Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Abstract

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

“…Then mathematical programming [24] is used to solve the problem, but the traditional solution method can only obtain an optimal solution each time, and the relationship between the objectives needs to be studied to transform the multi-objective solution into a single-objective solution, which is complex and inefficient. Intelligent algorithms based on Pareto optimal solutions mainly include multi-objective genetic algorithms (NSGA, NSGA-II, NSGA-III) [46], multi-objective particle swarm optimization (MOPSO) [47], and multi-target human worker ant colony algorithm (MOABC) [48], Their essence is to use modern intelligent algorithms to calculate the Pareto solution set of the multi-objective optimization model and can generate multiple Pareto optimal solutions at a time, which has been widely concerned by scholars at home and abroad. Genetic algorithm (GA) is a kind of search algorithm that simulates the genetic and evolutionary processes of natural organisms and has good applicability to complex nonlinear and multi-dimensional space optimization problems.…”