A Framework for Smart Freight Mobility with Crowdsourcing

Chandra, Shailesh; Naik, Ramakanta; Jimenez, Jose

doi:10.1016/j.trpro.2020.08.055

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In transportation planning, crowdsourcing is an effective tool that can consolidate data from a large group of individuals on the same platform in order to address a shared problem among its members [34]. Key areas in which crowdsourcing proved to be very useful in transportation are the following: (1) development of strategies for managing traffic flow in an urban environment; (2) efficient detour routing of vehicles to avoid traffic congestion; (3) monitoring and assessment of road conditions; (4) data analysis on traffic accident and road crime; and many more [35][36][37][38][39]. Transport modeling provides insights for the possible future development of a particular area [40].…”

Section: Introductionmentioning

confidence: 99%

Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines

2021

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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.

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