Journal of Transportation Systems Engineering and Information Technology ›› 2014, Vol. 14 ›› Issue (1): 235-241.

Previous Articles    

Parking Demand Forecasting Method in Old Town Based on Current Survey

WU De-hua   

  1. College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
  • Received:2013-05-20 Revised:2013-07-21 Online:2014-02-25 Published:2014-07-07

基于现状调查的城市老城区停车需求预测方法

吴德华   

  1. 福州大学 土木工程学院, 福州 350108
  • 作者简介:吴德华(1978-),男,福建莆田人,副教授,博士.
  • 基金资助:

    福州大学人才基金资助项目(022287).

Abstract:

To overcome the limitations of traditional urban parking demand forecasting method, which predict the parking demand of the old town, the paper studys the parking demand forecasting method of the old town. On the base of contrasting the advantages and disadvantages among traditional parking demand forecasting methods, a new parking demand forecasting method is put forward to use the correlation between the motor vehicle growth rates and parking demand. The method has obvious advantages than traditional prediction method in predicting the reliability, costs of investigation and prediction parking distribution depth. Case study results show that the relative error of the recent forecast less than 5% compared with the method based on the number of cars, the investigation cost savings 30%-40% than the motor vehicle OD prediction method and traffic-parking demand forecasting method, and the forecast parking distribution can be obtained for each traffic survey within the district. The results also show that the method can be applied and promoted in the old town parking demand forecasting.

Key words: urban traffic, forecasting method, current survey, parking demand, old town

摘要:

为了克服传统常用城市停车需求预测方法在对老城区停车需求进行预测时的局限性,本文对城市老城区停车需求预测方法进行研究。在对比各传统常用停车需求预测方法优缺点的基础上,利用机动车增长率和停车需求之间的相关性,提出一种基于现状调查的停车需求预测方法和思路,该方法在预测可靠性、调查成本和预测停车分布的深度方面比传统的预测方法具有明显的优势.案例分析揭示,预测结果与基于小汽车数量法相对误差在5%以内;调查成本比机动车OD预测法和交通量-停车需求预测法节约30%-40%;预测可以得到每个交通调查小区内的详细停车分布状况.研究结果表明,该方法可以在老城区停车需求预测中应用和推广.

关键词: 城市交通, 预测方法, 现状调查; 停车需求, 老城区

CLC Number: