# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## 1 Introduction  
The number of vehicles on the roads has been increasing exponentially, both in developing and developed countries. The reasons are consumerism, reduced prices, finance options, competition and the requirement of faster and convenient transport. An inevitable outcome of this continuous production and buying of these vehicles, especially the private ones, has been an increase in the noise and air pollution levels due to the hard traffic congestion, especially in critical areas of the cities [1; 2; 3]. The effects of air pollution are well known to the general public. To address the problem of environmental pollution, many predictive models and experimental studies have been developed for the traffic management as efficient solution to reduce vehicle journey times and, consequently, environmental impact. For instance, in [4], carbon dioxide (CO\({}_{2}\)) emissions and unnecessary fuel consumption are addressed in the framework of route management for autonomous vehicles in urban areas. Conversely, there is a relatively lesser awareness of the harmful effects of traffic noise on the human population. Some of the adverse effects [5; 6; 7; 8] include sleep disturbance, speech interference, annoyance, cardio-vascular effects and loss of fertility. In rare cases, such as with traffic policemen who are exposed to high sound pressure levels and long-term road traffic noise, hearing loss has also been observed [9]. As for the prediction of traffic volumes and the adoption of traffic management strategies to reduce noise pollution levels, a lot of research has been done on the effects of noiseon human health [10, 11]. Traffic noise prediction models play a very important role not only in the design and modification of road and traffic infrastructure [12] but also in the assessment of the noise level, based on certain critical parameters. Different types of traffic noise prediction models include country or region-specific models like the \"Federal Highway Administration model\" (FHWA, USA), the \"UK Calculation of Road Traffic Noise\" (CoRTN, UK), the \"Acoustical Society of Japan Road Traffic Noise model\" (ASJ, Japan) and the \"Richtlinien fur den Larmschutz an Straben\" (RLS-90, Germany). A critical review of the most used models can be found in [13, 14, 15, 16]. Other categories of models include static and dynamic, stochastic and deterministic and artificial intelligence or machine learning based models [17, 18, 19, 20, 21, 22]. In particular, several applications to traffic noise prediction can be found in literature, for instance [23, 24, 25, 26, 27], in which the authors adopt artificial intelligence techniques, such as artificial neural networks (ANN) and genetic algorithms (GA). In most recent studies, hybrid models combining systems of ANN and fuzzy interference systems (FIS) (i.e., adaptive neuro fuzzy inference system--ANFIS) have been developed too [28]. Other hybrid models are represented by the integration between time series analysis techniques and ANN [29]. The different parameters that are generally considered in these models are traffic volume (number of vehicles per unit of time), percentage of heavy vehicles, average speed of vehicles, type-road interaction, building height, road width and gradient etc. One parameter that has generally been not considered and is quite prevalent in the Indian road conditions is honking noise. The relevance of the honking in the Indian scenario has been demonstrated in several studies. In [30], for example, it is reported how the expansion of the transportation industry in the city of Nagpur led to an increment of 5-6 dBA in noise level, 4-6 times in honking and 1.7 times in traffic volume. The Indian roads traffic conditions are quite different because of the size of the population, topography of the roads, vehicle types and conditions, driving habits and culture.