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Development of a Merge Capacity Model under Connected Automated Vehicle Environment
Technical Paper
2020-01-5202
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In recent years, research on connected and automated vehicles (CAVs) are gradually becoming a hot topic in traffic industry. Recent technological advances in wireless communication, artificial intelligence, and sensory systems lay a foundation for the development of CAV. Because the merging section is an important node in city road system, it is necessary to analyze the capacity of it. Researches on CAVs are mainly based on VISSIM and other traffic simulation software. In this paper, the capacity calculation formula of merging area based on the Cowan M3 distribution is established. The formula includes the number of mainline lanes, the critical merge gap and other parameters. Besides, the capacity of the merging area under two simulation environments is discussed in this paper, which is CAV in cooperative control method and automated vehicles (AVs) without cooperative merging control. The paper takes the three mainline lanes as an example. The maximum traffic throughput of on-ramp under different traffic demands is described by drawing three-dimensional figures. After comparing and analysis of the calculation results, it can be seen that the throughput of on-ramp under CAV environment is significantly greater than that under AV environment when main road traffic flow is not saturated. When the minimum following headway is 1 s and the critical merging gap is 2.4 s, the difference of on-ramp capacity under the two control methods reaches the maximum when the traffic demand of the main road is 6000 pcu/h, which is about 2600 pcu/h. At this time, the capacity of on-ramp is increased by about 2.6 times compared with that under AV environment. On the contrary, when mainline overflows, no on-ramp vehicles can merge into the main road.
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Zhou, Y., Li, Z., and Tao, C., "Development of a Merge Capacity Model under Connected Automated Vehicle Environment," SAE Technical Paper 2020-01-5202, 2020, https://doi.org/10.4271/2020-01-5202.Also In
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