GRAPH ANALYSIS FOR CONGESTION DETECTION ON ROAD NETWORK

• Main Author: RAMDLANI (NIM: 23514094), APIP
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/21182
• Institution: Institut Teknologi Bandung

This research is development framework for detecting congestion on the urban road network. ATCS <br /> <br /> data (Area Traffic Control System) in Bandung city with traffic volume used in congestion detection <br /> <br /> process. Traffic flow data is colle- cted by vehicles detector located at a crossroads within of 15 <br /> <br /> minutes. The data also collects flow direction and location of intersections in the road ne- twork. <br /> <br /> To compute spatial correlation, graph modelling used in the adjacency matrix. Assuming the location <br /> <br /> of the detector as the vertices and the direction of the vehicle as the edge, the graph modeled <br /> <br /> with vehicle’s detector location and the flow direction at nine locations on road nework. Results, <br /> <br /> graph repre- sented matrix for easiest way to correlation calculation. The adjacency matrix used <br /> <br /> consists of 3 matrices in each period of time, which describes the order of spatial distances <br /> <br /> traveled by vehicle at the intersection location. To calculate spatial correlation, the <br /> <br /> autocorrelation function and the cross-correlation fun- ction which are derived from Pearson’s <br /> <br /> simple correlation is used to looking influence at each location on road network. Spatial <br /> <br /> correlation is performed on 4 (four) time periods, namely: at 24 hour (00: 00-24: 00), AM <br /> <br /> Peak(06: <br /> <br /> 00-09: 00), Interpeak (11: 00-14: 00) and PM peak(16: 00-19: 00) The result of calculation of <br /> <br /> spatial correlation, shows the existence of seasonal pattern on the autocorrelation results even <br /> <br /> though the value scale is getting smaller as it increases time lags. This provides that the process <br /> <br /> of calculating cross- correlation functions and it can be concluded that the volume of vehicles at <br /> <br /> each location that are connected in the road network can be known by making observations in the <br /> <br /> time series of previous seasonal periods. observing conges- tion levels intersection location based <br /> <br /> on Level of Service is used ti proove teh result of congestion detection. The conclusion that can <br /> <br /> be formulated that graph modeling is needed to simplify the spatial correlation calculation process <br /> <br /> by performing the graph representation into a matrix. The Simpson rules on cross-correlation <br /> <br /> results, can be detected congestion at intersection locations on the road network to find the most <br /> <br /> critically locations causing congestion on <br /> <br /> the road network at time periods. <br />