The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban data in planning tools capable of offering the flexibility of scenario-based design. This article introduces Berkeley Integrated System for Transportation Optimization (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. It 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. The portfolio of transportation intervention strategy alternatives eventually chosen achieves high-level regional planning goals developed through participatory stakeholder engagement practices.

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小酒馆

当前的城市化趋势以及采用灵活和创新的移动技术将对城市交通系统产生复杂且难以预测的影响。尚不存在能够解释城市交通景观未来状态日益不确定的综合方法框架。此外，很少有方法能够在能够提供基于场景设计的灵活性的规划工具中大量摄取城市数据。本文介绍了伯克利交通优化集成系统 (BISTRO)，这是一种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制定针对可能的技术中断和增长情景的适应性计划。新框架是在 Uber Technologies, Inc. 举办的机器学习竞赛的背景下进行评估的，400 多名工程师和数据科学家参加了该竞赛。出于本次比赛的目的，一个基于南达科他州苏福尔斯市的基准模型适用于 BISTRO 框架。这项研究的一个重要发现是，尽管在赛前进行了严格的分析和测试，但得分最高的两个团队发现了搜索空间的无限区域，使得解决方案在很大程度上无法解释为决策支持的目的。另一方面，后续研究旨在修复目标函数。它展示了 BISTRO 作为人在环网络物理系统的实用性：一种使用基于场景的优化算法作为反馈机制，以帮助城市规划者迭代改进干预策略的目标函数和约束规范。最终选择的交通干预策略替代方案组合实现了通过参与性利益相关者参与实践制定的高水平区域规划目标。