Integrating an Automated Bottleneck Detection Tool into an Online Freeway Data Archive

Bottlenecks are key features of any freeway system. Their impacts are of increasing importance as congestion worsens in major urban areas. In the U.S., the FHWA has been working to identify and monitor key bottlenecks in each state. In Oregon, a freeway data archive known as PORTAL records count, occupancy, and speed measurements from over 600 freeway locations. This archive has enabled development of online freeway performance and reliability analysis tools. This paper describes the results of a project aimed at developing an automated tool for identifying recurrent freeway bottlenecks using historical data within the framework of the data archive. Efforts have focused on identification and display of active bottleneck features using graphical tools and the selection of optimal variables that enable the careful identification of active bottlenecks. This research aims to detect bottleneck activation in real time and to expand the use of reliability techniques. Ultimately the results of this research will improve the prioritization of improvements and implementation of operational strategies on the freeway network. INTRODUCTION Understanding traffic dynamics at freeway bottlenecks is a foundation for understanding how the freeway system operates. A bottleneck is defined as a point upstream of which one finds a queue and downstream of which one finds freely flowing traffic. Bottlenecks can be static (e.g. a tunnel entrance) or dynamic (e.g., an incident or a slow moving vehicle). Bottlenecks can be activated or deactivated due to a decrease in demand or spillover from a downstream bottleneck (1). Congestion imposes great costs on the movement of people and freight, motivating the development of new freeway performance measures and reporting systems. In Oregon, the Portland Oregon Regional Transportation Archive Listing (PORTAL—see http://portal.its.pdx.edu/) has been established to collect count, occupancy, and speed data from over 600 locations at 20-second intervals. In addition to archived data, performance measurement tools and analytical tools are available. The objective of this paper is to describe the development of an automated system to identify freeway bottlenecks using historical data within the PORTAL environment. Using ground truth knowledge of when and where bottlenecks occurred during a sample period, a working prototype was developed and tested with the intent to accurately identify, track, and display active bottleneck features using graphical tools. Previous research, conducted in California by Chen et al. (2), was validated for use in PORTAL, and suggestions for further research are provided. BACKGROUND Past research has sought a greater understanding of where freeway bottlenecks formed and how and when they were activated. One method uses oblique curves of cumulative vehicle arrival number versus time and cumulative occupancy (or speed) versus time constructed from data measured at neighboring freeway loop detectors (3, 4, 5, 6, 7). Using this method it is possible to observe transitions between freely-flowing and queued conditions and to identify important features. This method has been applied here to systematically define bottleneck activations and deactivations as ground truth for testing and implementing an automated bottleneck detection algorithm. Previous research has also developed techniques for automatic bottleneck activation and deactivation identification by comparing measured traffic parameters and applying thresholds for those values. Notably, Chen et al. (2) developed an algorithm to identify bottleneck locations and their activation and deactivation times using 5minute aggregations of freeway loop detector data from San Diego, California, focusing on speed differences between adjacent detectors. Zhang and Levinson (8) implemented a similar system to identify bottlenecks using occupancy differentials. Banks (9) used 30-second speed drop thresholds to identify bottleneck activation in San Diego, and Hall and Agyemang-Duah (10) developed a threshold using the ratio of 30-second occupancy divided by flow on a Canadian freeway. Bertini (11) tested other signals including the variance of 30-second count as characterizing bottleneck activation at several sites. Building on the past research, the objective of this paper is to test the Chen method using a sensitivity analysis approach with data from a freeway in Portland, Oregon. EXTENDING PORTAL’S FUNCTIONALITY In addition to providing loop detector data at different aggregation levels, the PORTAL archive includes automatic data analysis tools such as oblique plots, travel time analysis, congestion reports, and information layers in timespace surface plots with incident and variable message sign information (12). In response to the FHWA’s initiative of identifying bottleneck locations, a new PORTAL module is being developed to provide bottleneck identification and analysis capabilities. One of PORTAL’s main features is the use of surface plots to represent the time-space TRB 2009 Annual Meeting CD-ROM Paper revised from original submittal. Wieczorek, Li, Fernández-Moctezuma and Bertini 3 evolution of a particular measurement. One goal for the bottleneck identification module is to visually represent the bottleneck activation and deactivation times, as shown hypothetically in Figure 1. Bottleneck activation is marked as well as the average propagation speed of the shockwave. This tool will provide the most relevant information to the user: activation time, duration, deactivation time, location, and shock propagation speed.