Abstract

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 minutes. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-030-17798-0_2 under the license http://www.springer.com/tdm
https://link.springer.com/chapter/10.1007/978-3-030-17798-0_2,
https://arxiv.org/pdf/1911.03441,
https://arxiv.org/abs/1911.03441,
https://escholarship.org/uc/item/1s88z4pw,
https://ui.adsabs.harvard.edu/abs/2019arXiv191103441W/abstract,
https://rd.springer.com/chapter/10.1007/978-3-030-17798-0_2,
https://export.arxiv.org/abs/1911.03441,
https://academic.microsoft.com/#/detail/2943846038
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1007/978-3-030-17798-0_2
Licence: Other

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