Document Type : Research Paper

Authors

• Mohammad Bagheri
• Hossein Ghafourian
• Morteza Kashefioasl
• Mohammad Taghi Sadatipour
• Mohammad Rabbani

Department of Environmental Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Today, air pollution and energy consumption are metropolitans’ main transportation issues. In these cities, most people consider their mode of transportation based on the appropriate means, including passenger, travel characteristics, population growth, urban space, and transportation. Therefore, the systematic optimization of travel demand in the actual road network in urban areas is necessary. Travel Demand Management (TDM) is one of the well-known methods to solve these problems in congested areas. TDM is a strategy to reduce the efficiency of the urban transportation system by granting special concessions to public transportation methods, implementing the ban on private cars in certain places or times, and increasing the cost of using some facilities such as parking in busy areas pricing network. Transportation demand management is one of the most effective methods to reduce traffic and control air pollution, especially in crowded areas of the city center. A few studies have optimized urban transportation in congested cities by combining Markov decision processes based on decision processes with reward and evolution-based algorithms and simultaneously considering customers and travel characteristics. Therefore, this study provided a new network traffic management for urban cities with multiple objective functions related to the expected reward value of the Markov decision system using a genetic algorithm. Shiraz is considered as a benchmark to evaluate the performance of the proposed approach. Then, the impact of toll levels was evaluated on changes in user and operator cost components.

Keywords

• Traffic management
• Transportation network
• Travel demand
• Decrease air pollution