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Register a new userExamining Passenger Vehicle Miles Traveled and Carbon Emissions in the Boston Metropolitan Area
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With spatial analytic, econometric, and visualization tools, this book chapter investigates greenhouse gas emissions for the on-road passenger vehicle transport sector in the Boston metropolitan area in 2014. It compares greenhouse gas emission estimations from both the production-based and consumption-based perspectives with two large-scale administrative datasets: the vehicle odometer readings from individual vehicle annual inspection, and the road inventory data containing road segment level geospatial and traffic information. Based on spatial econometric models that examine socioeconomic and built environment factors contributing to the vehicle miles traveled at the census tract level, it offers insights to help cities reduce VMT and carbon footprint for passenger vehicle travel. Finally, it recommends a pathway for cities and towns in the Boston metropolitan area to curb VMT and mitigate carbon emissions to achieve climate goals of carbon neutrality.
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The Metropolitan Seoul Subway system, consisting of 380 stations, provides the major transportation mode in the metropolitan Seoul area. Focusing on the network structure, we analyze statistical properties and topological consequences of the subway system. We further study the passenger flows on the system, and find that the flow weight distribution exhibits a power-law behavior. In addition, the degree distribution of the spanning tree of the flows also follows a power law.
Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban commuters, we provide a direct price comparison between Uber and the Yellow Cab company in New York. We discover that Uber, despite its lower standard pricing rates, effectively charges higher fares on average, especially during short in length, but frequent in occurrence, taxi journeys. Building on this insight, we develop a smartphone application, OpenStreetCab, that offers a personalized consultation to mobile users on which taxi provider is cheaper for their journey. Almost five months after its launch, the app has attracted more than three thousand users in a single city. Their journey queries have provided additional insights on the potential savings similar technologies can have for urban commuters, with a highlight being that on average, a user in New York saves 6 U.S. Dollars per taxi journey if they pick the cheapest taxi provider. We run extensive experiments to show how Ubers surge pricing is the driving factor of higher journey prices and therefore higher potential savings for our applications users. Finally, motivated by the observation that Ubers surge pricing is occurring more frequently that intuitively expected, we formulate a prediction task where the aim becomes to predict a geographic areas tendency to surge. Using exogenous to Uber datasets we show how it is possible to estimate customer demand within an area, and by extension surge pricing, with high accuracy.
The burgeoning of misleading or false information spread by untrustworthy websites has, without doubt, created a dangerous concoction. Thus, it is not a surprise that the threat posed by untrustworthy websites has emerged as a central concern on the public agenda in many countries, including Czechia and Slovakia. However, combating this harmful phenomenon has proven to be difficult, with approaches primarily focusing on tackling consequences instead of prevention, as websites are routinely seen as quasi-sovereign organisms. Websites, however, rely upon a host of service providers, which, in a way, hold substantial power over them. Notwithstanding the apparent power hold by such tech stack layers, scholarship on this topic remains largely limited. This article contributes to this small body of knowledge by providing a first-of-its-kind systematic mapping of the back-end infrastructural support that makes up the tech stacks of Czech and Slovak untrustworthy websites. Our approach is based on collecting and analyzing data on top-level domain operators, domain name Registrars, email providers, web hosting providers, and utilized website tracking technologies of 150 Czech and Slovak untrustworthy websites. Our findings show that the Czech and Slovak untrustworthy website landscape relies on a vast number of back-end services spread across multiple countries, but in key tech stack layers is nevertheless still heavily dominated by locally based companies. Finally, given our findings, we discuss various possible avenues of utilizing the numeral tech stack layers in combating online disinformation.
The master equation approach is proposed to describe the evolution of passengers in a subway system. With the transition rate constructed from simple geographical consideration, the evolution equation for the distribution of subway passengers is found to bear skew distributions including log-normal, Weibull, and power-law distributions. This approach is then applied to the Metropolitan Seoul Subway system: Analysis of the trip data of all passengers in a day reveals that the data in most cases fit well to the log-normal distributions. Implications of the results are also discussed.
In modern metropolitan cities, the task of ensuring safe roads is of paramount importance. Automated systems of e-challans (Electronic traffic-violation receipt) are now being deployed across cities to record traffic violations and to issue fines. In the present study, an automated e-challan system established in Ahmedabad (Gujarat, India) has been analyzed for characterizing user behaviour, violation types as well as finding spatial and temporal patterns in the data. We describe a method of collecting e-challan data from the e-challan portal of Ahmedabad traffic police and create a dataset of over 3 million e-challans. The dataset was first analyzed to characterize user behaviour with respect to repeat offenses and fine payment. We demonstrate that a lot of users repeat their offenses (traffic violation) frequently and are less likely to pay fines of higher value. Next, we analyze the data from a spatial and temporal perspective and identify certain spatio-temporal patterns present in our dataset. We find that there is a drastic increase/decrease in the number of e-challans issued during the festival days and identify a few hotspots in the city that have high intensity of traffic violations. In the end, we propose a set of 5 features to model recidivism in traffic violations and train multiple classifiers on our dataset to evaluate the effectiveness of our proposed features. The proposed approach achieves 95% accuracy on the dataset.
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