Global Journal of Researches in Engineering, J: General Engineering, Volume 22 Issue 1

the system If n = 6 Trucks Probability of having 6 Trucks or more in the system = 0.96 6 = 0.7828 or 78.28% Table 3: Summary of Model Results IV. R esults and D iscussion In this study, traffic intensity stood at 0.96 which was close to 1, indicating that the queue situation was getting to an infinite state in which long queue could be observable at the dumpsite throughout the day. As revealed in table 3, the average number of trucks in the system, average number of trucks in the queue (length of queue) and average number of trucks in the queue (where there is queue) were 24 trucks, 23 trucks and 25 trucks respectively. Average waiting time in the queue and in the system accounted for 28mins and 30mins respectively. The study’s results also showed 96% probability that a truck will queue on arrival before being served while the probability that a truck will not queue on arrival is just 4%. The probability of having exactly ‘n’ number of Truck in the system stood at 3% and probability of having ‘n’ or more trucks in the system was 78%. Probability of having no Truck at all in the system (idle time) was 4%. This is so as the traffic intensity is running to 1 and queue development is bound to occur, meaning that the system will always be busy from time to time. Again, it is clear that an average of 25trucks will be waiting in line and each will spend at least 28mins before receiving service. Apart from this, the 96% of traffic waiting in line is indicative that the long queue is affecting the truck drivers negatively. Consequently, some of them may want to leave without being served (baulking or abandonment), or look for available space upfront to enter the queue illegitimately (shunting), thereby worsening the traffic situation on the major road due to frustration. a) Model Validation In this study, Chi square test (X2) was used to test whether the values of the arrival rate followed Poisson distribution or not at 5% level of significance and this was carried out in relation to its variance. Table 4: Model Hypothesis Mean (x) Variance X2 4 2.333 1.8955 Ho: Trucks Arrival rate does not follow a Poisson distribution at the dumpsite. H1: Trucks Arrival rate follows a Poisson distribution at the dumpsite. The expected or predicted service rate was set at 4 trucks/hour. The critical value of X 2 (X 2 CV) was set at =0.05 and 11 degree of freedom. The table value is 19.675. Since the calculated (P-value) value (1.8955) is less than the table value (19.675), the result is not significant. Ho is accepted – meaning that the truck arrival rate does not follow a Poisson distribution at the dumpsite. Although there exists a close fit in the observed arrival rate and the predicted service rate as well as X2 value (1.8955) and the value of the variance (2.3333). V. C onclusion Applying queue model for trucks management at Solous III dumpsite in Igando showed that queue always exists at the dumpsite as revealed by the level of the traffic intensity but this can be reduced drastically if more service points are created by the management. The simple queue model and possibly multiple server model could be applied as monitoring or evaluation tools for either the dumpsite performance or management of the trucks that arrive the dumpsite for waste disposal. It is believed that better improvement can be achieved by using the multiple queue model to manage trucks at the dumpsite by the Lagos Waste Management Authority (LAWMA). This can go a long Improving Trucks Management at Dumpsites through the Application of Queue Theory- The Case of Solous III Dumpsite, Igando, Lagos State lobal Journal of Researches in Engineering ( ) Volume XxXII Issue I Version I J G 35 Year 2022 © 2022 Global Journals 4. Probability of having ‘n’ or more cars in the system = Rn 5. Probability of having no Truck at all in the system P(o) = (R) o (1 – R) = (0.96) o x (1 – 0.96) = 0.04 or 4%