A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics

This research was funded by the Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia, under Tamayuz research grant number 2/710.Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia 2/71

Internet of things-based framework for public transportation fleet management in the Free State

Thesis (Masters: Information Technology) -- Central University of Technology, Free State, 2019The poor service delivery by the Free State public transportation system inspired us to design a framework solution to improve the current system. This qualitative study focuses on improving the management of the public transportation fleet. One of the most recently developed technologies in Information and Communication Technology (ICT), namely the Internet of Things (IoT), was utilised to develop this framework. Existing problems were identified through research observations, analyses of the current system, analyses of the current problem areas, as well as participants’ questionnaire answers and recommendations, the participants being the passengers, drivers and vehicle owners. The framework was developed in two phases, namely a hardware phase that makes use of ICT sensors (e.g. RFID, GPS, GPRS, IR, Zigbee, WiFi), and a software phase that uses an internet connection to communicate with the different ICT devices. The software utilised a Graphic User Interface (GUI) to ensure that the software is user-friendly and addresses possible problems and barriers such as multiple language interfaces and different ICT skills levels. The newly designed framework offers different services and solutions to meet the participants’ needs, such as real-time tracking for public transport vehicles to help passengers manage their departure and arrival times, as well as for vehicle owners to monitor their own vehicles. In turn, vehicle arrival notifications will encourage passengers to be on time so that vehicles will not be delayed unnecessarily. Another feature is counting devices that can be installed inside the vehicles, which will inform vehicle owners how many passengers are being transported by a vehicle. The passenger pre-booking system will support the drivers when planning their trips/routes. Finally, the framework was designed to fulfil all the participants’ needs that were indicated in the questionnaires in order to achieve the goal of the research study