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dc.contributor.author
Gächter, Joel
dc.contributor.author
Zanardi, Alessandro
dc.contributor.author
Ruch, Claudio
dc.contributor.author
Frazzoli, Emilio
dc.date.accessioned
2021-11-08T08:06:25Z
dc.date.available
2021-11-05T13:38:36Z
dc.date.available
2021-11-08T08:06:25Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7281-9077-8
en_US
dc.identifier.isbn
978-1-7281-9078-5
en_US
dc.identifier.other
10.1109/icra48506.2021.9561552
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/513970
dc.description.abstract
In recent years, mobility on demand has experienced a major revival due to various ride-hailing companies entering the market. Competing in this field requires an efficient operation. Therefore, the applied policy, which cares for vehicle-to-customer assignment and vehicle repositioning, has to achieve good customer service and minimize cost while trying to keep the impact on the environment as low as possible. A promising approach is to coordinate the control of the entire fleet, which is foreseen to become even easier with the possibility of autonomous vehicles in mind. Anticipating future demand requires a good understanding of the spatiotemporal distributions of request origins and destinations, and the resulting imbalance between vehicle demand and availability. This results from a multitude of topological, demographic, and social effects, which are almost impossible to sufficiently capture in a handcrafted model of reasonable complexity. This can be circumvented by leveraging machine learning approaches. In this paper, an image-like representation of the city and its fleet's state is introduced. It is comprehensive and intuitive to use as input to convolutional neural networks, which in the past have already been proven to capture spatial relationships very well. This allows operating on realistic, full-sized traffic networks without greatly increasing the number of parameters the neural network has to learn and, hence, keeps the training effort low. Additionally, this state is combined with a similarly constructed repositioning action, reflecting a 2D distribution of a well-performing operational policy. This approach allows replacement of complex, handcrafted mathematical models by a single, compact, auto-encoder-like neural network.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Image Representation of a City and Its Taxi Fleet for End-To-End Learning of Rebalancing Policies
en_US
dc.type
Conference Paper
dc.date.published
2021-10-18
ethz.book.title
2021 IEEE International Conference on Robotics and Automation (ICRA)
en_US
ethz.pages.start
8076
en_US
ethz.pages.end
8082
en_US
ethz.event
2021 IEEE International Conference on Robotics and Automation (ICRA 2021)
en_US
ethz.event.location
Xi'an, China
en_US
ethz.event.date
May 30 - June 5, 2021
en_US
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09574 - Frazzoli, Emilio / Frazzoli, Emilio
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09574 - Frazzoli, Emilio / Frazzoli, Emilio
en_US
ethz.date.deposited
2021-11-05T13:38:45Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-11-08T08:06:31Z
ethz.rosetta.lastUpdated
2022-03-29T15:53:05Z
ethz.rosetta.versionExported
true
ethz.COinS
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