Parking generation modelling of government hospitals in Peninsular Malaysia

Abstract

Parking has long been an issue in government hospital, especially the one which are located within urban environment. Scarcity of parking caused traffic issue, while over-providing would cost unnecessary expenditure on the infrastructures. There are few parking guidelines available, but none of these been established based on empirical data from actual site investigation. This study analyse the actual parking demand and develop a parking demand model for government hospitals in Malaysia. Quantitative study approach was carried out for the data collection via primary data survey at six (6) state hospitals from June 2016 to March 2017. Effective boundary for hospital compound were established and divided into zones which further assigned to the enumerators. Subsequently, Automated Traffic Counter (ATC) were installed for a week at each hospital to ascertain peak day which the parking survey shall be carried out. Then, license plate matching survey method was further carried out throughout a 12 hours period, from 7.00 a.m. to 7.00 p.m. where highest parking activity was anticipated. Parking information, including daily parking volume, hourly parking demand, average parking duration, parking turnover and parking index were captured from the survey and presented via descriptive statistical analysis. On the other hand, the independent variables for the modelling such as number of beds, professional staff, support staff, gross floor area, daily outpatient, and occupied bed were retrieved from Clinical Research Centre (CRC) or administration office from each hospital. Next, four type of predictive models based on regression modelling by utilising linear-, square-, inverse-, and natural logarithmic'based variable structures were developed which summed up to 60 different models been analysed. These models were evaluated based on the overall model F 'te s t; coefficient of determination (R2 ); mean square error (MSE); root mean square error (RMSE) and mean absolute error (MAE). Inverse 6 from inverse based model has the lowest MSE, RMSE and MAE as well as the highest R2 among all models which represented by number of beds, number of total staff and number of daily outpatients as the root predictors. These three measurements are non'confidential data which obtainable from the hospitals and does not include sensitive information. Model validation was conducted by comparing the estimates of parking demand by the developed model with the estimates by other available guidelines as well as the actual parking demand. The developed model was proved to yield the least error, proving that it could better mimic the demand of parking spaces at hospitals in Malaysia. This model could benefit relevant stakeholder in establishment of parking facilities, especially town planner in way to instigate sustainable development approach.