In this paper, single-lane and two-lane traffic model are established based on cellular automaton. Different values of vehicle arrival rate at the entrance and vehicle departure rate at the exit are set to analyze their effects on density, average speed and traffic flow. If the road exit is unblocked, vehicles can pass through the road smoothly despite of the arrival rate at the entrance. If vehicles enter into the road continuously, the traffic condition is varied with the departure rate at the exit. To avoid traffic jam, reasonable vehicle departure rate should be adopted.
In some cases, there are some special requirements for the vehicle routing problem. Personnel or goods geographically
scattered, should be delivered simultaneously to an assigned place by a fleet of vehicles as soon as possible. In this case
the objective is to minimize the distance of the longest route among all sub-routes. An improved genetic algorithm was
adopted to solve these problems. Each customer has a unique integer identifier and the chromosome is defined as a string
of integers. Initial routes are constructed randomly, and then standard proportional selection incorporating elitist is
chosen to guarantee the best member survives. New crossover and 2-exchange mutation is adopted to increase the
diversity of group. The algorithm was implemented and tested on some instances. The results demonstrate the
effectiveness of the method.