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Minqing Zhu

Publications and source records attributed to Minqing Zhu.

2 recordsLinked to original sources

How does Bike Absence Influence Mode Shifts Among Dockless Bike-Sharing Users? Evidence From Nanjing, China

Dockless bike-sharing (DBS) users often encounter difficulties in finding available bikes at their preferred times and locations. This study examines the determinants of the users' mode shifts in the context of bike absence, using survey data from Nanjing, China. An integrated choice and latent variable based on multinomial logit was employed to investigate the impact of socio-demographic, trip characteristics, and psychological factors on travel mode choices. Mode choice models were estimated with seven mode alternatives, including bike-sharing related choices (waiting in place, picking up bikes on the way, and picking up bikes on a detour), bus, taxi, riding hailing, and walk. The findings show that under shared-bike unavailability, users prefer to pick up bikes on the way rather than take detours, with buses and walking as favored alternatives to shared bikes. Lower-educated users tend to wait in place, showing greater concern for waiting time compared to riding time. Lower-income users, commuters, and females prefer picking up bikes on the way, while non-commuters and males opt for detours. The insights gained in this study can provide ideas for solving the problems of demand estimation, parking area siting, and multi-modal synergies of bike sharing to enhance utilization and user satisfaction.

econ.EM

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism). Due to possibly omitted source labels and unmeasured confounders, traditional methods cannot estimate individual treatment assignment probability and infer treatment effect effectively. Therefore, we propose to reconstruct the source label and model it as a Group Instrumental Variable (GIV) to implement IV-based Regression for treatment effect estimation. In this paper, we conceptualize this line of thought and develop a unified framework (Meta-EM) to (1) map the raw data into a representation space to construct Linear Mixed Models for the assigned treatment variable; (2) estimate the distribution differences and model the GIV for the different treatment assignment mechanisms; and (3) adopt an alternating training strategy to iteratively optimize the representations and the joint distribution to model GIV for IV regression. Empirical results demonstrate the advantages of our Meta-EM compared with state-of-the-art methods.

cs.AI