vehicle activity
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2022 ◽  
Vol 9 ◽  
Author(s):  
Laura W. Ploughe ◽  
Lauchlan H. Fraser

The global use of off-road vehicles (ORVs) in natural environments has accelerated rapidly over the last few decades, resulting in significant social and environmental consequences. As the demand, use, and promotion of light-duty ORVs like all-terrain vehicles (ATVs), motorcycles, four-wheel drive trucks and sport utility vehicles (SUVs) increases in remote wilderness, the landscape is becoming fragmented into disorganized and destructive networks of trails and roads. Substantial ecological impacts to a wide range of ecosystem structures and functions will likely result from ORV activity. Applying a global systematic review, we examine 105 publications about plant, soil, and wildlife responses to ORV traffic in different habitats to help guide the direction of future research, monitoring programs, and mitigation efforts. Most studies investigated impacts to animals, followed by soils, then vegetative responses. Soil studies primarily focused on physical impacts to the soil (i.e., compaction, erosion, rut depth), but some studies suggest that soil chemical and biological properties may also be impacted by ORV traffic. The literature on plant responses to ORV activities primarily explored vegetation loss, although impacts on the plant community were also investigated. Animal studies investigated impacts of ORV use on invertebrates, mammals, birds, and to a lesser extent reptiles/amphibians, including population-level, community-level, and behavioral responses. Overall, research on environmental impacts of ORV traffic is biased to coastal and desert ecosystems in the northern hemisphere (primarily in the US), often does not address mechanisms that may produce ecological impacts (e.g., intensity of vehicular disturbance and ecosystem- or species-specific sensitivity to ORV activities), and frequently focused on short-term responses. More research is needed to understand the mechanisms that cause the different responses of soil, plant, and animals to ORVs over the long-term in a broad range of ecosystems to support real-time management and conservation efforts.


Author(s):  
John W. Koupal ◽  
Allison DenBleyker ◽  
Gopi Manne ◽  
Maia Hill Batista ◽  
Thomas Schmitt

Eastern Research Group, Inc. evaluated the current state of personal vehicle telematics data with respect to emission inventory development, identifying relative strengths and weaknesses, and how these data could align better with the needs of emission modelers. A market survey of telematics firms provided an overview of available data, and identified several candidate sources for location-based and engine-based telematics data on personal vehicles. Data were then purchased from three different vendors: StreetLight Data, Moonshadow Mobile, and Otonomo. These data were applied in case studies conducted in the Denver metro area, U.S., to assess strengths and weaknesses of telematics for developing emission inventories. Case studies included using telematics to estimate regional vehicle miles traveled (VMT) for annual emission inventories; tracking the VMT impacts of COVID shutdown; generating location- and time-specific vehicle activity inputs for project scale “hot spot” air quality analysis; and estimating the distribution of fuel fill level from real-world data, which is important for evaporative emissions. These case studies confirmed that telematics can serve a growing range of emission inventory use cases, and use of these data may help improve emission inventory accuracy. However, there are also several limitations of the data to consider in preparing emission inventories; for example, it can be difficult to assess the representativeness of telematics data because of a lack of vehicle information. The authors encourage telematics firms to cater data products more directly to the needs of emission inventory modelers, to better harness the enormous potential of these data for refining vehicle emission inventory estimates.


Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6526
Author(s):  
Ali Walid Daher ◽  
Ali Rizik ◽  
Marco Muselli ◽  
Hussein Chible ◽  
Daniele D. Caviglia

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.


2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
James Van Hinsbergh ◽  
Nathan Griffiths ◽  
Phillip Taylor ◽  
Zhou Xu ◽  
Alex Mouzakitis

Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.


2021 ◽  
Author(s):  
Mauricio Osses ◽  
Néstor Rojas ◽  
Cecilia Ibarra ◽  
Víctor Valdebenito ◽  
Ignacio Laengle ◽  
...  

Abstract. This description paper presents a detailed and consistent estimate and analysis of exhaust pollutant emissions generated by Chile's road transport activity for the period 1990–2020. The complete database for the period 1990–2020 is available at doi: http://dx.doi.org/10.17632/z69m8xm843.2. Emissions are provided at high-spatial resolution (0.01° × 0.01°) over continental Chile from 18.5 S to 53.2 S, including local pollutants (CO, VOC, NOx, MP2.5), black carbon (BC) and greenhouse gases (CO2, CH4). The methodology considers 70 vehicle types, based on ten vehicle categories, subdivided into two fuel types and seven emission standards. Vehicle activity was calculated based on official databases of vehicle records and vehicle flow counts. Fuel consumption was calculated based on vehicle activity and contrasted with fuel sales, to calibrate the initial dataset. Emission factors come mainly from COPERT 5, adapted to local conditions in the 15 political regions of Chile, based on emission standards and fuel quality. While vehicle fleet has grown fivefold between 1990 and 2020, CO2 emissions had followed this trend at a lower rate and emissions of local pollutants have decreased, due to stricter abatement technologies, better fuel quality and enforcement of emission standards. In other words, there has been decoupling between fleet growth and emissions’ rate of change. Results were contrasted with EDGAR datasets, showing similarities in CO2 estimations and striking differences in PM, BC and CO; in the case of NOx and CH4 there is coincidence only until 2008. In all cases of divergent results, EDGAR estimates higher emissions.


2021 ◽  
Author(s):  
Chen Zhang ◽  
Karen Ficenec ◽  
Andrew Kotz ◽  
Kenneth Kelly ◽  
Darrell Sonntag ◽  
...  

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Burak Cankaya ◽  
Berna Eren Tokgoz ◽  
Ali Dag ◽  
K.C. Santosh

Purpose This paper aims to propose a machine learning-based automatic labeling methodology for chemical tanker activities that can be applied to any port with any number of active tankers and the identification of important predictors. The methodology can be applied to any type of activity tracking that is based on automatically generated geospatial data. Design/methodology/approach The proposed methodology uses three machine learning algorithms (artificial neural networks, support vector machines (SVMs) and random forest) along with information fusion (IF)-based sensitivity analysis to classify chemical tanker activities. The data set is split into training and test data based on vessels, with two vessels in the training data and one in the test data set. Important predictors were identified using a receiver operating characteristic comparative approach, and overall variable importance was calculated using IF from the top models. Findings Results show that an SVM model has the best balance between sensitivity and specificity, at 93.5% and 91.4%, respectively. Speed, acceleration and change in the course on the ground for the vessels are identified as the most important predictors for classifying vessel activity. Research limitations/implications The study evaluates the vessel movements waiting between different terminals in the same port, but not their movements between different ports for their tank-cleaning activities. Practical implications The findings in this study can be used by port authorities, shipping companies, vessel operators and other stakeholders for decision support, performance tracking, as well as for automated alerts. Originality/value This analysis makes original contributions to the existing literature by defining and demonstrating a methodology that can automatically label vehicle activity based on location data and identify certain characteristics of the activity by finding important location-based predictors that effectively classify the activity status.


2021 ◽  
Vol 12 (5) ◽  
pp. 101052
Author(s):  
Masoud Fallah Shorshani ◽  
Meredith Franklin ◽  
Marianne Hatzopoulou
Keyword(s):  

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