BISTRO：伯克利交通优化集成系统
BISTRO: Berkeley Integrated System for Transportation Optimization
摘要：
本文介绍了BISTRO，一个新的开源交通规划决策支持系统，它使用基于代理的模拟和优化方法来预测和开发适应可能的技术中断和增长场景的计划。新框架在Uber Technologies，Inc.举办的机器学习竞赛中进行了评估，超过400名工程师和数据科学家参加了此次会议。为了这次比赛的目的，一个基准模型，基于城市的苏福尔斯，南达科他州，适应BISTRO框架。这项研究的一个重要发现是，尽管在比赛前进行了严格的分析和测试，但两个得分最高的团队发现了搜索空间的一个无界区域，使得解决方案在很大程度上无法解释决策支持的目的。另一方面，一项旨在确定目标函数的后续研究证明了BISTRO作为人在回路网络物理系统的实用性：一种是使用基于MIMO的优化算法作为反馈机制，以帮助城市规划者迭代地细化目标函数和干预策略的约束规范，使得最终选择的交通干预策略备选方案的组合实现高性能，通过参与性的利益攸关方参与做法制定区域一级的规划目标。
英文摘要：
This article introduces BISTRO, a new open source transportation planning decision support system that uses an agent-based simulation and optimization approach to anticipate and develop adaptive plans for possible technological disruptions and growth scenarios. The new framework was evaluated in the context of a machine learning competition hosted within Uber Technologies, Inc., in which over 400 engineers and data scientists participated. For the purposes of this competition, a benchmark model, based on the city of Sioux Falls, South Dakota, was adapted to the BISTRO framework. An important finding of this study was that in spite of rigorous analysis and testing done prior to the competition, the two top-scoring teams discovered an unbounded region of the search space, rendering the solutions largely uninterpretable for the purposes of decision-support. On the other hand, a follow-on study aimed to fix the objective function, served to demonstrate BISTRO's utility as a human-in-the-loop cyberphysical system: one that uses scenario-based optimization algorithms as a feedback mechanism to assist urban planners with iteratively refining objective function and constraints specification on intervention strategies such that the portfolio of transportation intervention strategy alternatives eventually chosen achieves high-level regional planning goals developed through participatory stakeholder engagement practices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
