Development of an economical level of service estimation model using gps data in a mixed traffic condition

Abstract

This research presents the development of an economical Level-of-Service (LOS) estimation model using GPS data in a mixed traffic condition, with a specific focus on defining clusters based on the categories within the existing Highway Capacity Manual (HCM) definitions of motorised LOS, for practical application. The study aims to enhance the representation of Sri Lankan traffic conditions, predominantly observed on 2-lane roads, particularly within the LOS D and E categories where the majority of typical Sri Lankan traffic situations occur. The data collection scope encompasses the entirety of Sri Lanka to ensure the generation of more representative values for the defined clusters. As clusterisation parameters, Average Travel Speed which is a reflection of mobility, Percentage Time Spent Following another vehicle and the Percentage Free Flow Speed which is a ratio of current speed to the posted speed limit were used in the same manner as HCM 2016 - 15-2. It showcases the utilisation of two CNN based image processing models developed, one for assessing the ‘following’ and ‘non-following’ states and the other to assess the types of road (road classes), using the Google Colaboratory platform, for the analysis of geo-tagged video collected through the Transcend DrivePro 250 and their combination with 1 Hz GPS data collected by the Qtravel GPS device, which includes parameters such as speed, heading local date and time. Additionally, application of unsupervised K means clustering, which finds k centroids and then assigns each data point to the closest cluster while minimising the size of the centroids, to define clusters corresponding to the HCM definitions. The proposed methodology and model aim to provide an improved representation of LOS in Sri Lanka's traffic conditions, considering the unique characteristics of the road network and the predominant traffic scenarios observed in the country. The research findings, produce a table containing parameters similar to HCM 15-2 (Motorised LOS parameters for 2 lane roads) but in a practical sense instead of a planning tool. PFFS exceeded 100% due to speed limit choice (50 km/h) for class 03 roads and FCD non-compliance. Cluster 5 needs to be checked against road capacity levels. Adjusting limits in the clustering model can eliminate any potential issues. However, the primary objective has been achieved for representative LOS clusterisation from GPS and geo-tagged video data.