City infrastructure agencies routinely implement road projects that address various elements of urban infrastructure. The majority of these projects are short-term in nature (e.g., utility repair), as they are completed in a few hours within 8:00 a.m. to 5:00 p.m. of a workday. The implementation of these projects during working hours, in spite of the inconvenience imposed on road users, helps the agency avoid extra labor costs associated with nonregular working hours. Careful scheduling of these projects can prevent unduly increased travel delays (road users’ interest) while keeping project costs low (the agency's interest). This study presents a bi-level framework for scheduling short-term urban road projects that analyzes the implicit tradeoffs between the two stakeholders’ interests. The upper-level model establishes the optimal schedule considering the project characteristics, such as cost and duration. The lower-level model captures the dynamic user equilibrium conditions that yield the road users’ path and departure time choices. The bi-level model is a mixed-integer program with nonlinear constraints. Recognizing the relatively low efficiency of traditional solution methods, this paper proposes a deep-neural-network-ensemble-assisted active learning (DN2EA2L) algorithm and adopts a fixed-point algorithm for solving the bi-level model. The numerical experiment uses the Sioux Falls network to demonstrate the efficiency of the DN2EA2L, compared to conventional metaheuristic methods. It is shown that travel time increases due to the project implementation during the peak hours can outweigh the agency's saving in wage costs. Further, it is shown that a significant reduction in the road users’ value of time enables the agency to schedule more projects during regular working hours.

中文翻译：

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一种使用深度神经网络和主动学习安排日内道路工作区的方法

城市基础设施机构定期实施解决城市基础设施各种要素的道路项目。这些项目中的大多数都是短期性质的（例如，公用设施维修），因为它们会在工作日上午 8:00 至下午 5:00 的几个小时内完成。尽管给道路使用者带来不便，但在工作时间实施这些项目有助于该机构避免与非正常工作时间相关的额外人工成本。仔细安排这些项目可以防止不当增加的旅行延误（道路使用者的利益），同时保持较低的项目成本（机构的利益）。本研究提出了一个用于安排短期城市道路项目的双层框架，该框架分析了两个利益相关者利益之间的隐含权衡。上层模型根据成本和工期等项目特征建立最优进度表。较低级别的模型捕获动态用户均衡条件，这些条件会产生道路用户的路径和出发时间选择。双层模型是一个具有非线性约束的混合整数规划。认识到传统求解方法效率相对较低，本文提出了深度神经网络集成辅助主动学习（DN2EA2L）算法，并采用定点算法求解双层模型。与传统的元启发式方法相比，数值实验使用 Sioux Falls 网络来证明 DN2EA2L 的效率。结果表明，由于在高峰时段实施项目而增加的旅行时间可以超过该机构的“ 节省工资成本。此外，研究表明，道路使用者时间价值的显着降低使该机构能够在正常工作时间内安排更多项目。