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Due to globalization and rapid development, the demand for travel and congestion are increasing, stressing the road networks. Furthermore, road networks are subjected to planned and unplanned disruptions, and these disruptions affect the road network performance by either damaging its components, fluctuating the travel demand, or doing both at the same time. Thus, assessing the change in travel demand and travel patterns during such disruptions is essential. Conventionally, travel demand has been estimated using a household travel survey (HTS). However, the HTS is not feasible for disrupted conditions because of its large sample size requirement and time-consuming methodology. However, with the advent of crowdsourced data, new methodologies have been proposed for estimating the travel demand, but most of these studies have focused on estimating demand for a typical day and not disrupted scenarios. Moreover, data sources utilized till now have low sample sizes or are unavailable for developing countries. To bridge this gap, the current study utilizes an automated planning tool called Rapidex to estimate the travel demand and pattern change for a planned disruption, i.e., the 18th G20 Summit, Delhi, using crowdsourced travel time data from TomTom API. Upon comparing the results of travel demand and travel patterns of typical days with G20 days, it was observed that travel demand was lower during G20. Moreover, it was observed that the zones where railway stations, interstate bus terminals, and G20 activities were focused had higher generation and attraction share than typical days. Other zones within the regulated area had lower generation and attraction rates, which can be because of travel restrictions imposed in these zones. [ABSTRACT FROM AUTHOR]

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data. [ABSTRACT FROM AUTHOR]

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of

Accurate mapping of streams that maintain surface flow during annual baseflow periods in mountain headwater streams is important for informing water availability for human consumption and is a fundamental determinant of in‐channel conditions for stream‐dwelling organisms. Yet accurate mapping that captures local spatial variability and associated local controls on surface flow presence is limited. An empirical random‐forest model was developed to predict streamflow permanence (late summer surface‐flow presence) for Mount Rainier National Park and the surrounding mountainous area in western Washington, USA. This model was developed to improve upon the existing multi‐state, regional‐scale probability of stream permanence developed for the greater Pacific Northwest Region (PROSPERPNW). The model was trained on 544 wet/dry observations collected during the late summer, baseflow period from 2018 to 2020 using the crowd‐source mobile application, FLOwPER. Final model accuracy was 0.74 with drainage area and covariates describing geology, topography, and land cover as top predictors of streamflow permanence compared to coarser resolution climatic covariates. The prevalence of static covariates over climatic covariates as top ranked important covariates highlights the importance of scale when evaluating controls on streamflow permanence. Cross validation of the model indicates that streamflow permanence probabilities from this model is an improvement over the regional‐scale PROSPERPNW model demonstrating the utility of relatively simple, crowd‐sourced data to address water resource needs, and that determination of important predictors of streamflow permanence is influenced by the spatial and temporal resolution of analysis. [ABSTRACT FROM AUTHOR]

Ramadan intermittent fasting during the COVID-19 lockdown (RIFL) may present unique demands. We investigated training practices (i.e., training load and training times) of athletes, using pre-defined survey criteria/questions, during the ‘first’ COVID-19 lockdown, comparing RIFL to lockdown-alone (LD) in Muslim athletes. Specifically, a within-subject, survey-based study saw athletes (n = 5,529; from 110 countries/territories) training practices (comparing RIFL to LD) explored by comparative variables of: sex; age; continent; athlete classification (e.g., world-class); sport classification (e.g., endurance); athlete status (e.g., professional); and level of training knowledge and beliefs/attitudes (ranked as: good/moderate/poor). During RIFL (compared to LD), athlete perceptions (ranges presented given variety of comparative variables) of their training load decreased (46–62%), were maintained (31–48%) or increased (2–13%). Decreases (≥ 5%, p < 0.05) affected more athletes aged 30–39 years than those 18–29 years (60 vs 55%); more national than international athletes (59 vs 51%); more team sports than precision sports (59 vs 46%); more North American than European athletes (62 vs 53%); more semi-professional than professional athletes (60 vs 54%); more athletes who rated their beliefs/attitudes ‘good’ compared to ‘poor’ and ‘moderate’ (61 vs 54 and 53%, respectively); and more athletes with ‘moderate’ than ‘poor’ knowledge (58 vs 53%). During RIFL, athletes had different strategies for training times, with 13–29% training twice a day (i.e., afternoon and night), 12–26% at night only, and 18–36% in the afternoon only, with ranges depending on the comparative variables. Training loads and activities were altered negatively during RIFL compared to LD. It would be prudent for decision-makers responsible for RIFL athletes to develop programs to support athletes during such challenges.

