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Analysis of E-bike Trip Duration and Frequency by Bayesian Duration and Zero-inflated Count Models
Abstract E-bike trip duration and frequency are two essential variables to identify factors affecting e-bike travel demand. This study aimed to investigate the factors affecting e-bike trip duration and frequency. The Bayesian hazard-based duration models with random effect were developed to investigate the contributing factors to the travel time of e-bike trip. The random effect was included to capture the unobserved heterogeneity. Estimation results showed that trip purpose, traffic volume, population, departure time, age and occupation are the main contributing factors to e-bike trip duration. And the factors affecting travel time of e-bike trip are distinct between males and females. The validation results indicated that the predictive performance of the developed duration models are satisfactory. The Bayesian zero-inflated count models with random effect were then used to investigate the contributing factors to the e-bike trip frequency. The zero-inflated count model assumes that the frequency of e-bike trip is generated by two states, including zero-frequency state which determines whether people use e-bikes for travel, and negative binomial state which determines e-bike trip frequency.
Analysis of E-bike Trip Duration and Frequency by Bayesian Duration and Zero-inflated Count Models
Abstract E-bike trip duration and frequency are two essential variables to identify factors affecting e-bike travel demand. This study aimed to investigate the factors affecting e-bike trip duration and frequency. The Bayesian hazard-based duration models with random effect were developed to investigate the contributing factors to the travel time of e-bike trip. The random effect was included to capture the unobserved heterogeneity. Estimation results showed that trip purpose, traffic volume, population, departure time, age and occupation are the main contributing factors to e-bike trip duration. And the factors affecting travel time of e-bike trip are distinct between males and females. The validation results indicated that the predictive performance of the developed duration models are satisfactory. The Bayesian zero-inflated count models with random effect were then used to investigate the contributing factors to the e-bike trip frequency. The zero-inflated count model assumes that the frequency of e-bike trip is generated by two states, including zero-frequency state which determines whether people use e-bikes for travel, and negative binomial state which determines e-bike trip frequency.
Analysis of E-bike Trip Duration and Frequency by Bayesian Duration and Zero-inflated Count Models
Xu, Chengcheng (author) / Wang, Chen (author)
KSCE Journal of Civil Engineering ; 23 ; 1806-1818
2019-01-21
13 pages
Article (Journal)
Electronic Resource
English
Analysis of E-bike Trip Duration and Frequency by Bayesian Duration and Zero-inflated Count Models
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