Quantifying changes in bicycle volumes using crowdsourced data

2022 ◽  
pp. 239980832110661
Author(s):  
Ali Al-Ramini ◽  
Mohammad A Takallou ◽  
Daniel P Piatkowski ◽  
Fadi Alsaleem
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Crowdsourced Data ◽  
Machine Learning Approach ◽  
Bicycle Infrastructure ◽  
The Difference ◽  
Infrastructure Investments ◽  
Using Data ◽  
The Impact ◽  
The City

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

scholarly journals Evaluating the Impact of Floods on Housing Price Using a Spatial Matching Difference-In-Differences (SM-DID) Approach

2021 ◽  
Vol 13 (2) ◽  
pp. 804
Author(s):  
Jean Dubé ◽  
Maha AbdelHalim ◽  
Nicolas Devaux
Keyword(s):  
Housing Price ◽  
Hedonic Pricing ◽  
Environmental Amenities ◽  
Sale Price ◽  
Difference In Differences ◽  
Hedonic Pricing Model ◽  
The Difference ◽  
And Control ◽  
The Impact ◽  
The City

Many applications have relied on the hedonic pricing model (HPM) to measure the willingness-to-pay (WTP) for urban externalities and natural disasters. The classic HPM regresses housing price on a complete list of attributes/characteristics that include spatial or environmental amenities (or disamenities), such as floods, to retrieve the gradients of the market (marginal) WTP for such externalities. The aim of this paper is to propose an innovative methodological framework that extends the causal relations based on a spatial matching difference-in-differences (SM-DID) estimator, and which attempts to calculate the difference between sale price for similar goods within “treated” and “control” groups. To demonstrate the potential of the proposed spatial matching method, the researchers present an empirical investigation based on the case of a flood event recorded in the city of Laval (Québec, Canada) in 1998, using information on transactions occurring between 1995 and 2001. The research results show that the impact of flooding brings a negative premium on the housing price of about 20,000$ Canadian (CAN).

scholarly journals Relationship between Changes over Time in Factors, Including the Impact of Meteorology on Photochemical Oxidant Concentration and Causative Atmospheric Pollutants in Kawasaki

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 446
Author(s):  
Akinori Fukunaga ◽  
Takaharu Sato ◽  
Kazuki Fujita ◽  
Daisuke Yamada ◽  
Shinya Ishida ◽  
...  
Keyword(s):  
Moving Average ◽  
Atmospheric Pollutants ◽  
Important Indicator ◽  
Photochemical Oxidant ◽  
Oxidant Concentration ◽  
Using Data ◽  
Changes Over Time ◽  
The Impact ◽  
The Relationship ◽  
The City

To clarify the relationship between changes in photochemical oxidants’ (Ox) concentrations and their precursors in Kawasaki, a series of analyses were conducted using data on Ox, their precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), and meteorology that had been monitored throughout the city of Kawasaki for 30 years from 1990 to 2019. The trend in air temperature was upward, wind speed was downward, and solar radiation was upward, indicating an increasing trend in meteorological factors in which Ox concentrations tend to be higher. Between 1990 and 2013, the annual average Ox increased throughout Kawasaki and remained flat after that. The three-year moving average of the daily peak increased until 2015, and after that, it exhibited a slight decline. The amount of generated Ox is another important indicator. To evaluate this, a new indicator, the daytime production of photochemical oxidant (DPOx), was proposed. DPOx is defined by daytime averaged Ox concentrations less the previous day’s nighttime averaged Ox concentrations. The trend in DPOx from April to October has been decreasing since around 2006, and it was found that this indicator reflects the impact of reducing emissions of NOx and VOCs in Kawasaki.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

The Impact of Customer Loyalty and Restaurant Sanitation Grades on Revisit Intention and the Importance of Narrative Information: The Case of New York Restaurant Sanitation Grading System

2017 ◽  
Vol 59 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Min Gyung Kim ◽  
Hyunjoo Yang ◽  
Anna S. Mattila
Keyword(s):  
New York ◽  
New York City ◽  
Customer Loyalty ◽  
York City ◽  
Letter Grades ◽  
The Difference ◽  
Revisit Intentions ◽  
Using Data ◽  
The Impact ◽  
Loyal Customers

New York City launched a restaurant sanitation letter grade system in 2010. We evaluate the impact of customer loyalty on restaurant revisit intentions after exposure to a sanitation grade alone, and after exposure to a sanitation grade plus narrative information about sanitation violations (e.g., presence of rats). We use a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C, or pending) between-subjects full factorial design to test the hypotheses using data from 547 participants recruited from Amazon MTurk who reside in the New York City area. Our study yields three findings. First, loyal customers exhibit higher intentions to revisit restaurants than non-loyal customers, regardless of sanitation letter grades. Second, the difference in revisit intentions between loyal and non-loyal customers is higher when sanitation grades are poorer. Finally, loyal customers are less sensitive to narrative information about sanitation violations.

