Volume 41 Issue 4
Aug.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(4): 132-142
SHEN Jinxing, JIANG Wenfeng, CAO Huimin, MA Changxi. A Study on Impact of Curb Parking on Road Capacity Using Cellular Automata[J]. Journal of Transport Information and Safety, 2023, 41(4): 132-142. doi: 10.3963/j.jssn.1674-4861.2023.04.014
Citation: SHEN Jinxing, JIANG Wenfeng, CAO Huimin, MA Changxi. A Study on Impact of Curb Parking on Road Capacity Using Cellular Automata[J]. Journal of Transport Information and Safety, 2023, 41(4): 132-142. doi: 10.3963/j.jssn.1674-4861.2023.04.014
shu

A Study on Impact of Curb Parking on Road Capacity Using Cellular Automata

doi: 10.3963/j.jssn.1674-4861.2023.04.014
  • 1.

    College of Civil Engineering and Transportation Engineering, Hohai University, Nanjing 210024, China

  • 2.

    School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • Received Date: 2023-03-15
    Available Online: 2023-11-23
    Abstract
  • The installation of curb parking is a crucial solution to address the imbalance between supply and demand of parking. However, unreasonable installation of curb parking spaces may fail to solve the problems of parking, and may reduce road capacity, resulting in traffic congestion and a series of related traffic issues. To investigate the impact of curb parking supply, demand, and convenience on road section capacity at a microlevel, an improved one-way two-lane cellular automata model is developed. The model proposed in this study includes three components: a car-following model, a lane-changing model, and a curb-parking model. In the car-following model, the difference between regular driving and cruising parking processes is studied by setting different slowing probabilities for the parking and non-parking road sections. In this model, the impact of curb parking convenience on road capacity is resolved by setting the duration of parking maneuvers. Based on the field survey data, the operational rules for regular driving and cruising-parking vehicles are calibrated separately, and the reliability of the model is verified. The comparison results with the existing models show that the proposed model exhibits superior fitting performance and the average absolute error index can be reduced by 53.74% to 75.71%. It indicates that the proposed model can provide a more accurate reflection of the impact of curb parking process on traffic flow of road section. Besides, as traffic flow density increases, the average absolute error index of the proposed model can be reduced by 16.39% to 52.85%. The results show that the model can avoid overestimating the negative impact of curb parking on road section capacity, making it a valuable addition to current studies. Road section capacity under the scenarios of changing parking demand, parking convenience, and supply capacity are studied in open boundary conditions. The results show that the road section capacity fluctuates significantly under all three scenarios. The most significant impact is observed from changes in parking demand. Specifically, when parking demand increases from 10% to 30% of road traffic flow, there is a 44.12% decrease in capacity. When the parking convenience decreases and the duration of parking maneuvers increases from 5 s to 15 s, the capacity decreases by 24.44%. On a 500 m road segment, when the parking supply capacity increases from 20 parking spaces to 50, a 39.39% decrease in capacity is observed.

     

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