2021
DOI: 10.3390/su13126682
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Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines

Abstract: The general framework of the bottom-up approach for modeling mobile emissions and energy use involves the following major components: (1) quantifying traffic flow and (2) calculating emission and energy consumption factors. In most cases, researchers deal with complex and arduous tasks, especially when conducting actual surveys in order to calculate traffic flow. In this regard, the authors are introducing a novel method in estimating mobile emissions and energy use from road traffic flow utilizing crowdsource… Show more

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Cited by 7 publications
(2 citation statements)
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“…Delussu et al 2021 [15] deployed heuristic and exhaustive algorithms to generate Bayesian networks among sensor monitored traffic and environment data to predict the fuel consumption dependent on the context of traffic conditions. In another inferred case of predicting consumption, the authors in Rito et al 2021 [16] extracted vehicle count data from google map and deduced the energy consumption data. Then they aggregated the traffic variable data with the consumption data and multiplied assumed emission factors to estimate the emissions.…”
Section: Transportation Emission Estimation Based On Causal Relationsmentioning
confidence: 99%
“…Delussu et al 2021 [15] deployed heuristic and exhaustive algorithms to generate Bayesian networks among sensor monitored traffic and environment data to predict the fuel consumption dependent on the context of traffic conditions. In another inferred case of predicting consumption, the authors in Rito et al 2021 [16] extracted vehicle count data from google map and deduced the energy consumption data. Then they aggregated the traffic variable data with the consumption data and multiplied assumed emission factors to estimate the emissions.…”
Section: Transportation Emission Estimation Based On Causal Relationsmentioning
confidence: 99%
“…Delussu et al 2021 [15] deployed heuristic and exhaustive algorithms to generate Bayesian networks among sensor monitored traffic and environment data to predict the fuel consumption dependent on the context of traffic conditions. In another inferred case of predicting consumption, the authors in Rito et al 2021 [16] extracted vehicle count data from google map and deduced the energy consumption data. Then they aggregated the traffic variable data with the consumption data and multiplied assumed emission factors to estimate the emissions.…”
Section: Transportation Emission Estimation Based On Causal Relationsmentioning
confidence: 99%