Objective

1) Quantify variations among different bottleneck sites in terms of daily averages of critical lane flow ratios, time gaps, and passage times and identify any relationships among these variables;

2) Quantify the variability in daily average pre-breakdown and queue discharge flows at individual sites and explain differences in the variability of these flows among sites in terms of observable site, vehicle population, and driver population characteristics;

3) Explain variations in site-to-site bottleneck capacity by (a) identifying and quantifying relationships between average time gaps, critical lane flow ratios, and passage times and observable characteristics of bottleneck sites, vehicle populations, and driver populations and (b) using these relationships to predict bottleneck flow (both maximum pre-breakdown flow and queue discharge flow); and

4) Develop improved capacity analysis procedures incorporating these relationships.

Methodology

Includes identification and documentation of traffic, site, vehicle, and driver population characteristics for study sites and graphical and statistical analysis of data. Peak period traffic data (volumes, occupancies, and speeds) will be analyzed for a minimum of 50 days at a minimum of 20 freeway bottleneck sites to be identified in consultation with Caltrans personnel and researchers outside California. Data analysis will include use of re-scaled cumulative plots to identify periods of pre-queue and queue discharge flow; quantification of means, standard deviations, lane flow ratios, average time gaps, and passage times for these flow conditions; statistical analysis of relationships among these characteristics; and statistical analysis of relationships between capacity and site characteristics, vehicle characteristics, and driver population characteristics using critical lane flow ratios, average critical lane time gaps and average critical lane passage times as intervening variables.

Research Tasks

1) Identify and document local study sites. Identify freeway bottlenecks in the San Diego area, investigate their suitability as study sites, and document site characteristics

2) Identify and document non-local study sites. Contact traffic operations personnel and researchers outside California to identify bottlenecks, determine availability and suitability of data, and document site characteristics.

3) Update data reduction software. Review and modify as necessary software used to extract and reduce San Diego traffic data. Write any additional software needed to reduce non-local data.

4) Collect and analyze field data. For local sites, videotape traffic flow and conduct hand counts to classify vehicles and to determine lane volume distributions for locations away from detectors.

5) Collect and reduce traffic data. Extract San Diego data from daily data sets and prepare reduced data sets for graphical and statistical analysis. Secure non-local data and perform any necessary extraction and/or reduction to prepare reduced data sets.

6) Analyze data for individual sites. Determine daily averages for time gaps, flow ratios, pre-breakdown flow, and queue discharge flow. Calculate average and standard deviation of daily pre-breakdown flows and queue discharge flows.

7) Compare sites and identify relationships. Use statistical analysis techniques to identify relationships among the traffic variables and between traffic variables and site characteristics, vehicle population characteristics, and/or driver population characteristics.

8) Develop implementation package. Develop procedures for using results in capacity analysis and incorporate these procedures in a set of spread sheets or other software package. Write instructions for use of the implementation package.