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A platform for research: civil engineering, architecture and urbanism
What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving
The average hourly income of taxi drivers could be improved by understanding the realized income of taxi drivers and investigating the variables that determine their income. Based on 4.85 million taxi-trajectory GPS records in Shenzhen, China, this study built a multi-layer road index system in order to reveal the behavioral patterns of drivers with varying income levels. On this basis, late-shift drivers were further selected and classified into two categories, namely high-earning and low-earning groups. Each driver within these groups was further classified into three income levels and four categories of factors were defined (i.e., occupied trips and duration, operational region, search speed, and taxi service strategies). The sample-based multinomial logit model was used to reveal the significance of these income-influencing factors. The results indicate significant differences in the drivers’ behavioral habits and experience. For instance, high-earning drivers focused more on improving efficiency using mobility intelligence, while low-earning drivers were more likely to invest in working hours to boost their revenue.
What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving
The average hourly income of taxi drivers could be improved by understanding the realized income of taxi drivers and investigating the variables that determine their income. Based on 4.85 million taxi-trajectory GPS records in Shenzhen, China, this study built a multi-layer road index system in order to reveal the behavioral patterns of drivers with varying income levels. On this basis, late-shift drivers were further selected and classified into two categories, namely high-earning and low-earning groups. Each driver within these groups was further classified into three income levels and four categories of factors were defined (i.e., occupied trips and duration, operational region, search speed, and taxi service strategies). The sample-based multinomial logit model was used to reveal the significance of these income-influencing factors. The results indicate significant differences in the drivers’ behavioral habits and experience. For instance, high-earning drivers focused more on improving efficiency using mobility intelligence, while low-earning drivers were more likely to invest in working hours to boost their revenue.
What Makes a Good Cabman? Behavioral Patterns Correlated with High-Earning and Low-Earning Taxi Driving
Shuxin Jin (author) / Juan Su (author) / Zhouhao Wu (author) / Di Wang (author) / Ming Cai (author)
2022
Article (Journal)
Electronic Resource
Unknown
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