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Taxi passenger hotspot prediction using automatic ARIMA model
Jamil M.S.a, Akbar S.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 IEEE.As a transportation mode, taxis facing low occupancy rate problems at certain time and over-demand at another. This issue is due to imbalance of supply and demand of taxi service. According to some studies the imbalance caused by inefficient taxis distribution. Recently, there are data sources available by utilizing GPS technology that can be used to obtain spatio-temporal information. This spatio-temporal information is valuable to develop intelligent taxi system. There are a lot of previous research related to intelligent taxi system in several research topics, one of which on the analysis hotspot area. This study utilizes Automatic ARIMA Model to perform time-series analysis to predict the passenger’s hotspot area based on the spatio-temporal data provided from local taxi firm in Bandung. The challenge of this research is to use the right method in the data-preprocessing phase so the Automatic ARIMA Model can process the spatio-temporal data. Some of the alternatives proposed for preprocessing phase and experimental results show that grid mapping method can be used well in the preprocessing phase. Research results show that the Automatic ARIMA can be used to conduct an analysis of the spatio-temporal data. This is indicated by Mean Absolute Scaled Error (MASE) value 0.8797 for New York City dataset and 0.6338 for Bandung dataset. Cross-validation analysis also showed satisfactory results when the actual demand is quite large. However, if the actual demand is close to zero, the result of the analysis becomes less reliable. This can be understood as a lack in the quality of the data, not in the prediction model.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ARIMA,Cross-validation analysis,Grid mapping,Spatio temporal,Spatio-temporal data,Spatiotemporal information,taxi,Transportation mode[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ARIMA,grid mapping,Prediction,spatio-temporal,taxi,time-series[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICSITech.2017.8257080[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]