Cyber Place Management and Crime Prevention: The Effectiveness of Cybersecurity Awareness Training Against Phishing Attacks

2021 ◽  
pp. 104398622110016
Author(s):  
Sinchul Back ◽  
Rob T. Guerette
Keyword(s):  
Crime Prevention ◽  
Research University ◽  
Management Strategies ◽  
Large Body ◽  
The United States ◽  
Place Management ◽  
Online Setting ◽  
Using Data ◽  
Virtual Domain ◽  
The Impact

Criminologists and crime prevention practitioners recognize the importance of geographical places to crime activities and the role that place managers might play in effectively preventing crime. Indeed, over the past several decades, a large body of work has highlighted the tendency for crime to concentrate across an assortment of geographic areas, where place management tends to be absent or weak. Nevertheless, there has been a paucity of research evaluating place management strategies and cybercrime within the virtual domain. The purpose of this study was to investigate the effectiveness of place management techniques on reducing cybercrime incidents in an online setting. Using data derived from the information technology division of a large urban research university in the United States, this study evaluated the impact of an anti-phishing training program delivered to employees that sought to increase awareness and understanding of methods to better protect their “virtual places” from cybercrimes. Findings are discussed within the context of the broader crime and place literature.

Overeducation and the Earnings of Black, Hispanic, and White Male Workers

1988 ◽  
Vol 31 (2) ◽  
pp. 190-212 ◽  
Author(s):  
Richard R. Verdugo ◽  
Naomi Turner Verdugo
Keyword(s):  
United States ◽  
Mexican Americans ◽  
Minority Groups ◽  
Puerto Ricans ◽  
White Male ◽  
The United States ◽  
Using Data ◽  
The Impact ◽  
Male Workers ◽  
White Minority

This study addresses two issues: (1) the impact of overeducation on the earnings of male workers in the United States, and (2) white-minority earnings differences among males. Given that educational attainment levels are increasing among workers, there is some suspicion that earnings returns to education are not as great as might be expected. This topic is examined by including an overeducation variable in an earnings function. Regarding the second issue addressed in this article, little is actually known about white-minority differences because the bulk of such research compares whites and blacks. By including selected Hispanic groups in this analysis (Mexican Americans, Puerto Ricans, Cubans, and Other Hispanics) we are able to assess white-minority earnings differences to a greater degree. Using data from a 5% sample of the 1980 census to estimate an earnings function, we find that overeducated workers earn less than either undereducated or adequately educated workers. Second, we find that there are substantial earnings differences between whites and minorities, and, also, between the five minority groups examined.

Impact of Health Plan Deductibles and Health Insurance Marketplace Enrollment on Health Care Experiences

2018 ◽  
Vol 77 (5) ◽  
pp. 483-497
Author(s):  
Weiwei Chen ◽  
Timothy F. Page
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
The United States ◽  
Health Plans ◽  
Medical Needs ◽  
Care Access ◽  
Health Care Experiences ◽  
Monitoring Survey ◽  
Using Data ◽  
The Impact

High-deductible health plans (HDHPs) have become increasingly prevalent among employer-sponsored health plans and plans offered through the Health Insurance Marketplace in the United States. This study examined the impact of deductible levels on health care experiences in terms of care access, affordability, routine checkup, out-of-pocket cost, and satisfaction using data from the Health Reform Monitoring Survey. The study also tested whether the experiences of Marketplace enrollees differed from off-Marketplace individuals, controlling for deductible levels. Results from multivariable and propensity score weighted regression models showed that many of the outcomes were adversely affected by deductible levels and Marketplace enrollment. These results highlight the importance of efforts to help individuals choose the plan that fits both their medical needs and their budgets. The study also calls for more attention to improving provider acceptance of HDHPs and Marketplace plans as these plans become increasingly common over time.

Strangers in the City: Transmigration from Eastern Europe and its Impact on Berlin and Hamburg 1880–1914

2016 ◽  
Vol 2 (2) ◽  
pp. 223-246
Author(s):  
Tobias Brinkmann
Keyword(s):  
United States ◽  
New York ◽  
Eastern Europe ◽  
The United States ◽  
Urban Traffic ◽  
European Cities ◽  
Massive Growth ◽  
Transit Migration ◽  
The Impact ◽  
The City

This article examines the impact of transit migration from the Russian and Austro-Hungarian Empires on Berlin and Hamburg between 1880 and 1914. Both cities experienced massive growth during the last three decades of the nineteenth century, and both served as major points of passage for Eastern Europeans travelling to (and returning from) the United States. The rising migration from Eastern Europe through Central and Western European cities after 1880 coincided with the need to find adequate solutions to accommodate a rapidly growing number of commuters. The article demonstrates that the isolation of transmigrants in Berlin, Hamburg (and New York) during the 1890s was only partly related to containing contagious disease and ‘undesirable’ migrants. Isolating transmigrants was also a pragmatic response to the increasing pressure on the urban traffic infrastructure.

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