Introduction

Recent disruptions in the transportation sector, such as the growing popularity of ridesourcing services, have substantially impacted traditional transportation services (such as taxis). However, the factors that determine usage rates of ridesourcing options are still not fully understood. The intent of the current paper is to develop a statistical model of individuals’ usage rates of ridesourcing services. Methods: Using a sample of recently collected data, a mixed logit model (multinomial logit model with random parameters) of ridesourcing-usage rate was estimated and, in addition to traditional socio-demographic factors, travel and health-related variables were also considered.

Results

Besides some socio-demographic variables, the travel and health-related variables were found to play statistically significant roles in ridesourcing usage. Specifically, age, gender, income, household size, vehicle ownership, typical parking time, and the nature of commutes were some of the significant variables found in model estimation results. In addition, self-assessed health, high body mass index (BMI), and registration for other shared mobility services were all found to play roles in ridesourcing usage.

Conclusions

The findings suggest that lower income, older age, and the presence of small children in the household are characteristics that should be targeted as means of reducing transportation inequity. They also suggest that policy efforts encouraging sharing ridesourcing trips could be beneficial on multiple fronts and lead the transportation system into the future by altering behavioral economics and system efficiency.

中文翻译：

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社会人口和健康相关因素在骑乘行为中的作用的探索性分析

介绍

最近运输领域的中断，例如拼车服务日益普及，已经极大地影响了传统的运输服务（例如出租车）。但是，决定出行选择的使用率的因素仍然不完全清楚。本文的目的是建立一个个人骑乘服务使用率的统计模型。方法：使用最近收集的数据样本，估算出乘车使用率的混合logit模型（带随机参数的多项式logit模型），除传统的社会人口统计学因素外，还考虑了旅行和健康相关的变量。

结果

除了一些社会人口统计学变量外，人们还发现与旅行和健康相关的变量在乘车出行中起着统计学上的重要作用。具体来说，年龄，性别，收入，家庭人数，车辆拥有量，典型的停车时间和通勤性质是模型估计结果中发现的一些重要变量。此外，人们发现自我评估的健康状况，高体重指数（BMI）以及其他共享出行服务的注册在骑行外包使用中也发挥了作用。

结论

研究结果表明，较低的收入，较高的年龄以及家庭中有小孩的情况是减少交通不平等现象的目标。他们还建议，鼓励共享拼车出行的政策努力可能在多个方面都是有益的，并且通过改变行为经济学和系统效率，将运输系统带入未来。