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TDNet: A Generative Model for Taxi Demand Prediction
Linköping University, Department of Computer and Information Science, Software and Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
TDNet : En Generativ Modell för att Prediktera Taxiefterfrågan (Swedish)
Abstract [en]

Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and one in South America. In northern europe, an error of one taxi or less per hour per zone was achieved in 64% of the cases, in South America the number was 40%. In both cities, it beat the SARIMA and stacked ensemble benchmarks. This performance has been achieved by tuning the hyperparameters with a Bayesian optimization algorithm. Additionally, weather and holiday features were added as input features in the northern European city and they did not improve the accuracy of TDNet.

Place, publisher, year, edition, pages
2019. , p. 59
Keywords [en]
Taxi Demand, TDNet, WaveNet, Tree Parzen Estimator, Predictive Models, Convolutional Neural Networks, Time Series Forecasting, Traffic Forecasting, Sequence Learning Models
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-158514ISRN: LIU-IDA/LITH-EX-A--19/046--SEOAI: oai:DiVA.org:liu-158514DiVA, id: diva2:1334506
External cooperation
TaxiCaller Nordic AB
Subject / course
Computer Engineering
Presentation
2019-06-14, 20:07
Supervisors
Examiners
Available from: 2019-07-03 Created: 2019-07-02 Last updated: 2019-07-03Bibliographically approved

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CiteExportLink to record
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