How Technology is Revolutionising Intersection Performance Evaluation
Highway Capacity Manual (HCM) traditionally proposes an 8-step method to evaluate the performance of signalized intersections. This evaluation begins from the calculation of the saturation flow rate all the way to estimating the Level of Service (LOS) of the intersection. This method makes use of a volume-delay function based on a traffic simulation model developed in early 1970s. The HCM and similar methods although provide an “estimation” of the intersection performance, there are several disadvantages associated with it that can be also the sources for inaccuracies:
- Extensive data collection is required including vehicles and pedestrian volumes, percentage of heavy vehicles, estimation of base saturation flow rate, bus stops/frequency etc.
- The performance evaluation is limited to the day/hour for which the data was collected
- Inaccuracies in estimating important factors such as saturation flow rate, lost time, etc.
- Weak adjustments for corridor intersections and platooning effects
- Turning Movement Counts represent the number of vehicles that have passed during green phase therefore under-representing the traffic conditions in over-saturated traffic
Technological advances in the past few years have allowed transportation agencies to directly obtain traffic travel time and delay information that allow them to directly measure traffic performance. These technologies include high-resolution GPS data, and wireless Bluetooth/Wi-Fi MAC address detection data. Methods such as running GPS probes along the roadway are not only expensive and polluting, but they also provide a small sample size of travel times that are subject to sampling error and/or bias. Among these technologies, Bluetooth/Wi-Fi data provides the lowest cost for each travel time data point collected.
With the help of travel time data collection technologies, such as Bluetooth/Wi-Fi, agencies are able to monitor the performance of their traffic network in real-time 24-7. The system can effectively identify and report anomalies in the traffic network compared to similar historical data of the same hour/day of the week. At the management and planning level, this information allows agencies to maximize the effects of their investments by prioritizing locations with the worst conditions that require immediate mitigation. This information is the basis for data driven decision-making and management for transportation agencies.