Identifying Types of Staying Facilities from Traffic Behavior Log Data

AbstractTraffic behavior surveys by hand require both a lot of money and human resources. Recently, traffic behavior surveys using information technology have been carried out. In this study, we propose a method to extract staying points from GPS-based positional data and identify the types of staying facilities by using Google Places API, a facility ontology, the regularity which is analyzed from trip chains about traffic behavior. This method could identify 68.5% types of staying facilities correctly in the evaluation using GPS location data from the Traffic Behavior Survey in Nagasaki

Transportation System Performance and Traveler Behavior in the Context of a Systemwide Shock: Applications of Data Science Toward a Sustainable Future

The COVID-19 pandemic, a systemwide shock, has left a long-lasting and significant impact on transportation systems. It has contributed to a shift in travel behavior, with many people turning to work from home (WFH) and online shopping. This shift has led to a reduction in vehicular travel. However, the pandemic witnessed increased crash fatalities despite a reduction in overall crashes, disproportionately affecting disadvantaged communities (DACs). The main question arising from these pandemic-related issues is what we can learn to improve transportation systems and shape future travel behavior. Therefore, this dissertation aims to investigate how the transportation system changed during COVID-19 and explore the future implications while examining the travel behavior, technology adoption behavior, and road safety aspects in DACs compared with non-DACs during COVID-19. As such, this dissertation first explores the interaction between WFH, online shopping, and in-person shopping behaviors, revealing nuanced relationships that have evolved amidst the pandemic. Second, comprehensive safety data are utilized to dissect why crash fatalities increased during COVID-19. Third, transportation safety in DACs is investigated by leveraging safety data covering COVID-19 periods and the comprehensive DAC indicators developed by the US Department of Transportation. Fourth, DACs’ shopping behavior during COVID-19 is analyzed by focusing on the interplay of emerging online delivery components (retail, grocery, and food) and in-person activities. Finally, the study compares technology adoption behaviors between DACs and non-DACs by exploring infrastructure and socio-economic barriers. Methodologically speaking, this dissertation employs various state-of-the-art statistical and explainable artificial intelligence techniques. Overall findings indicate that compared to pre-COVID-19, the surge in WFM and e-commerce trends was associated with a substantial reduction in physical shopping trips during COVID-19. Speeding and reckless behaviors were strongly associated with the increased road fatalities. DACs experienced heightened adversity than non-DACs, associated with a higher rate of fatal crashes (an increase of 8% to 57%). Online orders were considerably less frequent in DACs than non-DACs (2% to 7%), emphasizing disparity in digital infrastructure. Additionally, technology adoption rates were significantly lower in DACs. These findings underscore the importance of better preparedness and planning for such communities to be equipped to handle future systemic shocks

Assessing visitor satisfaction with tourism rejuvenation policies: the case of Rimini, Italy.

In this paper we assess the appeal of potential interventions on the tourism offer of Rimini, a popular Italian seaside holiday destination, by means of a choice modelling analysis. Tourism can be viewed as a composite good, its overall utility depending on the arrangement of the component characteristics. Our discrete choice experiments incorporate as attributes a number of possible changes to current tourist activities (the subject of public debate), including them in hypothetical alternative holiday packages. The conditional logit analysis indicates that tourists show lesser preference for interventions aimed at protecting the environmental integrity of the beach and greater preference for those, such as the creation of a pedestrianised seafront with late-night opening of amenities and facilities, that are likely to diminish the role of the traditional sea, sun and sand component of the overall holiday experience.

PROGRAMS WITH DATA MINING CAPABILITIES

The fact that the Internet has become a commodity in the world has created a framework for anew economy. Traditional businesses migrate to this new environment that offers many features and options atrelatively low prices. However competitiveness is fierce and successful Internet business is tied to rigorous use of allavailable information. The information is often hidden in data and for their retrieval is necessary to use softwarecapable of applying data mining algorithms and techniques. In this paper we want to review some of the programswith data mining capabilities currently available in this area.We also propose some classifications of this softwareto assist those who wish to use such software

Factors Affecting Demand for Plug-in Charging Infrastructure: An Analysis of Plug-in Electric Vehicle Commuters

