A Genetic Predictive Model Approach for Smart Traffic Prediction and Congestion Avoidance for Urban Transportation

Algorithm 1

TPCA algorithm with modified FCM algorithm.

Step 1: Value initialization – input parameters: total population Tp, arriving intervals Ai, arrival time At, genetic criteria Gc

Step 2: Redefined TPCA clustering algorithm:

Arbitrarily select vehicles as the initial centers

Until no change, do

Step 3: Assign the vehicle to the cluster with the similar parameters.

Improve the quality of the centers selected

Step 4:For each pair of selection B and other vehicles V

 Calculate the cost

 Derive membership function

  Subject to ,

  Where , if

  =0

  1 if _,

  0 if _,

Step 5: Calculate the fitness value of each object .

Step 6: Initial genetic criteria

Step 7: Perform genetic functions like mutation of the original arrival time of the vehicles

Step 8: Compute mutation results

 i. Calculate new membership function

 ii. Calculate new fitness value

Step 9: Fitness comparison: if , replace the old values with the new objects.

 Else replace the old value with probability factor , where is the constant

Step 10: Arrive at the CDF with respect to time and fuel consumption

Step 11: Output: fitness and CDF values