TY - JOUR
T1 - Agent-based modelling with geographically weighted calibration for intra-urban activities simulation using taxi GPS trajectories
AU - Gong, Shuhui
AU - Dong, Xiangrui
AU - Wang, Kaiqi
AU - Lei, Bingli
AU - Jia, Zizhao
AU - Qin, Jiaxin
AU - Roadknight, Chris
AU - Liu, Yu
AU - Cao, Rui
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China [grant numbers 42101472 , 62172373 , 61872325 ], Fundamental Research Funds for the Central Universities [Grant No. 2-9-2020-028 ], GHFUND B under Grant ghfund202107021958 , and the Hong Kong Polytechnic University Start-Up under Grant BD41 .
Publisher Copyright:
© 2023 The Authors
PY - 2023/8
Y1 - 2023/8
N2 - Human motivations are an important factor in influencing human movement. However, most existing studies on passenger travel demand prediction focus on external characteristics of movement, but neglect the influence of activities and the motivations behind them, on the citizen's trip decisions. In this study, we proposed an agent-based model, to predict passengers’ travel behaviour over a period of time, particularly when the urban structure changes. The model includes passenger characteristics, transitions in travel demands between activities over time, and their movement in space and time. In addition, we innovatively calibrated the agent-based model locally using Geographically Weighted Regression (GWR) to account for geographical variations in the parameters of destination attractiveness and travel cost in the agent-based model. We conducted a case study in Ningbo, China, using trip diaries, census data, and over 1.5 million taxi trip records. Our agent-based model showed superior performance in predicting citizens’ movements and activities after a new shopping area in Ningbo was built, compared with a model without local calibration. The results also revealed the geographic sensitivity to destinations and the effects of a passenger's motivations that underpin human movement.
AB - Human motivations are an important factor in influencing human movement. However, most existing studies on passenger travel demand prediction focus on external characteristics of movement, but neglect the influence of activities and the motivations behind them, on the citizen's trip decisions. In this study, we proposed an agent-based model, to predict passengers’ travel behaviour over a period of time, particularly when the urban structure changes. The model includes passenger characteristics, transitions in travel demands between activities over time, and their movement in space and time. In addition, we innovatively calibrated the agent-based model locally using Geographically Weighted Regression (GWR) to account for geographical variations in the parameters of destination attractiveness and travel cost in the agent-based model. We conducted a case study in Ningbo, China, using trip diaries, census data, and over 1.5 million taxi trip records. Our agent-based model showed superior performance in predicting citizens’ movements and activities after a new shopping area in Ningbo was built, compared with a model without local calibration. The results also revealed the geographic sensitivity to destinations and the effects of a passenger's motivations that underpin human movement.
KW - Activity-based analysis
KW - Agent-based modelling (ABM)
KW - Geographically weighted regression (GWR)
KW - Huff model
KW - Taxi GPS trajectories
UR - http://www.scopus.com/inward/record.url?scp=85163865263&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2023.103368
DO - 10.1016/j.jag.2023.103368
M3 - Review article
AN - SCOPUS:85163865263
SN - 1569-8432
VL - 122
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
M1 - 103368
ER -