The public sector and the private sector, which includes automakers and charging network companies, are increasingly investing in building charging infrastructure to encourage the adoption and use of plug-in electric vehicles (PEVs) and to ensure that current facilities are not congested. However, building infrastructure is costly and, as with road congestion, when there is significant uptake of PEVs, we may not be able to “build out of congestion.” We modelled the choice of charging location that more than 3000 PEV drivers make when given the options of home, work, and public locations. Our study focused on understanding the importance of factors driving demand such as: the cost of charging, driver characteristics, access to charging infrastructure, and vehicle characteristics. We found that differences in the cost of charging play an important role in the demand for charging location. PEV drivers tend to substitute workplace charging for home charging when they pay a higher electricity rate at home, more so when the former is free. Additionally, socio-demographic factors like dwelling type and gender, as well as vehicle technology factors like electric range, influence the choice of charging location

MIXED-USE SAFETY ON RURAL FACILITIES IN THE PACIFIC NORTHWEST: Consideration of Vehicular, Non-Traditional, and Non-Motorized Users

In the United States, one in 12 households do not own a personal automobile and approximately 13% of those who are old enough to drive do not. Trips by these individuals are being made in one of many other possible modes, creating the need to “share space” between many forms of travel. The goal of this project is to: improve safety and minimize the dangers for all transportation mode types while traveling in mixed-use environments on rural facilities through the development and use of engineering and education safety measures. To that end, this report documents three specific efforts by the project team. First, a comprehensive literature review of mixed-use safety issues with consideration of non-motorized and non-traditional forms of transportation. Second, a novel analysis of trauma registry data. Third, development, execution and analysis of the Pacific Northwest Transportation Survey geared toward understanding safety perceptions of mixed-use users. Most notably, findings indicate that ATVs (and similar non-traditional-type vehicles) are used on or near roads 24% of the time and snowmachines are used on or near roads 23% of the time. There are significantly more (twice as many) ATV-related on-road traumas in connected places than isolated places in Alaska and three times more traumas in highway connected places than in secondary road connected places. Comparably, bicycles had 449 on-road traumas between 2004 and 2011 whereas ATVs had 352 on-road traumas. Users of all modes who received formalized training felt safer in mixed-use environments than those who reported having no training at all

Prediction of Air Traffic Controller Trainee Selection and Training Success Using Cognitive Ability and Biodata

The Federal Aviation Administration (FAA) has experienced decreased return on investment caused by hiring too many air traffic controller specialists (ATCSs) who performed poorly in field training, thus failing to become certified professional controllers (CPCs). Based on Schmidt and Hunter\u27s theory of job performance and biodata theory, this quantitative, archival study examined whether factors of cognitive ability and biodata could predict job performance status of 2 generations of ATCSs, poststrike (PS) and next generation (NG) controllers. For each generation of controllers, binary logistic regression analysis was conducted to determine if any of the independent variables---transmuted composite (TMC) score for PS controllers, Air Traffic and Selection and Training (AT-SAT) test score for NG controllers, average of high school arithmetic/math letter grade, overall high school average letter grade, self-estimation of time to become fully effective in the ATCS role, self-estimation of percentile ranking in the FAA program relative to the class, size of neighborhood raised, or socioeconomic status---are significant predictors of job performance status for controllers as measured by whether they pass the field OJT (i.e., certified or still in training, or failed certification or left training). The regression results for the PS and NG controllers were found to be statistically significant (chi2 (23) = 68.377, p \u3c .001) and (chi 2 (17) = 99.496, p \u3c .001), respectively. Findings that overall high school grade point average and socioeconomic status significantly predicted ATCS job performance for both PS and NG controllers could influence the FAA\u27s use of revised biodata to better predict ATCS job performance. Further research should include studies of socioeconomic status, gender, and race to address new evidence that the AT-SAT has adverse impact

Toward a sustainable cybersecurity ecosystem

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Cybersecurity issues constitute a key concern of today’s technology-based economies. Cybersecurity has become a core need for providing a sustainable and safe society to online users in cyberspace. Considering the rapid increase of technological implementations, it has turned into a global necessity in the attempt to adapt security countermeasures, whether direct or indirect, and prevent systems from cyberthreats. Identifying, characterizing, and classifying such threats and their sources is required for a sustainable cyber-ecosystem. This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in the Internet of Things (IoT). The cybersecurity of emerging technologies such as smart cities is also discussed. In addition, associated solutions based on artificial intelligence and machine learning frameworks to prevent cyber-risks are also discussed. Our review will serve as a reference for policy-makers from the industry, government, and the cybersecurity research community