Ramadan intermittent fasting during the COVID-19 lockdown (RIFL) may present unique demands. We investigated training practices (i.e., training load and training times) of athletes, using pre-defined survey criteria/questions, during the 'first' COVID-19 lockdown, comparing RIFL to lockdown-alone (LD) in Muslim athletes. Specifically, a within-subject, survey-based study saw athletes (n = 5,529; from 110 countries/territories) training practices (comparing RIFL to LD) explored by comparative variables of: sex; age; continent; athlete classification (e.g., world-class); sport classification (e.g., endurance); athlete status (e.g., professional); and level of training knowledge and beliefs/attitudes (ranked as: good/moderate/poor). During RIFL (compared to LD), athlete perceptions (ranges presented given variety of comparative variables) of their training load decreased (46--62%), were maintained (31--48%) or increased (2--13%). Decreases (≥ 5%, p < 0.05) affected more athletes aged 30--39 years than those 18--29 years (60 vs 55%); more national than international athletes (59 vs 51%); more team sports than precision sports (59 vs 46%); more North American than European athletes (62 vs 53%); more semi-professional than professional athletes (60 vs 54%); more athletes who rated their beliefs/attitudes 'good' compared to 'poor' and 'moderate' (61 vs 54 and 53%, respectively); and more athletes with 'moderate' than 'poor' knowledge (58 vs 53%). During RIFL, athletes had different strategies for training times, with 13--29% training twice a day (i.e., afternoon and night), 12--26% at night only, and 18--36% in the afternoon only, with ranges depending on the comparative variables. Training loads and activities were altered negatively during RIFL compared to LD. It would be prudent for decision-makers responsible for RIFL athletes to develop programs to support athletes during such challenges. [ABSTRACT FROM AUTHOR]

Flooding causes significant damage to lives and property. Moreover, it affects the economy and the lifestyles of people in society for both the short term and long term. As a consequence, this research aimed to demonstrate techniques and flood detection analysis through digital images and web application development for receiving reports and inspection data about flood situations in every area. The process requires crowdsource data and uses 3S technology so it can receive accurate, real-time data for making decisions in flood management. It also offers aid to the people in the disaster areas. In this research, a convolutional neural network was applied for flood detection and classification using digital images and data from people or the victims. According to the study, it was found that the convolutional neural network for flood classification has data accuracy at high levels of 95. 50%, 93. 00%, 97. 89%, and 0. 91, which are the results of accuracy, producer accuracy, user accuracy, and kappa statistics, respectively. Besides, the use of this technique saves costs, time, and labour. Furthermore, the method could be applied to other disasters such as landslides, earthquakes, and fires. It is able to monitor the incidents in each type of disaster and also examine the damaged site after the incident. [ABSTRACT FROM AUTHOR]

A well developed and maintained highway infrastructure is essential for the economic and social prosperity of modern societies. Highway maintenance poses significant challenges pertaining to the ever-increasing ongoing traffic, insufficient budget allocations and lack of resources. Road potholes detection and timely repair is a major contributing factor to sustaining a safe and resilient critical road infrastructure. Current pothole detection methods require laborious manual inspection of roads and lack in terms of accuracy and inference speed. This paper proposes a novel application of Convolutional Neural Networks on accelerometer data for pothole detection. Data is collected using an iOS smartphone installed on the dashboard of a car, running a dedicated application. The experimental results show that the proposed CNN approach has a significant advantage over the existing solutions, with respect to accuracy and computational complexity in pothole detection. [ABSTRACT FROM AUTHOR]