AI 中文总结
本研究提出可控拥堵度量指标，基于METANET模型结合MPC框架估算，可区分不可避免与可调控拥堵，指导ITS的成本效益部署。
AI 中文摘要
新兴智能交通系统（ITS）的进步在减少拥堵、排放和事故方面展现出巨大效益，且为高速公路车道扩建提供了成本更低的替代方案。然而，目前各机构尚无标准度量指标来评估这些干预措施的改进潜力，可能导致无法在最具前景的区域部署基础设施，或投资于无法显著提升性能的基础设施。本研究旨在开发一种度量指标，用于量化通过控制高速公路限速实现的拥堵改进上限，以指导ITS的部署。我们提出了可控拥堵（controllable congestion）这一度量指标，用于量化系统延误的最大可实现减少量。为估算可控拥堵，我们开发了一种基于METANET宏观交通模型重构的非线性优化框架，并采用模型预测控制（MPC）求解。通过合成场景和来自田纳西州I-24 SMART Corridor的高速公路数据，我们发现可控拥堵在很大程度上与总延误无关，这表明在两天旅行时间相同的情况下，可控拥堵可在13%至80%之间变化。我们进一步证明，该上限的可实现份额取决于施加的运行约束：在I-24上，最低公布限速对可控拥堵影响很小，而限速更新频率与相邻龙门架间的最大差值则有显著影响。该框架使高速公路运营方能够区分结构性不可避免的拥堵和对ITS高度敏感的拥堵，从而实现基于控制的交通基础设施的更具成本效益的部署。
英文摘要
Advancements in emerging intelligent transportation systems (ITS) have shown immense benefit in reducing congestion, emissions, and accidents and enable lower-cost alternatives to highway lane expansion. However, there currently exists no standard metric by which agencies can assess the improvement potential from these interventions. As a result, they may fail to deploy infrastructure where it will be most promising or risk investing in infrastructure that does not meaningfully enhance performance. Our objective is to guide ITS deployment by developing a metric that quantifies the upper bound of congestion improvement from controlling speed limits on a highway. We propose controllable congestion, a metric that quantifies the maximum achievable reduction in system delay. To estimate controllable congestion, we develop a nonlinear optimization framework grounded in a reformulation of the METANET macroscopic traffic model and solved using model predictive control (MPC). Using both a synthetic scenario and highway data from the I-24 SMART Corridor in Tennessee, we find that controllable congestion is largely independent of total delay, revealing that for two days with identical travel times, controllable congestion can vary from 13\% to 80\%. We further show that the realizable share of this upper bound depends on the operational constraints imposed. On I-24, the minimum posted speed limit has little effect on controllable congestion, while frequency of speed limit update and maximum difference between adjacent gantries have large impacts. This framework allows highway operators to distinguish between congestion that is structurally unavoidable and congestion that is highly responsive to ITS, enabling more cost-effective deployment of control-based traffic infrastructure.
CommentsSubmitted to Transportation Research Part C: Emerging Technologies
