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Point of Interest and Stop Type Prediction for Paratransit Transportation Data
Putri E.P.a, Nurmalasari R.R.a, Prihatmanto A.S.a, Yusuf R.a, Wijaya R.b
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, Indonesia
b Telkom University, Engineering Faculty of Electrical Engineering, 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]© 2020 IEEE.Paratransit vehicles often do not have an uncertain stop point Sometimes it causes a traffic jam, especially in Southeast Asia. In Indonesia, Angkot is a well-known paratransit vehicle. Angkot often causes congestion because it can stop anywhere and anytime. It is quite difficult to predict when and where the Angkot stops. This paper proposes a machine learning model design that can predict the types of Angkot stops, as well as its Point of Interest. This paper using Angkot trips history data on Bandung City in 2018. Data processing is carried out in several stages, consisting of manual labeling with google maps and google road maps as a reference, grouping data, increasing the correlation matrix to the output data, normalizing GPS data, finally entering the data into the Multi-Layer Perceptron (MLP) model for training. Several experiments were tested, such as conducting training on the Artificial Neural Network model without normalizing the data beforehand or without entering the clustering results into the training data as input. A model that has been trained can labeling raw data that has the same route as trained data. The model also shows us 95% accuracy with the GPS data that has been normalized and clustered first.[/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]Artificial neural network modeling,Clustering results,Correlation matrix,Machine learning models,Multi layer perceptron,Paratransit vehicles,Point of interest,Type predictions[/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]big data,geospatial data,k-means clustering,machine learning,multi-layer perceptron,transportation data[/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/ICIDM51048.2020.9339601[/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]