Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Abstract

This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.

An Overview of Current Challenges and Emerging Technologies to Facilitate Increased Energy Efficiency, Safety, and Sustainability of Railway Transport

“…Thus, it represents a key enabling technology to facilitate automated rail traffic control and energy use optimisation and their integration in a straightforward fashion. This has been illustrated by previous examples in references [149,150], wherein the advanced information and communication technologies, data from remote sensor networks, and artificial intelligence have been successfully used to improve railway transportation safety and energy efficiency. Similarly, the utilisation of sensor suites on-board traffic entities coupled with massive data throughput inherent to 5G radio networks was identified in [156] as enabling technologies to increase the safety of autonomous transport.…”

“…Moreover, track conditions can be used to optimise the energy (fuel) expenditures based on a suitable computer model supplied with the real-time track condition information, thus supporting the transportation sector's "greening" and sustainability [36]. Another example of possible NB-IoT sensor use in conjunction with AI for increased traffic safety was presented in [150]. A traffic entity management algorithm based on fuzzy logic was designed to reduce traffic congestion in the case of a partial restriction of one of the two routes of the transport corridor, which is a typical problem in railways (Figure 8).…”

“…A traffic entity management algorithm based on fuzzy logic was designed to reduce traffic congestion in the case of a partial restriction of one of the two routes of the transport corridor, which is a typical problem in railways (Figure 8). Under the assumptions of available traffic entity (vehicle) queue lengths (e.g., by using NB-IoT remote sensor nodes for vehicle registration and tracking) the proposed fuzzy logic-based traffic entity scheduling algorithm was compared with conventional traffic scheduling, and it demonstrated the ability to effectively minimise vehicle queue lengths under stochastic conditions of incoming traffic flows [150]. Another example of possible NB-IoT sensor use in conjunction with AI for increased traffic safety was presented in [150].…”

“…Under the assumptions of available traffic entity (vehicle) queue lengths (e.g., by using NB-IoT remote sensor nodes for vehicle registration and tracking) the proposed fuzzy logic-based traffic entity scheduling algorithm was compared with conventional traffic scheduling, and it demonstrated the ability to effectively minimise vehicle queue lengths under stochastic conditions of incoming traffic flows [150]. Another example of possible NB-IoT sensor use in conjunction with AI for increased traffic safety was presented in [150]. A traffic entity management algorithm based on fuzzy logic was designed to reduce traffic congestion in the case of a partial restriction of one of the two routes of the transport corridor, which is a typical problem in railways (Figure 8).…”

“…Figure10. Example of autonomous vehicle using the concept of virtual tracks and equipped with 5G remote sensing and communication platform[150].…”