Predictive analytics of driver turnover

在当前的大数据时代，在运输和卡车运输行业中，可以轻松地以高速率收集大量关于司机和路线的各种有价值的数据。这些大数据中包含了有价值的信息和知识。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，以前未知的和潜在有用的信息和知识。对于这个参与项目，**DecisionWorks咨询公司报告说，司机流动率的问题对 ** 运输和卡车运输行业的客户来说很重要，因为培训一名新司机的成本大约为5000美元。此外，坏 ** 司机（例如，那些对设备很苛刻的人安全记录很差，或者在良好的道路条件下驾驶很困难的人）正在产生成本问题，例如延迟或管理不善的交付，高于平均水平的维护成本等。因此，拥有数据科学解决方案来发现关于 ** 驾驶员和路线的知识和信息，有助于揭示“安全”和“危险”路线上的“好”和“坏”驾驶员的特征，** 这反过来又有助于DecisionWorks增强其业务分析模型，以（a）降低 ** 的运营成本和（B）为其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的安全性、行为 ** 和保持力很重要。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。其他关系 **（例如，因果关系）而不是相关性可以存在于预定义的参数之间。此外，除了在相关性分析中使用的那些预定义参数之外，还可能存在影响驾驶员性能的一些因素。在这项拟议的工作中，我们计划设计和开发一个数据解决方案，用于对驾驶员进行预测性分析。特别是，在与行业合作伙伴的合作中，我们的解决方案有望 ** 通过预测分析组件执行模式匹配，以预测面临营业额风险的好司机。

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.