Development of an intelligent traffic management system using dynamic time allocation technology
| dc.contributor.author | Nwaogwugwu, Nnanyereugo Kelechi Clinton | |
| dc.date.accessioned | 2026-04-17T14:43:25Z | |
| dc.date.available | 2026-04-17T14:43:25Z | |
| dc.date.issued | 2024-06 | |
| dc.description | This thesis is for the award of Doctor of Philosophy (Ph.D) in Computer Engineering | |
| dc.description.abstract | Considering the increase rate at which traffic congestion occur in urban cities which is associated with longer waiting time of vehicles on traffic queues as well as loss of productivity, fuel, time, fatigue and other health-threatening conditions; it is imperative to apply innovative, fully functional and affordable technologies to curb this challenge. Unfortunately, state-of-the-art traffic management systems are not able to solve this problem as they do not employ intelligent traffic control techniques at road junctions. This research developed an intelligent road traffic management system using Dynamic Time Allocation Technology (DTAT). In doing this, Modelio, an open-source Unified Model Language (UML) tool, was utilized in conjunction with object-oriented system analysis and design in analysing and modelling of typical traffic scenario at road junctions. A sensor network for the detection of vehicular presence and movement was designed using motion sensors and IP cameras. Furthermore, a camera system for capturing vehicle plate number of offending drivers, as well as traffic offence SMS gateway for communicating with offenders and appropriate authorities was developed. Fuzzy Logic and Artificial Neural Network (ANN) techniques as well as load balancing and remote procedural call (RPC) were applied in implementing the fundamental operations of the system. The system was simulated using Proteus 8 Professional in which a microcontroller was used to run the fuzzy logic operations, while the ANN runs on the data storage server and was used to analyse the patterns of stored data so that the system can learn traffic situations of the road with time. Results obtained indicate that the frequency at which traffic flows at a particular lane of the road within a period of time is a function of the number of vehicles that enter and leave the traffic zone at that point. Performance of the developed system shows that there is a balance in the flow of traffic for different lanes of the road intersecting at a point. | |
| dc.identifier.citation | Nwaogwugwu, N. K. C. (2024). Development of intelligent traffic management system using dynamic time allocation technology [Unpublished Doctoral Thesis]. Federal University of Technology, Owerri, Nigeria | |
| dc.identifier.uri | https://repository.futo.edu.ng/handle/20.500.14562/2679 | |
| dc.language.iso | en | |
| dc.publisher | Federal University of Technlogy, Owerri | |
| dc.subject | Road traffic | |
| dc.subject | fuzzy logic | |
| dc.subject | ANN | |
| dc.subject | load balancer | |
| dc.subject | RPC | |
| dc.subject | object-oriented system analysis and design | |
| dc.subject | UML and DTAT | |
| dc.subject | Department of Electrical and Electronic Engineering | |
| dc.title | Development of an intelligent traffic management system using dynamic time allocation technology | |
| dc.type | Doctoral Thesis |