考虑车内拥挤状态的公交弹性发车间隔优化

为提高常规公交的出行服务质量，建立了考虑车内拥挤状态的公交弹性发车间隔优化模型.通过量化车内拥挤状态，给出各拥挤状态下的乘车成本;建立了线路各区间的拥挤状态转移函数.考虑乘客的车内拥挤状态感知，建立站点乘客随时间的上下车数量的度量模型;构建乘客出行成本度量模型，以乘客出行成本与车辆运营亏损之和最小化为目标函数，建立了公交弹性发车间隔优化模型;并设计遗传算法对模型进行求解.结果表明:优化模型能够有效降低出行成本.模型对等待成本有着“补短板”效果，对乘车成本有着“削峰”效果，即通过增加乘客到站量小站点的等待成本，降低乘客到站量大站点的等待成本，削弱乘车成本峰值的手段，达到总成本最低的效果.

Optimization of Buses' Flexible Departure Intervals Considering Crowdedness State

In order to improve the travel service quality of conventional bus， an optimization model of buses’ flexible departure intervals considering crowdedness state in the bus is established. By quantifying the crowdedness state in the bus， the travel cost under each crowdedness state is given， and the crowdedness state transfer function of each section of the line is established. Considering passengers’perception of the crowdedness state in the bus， the measurement model of the number of passengers getting on and off the bus over time is established; the travel cost measurement model is established， and the optimization model of buses’ flexible departure intervals is established with the objective function of minimizing the sum of passenger travel cost and vehicle operating loss; and the genetic algorithm is designed to solve the model. The results show that the optimization model can effectively reduce the travel cost. The model has a “short board filling” effect on the waiting cost， and a “peak cutting” effect on the travel cost. That is， by increasing the waiting cost of the stop with fewer passenger arrivals， reducing the waiting cost of the stop with more passenger arrivals and weakening the peak value of the travel cost， the total cost can be minimized.