AN AGENT-BASED MODELLING FOR RIDE SHARING OPTIMIZATION USING A* ALGORITHM AND CLUSTERING APPROACH
- 1Faculty of Civil Engineering, Islamic Azad University Ramsar Branch, Iran
- 2Faculty of Geodesy and Geomatics, K.N.Toosi University of Technology, Iran
- 3Electrical and Computer School, Electrical Engineering Department, University of Ghiaseddin Jamshid Kashani, Qazvin, Iran
- 4Department of Geo-Spatial Information System, Faculty of Geomatic and Geodesy, University of K. N. Toosi of Technology, Tehran, Iran
Keywords: Ride Sharing, Urban Traffic, Agent-based Modelling, Clustering, NetLogo
Abstract. Today, city management is one of the great challenges facing the world. The growth of population, industries, and services is in urgent need of transportation on a large scale. Meanwhile, transportation has great importance in urban management. Therefore, it is necessary to solve the traffic problem with scientific methods and reduce the traffic load of cities. An interesting way to reduce urban travels is using 2,3, or 4 people from one car that it is known as “Ride Sharing”. In this research, the NetLogo software is used to simulate travel sharing scenarios. The three considered parameters are the number of passengers, the acceptable travel sharing radius, and the acceptable waiting time. The proposed algorithm uses a clustering method to find the best candidates to share a ride. Several scenarios were performed to evaluate numerical results. The number of passengers was 100, and 500, the radius of the trip was 1,000 and 2,000 meters, and the waiting time was 10 and 20 minutes. So, 8 experiments were carried out. The least amount of travel sharing was observed in the first scenario (100 passengers, 1000 m travel sharing radius and 10 minutes waiting time), in which 2% of single trips dropped out. The most sharing trips were in the final scenario (500 passengers, 2000 meters radius and 20 minutes waiting time), which saw a decrease of 36.4% of single trips. So, it can be said that sharing a trip can reduce traffic in cities and consequently reduce urban costs and either air pollution or noise pollution.