High-resolution traffic data was collected by the SMART-SIGNAL system (Liu et al., 2009), which is capable of continuously collecting and archiving high-resolution vehicle-detector actuation and signal phase change data. The raw data contains information of individual vehicles’ arrival and departure time. From that, the occupancy time (i.e., the time the detector is occupied by a vehicle), individual vehicle speed and time gap can be derived.

the data collected from stop-bar detectors are used to directly identify RLR cases. The basic idea is very intuitive: if a vehicle approaches stop-bar with a relatively high speed (higher than a threshold value), we conclude that the driver decides to run through the intersection. For the running cases, if the signal indication is red when the vehicle passes through the stop-bar detector, it is an RLR

This research focuses on estimating both rear-end and crossing (i.e. right-angle) traffic conflicts. The rear-end conflicts include the same-direction conflicts from all approaches, and the crossing traffic conflicts mainly are the conflicts between through movement and conflicting left-turns. To estimate the potential conflicts, first we need to estimate vehicles’ trajectories using high-resolution data. With the trajectory data, we then apply the traffic conflict technique to estimate both types of traffic conflicts at intersections. The overall intersection safety can be evaluated based on a combination of the probability of two types of potential conflicts.

Using the event-based data, a set of arterial performance measures, especially intersection queue length can be estimated. Queue length is unarguably the most important performance measure at a signalized intersection, since other performance measures such as average delay and level of services can be easily derived from queue length information. A major shortcoming of traditional input-output models for estimating queue length has been their inability to determine queue length under saturated conditions—i.e., when the queue of cars waiting to pass through an intersection extends beyond the upstream vehicle detector. Under saturated conditions, data on incoming traffic flow are no longer available and the input side of the input-output model breaks down. The SMART-Signal developers overcame this limitation by developing a new algorithmic approach to queue length estimation based on traffic shockwave theory.