It identifies TNCs currently operating in Greater Philadelphia and reviews some of the more innovative services around the world. It presents four alternative future scenarios for their growth: Filling a Niche, A Tale of Two Regions, TNCs Take Off, and Moore Growth. It then creates a future vision for an integrated, multimodal transportation network and identifies infrastructure needs, institutional reforms, and regulatory recommendations intended to help bring about this vision