TY - GEN
T1 - State-Based Behavioral Modeling Framework for Electric Taxis
T2 - 10th International Conference on Intelligent Transportation Engineering, ICITE 2025
AU - Lu, Zhixuan
AU - Zhong, Jiahao
AU - Shao, Ziyun
AU - Jian, Linni
N1 - Publisher Copyright:
© 2026 The Authors.
PY - 2026/1/8
Y1 - 2026/1/8
N2 - As electric and autonomous mobility advances, understanding electric taxi (ET) behavior is vital for urban planning. Traditional ETs (TETs) are limited by driver rest and subjective decisions, reducing service consistency. Unmanned ETs (UETs) can overcome these constraints through automation, yet their large-scale urban impacts remain underexplored. This study develops a state-based simulation framework modeling TET and UET operations are modeled across four states (search, work, charge, rest), with transitions determined by time, energy, and spatial demand. Using Shenzhen GPS data, the TET model is validated for realism, then adapted for UETs by removing rest constraints and adding adaptive exploration and charging. Simulations show UETs achieve higher, more stable pick-up rates and more balanced, distributed charging, highlighting their potential to enhance service efficiency and infrastructure use.
AB - As electric and autonomous mobility advances, understanding electric taxi (ET) behavior is vital for urban planning. Traditional ETs (TETs) are limited by driver rest and subjective decisions, reducing service consistency. Unmanned ETs (UETs) can overcome these constraints through automation, yet their large-scale urban impacts remain underexplored. This study develops a state-based simulation framework modeling TET and UET operations are modeled across four states (search, work, charge, rest), with transitions determined by time, energy, and spatial demand. Using Shenzhen GPS data, the TET model is validated for realism, then adapted for UETs by removing rest constraints and adding adaptive exploration and charging. Simulations show UETs achieve higher, more stable pick-up rates and more balanced, distributed charging, highlighting their potential to enhance service efficiency and infrastructure use.
KW - Behavior model
KW - Charging distribution
KW - Traditional Electric taxis (TETs)
KW - Unmanned electric taxis (UETs)
UR - https://www.scopus.com/pages/publications/105028162696
U2 - 10.3233/ATDE251471
DO - 10.3233/ATDE251471
M3 - 会议稿件
AN - SCOPUS:105028162696
T3 - Advances in Transdisciplinary Engineering
SP - 65
EP - 72
BT - Intelligent Transportation Engineering - Proceedings of the 10th International Conference on Intelligent Transportation Engineering, ICITE 2025
A2 - Chen, Yanyan
PB - IOS Press BV
Y2 - 24 October 2025 through 26 October 2025
ER -