Dynamic network traffic control |
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| Institution: | 1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. Department of Civil Engineering, Monash University, Clayton, VIC 3800, Australia;4. Institute of Transport Studies, Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;1. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China;2. School of Intelligent Systems Engineering, Sun Yat-sen University, Guangdong, China;1. Global Academy for Agriculture and Food Security, University of Edinburgh Easter Bush Campus, Midlothian EH25 9RG, United Kingdom;2. Royal (Dick) School of Veterinary Studies and Roslin Institute University of Edinburgh Easter Bush Campus, Midlothian EH25 9RG, United Kingdom |
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| Abstract: | This study developed a dynamic traffic control formulation designated as dynamic intersection signal control optimization (DISCO). Traffic in DISCO is modeled after the cell-transmission model (CTM), which is a convergent numerical approximation to the hydrodynamic model of traffic flow. It considers the entire fundamental diagram and captures traffic phenomena such as shockwaves and queue dynamics. As a dynamic approach, the formulation derives dynamic timing plans for time-variant traffic patterns. We solved DISCO based on a genetic algorithm (GA) approach and applied it to a traffic black spot in Hong Kong that is notorious for severe congestion. For performance comparisons, we also applied TRANSYT to the same scenarios. The Results showed that DISCO outperformed TRANSYT for all the scenarios tested especially in congested traffic. For the congested scenarios, DISCO could reduce delay by as much as 33% when compared with TRANSYT. Even for the uncongested scenarios, DISCO’s delays could be smaller by as much as 23%. |
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