TY - GEN
T1 - Realistic Transport Simulation for Studying the Impacts of Shared Micromobility Services
AU - Khalil, Jalal
AU - Yan, Da
AU - Guo, Guimu
AU - Sami, Mirza Tanzim
AU - Roy, Joy Bhadhan
AU - Sisiopiku, Virginia P.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by NSF OAC-2106461, NSF DGE-1723250, and the Southeastern Transportation Research, Innovation, Development, and Education Center (STRIDE; Projects I2 and B3). Guimu Guo acknowledges financial support from the Alabama Graduate Research Scholars Program (GRSP) funded through the Alabama Commission for Higher Education and administered by the Alabama EPSCoR. REFERENCES
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Micromobility refers to small, lightweight vehicles such as shared bicycles and electric scooters (e-scooters). Recently, shared micromobility services see increasing deployment in urban areas, especially for trips where the travel distance is considered long for walking, but not worth driving a car (e.g., to avoid parking). A key question to ask when deciding whether to deploy a shared micromobility service in an area is: how much car traffic can be reduced during peak hours if this service is deployed? This work answers this question by agent-based transportation simulation. The key contribution is to generate a realistic synthetic population of transportation users in the target area along with their travel day-plans, using an area-specific travel survey plus openly available data sources. We demonstrate our approach through a case study on the deployment of dockless e-scooters in Birmingham, AL, with a demo at https://youtu.be/zh_mHQ6ck4U.
AB - Micromobility refers to small, lightweight vehicles such as shared bicycles and electric scooters (e-scooters). Recently, shared micromobility services see increasing deployment in urban areas, especially for trips where the travel distance is considered long for walking, but not worth driving a car (e.g., to avoid parking). A key question to ask when deciding whether to deploy a shared micromobility service in an area is: how much car traffic can be reduced during peak hours if this service is deployed? This work answers this question by agent-based transportation simulation. The key contribution is to generate a realistic synthetic population of transportation users in the target area along with their travel day-plans, using an area-specific travel survey plus openly available data sources. We demonstrate our approach through a case study on the deployment of dockless e-scooters in Birmingham, AL, with a demo at https://youtu.be/zh_mHQ6ck4U.
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U2 - 10.1109/BigData52589.2021.9671681
DO - 10.1109/BigData52589.2021.9671681
M3 - Conference contribution
AN - SCOPUS:85125320430
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 5935
EP - 5937
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
Y2 - 15 December 2021 through 18 December 2021
ER -