WATI: Warning of Traffic Incidents for Fuel Saving

Abstract

Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the rolling resistance coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%.

Logical Structure of Actions to Reduce the Impacts of Traffic Incidents

“…Analyzing the environmental impact, incidents generate an increase in emissions of air pollutants (Thomas and Jacko, 2007;Islam, 2019) and GHG (Baltar et al, 2020b;2021a), an increase in noise pollution (Riedel et al, 2017) and the depletion of natural resources due to increased consumption of fossil fuels (Corcoba et al, 2016).…”

“…Furthermore, traffic incidents (heavy traffic, adverse weather conditions and traffic accidents) cause an increase in the frequency and intensity of acceleration and deceleration. The result is a very significant increase in fuel consumption (Corcoba et al, 2016). Dia, Gondwe and Panwai (2006) estimated that reducing the duration of a dual lane incident from 30 to 15 minutes resulted in 11.2% reductions in fuel consumption.…”

Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving

“…Ferri (2016) used decision trees, logistic regression, Naïve Bayes, KNN (K-nearest neighbours), SVM (support vector machines) and MDA (Mixture Discriminant Analysis) to extract information from GPS traces. Corcoba Magaña and Muñoz-Organero (2016) used several classification techniques applied to onvehicle telemetry data to detect traffic incidents. Magaña, Organero, Fisteus, and Fernández (2016) made used of deep learning methods to detect the driver state while driving based on GPS data from a mobile device and a heart rate wearable sensor.…”