This paper introduced a network centrality-based method to estimate the volume of trip attraction in traffic analysis zones. Usually trip attraction volumes are estimated based on land use characteristics. However, ex...This paper introduced a network centrality-based method to estimate the volume of trip attraction in traffic analysis zones. Usually trip attraction volumes are estimated based on land use characteristics. However, executing of land use-based trip attraction models are severely constrained by the lack of updated land use data in developing countries. The proposed method used network centrality-based explanatory variables as ＂connectivity＂, ＂local integration＂ and ＂global integration＂. Space syntax tools were used to compute the centrality of road segments. GIS-based kernel density estimation method was used to transform computed road segrnent-based centrality values into traffic analysis zone. Trip attraction values exhibited significant high correlation with connectivity, global and local integration values. The study developed and validated model to estimate trip attraction by using connectivity, local integration and global integration values as endogenous variables with an accepted level of accuracy （R2 〉 0.75）. The proposed approach required minimal data, and it was easily executed using a geographic information system. The study rec- ommended the proposed method as a practical tool for transport planners and engineers, especially who work in developing countries and where updated land use data is unavailable.展开更多
文摘This paper introduced a network centrality-based method to estimate the volume of trip attraction in traffic analysis zones. Usually trip attraction volumes are estimated based on land use characteristics. However, executing of land use-based trip attraction models are severely constrained by the lack of updated land use data in developing countries. The proposed method used network centrality-based explanatory variables as ＂connectivity＂, ＂local integration＂ and ＂global integration＂. Space syntax tools were used to compute the centrality of road segments. GIS-based kernel density estimation method was used to transform computed road segrnent-based centrality values into traffic analysis zone. Trip attraction values exhibited significant high correlation with connectivity, global and local integration values. The study developed and validated model to estimate trip attraction by using connectivity, local integration and global integration values as endogenous variables with an accepted level of accuracy （R2 〉 0.75）. The proposed approach required minimal data, and it was easily executed using a geographic information system. The study rec- ommended the proposed method as a practical tool for transport planners and engineers, especially who work in developing countries and where updated land use data is unavailable.
