A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User’s Driving Preference

Table 8

The improved differential perceptron algorithm.

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Algorithm 3: Improved differential perceptron

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Input: OBD data, habit weight vector

Output: Improved new habit weight vector

11:  While n ! = 2000 or

12:   For each road classification do

13:    Calculate the total length of the section in AP and PP respectively

14:    

15:   End for

16:   For each habit factor do

17:    Calculate the total cost in AP and PP respectively

18:    

19:    Calculate weight change volume respectively

20:    

21:    Calculate and normalize the new driving style weight

22:    

23:   End for

24:   n = n + 1

25:   Re-quantify road network with

26:   Replan the optimal path with the Tabu search algorithms

27:  End while

28:  Return

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