Data collection, accuracy of data and the ability to collect data cost effectively are important components of development and calibration of travel demand models. Speed and delay data is one of the key inputs to calibrate travel demand models to be representative of field conditions. To collect peak period and off-peak speed and delay data, travel time observations are made to collect data along the study corridor and usually on several feeder and competing routes. This data is usually collected in the field by using Global Position System (GPS) receivers. Time and location coordinates are gathered from the GPS receiver at a fixed preset interval (like every 0.1 miles) and using this data the average operating speed of the roadway is calculated.

In addition to model calibration, travel time and delay data also provide a performance metric for gauging a transportation system’s performance and for planning the future improvements, again emphasizing the need for accurate data. As vehicle flow increases and nears the capacity of the roadway, speed decreases and travel time increases and this behavior needs to be modeled reasonably well specially to forecast demand for tolled managed lanes. With the evolution of technology, newer methods including mobile applications are available to collect speed and delay data in the field. Also Bing maps and other web based applications report congestion characteristics for drivers by time of day.

The purpose of this research is to compare the speed and delay data collected by different methods including the newer technology applications. This paper also looks at:

• Using GIS based application for analyses and presentation of speed and delay data.

• Advantages/shortcomings of data collected by different methods and their accuracy/application for model calibration purposes.

• Cost effectiveness of different methods of data collection and the accuracy of each for use in modeling.

• Industry evolution, future/evolving methods to collect this type of data.

The analyses presented in this paper is based on actual field data from ongoing projects and the utilization of GIS technology to analyze and compare available web based data used to support the analysis.