Modelling and simulation of a multi-phase traffic light controlled T-type junction using timed coloured petri nets

Abstract

The emergence of traffic and subsequently traffic congestion in urban road networks are increasing worldwide with the growing number of vehicles. In the worst case, traffic congestion results in excess delays, reduced safety and increased environmental pollution. As a result, to proffer effective solutions to the aforementioned congestion problems, a vast amount of literature offered fixed-time or traffic-response control strategies, each focusing on modelling traffic light controlled intersections. In this paper, a Timed Coloured Petri Net (TCPN) formalism is employed to model and simulate a multi-phase traffic light controlled intersection with an associated fixed signal timing plan using T-type junction located in Federal Capital Territory, Abuja, Nigeria, as a case study. Besides, the developed model is simulated via CPN tools while its validation is carried out on the basis of real traffic data measured by detectors positioned at each input link of the T-type junction under consideration.

Modelling and Simulation of Deposit Slip Mode of Bank Cash Deposit Transactions Using Hierarchical Timed Coloured Petri-Nets

“…When time concepts are introduced into a Coloured Petri Net model, a Timed Coloured Petri Net (TCPN) model is obtained [1]. Thus, with a Hierarchical Timed Coloured Petri Net, it is possible to predict performance measures, such as the time at which a customer arrives and receives service, average flow time and percentage of customers attended to.…”

Help the Genetic Algorithm to Minimize the Urban Traffic on Intersections

“…But the article again after 10,000 generations, better solutions may also be found (Figure 8). The horizontal axis in (Figure 8), the numbers of parameters x1 to x14 is and the vertical axis represents the difference seated normalized values obtained from simulation and actual values is even in the worst case [26], the difference between 0.77 percent and 23 percent error. The difference is 1.00, the best value reaches zero.…”

Density based auto traffic light control system with GSM based remote override for enugu metropolis

“…Experimental results show that the proposed method can estimate the unknown traffic volume using only the known traffic volumes. In Ganiyu et al (2011), performance evaluation study was carried out by utilizing the existing vehicle-based sensors in taxies for traffic monitoring and vehicle density calculation in order to reduce traffic congestion. Here, two types of traffic status-estimation algorithms, i.e., the link-based and the vehicle-based, are introduced and analyzed with results that show that estimations of the traffic status based on these imperfect data are reasonably accurate.…”

“…The intelligent traffic light control system has the capacity to detect any lane in a t-road or cross-road junction with higher density of vehicles and allot to it the right of way and blocking every other lane with less density of vehicles. In addition and most importantly, it has a remote control facility to override the set timing by providing instantaneous green signal in the desired direction while blocking the other lanes by red signal for some time (Ganiyu, Olabiyisi, Omidiora, Okediran & Alo, 2011;Jaiswal, Agarwal, Singh & Lakshita, 2013;Mainali & Mabu, 2010;Li et al, 2009). The remote control overriding authority may usually be vested only on the FRCN officials and the special road users to override the set timing in the event of emergencies.…”