Ride-hailing Emissions Modeling and Reduction through Ride Demand Prediction

Tanmay Bansal, Ruchika Dongre, Kassie Wang, Sam Fuchs

Abstract

Transportation is the largest contributor of greenhouse gas emissions in the United States. As Transportation Network Companies (TNCs), such as Uber and Lyft, grow in prevalence, it is imperative to quantify their emissions impact. We studied the case of Austin, Texas through its primary ride-hailing service - RideAustin - that has released data on 1.4+ million individual rides over an 11-month period. We estimated a total of 6014.95 metric tonnes of CO2 emissions from deadheading (when there are no passengers in freight) over the given time period. We clustered Austin into different zones and built an LSTM-based neural network for hourly ride demand forecasting on each zone through spatiotemporal features (weather, federal holidays, day of the week, and a look-back interval). Despite a large out-of-time validation window (7 months), our model outperforms the XGBoost-based baseline model by 34.86% and the next best comparable model in current literature by 15.3% in terms of MAE. In addition, we estimated a 10.624% reduction in total deadheading emissions for the same period given that the ride-hailing drivers on road are routed according to the proposed hourly ride demand forecasts.

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Paper Citation


in Harvard Style

Bansal T., Dongre R., Wang K. and Fuchs S. (2021). Ride-hailing Emissions Modeling and Reduction through Ride Demand Prediction. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 192-200. DOI: 10.5220/0010460801920200


in Bibtex Style

@conference{vehits21,
author={Tanmay Bansal and Ruchika Dongre and Kassie Wang and Sam Fuchs},
title={Ride-hailing Emissions Modeling and Reduction through Ride Demand Prediction},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={192-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010460801920200},
isbn={978-989-758-513-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Ride-hailing Emissions Modeling and Reduction through Ride Demand Prediction
SN - 978-989-758-513-5
AU - Bansal T.
AU - Dongre R.
AU - Wang K.
AU - Fuchs S.
PY - 2021
SP - 192
EP - 200
DO - 10.5220/0010460801920200