We consider optimizing a truck's choice of path and speed profile to minimise fuel consumption, exploiting real-time predictive information on dynamically varying traffic conditions. Time-varying traffic conditions provide particular challenges, both from network-level interactions (e.g. slowing to consume more fuel locally may be beneficial to avoid congested periods downstream) and link-level phenomena (e.g. interaction between acceleration and gradient profiles). A multi-level, discrete-time decomposition of the problem is presented in which: (i) [sub-problems] speed profiles are optimized within each link, given boundary conditions of entry/exit times and speeds; (ii) [master problem] a space-time extended network representation is used to encode the dynamic interactions, within which the joint choice of path and speed profile is made. By instantiating the space-time network in (ii) with the optimal link profiles from (i), we are able to devise a tractable algorithm while optimizing speed profiles over a fine timescale. The solution approach is to pre-solve offline the computationally-intensive step (i), meaning that the representation in (ii) can be efficiently produced online in response to the real-time predictive information, whereby optimization of the path and speed profile is solved by a single shortest path search in the space-time network, for which many exact algorithms exist. The method is extended to additionally consider choice of discretionary stops and (pre-trip) departure time. Two representations are presented and investigated, depending on whether constraints are additionally imposed to ensure consistency of speed profiles across link boundaries. Numerical experiments are reported on a small illustrative example and a case-study network.

我们考虑优化卡车的路径选择和速度曲线，以最大限度地减少油耗，利用动态变化的交通状况的实时预测信息。随时间变化的交通状况带来了特殊的挑战，既来自网络层面的相互作用（例如，放慢速度以局部消耗更多燃料可能有利于避免下游的拥堵期），也来自链路层面的现象（例如，加速度和梯度曲线之间的相互作用）。提出了问题的多层次、离散时间分解，其中：(ii) [主要问题] 时空扩展网络表示用于对动态交互进行编码，其中路径和速度配置文件的联合选择。通过使用 (i) 中的最佳链路配置文件实例化 (ii) 中的时空网络，我们能够设计出一种易于处理的算法，同时在精细的时间范围内优化速度配置文件。解决方法是离线预先解决计算密集型步骤 (i)，这意味着 (ii) 中的表示可以根据实时预测信息有效地在线生成，从而优化路径和速度配置文件通过时空网络中的单个最短路径搜索来解决，为此存在许多精确的算法。扩展该方法以额外考虑自主停靠站和（出行前）出发时间的选择。根据是否额外施加约束以确保跨链路边界的速度配置文件的一致性，提出并研究了两种表示形式。