TY - GEN
T1 - A Route-Affecting Region Based Approach for Feature Extraction in Transportation Route Planning
AU - Lin, Fandel
AU - Hsieh, Hsun Ping
AU - Fang, Jie Yu
N1 - Funding Information:
This work was partially supported by Ministry of Science and Technology (MOST) of Taiwan under grants 108-2221-E-006-142 and 108-2636-E-006-013. Meanwhile, we are grateful to Tainan City Government for providing the bus ticket data.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Traffic deployment is highly correlated with the quality of life. Current research for passenger flow estimation in transportation route planning focuses on origin-destination matrices (OD) analysis; however, we claim that urban functions and geographical environments around passing area and stations should also be considered because they affect the demand of public transportation. For the route-based demand prediction task, we therefore define route-affecting region (RAR) to model the influential region of routes. Based on the proposed RAR, we further proposed route-based feature extraction approaches along with adopting several regression models to do high accurate inference. Given heterogeneous features and faced with the competitive and transfer effects of existing routes, our proposed RAR-based feature engineering methods are effective for handling and combining dynamic and static data which are high-correlated with passenger volumes. The experiments on bus-ticket data of Tainan and Chicago, with public transit network structures different from each other, show the adaptability and better performance of our proposed RAR-based approach compared to traditional OD-based feature extraction strategies.
AB - Traffic deployment is highly correlated with the quality of life. Current research for passenger flow estimation in transportation route planning focuses on origin-destination matrices (OD) analysis; however, we claim that urban functions and geographical environments around passing area and stations should also be considered because they affect the demand of public transportation. For the route-based demand prediction task, we therefore define route-affecting region (RAR) to model the influential region of routes. Based on the proposed RAR, we further proposed route-based feature extraction approaches along with adopting several regression models to do high accurate inference. Given heterogeneous features and faced with the competitive and transfer effects of existing routes, our proposed RAR-based feature engineering methods are effective for handling and combining dynamic and static data which are high-correlated with passenger volumes. The experiments on bus-ticket data of Tainan and Chicago, with public transit network structures different from each other, show the adaptability and better performance of our proposed RAR-based approach compared to traditional OD-based feature extraction strategies.
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U2 - 10.1007/978-3-030-67667-4_17
DO - 10.1007/978-3-030-67667-4_17
M3 - Conference contribution
AN - SCOPUS:85103254437
SN - 9783030676667
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 290
BT - Machine Learning and Knowledge Discovery in Databases
A2 - Dong, Yuxiao
A2 - Mladenic, Dunja
A2 - Saunders, Craig
PB - Springer Science and Business Media Deutschland GmbH
T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
Y2 - 14 September 2020 through 18 September 2020
ER -
