Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization

Abstract

Noise maps and action plans represent the main tools in the fight against citizens’ exposure to noise, especially that produced by road traffic. The present and the future in smart traffic control is represented by Intelligent Transportation Systems (ITS), which however have not yet been sufficiently studied as possible noise-mitigation tools. However, ITS dedicated to traffic control rely on models and input data that are like those required for road traffic noise mapping. The present work developed an instrumentation based on low-cost cameras and a vehicle recognition and counting methodology using modern machine learning techniques, compliant with the requirements of the CNOSSOS-EU noise assessment model. The instrumentation and methodology could be integrated with existing ITS for traffic control in order to design an integrated method, which could also provide updated data over time for noise maps and action plans. The test was carried out as a follow up of the L.I.S.T. Port project, where an ITS was installed for road traffic management in the Italian port city of Piombino. The acoustic efficacy of the installation is evaluated by looking at the difference in the acoustic impact on the population before and after the ITS installation by means of the distribution of noise exposure, the evaluation of Gden and Gnight, and the calculation of the number of highly annoyed and sleep-disturbed citizens. Finally, it is shown how the ITS system represents a valid solution to be integrated with targeted and more specific sound mitigation, such as the laying of low-emission asphalts.

Accuracy Improvement of Vehicle Recognition by Using Smart Device Sensors

“…In their video measurement system (VMS), Ref. [69] used magnetic sensors, infrared sensors, photoelectric sensors, Doppler and radar sensors, inductive loops, and video camera systems to analyze traffic flow and speed measurement. While the system integrated visual detection as part of the vehicle identification mechanism, it used multiple low-cost sensors to integrate into existing intelligent transportation systems to control traffic.…”

Assessment of road traffic noise pollution in Basudevpur – A case study on the sub‐urban area of Bhadrak, Odisha, India

“…Fredianelli et al. (2022) analyzed the traffic flow through camera images and a noise map developed through Machine Learning Methods in ITS (Fredianelli et al., 2022).…”

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

“…Furthermore, the automatic fare collection systems for public transit collect a large amount of data, such as passenger flows. These data can be used for traffic counting and analysis, such as road traffic noise mapping computations or traffic pattern recognition [ 4 , 5 ].…”

“…Degree of improvement (DI) is measured byDI = MAPE BL −MAPE DR MAPE BL, where MAPE BL and MAPE DR denote the MAPEs of the baseline and DR models, respectively. 3 Website (highwaysengland.co.uk) 4. PeMS (Caltrans Performance Measurement System) 5.…”