Floating Car Data (FCD) Applications
In our previous article, we have explained what FCD stands for. If you want to have more detailed information about FCD before reading this one, take a glimpse here first :)
So what kind of analysis can FCD be used in? I want to talk about some of them. FCD is extremely valuable for the traffic sector, especially when the cost and maintenance of sensors are taken into account.
We have two types of FCD, one is static data and the other is a dynamic one. Static data includes the fixed information of the road segments. Such as segment length, coordinates, and speed limit …etc. While dynamic data consists of segment-based travel time information that is updated every one minute. With the use of this information, we can develop real-time traffic density analysis; before/after analysis, real-time incident detection, and real-time dynamic junction management applications.
Speed Profile
The speed profile is the basis of most of the applications I have mentioned above. It plays an important role in determining the characteristics of junctions and corridors.
Incoming dynamic data is merged with static data to make sense of FCD. This way, we can calculate segment-based speed using the travel time of the segment and the length of that segment. Afterward, we can transform this into meter-based speed information with interpolation methods.
Queue Length Estimation
After the speed profile calculation, a predefined speed threshold can be used to estimate the queue lengths in traffic sections. This threshold value means the queue speed. It may vary according to each road class. In other words, with a method of your choice, you can obtain meters where the speed values under the threshold. So you get the queue length.
Incident Detection
The critical issue in incident detection applications is to be able to manage the system and give warnings in real-time. We said that speed profile defines the characteristic of the traffic sections. With the unusual changes in this characteristic, incident detection becomes possible. Todo so, we need archive data, and of course, a user interface where we can trigger a warning wouldn’t be bad, right? :)
The inputs for this part are the real-time calculated speed profile and archive speed profile. Current speed values for a traffic corridor are compared with the archive speed values of the same corridor and the existence of an incident is examined.
If an incident occurs, an alarm is shown on the user interface. Traffic operators are expected to mark this incident alarm as true or false. According to their mark, found incidents will either be included in the archive or excluded. An example of a found incident is shown below.
Delay Calculation
By calculating the queue length discharge duration and comparing it with the signal durations, the delay time can be calculated. An example plot for estimated queue length and calculated delay time is shown below.
Dynamic Junction Management
With the calculated queue length, we can obtain the demand outside the area covered by the sensors. Queue length estimation is included in the dynamic intersection management algorithm. It allows optimizing and changing the predefined signal durations in constant cycle duration. In this way, junction management becomes more effective and efficient. In the figure below, the number of vehicles in the directions is the same for both scenarios.
