Shock Wave Boundary Identification Using Cloud-Based Probe Data

An important component of active management of freeways is the systematic identification of recurring and nonrecurring congestion, particularly the location of the shock wave boundary between the two flow regimes. In the past five decades, many publications have described point-based detection models. The emerging widespread availability of true space mean speed data obtained from probe vehicles greatly simplifies the incident detection problem. This paper describes the use of cloud-based crowdsourced probe data for detecting the boundary between uncongested and congested conditions. Time-stamped freeway segment speed data can be retrieved from a cloud source with standard web communication and data protocols on a predetermined time interval. After data retrieval, differences in speeds of adjacent roadway segments are computed for all segments across the state. The calculated differences are called the "delta speeds." A threshold is then set for the computed delta speeds, and any record that surpasses the threshold triggers a warning via an incident management website to alert traffic management personnel. The delta speed data can also be aggregated over time for strategizing the management of shifting work zones or making capital investment decisions. This study used delta speed values to examine an incident on I-69 in northeast Indianapolis, Indiana. Data within a 5-h period were collected to analyze the dynamics of the back of the queue during reduced capacity caused by incident investigation. In addition, the area of analysis was expanded to 150 mi of I-69 over a 1-month period for finding locations with recurring backs of queues.