| dc.description.abstract | Respecting traffic volume has significantly grown and private cars become more popular than before in Taiwan. Therefore, carpool that enhances the car occupancy rate can not only relieve traffic congestion, but also reduce the travel cost which comes from the price of global petroleum going up, even save energy. As regards in Taiwan, carpool matching is manually performed by planning personnels with experience in current practice, without a systematic analysis. Such a manual approach is considered to be inefficient and ineffective. In other words, stochastic disturbances arising from variations in car travel times in actual operations are neglected. In the worst scenario, where car travel times fluctuate wildly during operations, the planned schedule could be disturbed enough to lose its optimality. Therefore, focusing on many-to-many origin-destination (OD), we constructed a stochastic carpool matching model that considers the influence of stochastic travel times. The matching model is expected to be an effective tool for the planner to solve carpool members matching.
We employed network flow techniques to construct the stochastic carpool matching model, including multiple CVG (a carpool member group who can provide a vehicle) vehicle-flow networks, CVG passenger-flow networks and multiple CNG (a carpool member group who cannot provide any vehicle) passenger-flow networks to formulate the flows of CVGs and CNGs in the dimensions of time and space. Then, we modified the stochastic travel times in the stochastic carpool matching model as an average travel time to develop a deterministic scheduling model. The two models are formulated as special integer multiple commodity network flow problems, which are characterized as NP-hard. Since the problem sizes are expected to be huge in real practice, the models are difficult to be solved in a reasonable time. Therefore, we also developed a heuristic algorithm for efficiently solving matching problems. In addition, to evaluate the stochastic and deterministic carpool matching models, we also developed a simulation-based evaluation method. The performance of the solution method in practice is evaluated by carrying out a case study using real data and suitable assumptions, and then sensitive analysis is performed for different parameters. The test results show the model to be good and that the solution method could be useful in practice.
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