Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Mining underlying factors affecting the income level will not only benefit the newcomers and low-income drivers but also assist in developing effective optimization algorithms for taxi operations. This paper intends to disclose the factors affecting incomes along with their quantitative influence by mining over 167 million GPS records from nearly 8000 taxis in Shanghai. We first identify a marked difference in drivers' incomes and categorize drivers into three income levels accordingly. We next investigate the overall search-delivery process, thereby defining several factors that may affect the income level. We then develop a generalized multi-level ordered logit (GMOL) model to find the significant factors that influence incomes. Finally, we compute the elasticity for those significant factors and present their contributions, as well as challenge some preconceived ideas regarding how to earn high incomes.

BackgroundExisting studies reporting on the levels of physical fitness among high school students use relatively few fitness tests for indicators of physical fitness, thus, incomprehensively evaluating the levels of physical fitness. Therefore, this study investigated the relationship between body mass index (BMI) and physical fitness index (PFI) by investigating five physical fitness indicators and calculating PHI.MethodAnthropometric measurements and indicators from five measures of physical fitness (50-m sprint, sit and reach, standing long jump, 800/1,000-m run, pull-up/bent-leg sit-up) were assessed. BMI was calculated to classify individuals into underweight, normal weight, overweight, and obese categories. Z-scores based on sex-specific mean and standard deviation were calculated, and the sum of Z-scores from the six fitness tests indicated the PFI. The findings were fitted to a linear regression model to elucidate the potential relationship between BMI and PFI.ResultsIn total, 176,655 high school students (male: 88,243, female: 88,412, age: 17.1 ± 1.05 years, height: 168.87 ± 11.1 cm, weight: 62.54 ± 15.15 kg) in Jinan, China, completed the physical fitness tests between 2020 and 2021. The one-way ANOVA models showed that PFI in the normal category was significantly higher as compared to all the other BMI categories within both male and female groups (p < 0.001), and PFI in the obese category was significantly lower as compared to all the other BMI categories for both male and female groups (p < 0.001). The association between PFI and BMI showed an inverted U-shape relationship.ConclusionsThis study demonstrated that BMI affects the PFI in both males and females. As compared to the obese and overweight categories based on BMI, significantly higher scores of PFI were observed for males and females.

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Adverse weathers are well-known to impact the operation of transportation systems, including taxis. This paper utilizes taxi GPS waypoint data to investigate the quantitative impact of rainfall on taxi hailing and taxi operations to help improve service quality on rainy days. Through statistical analysis, the study proves that it is more difficult to hail taxis on rainy days, especially during morning peak hours. By modelling the difference value of factors for rainfall and nonrainfall conditions in a multivariate regression model and attaining the significance and elasticity of each factor, passenger demand, taxi supply, search time and velocity are proved to be the significant factors that lower the taxis’ level of service on rainy days. Among them, the number of passengers and taxis are two factors that have the greatest impact. It is also shown that there is no significant difference in the total taxi supply and passenger demand between rainfall and nonrainfall conditions, but a dramatic change in the spatial distribution is discovered. The results suggest that instead of simply providing more taxis on rainy days, optimally dispatching taxicabs to high demand regions can be a more effective solution.

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