Chengdu taxi data for November 19, 2016.

This study proposes a novel GPS-based methodology for Macroscopic Fundamental Diagram (MFD) estimation to overcome limitations of fixed detectors and inaccurate penetration rate assumptions. The approach dynamically identifies stop-line positions using spatiotemporal floating car data, calculates maximum queue lengths per signal cycle by combining floating car positions with estimated arriving vehicle lengths, and establishes a speed-based nonlinear model to determine queuing vehicle counts. A dynamic scaling coefficient derived from maximum queue lengths enables assumption-free estimation of total regional vehicles when applied to the floating car population. Validation using Chengdu data demonstrates significant improvements: unary cubic curves achieve optimal fitting for MFD relationships (R2 up to 0.9157); the HMM-CRF hybrid map-matching algorithm reduces average position error by 29% and intersection mismatch rate by approximately 40%; simulation results show queue length estimation accuracy of RMSE 22.8m and MAPE 18.5%, while MFD estimation error for maximum network flow drops from −17.5% to −3.5%, representing an 80% relative accuracy improvement. The proposed methodology provides robust technical support for urban road network assessment and management by enabling high-precision acquisition of MFDs from floating car data, effectively addressing critical challenges in macroscopic traffic modeling and monitoring. This advancement presents potential value for perimeter control applications and other MFD-based traffic management strategies.本研究提出了一种基于GPS的新型宏观基本图（Macroscopic Fundamental Diagram, MFD）估计方法，以克服固定检测器的固有局限以及浮动车渗透率假设不准确的缺陷。该方法借助时空浮动车数据动态识别停止线位置，结合浮动车点位与预估的到达车辆长度，计算每个信号周期内的最大排队长度，并构建基于速度的非线性模型以确定排队车辆数。从最大排队长度中推导得到的动态缩放系数，可在应用于浮动车群体时实现区域总车辆数的无假设估计。利用成都数据集开展的验证实验表明，该方法取得了显著的性能提升：一元三次曲线可实现MFD关系的最优拟合（决定系数R²最高可达0.9157）；隐马尔可夫-条件随机场（Hidden Markov Model-Conditional Random Field, HMM-CRF）混合地图匹配算法可将平均位置误差降低29%，并将交叉口匹配错误率降低约40%；仿真结果显示，排队长度估计的均方根误差（Root Mean Square Error, RMSE）为22.8米，平均绝对百分比误差（Mean Absolute Percentage Error, MAPE）为18.5%；而最大网络流量的MFD估计误差从-17.5%降至-3.5%，相对精度提升达80%。所提方法可通过浮动车数据高精度获取宏观基本图，为城市道路网络评估与管理提供可靠的技术支撑，有效解决了宏观交通建模与监测领域的关键难题。该研究成果可为区域边界控制（perimeter control）以及其他基于MFD的交通管理策略提供潜在应用价值。