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Study On The Urban Parking Demand Prediction Based On Location

Posted on:2013-06-17Degree:MasterType:Thesis
Country:ChinaCandidate:T T NieFull Text:PDF
GTID:2232330392459467Subject:Transportation engineering
Abstract/Summary:PDF Full Text Request
Along with the rapid development of national economy, the city scale and car ownershipincrease unceasingly. Parking problems in city become more and more serious. The existingparking facilities haven’t meet the growing parking demand. The research of urban parkingfacility planning was gradually mentioned on schedule. Parking demand forecasting is thebasic content of parking planning, and also the basis for parking development planning.Parking demand is the foundation of parking facilities construction scheme. Therefore,improve the accuracy of parking demand forecasting is very important. This paper accordingto the actual situation of parking plan in China, borrow ideas from parking demand forecastresearch both in domestic and abroad. Set up parking demand forecasting model base on thetheories of space locations.In this paper, author discussed the improvement of the parking demand forecastingmodel. First and foremost, author detailed analysis the trip characteristics of parker and theparking characteristics of motor vehicle, recommend the concept and influencing factors ofurban parking demand, discussed the interactive relationship between the parking supply andthe urban traffic. Besides, This paper expounds the conventional prediction method of parkingdemand forecast, analysis the models characteristics. Use theories of space locations toquantify the regional location features. Set up a parking demand forecasting model based onthe theories of space locations, revise the models by berths turnover. Last but not least, thisarticle use Xiaozhai as example which is the second largest business circles in Xi’an. Makeprediction model practically by solving the parameter of location theory and parking demandforecasting model.
Keywords/Search Tags:Theories of space locations, Parking generation, Parking characteristics, Parking demand prediction
PDF Full Text Request
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