We propose to enhance the efficiency of road bottlenecks by strategically implementing metering-based priority (MBP) schemes. Under MBP, a portion of the bottleneck capacity is reserved for priority users but made available for nonpriority users when no priority users are queueing. Previous studies have found that MBP is Pareto-improving regarding individual trip costs with homogeneous users, but its effectiveness becomes ambiguous when users have heterogeneous scheduling preferences. To address this, we consider a finite number of user groups with group-specified scheduling preferences. The design of optimal MBP schemes to minimize the total trip cost is formulated as a bilevel problem, allowing for varying fractions of priority users across groups. Under the identified conditions, convex optimization algorithms can be used to solve optimal MBP schemes. When these conditions are not met, we propose a general solution framework to find solutions with satisfactory accuracy. We study the theoretically optimal system efficiency achievable by MBP through numerical simulations. We also explore the benefits of integrating MBP with other travel demand management policies, such as high-occupancy vehicle lanes. Importantly, the implementation challenges of MBP schemes are also discussed, particularly the difficulty of distinguishing users based on their preferences. We investigate the efficiency of implementing optimal MBP schemes in an aggregated manner, emphasizing the significance of selecting appropriate aggregating patterns. We also propose a type of heuristic MBP scheme that ensures that nonpriority users’ departures can be unaffected by MBP. Such schemes are Pareto-improving and remarkably do not require observing individual preferences. These heuristic schemes can be decentralized by assigning priority status through pricing in certain cases. Numerical results demonstrate that the heuristic approach achieves comparable efficiency levels to optimal MBP schemes in considered scenarios.

中文翻译：

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在具有一般用户异构性的瓶颈模型中定制优先级分配


我们建议通过战略性地实施基于计量的优先 （MBP） 计划来提高道路瓶颈的效率。在 MBP 下，一部分瓶颈容量保留给优先用户，但当没有优先用户排队时，非优先用户可以使用。以前的研究发现，MBP 在同质用户的个人出行成本方面是帕累托改进，但当用户具有不同的调度偏好时，其有效性变得模棱两可。为了解决这个问题，我们考虑了具有组指定的计划首选项的有限数量的用户组。将最小化总行程成本的最佳 MBP 方案的设计表述为双层问题，允许不同组的优先用户比例不同。在确定的条件下，可以使用凸优化算法来求解最优 MBP 方案。当这些条件不满足时，我们提出了一个通用的解框架来找到具有令人满意准确性的解决方案。我们通过数值模拟研究了 MBP 可以实现的理论上最优系统效率。我们还探讨了将 MBP 与其他出行需求管理策略（例如高占用率车道）集成的好处。重要的是，还讨论了 MBP 方案的实施挑战，特别是根据用户的偏好区分用户的困难。我们研究了以聚合方式实施最佳 MBP 方案的效率，强调了选择适当聚合模式的重要性。我们还提出了一种启发式 MBP 方案，确保非优先用户的离开不受 MBP 的影响。这样的方案是帕累托改进的，而且显然不需要观察个人偏好。 在某些情况下，可以通过定价分配优先级状态来分散这些启发式方案。数值结果表明，在所考虑的场景中，启发式方法实现了与最佳 MBP 方案相当的效率水平。