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Research On Price Discrimination Of Parking In Off-road Public Parking Lots

Posted on:2023-06-04Degree:MasterType:Thesis
Country:ChinaCandidate:C LiuFull Text:PDF
GTID:2532306830477254Subject:Architecture and civil engineering
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The rapid growth of motor vehicle ownership of urban residents has brought convenience to people’s travel,but also brought pressure to the operation of parking lots.The development of parking lot cannot keep up with the growth of motor vehicle ownership,and the two are seriously unbalanced,resulting in disorder and low efficiency during the high load parking period.In order to solve the problem of difficult parking,relevant national departments have successively issued relevant documents since 2015,improved the charging policy,promoted the development of parking facilities,combined with decentralization and management,recommended scientifically formulating differentiated charging standards in terms of pricing strategy,and gave parking facility operators the power to set prices independently.Based on the supporting policies of relevant national departments on parking lot pricing strategy,this paper focuses on the time-based differential pricing method of parking lot,and intends to systematically explore the construction and solution of differential pricing model from the two aspects of drivers’ parking choice behavior and parking lot users’ use behavior.Drivers are the main body of parking,and their parking choices directly determine whether the parking lot generates revenue.This study determines the influencing factors and degree of drivers’ parking choice behavior through questionnaire survey and the establishment and solution of discrete choice model.The discrete choice model quantifies drivers’ choice probability of different parking lots in the region under different conditions.The results show that the queuing time before entering the parking lot,the walking distance from the parking lot to the destination,the parking fee standard,the estimated parking time,and whether the parking demand is urgent all significantly affect the choice behavior.Therefore,the adjustment of the charging standard will have an impact on the selection of parking lots.When the charging standard is increased,it will inevitably cause a small number of vehicles to lose,alleviate the phenomenon of vehicle queuing during high load periods.Fully considering the personalized parking law of parking lot is the prerequisite for differential pricing.From the perspective of individual vehicles,analyze the behavior of each approaching vehicle,including frequency,date characteristics,parking time,etc.,classify vehicles with similar behavior into one category,and analyze the type composition of parking lot users.From the overall perspective of the parking lot,only the parking lot where the number of vehicles in the parking lot shows an obvious fluctuation law in a day and the high load period can basically reach saturation can adopt the differential pricing strategy to alleviate regional congestion and improve revenue.If the target parking lot is suitable for the differential pricing strategy,follow-up research can be carried out,and the parking high load period,medium load period and low load period can be divided by K-means cluster analysis.Finally,in the construction and solution of the differential pricing model,with the goal of increasing the total revenue,taking the charge standard of each time period divided by K-means clustering as the decision variable,and taking the number of vehicles on the scene and the upper and lower limits of the charge standard as the constraints,the optimal combination of charge standards with 30 minutes and 10 minutes as the charge interval is discussed through multiple iterations of genetic algorithm.The results show that the differential pricing method can significantly improve the revenue of parking lots.The maximum revenue of differential pricing with 10 minutes as the billing interval is greater than that with 30 minutes as the billing interval.
Keywords/Search Tags:Price discrimination, Parking choice behavior, Multinomial logit model, Genetic algorithm
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