Post-Construction Evaluation of Traffic Forecast Accuracy

This research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in Minnesota and identiÞes the factors inßuencing the inaccuracy in forecasts. The forecast traffic data for this study is drawn from Environmental Impact Statements(EIS), Transportation Analysis Reports (TAR) and other forecast reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010 or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of Traffic Forecasting and Analysis section at Mn/DOT. Based on recent research on forecast accuracy, the (in)accuracy of traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast (in)accuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts with factors such as highway type, functional classiÞcation, direction playing an inßuencing role. Roadways with higher volumes and higher functional classiÞcations such as freeways are subject to underestimation compared to lower volume roadways/functional classiÞcations. The comparison of demographic forecasts shows a trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of fundamental shifts and societal changes.Minnesota, Minneapolis, Travel Demand Model, Transportation Planning, Forecasting

Research on the Integration of Urban Traffic and Big Data

The powerful data processing ability of big data technology can allocate traffic resources more efficiently, and can deal with various sudden traffic problems flexibly. It is an unprecedented opportunity and challenge for urban transportation and smart cities to effectively collect and utilize traffic big data to meet the application requirements of high timeliness traffic administrative supervision, traffic enterprise management and traffic citizen service. This article expounds the concept and characteristics of big data, discusses the application research of big data in urban transportation at home and abroad in recent years, summarizes its application research scope and trend, points out that intelligent transportation is the focus of the application of big data in urban transportation, and finally looks forward to the future research direction

Enabling Data-Driven Transportation Safety Improvements in Rural Alaska

Safety improvements require funding. A clear need must be demonstrated to secure funding. For transportation safety, data, especially data about past crashes, is the usual method of demonstrating need. However, in rural locations, such data is often not available, or is not in a form amenable to use in funding applications. This research aids rural entities, often federally recognized tribes and small villages acquire data needed for funding applications. Two aspects of work product are the development of a traffic counting application for an iPad or similar device, and a review of the data requirements of the major transportation funding agencies. The traffic-counting app, UAF Traffic, demonstrated its ability to count traffic and turning movements for cars and trucks, as well as ATVs, snow machines, pedestrians, bicycles, and dog sleds. The review of the major agencies demonstrated that all the likely funders would accept qualitative data and Road Safety Audits. However, quantitative data, if it was available, was helpful