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Smart city article details
| Title | Taxi Demand Prediction Using An Lstm-Based Deep Sequence Model And Points Of Interest |
|---|---|
| ID_Doc | 54463 |
| Authors | Askari B.; Le Quy T.; Ntoutsi E. |
| Year | 2020 |
| Published | Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020 |
| DOI | http://dx.doi.org/10.1109/COMPSAC48688.2020.000-7 |
| Abstract | Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE). © 2020 IEEE. |
| Author Keywords | Deep Learning; LSTM; Machine Learning; POI; Urban Mobility Prediction |
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