Integrated model for highway-based travel time forecasting with application to truck transportation

Highways are increasingly congested in metropolitan areas, especially in major cities that rely on highway transportation. As a consequence, travel time forecasting on highways has become an active research field, playing an important role in Intelligent Transportation Systems (ITS) and Advanced Traveler Information Systems (ATIS). Based on current traffic information and forecasting techniques, fuzzy methods were created in this thesis to predict highway travel time to support decisions for travelers and transportation industries in real-time. The method starts from analyzing real-time data from single loop detectors and ultimately provides real-time travel time estimation for whole trips.; Two applications, truck-to-air connectivity and forecasts during a transit strike, were used to demonstrate the real-time travel time forecasting model. Express Package Transport carriers operate many local terminals within major cities as well as hub terminals. Trucks depart from local terminals carrying express shipments to airports with sorting facilities at the end of each day. It is important for trucks to arrive on time and for shipments to be processed on time, because truck delay can postpone sorting procedures and transferring processes. Another application that we investigated is travel time forecasting during a transit strike. Highways are especially susceptible to congestion during strikes because travelers have little opportunity to adjust and equilibrate their travel patterns. Hence, the prediction of travel time becomes more important for transportation industries during strikes.; A practical website was implemented along with the application of truck-to-air connectivity. We acquire real-time traffic information from loop detectors on highways and calculate real-time travel times for each truck schedule. By implementing a web-based decision support tool with real-time travel time forecasting, outbound aircraft from express companies can depart on schedule. The experiments conducted show that the fuzzy model can predict travel time during peak periods and abnormal traffic conditions. Morning rush hours, evening rush hours, and incident conditions have been evaluated to test the models. The proposed fuzzy model performed better then a Weighted Moving Average Model for the traffic conditions investigated.