Spatio-temporal analyses to estimate speed information for transport forecasting and scenario testing

Road transport, especially congestion, contributes about one-fifth of the European Union (EU)'s total emissions of carbon dioxide (CO2) and is considered as one of the major sources of global greenhouse gas emission. To understand the nature of congestion, speed information is vital, but not yet integrated into transport scenario models. The speed information is currently available via Information and Communication Technology (ICT) Infrastructure, so the possibility of introducing speed data into the transport model should be explored. Hence, the aim of the study was to integrate dynamic speed data from real-time urban navigation road network into a static transportation scenario testing model namely TRANS-TOOLS ("TOOLS for TRansport Forecasting ANd Scenario testing") model. In order to associate and integrate the speed data, a frame-work was designed and spatial interpolation methods being fixed width buffer method and Inverse Distance Weighting (IDW) were tested and compared in a case study area at Istanbul, Turkey. The major contribution of this study was to model the temporal dimension of the speed data via utilizing spatial analyze tools and associate such information with global scenario testing transportation models. The results were promising, where all speed information retrieved from urban navigation maps were associated with the TRANS-TOOLS network and simulated hourly. According to the results of analyzing, the free-speed assumed by the scenario testing transportation model was reduced drastically, being 77% on average, for weekdays. Hence, spatial analyze methods could successfully fill the gap in the transport model and could aid the policy making process.