Bluetooth Data collection technique is recently proven as a reliable data collection technique that provides the opportunity to modify traditional methodologies to improve system performance. Actual volume in the network is a result of the timing plans which are designed and modified based on the volume which is generated using existing timing plans in the system. This interdependency between timing plan and volume on the network is a dynamic process and should be captured to obtain actual traffic states in the network. The current practice is to calculate synthetic origin destination information based on detector volume that doesn't necessarily represent the volume scenario accurately. The data from Bluetooth technology can be utilized to calculate dynamic volume on the network which can be further used as input for signal timing design. Application of dynamic volume improves the system performance by providing the actual volume in system to design optimal timing plans. This thesis proposes a framework that can be used to integrate data obtained from the Bluetooth technology with the traditional methods to design timing plans. The proposed methodology utilizes the origin destination information obtained from Bluetooth data, detector data, characteristics of intersections such as number of lanes, saturation flow rate and existing timing plans as a basis for the calculation of the dynamic volume for the various movements at each intersection. The study shows that using the Bluetooth based OD matrix to calculate accurate dynamic volumes results in better system performance compared to the traditional way of using the static detector volume alone. / Master of Science

Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) utilize real-time information to apply measures improve the transportation system performance. Two key inputs for ATMS and ATIS are dynamic travel times and dynamic OD matrices. Bluetooth devices detection technology has been increasingly used to track vehicle movements on the network. This possibility naturally raises the question of whether this information can be used to improve the dynamic estimation of OD matrices. Previous research efforts rely entirely on the Bluetooth OD counts for estimation, which is why they require high penetration rates. In our study, we use Bluetooth data to supplement loop detector data while estimating dynamic OD matrices using Kalman filter. We use OD proportions as state variables and travel times, link counts, Bluetooth OD matrix and input and exit volumes as measurements. A simulation experiment is conducted in VISSIM and is designed such that the traffic network emulates the observed traffic patterns. Two case studies are performed for comparison. One uses Bluetooth OD matrices as input for estimation while the other does not. The Bluetooth ODs used in the Kalman filter estimation was found to improve the OD flow estimates. The developed methods were compared with synthetic OD estimation software (QueensOD) and were found to be more effective in obtaining dynamic OD flow estimates. A case of study with fewer detectors was also studied. When it was compared with a similar method developed by Gharat(2011), the errors were lower. / Master of Science