Evaluating the Impacts and Effectiveness of Traffic Calming Measures with Roadway Network Models

A multi-pronged investigation into the impacts and effectiveness of traffic calming measures (TCMs) at the road network level was evaluated, combining a systematic literature review, advanced microsimulation modeling, and innovative calibration techniques using connected vehicle data. While TCMs such as speed humps, raised crosswalks, and road diets are widely adopted to reduce vehicle speeds and improve road safety, existing studies have focused mainly on localized impacts and are limited by sparse or outdated data, incomplete methodologies, and inconsistent modeling practices. To better understand current practice and limitations, peer-reviewed articles were synthesized through a systematic review in Chapter 2, identifying key empirical models and highlighting deficiencies in data standardization, methodological robustness, and model transferability. The results from the review underscore the need for consistent, predictive approaches to estimate the trade-offs between reduced speed, traffic volume redistribution, noise, and emissions. To combat methodological robustness and model transferability, the current study uses microsimulation models to study the network-wide impact of TCMs using aggregated and disaggregated traffic data. A novel trip generation method is proposed in Chapter 3 to calibrate traffic models without relying on expensive origin-destination (O-D) pairs, achieving realistic simulations using sparse traffic count and speed data. Results demonstrate significant traffic diversion effects and speed reductions, emphasizing the broader systemic consequences of localized interventions. The current study extends this analysis in Chapter 4 by incorporating disaggregated INRIX connected vehicle trajectory data, scaled against pneumatic tubes ground truth. The model achieves high calibration accuracy using Simultaneous Perturbation Stochastic Approximation (SPSA) and successfully replicates post-TCM implementation behavior, including speed distribution narrowing and volume shifts. The hybrid framework developed in the current study provides a scalable, cost-effective, and transferable methodology for evaluating TCMs in data-limited urban settings.Collectively, this work advances the state of knowledge in transportation engineering by offering a comprehensive, scalable framework for evaluating the network-wide impacts of TCMs. The methods developed herein inform better decision-making for urban planners, policymakers, and engineers by addressing data scarcity, enhancing model validity, and emphasizing the interconnected nature of urban mobility systems.本研究针对路网层面的交通稳静化措施（traffic calming measures, TCMs）的影响与有效性开展多维度调研，结合系统文献综述、先进微观仿真建模，以及基于联网车辆数据的创新校准技术展开评估。尽管减速带、凸起人行横道、道路瘦身等交通稳静化措施已被广泛用于降低车辆行驶速度、提升道路安全，但现有研究多聚焦于局部影响，且受限于稀疏或过时的数据、不完善的研究方法，以及不一致的建模规范。为厘清当前研究现状与局限，本文第2章通过系统综述整合同行评议文献，梳理了核心实证模型，并指出当前研究在数据标准化、方法稳健性及模型可迁移性方面存在的不足。综述结果表明，亟需建立统一且具备预测能力的方法，以量化分析车速降低、交通流量再分配、噪声与污染物排放之间的权衡关系。针对方法稳健性与模型可迁移性的短板，本研究采用微观仿真模型，结合聚合与离散交通数据，探究交通稳静化措施的路网级影响。本文第3章提出一种新型出行生成方法，无需依赖成本高昂的起讫点（origin-destination, O-D）对即可完成交通模型校准，仅通过稀疏的交通流量与速度数据即可实现贴合实际的仿真效果。研究结果显示，该措施可产生显著的交通分流效果与车速降低效应，凸显了局部干预措施背后更广泛的系统性影响。本研究在第4章中拓展了上述分析，纳入离散化的INRIX联网车辆轨迹数据，并以气压管实测数据作为校准基准。本研究采用同时扰动随机逼近（Simultaneous Perturbation Stochastic Approximation, SPSA）算法实现高精度模型校准，并成功复现了交通稳静化措施实施后的交通行为特征，包括车速分布收窄与流量转移现象。本研究构建的混合框架，可为数据受限的城市场景下的交通稳静化措施评估提供可扩展、高性价比且具备可迁移性的研究方法。综上，本研究通过构建一套全面且可扩展的交通稳静化措施路网级影响评估框架，推动了交通工程领域的认知发展。本研究提出的方法通过解决数据稀缺问题、提升模型有效性，以及凸显城市交通系统的互联性，可为城市规划者、政策制定者与工程师提供更科学的决策依据。