Abstract
Motorway’s personnel tasks management and incidents monitoring, and response are critical processes that contribute to the motorway’s orderly and smooth operation. Existing management practices utilize SCADA technologies that control motorway actuator systems as well as various means of personnel communications mobile technologies. Nevertheless, contemporary motorways lack a unified incident response solution that tracks resources, sends notification alerts when necessary, and automates incident resolution. This paper presents a new holistic and unified management and response system called Resources Management System (RMS). This system was originally implemented as a generic motorways resources management system for the EGNATIA ODOS motorway that uses it in Greece. The implemented RMS provides real-time resources tracking, personnel utilization algorithms, and data mining capabilities towards incident confrontation. It operates as an incidents’ collection and resources central communication interface. It is also capable of incident response and completion time categorization; real-time tunnel exits sensory monitoring, staff mobilization, and tracking system. Furthermore, the RMS includes machine learning methodologies and smart agents (bots) for solving the problem of estimating and evaluating the response and completion time of incidents based on previous successful incidents’ confrontations.
Highlights
Each of the personnel team is closer to the incident, the Resources Management System (RMS) has stored the completion time used for the estimation of completion time of the incident, and the description to apply the second machine learning completion time algorithm
On the data set of the RMS as they have been inserted into the dataset and during the pilot application on the area of the 0–69 km of the motorway, from Ioannina to Igoumenitsa, the data filtering mechanism is applied
Incident dedescriptions are classified in descending order based on the frequency, and all of them are scriptions are classified in descending order based on the frequency, and all of them are displayed through the Intelligent Agent, so the RMS user can choose which displayed through the Intelligent Agent, so the RMS user can choose which one is the most suitable for the incident that occurred
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Resource management systems are the critical steps towards automation and safety as they offer the appropriate response protocols and resources management recommendations to the motorway operators [4,5] Such systems use supervised learning algorithms, deep learning, smart-bots, and automated suggestions via predefined assisted learning incidents planning towards industry 4.0 and autonomous systems. The proposed RMS, along with the resources management, incidents evaluation-estimation algorithms, and sensory interfaces, can achieve the following key objectives: (a) development of a holistic technological system for managing motorway human resources daily operations and equipment tracking; (b) data collection of sensory information ( implemented for tunnels smart exists) [6,7] acquired by operators of motorway traffic control centers; and (c) use of intelligent communications system and appropriate supervised and unsupervised machine learning algorithms to effectively deal with emergencies
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