Commuting rideshare routing optimization model and algorithm
The increase in road vehicles has led to increasingly serious urban traffic and environmental problems.Ride sharing is considered an effective way to reduce traffic congestion and reduce carbon emissions.Especially under the continuous impact of the corona virus disease 2019(COVID-19),commuters are more willing to use mutual assistance to travel.Considering the urgency of commuting time,commuters have commuting pressure and uncomfortable feeling of ride-sharing.In the absence of economic benefits,limits the matching range of ride-sharing routes,and adds penalty factors to improve the success rate of ride-sharing matching.In order to solve the larger scale problem,a greedy heuristic algorithm based on the optimal time interpolation is proposed,and three perturbation factors are added to improve the global search ability.Multiple groups of cases with different scales are used to test the disturbance effect.The results show that the designed algorithm can solve better results in a short time,and is more competitive than the exact algorithm,particle swarm algorithm and genetic algorithm in solving large-scale problems.In addition,the effect of ride-sharing can be improved by selecting employees who are far away and evenly distributed as pick-up.
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