The study uses time series and the Kalman prediction techniques along with modern technology such as the Global Positioning System (GPS) for accurate data collection and analysis. A greater understanding of travel time will help facilitate traffic system performance monitoring, control, planning, and informed route decisions for motorists accessing information from changeable message sings (CMS). The models used for estimations include the autoregressive integrated moving average (ARIMA) and the autoregressive moving average (ARMA). The study collects travel data for the peak hours of travel (3:30-5:00 p.m.) over an eight-month period on the busiest section of Highway 194 in Duluth, Minnesota. The predictions were conducted over two weeks during the summer of 2005. Observed and predicted travel times are charted carefully and report evaluations determine the success of the study.
Traffic flow study of the Miller Hill corridor on Highway 194 between Arlington Avenue and Haines Road, the most heavily traveled and congested roadway in the Duluth area. The study includes preliminary data collection using a non-intrusive Remote Traffic Microwave Sensor (RTMS), traffic flow modeling, traffic simulation and development of an efficient traffic signa l light timing plan. The author develops a reasonably accurate model to estimate traffic flow, which is used in association with the RTMS and a database system to conduct real-time traffic simulations. Results of the simulation were used to develop a traffic-signal optimization plan with Synchro 5.0 software. Discussion of problems faced and how improvements could be made through further research. The author concludes with a proposal that recommends the adoption of the newly developed signal-timing plan to alleviate the traffic congestion on the Miller Hill corridor.
