TY - GEN
T1 - Event-driven anomalies in spatiotemporal taxi passseger demand
AU - Wittmann, Michael
AU - Kollek, Michael
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - We propose a grid-based approach of mining historical taxi GPS data to study anomalous pickup patterns in spatiotemporal passenger demand caused by public events. The developed method is applied by way of example to the city of Munich on a large-scale taxi dataset. Information about occurring public events is collected from different online and offline sources. For further analysis, the data is aggregated on an equally distributed grid level based on geohashes. In a first step, an extension of robust principle component analysis (RPCA) is developed and applied to the dataset. It is shown that extended RPCA can separate anomalous pickup patterns from base demand in the given time series. Secondly, the combination of anomaly detection and knowledge about occurring public events allows the exploration of correlations in demand fluctuations. The results show that the proposed method can be used to discover the potential impact of public events on spatiotemporal passenger demand in large-scale mobility datasets.
AB - We propose a grid-based approach of mining historical taxi GPS data to study anomalous pickup patterns in spatiotemporal passenger demand caused by public events. The developed method is applied by way of example to the city of Munich on a large-scale taxi dataset. Information about occurring public events is collected from different online and offline sources. For further analysis, the data is aggregated on an equally distributed grid level based on geohashes. In a first step, an extension of robust principle component analysis (RPCA) is developed and applied to the dataset. It is shown that extended RPCA can separate anomalous pickup patterns from base demand in the given time series. Secondly, the combination of anomaly detection and knowledge about occurring public events allows the exploration of correlations in demand fluctuations. The results show that the proposed method can be used to discover the potential impact of public events on spatiotemporal passenger demand in large-scale mobility datasets.
KW - anomaly-detection
KW - on-demand mobility
KW - robust principle component analysis (RPCA)
KW - taxi-passenger demand
KW - time-series analysis
UR - http://www.scopus.com/inward/record.url?scp=85060438292&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2018.8569500
DO - 10.1109/ITSC.2018.8569500
M3 - Conference contribution
AN - SCOPUS:85060438292
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 979
EP - 984
BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Y2 - 4 November 2018 through 7 November 2018
ER -