CPS: Medium Collaborative Research: Smart Freight Transport Using Behavioral Incentives

该项目的目的是开发新的方法来提高货运活动的效率，从而减少与货运部门相关的空气污染和温室气体排放。 货运系统普遍效率低下，许多是由于系统中的行为者缺乏协调：铁路和卡车运输公司、航运公司、货主和港口运营商。有必要建立一个中央协调的货运管理系统，以优化铁路和公路运输网络之间的货运流量。 这些网络非常复杂：货运和客运共用相同的基础设施;需求存在时间和空间变化;有许多决策者影响系统。 我们使用优化和控制理论和技术结合行为模型来开发一个集中管理的控制系统。 其基本概念是有效的货运负载平衡，即在时间和空间上分配货运需求，以尽可能有效地满足整个需求。 为了实现最佳的负载平衡，我们必须解决两个问题：1）什么是最有效和可持续的时空分配的货物运输在整个道路网络，2）什么激励和定价工具将激励用户接受这种分配。 第二个问题是必要的，因为一个系统优化需要改变一些用户的行为相对于每个用户独立优化。我们的研究方法是协同仿真，控制和优化与BEECOME口头激励（SCOBE）。 我们使用仿真模型作为闭环优化系统的一部分，在该系统中，系统动态和用户行为被监控和更新。 一旦我们允许用户偏好的变化，解决系统优化问题的传统方法是不够的。 相反，我们必须直接考虑用户偏好，以确定发货分配和用户激励的最佳组合。该项目做出了以下贡献：首先，它通过开发一种用于用户偏好变化的系统优化方法来推进科学。 其次，将通过至少一家卡车运输公司的参与来证明所产生的方法，以评估其在现实世界中的价值。 第三，重型卡车排放的颗粒物是城市化地区影响人类健康的最大来源之一。 提高货运效率将减少颗粒物和其他排放，并减少温室气体排放。第四，该研究将用于促进代表性不足的本科生攻读研究生，并促进代表性不足的高中生在科学，技术，工程和数学（STEM）领域追求职业生涯。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The purpose of this project is to develop new methods to increase the efficiency of freight activity, thereby reducing air pollution and greenhouse gas emissions associated with the freight sector. There are widespread inefficiencies in the freight transport system, many due to lack of coordination across actors in the system: railroads and trucking firms, shipping companies, cargo owners, and port operators. There is a need for a centrally coordinated freight management system that will optimize the flow of freight across the rail and road transportation networks. These networks are very complicated: freight and passengers share the same infrastructure; there is temporal and spatial variation in demand; there are many decision-makers affecting the system. We use optimization and control theory and techniques combined with behavioral models to develop a centrally managed control system. The fundamental concept is efficient freight load balancing, meaning allocating freight demand across time and space to serve the entire set of demands as efficiently as possible. In order to accomplish optimal load balancing, we must address two questions: 1) What is the most efficient and sustainable spatio-temporal allocation of freight shipments across the road network, and 2) what incentives and pricing tools will motivate users to accept this allocation. The second question is necessary, because a system optimization requires changes in behavior for some users relative to each user optimizing independently.Our research approach is co-Simulation, Control and Optimization with BEhavioral incentives (SCOBE). We use simulation models as part of a closed loop optimization system where the system dynamics and user behavior are monitored and updated. Once we allow for variation in user preferences, conventional approaches to solving the system optimization problem are inadequate. Rather, we must take user preferences directly into account in order to determine the best combination of shipment allocations and user incentives. This project makes the following contributions: First, it advances science by developing a method for system optimization with variation in user preferences. Second, the resulting method will be demonstrated through the participation of at least one trucking company to evaluate its value in a real-world context. Third, particulate emissions from heavy duty trucks are one of the largest sources of human health impacts in urbanized areas. Increasing freight efficiency will reduce particulate and other emissions, as well as reduce greenhouse gas emissions. Fourth, the research will be used to promote under-represented undergraduate students to pursue graduate studies, and promote under-represented high school students to pursue careers in the science, technology, engineering and math (STEM) fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.