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 crowdsourced data from Google Maps. The method was applied on a major highway in the Philippines commonly known as EDSA. Results showed that a total of 370,855 vehicles traveled along EDSA on average per day in June 2019. In comparison to a government survey, only an 8.63% error was found with respect to the total vehicle count. However, the approximation error can be further reduced to 4.63% if cars and utility vehicles are combined into one vehicle category. The study concludes by providing the limitations and opportunities for future work of the proposed methodology.

Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Link-level

“…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.…”

Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Link-level

“…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.…”