Path matching algorithm that everyone can understand

Mobile phone number list for mass text marketing.
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amihaaharum
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Joined: Mon Apr 18, 2022 4:20 am

Path matching algorithm that everyone can understand

Post by amihaaharum »

In practical applications, the main reason for the deviation between positioning and map roads is the quality of GPS data collection. phone number list Since these problems are very common, we need to still achieve high path matching accuracy in this case. phone number list So, how to do it algorithmically? Imagine that a car is driving on the road. The three points 1, 2, and 3 are the GPS positioning results of the car. We can see that the positioning of the three points, the formed trajectory and the actual road have very obvious deviations. This is a common problem of matching the positioning trajectory with the map road. I believe that everyone has the experience of ordering takeout through Ele.me, or purchasing large items on JD.com. When we want to check the distance between the takeaway rider and the delivery person, the platform often just draws a straight line from the delivery point to the delivery address on the map. , the user cannot see the complete driving trajectory of the rider, and the displayed positioning is often significantly offset from the road. The main reason may be because in the scenario of take-out and home delivery.

It is not necessary to do accurate trajectory matching with the road. The reason for the inaccurate path matching In practical applications, the main reason for the deviation between positioning and map roads is the quality of GPS data collection. phone number list Low GPS sampling frequency, large positioning error, and loss of GPS signal will increase the inaccuracy of path matching. Since these problems are very common, we need to still achieve high path matching accuracy in this case. So, how to do it algorithmically? Algorithm Implementation of Path Matching 1. Anomaly point monitoring Through the outlier detection algorithm, the wrong data and unreasonable data in the driving GPS trajectory data are checked, and the outliers are eliminated before calculation and analysis. 2. Observation probability Observe the distance between the GPS track point of the vehicle and the surrounding roads, as well as the continuity of the road and other factors, and calculate the correct road for the vehicle to travel.

We calculate the distance between the red GPS points P1, P2, P3, P4 and the surrounding roads C1, C2, C3, C4, and determine the probability of which road the GPS point is actually located on according to the distance. The closer the distance, phone number list the higher the probability. At the same time, the continuity of the road will also be examined. For example, the two GPS points P2 and P3 are the closest to the two roads C2(1) and C3(1), but these two sections are not a continuous road. Simply use the closest distance to judge the probability. 3. Speed ​​Weight When a GPS point appears on an elevated road, it is difficult to determine the specific road through positioning. Using the vehicle speed weights, the exact position of the vehicle on an elevated or surface road can be calculated. Finally, in practical applications, we have accumulated and improved a rich and detailed map and road database, established a data index, which greatly improves the computing efficiency, and adopts the extremely efficient shortest path algorithm, which greatly improves the computing speed.
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