Whether the high-speed rail train operation plan is reasonable has an important impact on the transportation efficiency and benefits. This paper focuses on the optimization of high-speed train operation plan under the condition of unbalanced passenger flow. We formulate an integrated mixed integer linear programming model by adopting the form of flexible grouping. The model comprehensively considers constraints such as train unit allocation and passenger flow loading. It aims to minimize the number of empty seats in all trains. The inputs to the model are train units and passenger demand. The allocation of train units is completely driven by passenger demand. Taking the Beijing-Shanghai high-speed railway as an example, the results show that the load rate of train services is 96.42%, which is much higher than the fixed grouping. Therefore, a flexible grouping operation plan proposed in this paper can effectively improve train units' utilization and reduce enterprises' operating costs.
Following the rapid development of urban rail transit, networking operation will be an inevitable trend for rail transit. How to formulate reasonable train dispatching program and achieve total optimal efficiency has become the key issue to be addressed. The cross-line operation of the subway can effectively reduce the transfer station pressure and improve the service quality. This paper studies the train schedule optimization problem under the cross-line operation mode for a subway network, where the time-dependent passengers demand is considered. A mixed integer linear optimization (MILP) model is formulated with the objective of minimizing the passengers travel times and train operating costs. A genetic algorithm (GA) is designed to obtain high-quality solutions. The computational results illustrate that adopting cross-line operation can reduce the total travel time of passengers by 49.3% and train operating costs by 62.5%.
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