scholarly journals Computation and Assessment of Environmental Emissions Resulting from Traffic Operations at Roundabouts

2019 ◽  
pp. 130-145
Author(s):  
Göktuğ Tenekeci
Keyword(s):  
Traffic Operations ◽  
Environmental Emissions

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

scholarly journals Effect of carbon anode production parameters on anode cracking

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Salah Amrani ◽  
Duygu Kocaefe ◽  
Yasar Kocaefe ◽  
Dipankar Bhattacharyay ◽  
Mohamed Bouazara ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Crack Density ◽  
Raw Material ◽  
Carbon Anode ◽  
Know How ◽  
Production Parameters ◽  
Environmental Emissions ◽  
Carbon Anodes ◽  
Tomographic Analysis

AbstractCarbon anodes are used in the electrolytic production of aluminum. The quality of anodes is directly related to the production cost, carbon and energy consumption, and environmental emissions. It is desired that the anodes have high density, low porosity/cracks, low electrical resistivity as well as low air and CO2 reactivities. Low resistivity of anodes reduces energy required to produce aluminum during electrolysis. The presence of cracks and pores increases the anode electrical resistivity. Therefore, it is important to know how and when the pores and cracks form during the anode production so that the necessary actions could be taken to prevent their formation. A study was carried out to investigate the effect of different anode production parameters such as anode composition, type of raw material used, time and top-former bellow pressure of vibro-compactor, green anode cooling medium, and heating rate used during baking on the crack formation. The anodes are fabricated at the carbon laboratory of University of Quebec at Chicoutimi (UQAC) and characterized by measuring their properties (density, electrical resistivity, and surface crack density). The anode properties, hence the anode quality, were correlated with the anode production parameters. Also, their tomographic analysis was carried out to visualize and quantify the internal cracks. Graphical abstract

scholarly journals Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

2020 ◽  
Vol 13 (1) ◽  
pp. 290
Author(s):  
Seyed Hashem Mousavi-Avval ◽  
Shahin Rafiee ◽  
Ali Mohammadi
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Fuzzy Inference ◽  
Cost Ratio ◽  
Inference System ◽  
Benefit Cost Ratio ◽  
Multi Objective Genetic Algorithm ◽  
Neuro Fuzzy ◽  
Environmental Emissions ◽  
Benefit Cost

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS–MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS–MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems.

Minimization of environmental emissions through improved process integration

1990 ◽  
Vol 10 (4) ◽  
pp. 329-339 ◽  
Author(s):  
R. Smith ◽  
E.A. Petela ◽  
H.D. Spriggs
Keyword(s):  
Process Integration ◽  
Environmental Emissions

Application of LiDAR and Connected Vehicle Data to Evaluate the Impact of Work Zone Geometry on Freeway Traffic Operations

2018 ◽  
Vol 2672 (16) ◽  
pp. 1-13 ◽  
Author(s):  
Michelle M. Mekker ◽  
Yun-Jou Lin ◽  
Magdy K. I. Elbahnasawy ◽  
Tamer S. A. Shamseldin ◽  
Howell Li ◽  
...  
Keyword(s):  
Case Studies ◽  
Work Zone ◽  
Extensive Literature ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Traffic Operations ◽  
Work Zones ◽  
Geometric Data ◽  
Vehicle Data ◽  
The Impact

Extensive literature exists regarding recommendations for lane widths, merging tapers, and work zone geometry to provide safe and efficient traffic operations. However, it is often infeasible or unsafe for inspectors to check these geometric features in a freeway work zone. This paper discusses the integration of LiDAR (Light Detection And Ranging)-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. Connected vehicle speed data can be used at both a system-wide (statewide) or segment-level view to identify periods of congestion and queueing. Examples of regional trends, localized incidents, and recurring bottlenecks are shown in the data in this paper. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. In total, 350 directional miles were covered, resulting in approximately 360 GB of data. Two case studies, where geometric anomalies were identified, are discussed in this paper: a short segment with a narrow lane width of 10–10.5 feet and a merging taper that was about 200 feet shorter than recommended by the Manual on Uniform Traffic Control Devices. In both case studies, these work zone features did not conform to project specifications but were difficult to assess safely by an inspector in the field because of the high volume of traffic. The paper concludes by recommending the use of connected vehicle data to systematically identify work zones with recurring congestion and the use of LiDAR to assess work zone geometrics.

Sign in / Sign up

Export Citation Format

Share Document