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A Bilevel Programming Framework for Determining the Optimal Incentive-Based Traffic Demand Management Strategy

机译:确定基于最优激励的交通需求管理策略的双层编程框架

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Incentive-based traffic demand management (IBTDM) is a cost-effective alternative to increasing capacities and conventional traffic demand management strategies. This paper focuses on IBTDM strategy to provide incentives for commuting drivers' departure time shifts to balance temporal distribution of demand by proposing a bilevel programming framework to obtain optimal IBTDM strategy and to evaluate IBTDM strategy's impact on commuters' departure time choice behavior. In the upper-level, the objective function is to minimize total travel time with the total monetary compensation constraint by a pre-set budget, while decision variables are time-varying incentives for commuters according to their departure times. The optimal time-varying incentive profile is then passed on to the lower-level, within which the decision variable is personal departure time choice. The result indicates that such a time-varying linear incentive profile that reaches the highest at the "shoulders" of peak period while remains lowest during the most peak period achieves superior performance.
机译:基于激励的流量需求管理(IBTDM)是增加容量和常规流量需求管理策略的一种经济高效的替代方案。本文着重于IBTDM策略,通过提出一个双层编程框架以获得最佳IBTDM策略并评估IBTDM策略对通勤者出发时间选择行为的影响,从而为通勤驾驶员的出发时间转变提供激励,以平衡需求的时间分布。在上层,目标功能是通过预先设定的预算在总货币补偿约束下将总旅行时间减至最少,而决策变量是根据通勤者的出发时间随时间变化的诱因。然后,将最佳时变激励配置文件传递到下层,其中决策变量是个人出发时间选择。结果表明,这种随时间变化的线性激励曲线在高峰时段的“肩膀”达到最高,而在最高高峰时段保持最低,则表现出优异的性能。

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