Efficient Traffic Control System Using Fuzzy Logic with Priority

Abstract

The increase in the number of vehicles on the road is evident by the rate of traffic congestions on daily basis. Problems of traffic congestions are difficult to be measured. Emission of dangerous substances are some of the worrisome effects on weather, theft and delays to motorist are other effects. More and better road network connections have been found to be effective. However, road networks often have intersection(s) which introduces conflicts to right-of-way. These are solved using road traffic light control systems. In this work, an attempt to improve upon an existing programmed stationary road traffic light control system of the Kaduna Refinery Junction (KRJ) is considered. The KRJ is the major road connection to Kaduna main town from the southern part of the state. During working days, motorist from other parts of the country, public and private servants, students, business men, etc. traveling to other parts of the country through the southern part of the state, meet at the KRJ. Trucks conveying petroleum products from the Kaduna Refinery, and vehicles transporting workers, and business men affect the flow of traffic at the junction. An efficient model of fuzzy logic (FL) technique is developed for the optimal scheduling of traffic light control system using TraCI4MATLAB and Simulation of Urban Mobility (SUMO). An average improvement of 2.74% over an earlier result was obtained. Considering priority for emergency vehicles, an improvement of 66.79% over the static phase scheduling was recorded. This shows that FL can be effective on traffic control system.

Modeling of Cellular Automata Markov Chain for Predicting the Carrying Capacity of Ambon City

“…Fuzzy is a logical system that aims to formalize from estimates to reasoning represented in the form of interest levels that have a range of values 0-1 (Boolean) (Zadeh, 1994). According to Peter et al (2021) logic in fuzzy is a very good thing to interpret data that occurs continuously effectively and efficiently, this is a good way to do cellular automata-based modeling because it uses parallel computing consisting of interconnected cells and has a continuous value, so in this study I processed data driving factors using this fuzzy logic concept (Figure 3). Ambon City driving factor consists of six parameters (Figure 3 and Figure 4) after classification and weighting must be converted to fuzzy values (value range 0-1) to facilitate the next analysis process for CA MC modeling in Idrisi Selva software (Wang et al, 2022;Mwabumba et al, 2022).…”

Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata- Markov Chain

“…Logika fuzzy yang digunakan adalah logika besar, yang berarti skor besar akan menunjukkan tingkat kepentingan yang besar pula, semakin putih nilai fuzzy (mendekati 1) maka akan semakin sesuai dan semakin hitam nilai fuzzy (mendekati 0) semakin tidak sesuai (Lisanyoto et al, 2019). Peter et al, (2021) berpendapat bahwa logika Fuzzy merupakan alat yang sangat baik untuk menafsirkan data kontinu secara efektif dan efisien, ini merupakan cara yang baik untuk melakukan pemodelan berbasis cellular automata karena menggunakan komputasi paralel yang terdiri atas unit (sel) yang saling terkoneksi dan memiliki nilai kontinu. Klasifikasi tingkat kesesuaian setiap driving factor dapat dilihat pada Tabel 1 dan Keseluruhan driving factor yang telah dioverlay dapat dilihat pada Gambar 2.…”