The Temporal-Spatial Classification of Relative Attractiveness Factor in Fluid Analogy Method – A Case Study of Sun Yat-Sen Freeway

• Main Authors: Yu-Wei Chen, 陳昱維
• Other Authors: Yu-Chun Liao
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/88555309574190614785

碩士 === 中原大學 === 土木工程研究所 === 101 === This study mainly discussed the characteristics of Relative Attractiveness (Ae) of each off-ramp at various time period calculated by Time-Dependent Fluid Analogy Method (TDFAM). The study attempted to simplify and integrate varied Ae values for easy reference based on characteristics analysis of temporal-spatial distribution. In this way, the mean of temporal-spatial Ae could be easily used in planning and control of corridor system in addition to the estimation of origin-destination (O-D) flows. This study used the ramp flow data at North Section of Sun Yat-Sen Freeway from January to April of 2011. After reparation of the missing data, The Ae was calculated with each 5 minutes for every off-ramp by TDFAM. Moreover, the cluster analysis was applied to group Ae values with similar characteristics under the temporal-spatial characteristics. After classification, each Ae group is represented by a mean of temporal-spatial Ae which used to estimate the O-D flow in every 5 minutes. The O-D flow estimation ability of each temporal-spatial Ae is evaluated by Mean Absolute Percentage Error Rate (MAPE) and Root-Mean-Square Error (RMSE). The study results indicated that the error of O-D flow estimation ability could be maintained within 20% in average. It also indicated the temporal-spatial Ae could represent the characteristics of the temporal-spatial Ae before integration, it can also show the time-dependent characteristics of vehicle flow changing with time in the corridors a day. The temporal-spatial Ae after classification could be taken as the reference for the dimension of time period selection in TDFAM, and the reference for the calculation of real-time O-D flow in the future and the index of the real-time Ae value for each off-ramp in each time period.