As public agencies focus on optimized signal timing to reduce conges- tion, few have the ability to monitor network operations in real time. Little work has been done to provide a graphic presentation of surface street congestion. A way is introduced here to identify and monitor com- muter congestion on surface street arterials without using specialized equipment. Existing system detectors have been combined with the signal timing information to classify traffic congestion levels. The Utah Department of Transportation Traffic Operation Center is incorporat- ing the results to provide a real-time arterial congestion map. Occu- pancy, flow, and signal information are reported to the traffic operation center every 5 min. Then, a commuter congestion algorithm compares the actual measured volume with an estimated approach capacity. It is inappropriate to assume a fixed-green time per cycle because the net- work’s signalized intersections operate on coordinated–actuated con- trol. The algorithm compares the estimated approach capacity with the measured approach volume to give an estimated real-time volume/ capacity ratio. The approach capacity is estimated by an equation devel- oped through simulating network intersections under a range of con- gested conditions with various cycle lengths and approach green times. The methodology has been verified against field data from congested locations with actual volume and signal timing during peak periods. The predicted approach volumes were within 5% of observed congested con- ditions. This method allows traffic engineers to monitor and identify the congestion of a signal-controlled surface street network in real time without the need for new technologies. Traffic congestion is a major problem for many U.S. cities. The Utah Department of Transportation (UDOT) wants to address Salt Lake City’s growing congestion problem before it reaches the level of congestion found in many other large cities. Currently, UDOT and the Utah Traffic Operations Center (TOC) are able to monitor freeway congestion with speed detectors along the freeway. The detectors send vehicle speed information to the TOC via fiber optics. The speed data are analyzed and a congestion condition (green, amber, or red) for that road is displayed on a digital map of the free- way system. This system allows traffic engineers to observe real-time freeway congestion in Salt Lake City and its surrounding areas. To better serve the public, the TOC wants to expand this real-time observation capability and monitor congestion on surface street arte- rials. The TOC contracted with the Utah Traffic Laboratory at the University of Utah to determine whether it is possible to identify and monitor surface street congestion without purchasing additional detection hardware designed exclusively for the task. Currently, UDOT is in the process of installing system detectors near the inter- sections along the major arterials. Unlike the additional hardware, the primary purpose of these detectors is to detect vehicle presence, extend green time, record volume, and, in some cases, record vehi- cle speeds. The system detectors are embedded in the pavement about 90 m (300 ft) from the intersection. In addition to these detec- tors, UDOT is implementing a plan that has 650 intersection con- trollers communicate with the TOC. They communicate signal status via fiber-optic lines once per second. The intersection controllers used by UDOT measure volume (vehicle per time interval) and occupation (percent occupied per time interval). The minimum time interval is 5 min. Most of the arterial intersections have a 100- or 120-s cycle length (C ). Some inter- sections are partially actuated and allow detectors for the minor street detectors to extend the green time according to demand. The TOC records the cycle length and the permitted green time for each approach to the intersection. This research presents a method of iden- tifying commuter congestion with existing hardware such as system detectors, intersection controllers, and the communications systems currently operating between the TOC and the arterial intersections. Many transportation engineers use level of service (LOS) as defined in the Highway Capacity Manual (HCM) (1) to measure the operating conditions on a particular street. LOS is delimited by assigning a letter (A through F) to represent the average traveling conditions; A represents uninhibited or free-flow traveling condi- tions, and F represents congested or gridlock conditions. LOS can be described as the difference between the actual travel speeds along an arterial relative to the theoretical free-flow speed. ARTERIAL LOS LITERATURE REVIEW Arterial LOS is commonly defined as an average travel speed along an arterial. Many traffic organizations consider it a standard for traf- fic planning and evaluation. The equations and method of calculat- ing arterial LOS are found in the HCM (1). Current research is primarily focused on altering the HCM defined equation variables for the arterial LOS calculations. Researchers are looking to obtain more exact estimates. The HCM designation of arterial LOS is measured by timing a vehicle as it travels a minimum of 1.6 km (1.0 mi) along an arterial. It calculates vehicle speed by dividing distance (in miles) by time (in hours). The calculated speed is compared with HCM Exhibit 15- 2, Urban Street LOS by Class (1), which defines the LOS according to free-flow speeds and HCM urban street classifications. The HCM states that arterial LOS is strongly influenced by intersection fre- quency and operations (1). Intersections are typically the greatest capacity constraints for urban corridors. Congestion occurs at these points first and then progresses along the arterial. Previous research has focused on validating, challenging, and modifying definitions in the HCM. These definitions include the Monitoring Commuter Congestion on Surface Streets in Real Time Joseph Perrin, Peter T. Martin, and Brad Coleman University of Utah Traffic Laboratory, University of Utah, 122 South Central Campus Drive, Room 104, Salt Lake City, UT 84112-0561. Transportation Research Record 1811 ■ 107 Paper No. 02-3182