Guidance Effectiveness in Urban Intelligent Transportation Systems: Determinants, Energy-Saving and Carbon-Reduction Mechanisms, and Optimization Strategies

As a key technology in intelligent transportation systems, speed guidance improves vehicle eco-driving in urban areas, yet its potential failures can cause suboptimal performance. As a macro-level evaluation metric for evaluating the effectiveness of speed guidance, the Guidance Effectiveness Rate exerts a crucial impact on the optimization performance of the speed guidance. To address this, the study establishes a research pipeline encompassing large-scale data acquisition, algorithm development, mechanism analysis, and strategy design. An ensemble machine learning model (MVE) was developed to classify individual trajectory points as effective/ineffective guidance points, and then accurately estimate the trip-level Guidance Effectiveness Rate (GER) from vehicle trajectory data. Trained and validated on experimental data, the MVE model achieved a recall of 0.89. This model was then applied to classify over 1.1 million trajectory data points from a real-world speed guidance system, and the subsequent trip-level statistical analysis revealed an average GER of 0.79. The analysis identified two primary scenarios for low GER: on good roads (speeds of 60-80 km/h, acceleration <0.3 m/s 2 ) and in low-speed congestion (speeds of 15-40 km/h, acceleration >0.5 m/s 2 ). Furthermore, the proportion of aggressive driving peaked at a GER of approximately 0.75. Most critically, vehicle energy consumption and CO 2 emissions were found to be highest at low speeds (<20 km/h) and increased significantly within the GER band of 0.7 to 0.9. Consequently, it is recommended to restrict speed guidance applications in both free-flow and severely congested conditions to mitigate negative impacts. The research supports the practical application of speed guidance-type intelligent transportation systems.