ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

NEMO: Real-Time Noise and Exhaust Emissions Monitoring for Sustainable and Intelligent Transportation Systems

Research and development efforts on sustainable and intelligent transportation systems are accelerating globally as the transportation sector contributes significantly to environmental pollution and produces a variety of noise and emissions that impact the climate. With the emergence of ubiquitous sensors and Internet of Things (IoT) applications, finding innovative transport solutions, including adequate climate change mitigation, will all be vital components of a sustainable transport future. Thus, it is essential to continuously monitor noise and exhaust emissions from road vehicles, trains, and ships. As a contribution to addressing this as part of an effort of the European Union project called “NEMO: Noise and Emissions Monitoring and Radical Mitigation", in this paper, we propose the design and development of a real-time noise and exhaust emissions monitoring for sustainable and intelligent transportation systems. We report real-world field testing in some European cities where vehicle noise and exhaust emissions data are gathered in the cloud-enabled Nautilus platform and evaluated using artificial intelligence (AI) algorithms to determine their categorization into different classes of emitters and thereby enabling the infrastructure managers to define logic and actions to be taken by high emitters in near real-time. We outline the creation of a complete NEMO solution to monitor and reduce noise and emissions in real time for sustainable and intelligent transportation systems.acceptedVersio

NEMO: Internet of Things based Real-time Noise and Emissions MOnitoring System for Smart Cities

With the advent of ubiquitous sensors and Internet of Things (IoT) applications, research and development initiatives on smart cities are ramping up worldwide. It enables remote monitoring, management, and control of devices and the generation of fresh and actionable insight from huge quantities of real-time data. Real-time noise and emissions monitoring of vehicles remain indispensable in a smart city context. Effective management and control of noise and emissions of vehicles on the road are necessary and possible through analyzing lots of sensor data in real-time to take an actionable insight. To contribute to this, as part of an ongoing effort of the European Union project called ''NEMO: Noise and Emissions Monitoring and Radical Mitigation'', in this paper, we present the design and development of an IoT-based real-time noise and emissions monitoring system for vehicles in a smart city context. Real-world sensor data of the vehicles in some European cities are collected during the pilot tests. We have developed a complete application for infrastructure managers and analysts to monitor the sensor data related to noise and emissions of vehicles in real-time. The data of the individual road vehicles and trains in selected EU cities and from trains on a track in the Netherlands are collected in the cloud and analyzed with artificial intelligence (AI) algorithms for classification such as high emitter, medium emitter, and normal emitters. We present the development of a complete software solution that can be integrated with existing intelligent transportation systems in smart cities. Finally, we report the initial vehicle classification results from the Rotterdam (Netherlands) pilot test as a representative example for the NEMO monitoring system.acceptedVersio

Estimating vehicles emissions at signalized intersections in the highway capacity manual

Over the past decades, motor vehicle volumes have continued to increase at a high rate. As a result, engineers in the transportation field not only need more robust knowledge of traffic operation control and transportation planning, but more attention is also needed to understand and estimate the influences that this increasing volume of vehicles has on the environment, especially the influence on air quality. The EPA has stated that reducing carbon monoxide (CO) from vehicle emissions is the most significant way to control air pollution from the transportation sector. The Highway Capacity Manual is a national and international resource that has become a guideline for evaluating the operation of roadway, transit and pedestrian facilities. The Highway Capacity Manual assesses the operation of a roadway based on the perception of its users. Performance measures are used to describe the traffic operation of the roadway. At present, no measures are provided to describe the operation of the roadway based on environmental impacts. The incorporation of air pollution estimation into the Highway Capacity Manual will allow the roadway’s operation to be assessed both from an operational and environmental aspect, ultimately creating a sustainable development for both transportation and the environment. The objective of this dissertation is to develop MOVES-like estimation models of vehicle emissions for pollutants at a signalized intersection that can be incorporated into the Highway Capacity Manual. “EPA’s Motor Vehicle Emission Simulator (MOVES) is a state-of-the-art emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics.” (EPA, 2014). A thorough understanding is needed about what parameters, and influence of these parameters on vehicle emissions. This dissertation develops two kinds of models in order to estimate emissions caused by on-road vehicles. Two modeling approaches are used to estimate four kinds of emissions including CO, NO, NH3 and NOX separately. The following summarizes the work of this dissertation: The objective of this dissertation is to develop MOVES-like estimation models of vehicle emissions for pollutants at a signalized intersection that can be incorporated into the Highway Capacity Manual. “EPA’s Motor Vehicle Emission Simulator (MOVES) is a state-of-the-art emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics.” (EPA, 2014). A thorough understanding is needed about what parameters, and influence of these parameters on vehicle emissions. This dissertation develops two kinds of models in order to estimate emissions caused by on-road vehicles. Two modeling approaches are used to estimate four kinds of emissions including CO, NO, NH3 and NOX separately. The following summarizes the work of this dissertation: (1) Two modeling approaches are used to estimate vehicle emissions including: multiple linear regression and Artificial Neural Network (ANN). In the multiple linear regression modeling, two different models were developed including one model using operation modes as independent variables and another model using traffic related parameters as independent variables. Both model approaches and independent variables are used to estimate four types of pollutant emissions. Statistically, the emission models using traffic parameters as independent HCM related parameters are capable of providing a better emissions estimate based on the higher R square value. For CO, the variables found to be significant were volume to capacity ratio and grade with an R2 of 61.56%. For NO, the variables found to be significant were volume to capacity ratio and grade with an R2 of 99.47%. For NOx, the variables found to be significant were volume to capacity ratio and grade with an R2 of 99.47%. For NH3, the variables found to be significant were volume to capacity ratio and grade with an R2 of 99.25%. This study shows that volume to capacity dominate the emissions quality at a signalized intersection. The research found that for NOx, Idling and Moderate Speed Coasting were significant. For NH3, all variables were significant except Low Speed Coasting. For CO, Braking and Cruise/Acceleration were significant. It was also found that longer delay time reduces CO emissions, but it causes the other three pollutant emissions increase. (2) The ANN modeling method using the Levenberg-Marquardt method was used to train the HCM related variables and MOVES emissions outputs. The parameters of volume to capacity ratio, and road grade are used to estimate emissions. The Validated R value of the obtained ANN model is found

Comparison of Data Mining and Mathematical Models for Estimating Fuel Consumption of Passenger Vehicles

A number of analytical models have been described in the literature to estimate the fuel consumption of vehicles, most of which require a wide range of vehicle and trip related parameters as input data, which might limit the practical applicability of these models if such data were not readily available. To overcome this drawback, this study describes the development of three data mining models to estimate fuel consumption of a vehicle, including linear regression, artificial neural network and support vector machines. The paper presents comparison results with five instantaneous fuel consumption models from the literature using real data collected from three passenger vehicles on three routes. The results indicate that while the prediction accuracy of the instantaneous fuel consumption models varies across the data sets, those obtained by the regression models are significantly better and more robust against changes in input data

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reducing the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g. liters of fuel per kilometer. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. These varying factors introduce uncertainty in estimating the vehicles' energy efficiency. We propose in this paper an ensemble learning approach based on deep neural networks (ENN) that is designed to reduce the predictive uncertainty and to output measures of such uncertainty. We evaluated it using the publicly available Vehicle Energy Dataset (VED) and compared it with several baselines per vehicle and energy type. The results showed a high predictive performance and they allowed to output a measure of predictive uncertainty

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users