Scipediacontent: Scipediacontent moved page Draft Content 745886670 to Hou et al 2019a

2021-01-21T13:22:07Z

Scipediacontent moved page Draft Content 745886670 to Hou et al 2019a

Scipediacontent: Created page with " == Abstract == Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailin..."

2021-01-21T13:22:05Z

Created page with " == Abstract == Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailin..."

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== 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://arxiv.org/abs/1911.03441 http://arxiv.org/abs/1911.03441]

* [http://arxiv.org/pdf/1911.03441 http://arxiv.org/pdf/1911.03441]

* [https://escholarship.org/uc/item/1s88z4pw https://escholarship.org/uc/item/1s88z4pw]

* [http://link.springer.com/content/pdf/10.1007/978-3-030-17798-0_2 http://link.springer.com/content/pdf/10.1007/978-3-030-17798-0_2],
: [http://dx.doi.org/10.1007/978-3-030-17798-0_2 http://dx.doi.org/10.1007/978-3-030-17798-0_2] under the license http://www.springer.com/tdm

* [https://dblp.uni-trier.de/db/journals/corr/corr1911.html#abs-1911-03441 https://dblp.uni-trier.de/db/journals/corr/corr1911.html#abs-1911-03441],
: [https://link.springer.com/chapter/10.1007/978-3-030-17798-0_2 https://link.springer.com/chapter/10.1007/978-3-030-17798-0_2],
: [https://arxiv.org/pdf/1911.03441 https://arxiv.org/pdf/1911.03441],
: [https://arxiv.org/abs/1911.03441 https://arxiv.org/abs/1911.03441],
: [https://escholarship.org/uc/item/1s88z4pw https://escholarship.org/uc/item/1s88z4pw],
: [https://ui.adsabs.harvard.edu/abs/2019arXiv191103441W/abstract https://ui.adsabs.harvard.edu/abs/2019arXiv191103441W/abstract],
: [https://rd.springer.com/chapter/10.1007/978-3-030-17798-0_2 https://rd.springer.com/chapter/10.1007/978-3-030-17798-0_2],
: [https://export.arxiv.org/abs/1911.03441 https://export.arxiv.org/abs/1911.03441],
: [https://academic.microsoft.com/#/detail/2943846038 https://academic.microsoft.com/#/detail/2943846038]

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Hou et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 745886670 to Hou et al 2019a

Scipediacontent: Created page with " == Abstract == Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailin..."