Predictive analytics of driver turnover

在当前的大数据时代，运输和卡车运输行业可以很容易地以高速率收集到大量关于司机和路线的各种有价值的数据。这些大数据中蕴含着宝贵的信息和知识。数据科学解决方案——应用多学科技术，如数据挖掘、机器学习（包括深度学习）和统计建模——有助于从大数据中发现隐含的、以前未知的、潜在有用的信息和知识。对于这个Engage项目，**DecisionWorks咨询公司报告说，司机更替问题对**运输和卡车运输行业的客户来说非常重要，因为培训一名新司机大约需要5000美元。此外，糟糕的司机（例如，那些对设备很苛刻的人安全记录很差，或者在任何情况下都很难在良好的路况下驾驶的人）正在造成诸如延迟或管理不善的交付，高于平均水平的维护成本等问题。因此，拥有数据科学解决方案来发现关于**司机和路线的知识和信息，有助于揭示“好”和“坏”司机在“安全”和“危险”路线上的特征，从而帮助DecisionWorks增强其业务分析模型，以实现(a)降低**的运营成本和(b)为客户改善卡车司机环境。现有的手动或自动方法侧重于对预先定义的参数进行相关性分析，这些参数被认为对驾驶员安全、行为和留存率具有重要意义。不幸的是，这种假设可能并不适用于所有现实生活中的情况。* *(如其他关系。预定义参数之间可能存在相关性以外的因果关系。此外，除了在相关分析中使用的预定义参数之外，还可能存在一些影响驾驶员性能的因素。在这项拟议的工作中，我们计划设计和开发一个用于驾驶员预测分析的数据解决方案。特别是，在与行业合作伙伴的合作中，我们的解决方案有望**与预测分析组件进行模式匹配，以预测有周转风险的良好驱动程序。

In the current era of big data, high volumes of a wide variety of valuable data about drivers and routes can be**easily collected at a high rate in the transportation and trucking industry. Embedded in these big data are**valuable information and knowledge. Data science solutions--which apply multidisciplinary techniques such as**data mining, machine learning (including deep learning), and statistical modelling--help discover implicit,**previously unknown and potentially useful information and knowledge from big data. For this Engage Project,**DecisionWorks Consulting Inc has reported that the issue of driver turnover is significant for its clients in the**transportation and trucking industry because it costs roughly $5K to train a new driver. Furthermore, bad**drivers (e.g., those who are hard on equipment have poor safety records or who find driving under anything but**good road conditions difficult) are costing problems such as late or mismanaged deliveries, higher than average**maintenance costs, etc. Hence, having data science solutions to discover knowledge and information about**drivers and routes helps reveal the characteristics of "good" and "bad" drivers on "safe" and "dangerous" routes,**which in turn helps DecisionWorks to enhance its business analytic model for (a) reducing the operating cost of**and (b) enhancing the truck driver environment for its clients. Existing manual or automatic approaches focus**on correlation analysis of predefined parameters that are assumed to be significant in driver safety, behavior**and retention. Unfortunately, such an assumption may not hold in all real-life situations. Other relationships**(e.g., causality) other than correlation may exist among the predefined parameters. Furthermore, there may also**exists some factors that affect driver performance beside than those predefined parameters used in the**correlation analysis. In this proposed work, we plan to design and develop a data solution for predictive**analytics of drivers. In particular, in cooperation with the industrial partners, our solution is expected to**perform pattern matching with a predictive analytic component to forecast good drivers at risk of turnover.