Alicante-Murcia Freeway Scenario: A High-Accuracy and Large-Scale Traffic Simulation Scenario Generated Using a Novel Traffic Demand Calibration Method in SUMO

Abstract

The design, testing and optimization of Vehicle to Everything (V2X), connected and automated driving and Intelligent Transportation Systems (ITS) and technologies requires mobility traces and traffic simulation scenarios that can faithfully characterize the vehicular mobility at the macroscopic and microscopic levels under large-scale and complex scenarios. The generation of accurate scenarios and synthetic traces requires a precise modelling approach, and the possibility to validate them against real-world measurements that are generally not available for large-scale scenarios. This limits the open availability of realistic and large-scale traffic simulation scenarios. The purpose of this paper is to present a large-scale and high-accuracy traffic simulation scenario. The scenario has been implemented over the open-source SUMO traffic simulator and is openly released to the community. The scenario accurately models the traffic flow, the traffic speed and the road’s occupancy for 9 full days of traffic over a 97 km freeway section. The scenario models mixed traffic with light and heavy vehicles. The simulation scenario has been calibrated using a unique dataset provided by the Spanish road authority and a novel learning-based and iterative traffic demand calibration technique for SUMO. This technique, referred to as Clone Feedback, is proposed for the first time in this paper and does not require a pre-calibration to generate realistic traffic demand. Clone Feedback can generate calibrated mixed traffic (light and heavy vehicles) using as input only traffic flow measurements. The results obtained show that Clone Feedback outperforms two reference techniques for calibrating the traffic demand in SUMO.