Average minimum distance to visit a subset of random points in a compact region
该文推导了在紧凑区域内访问随机分布点集子集的最优路径期望长度的解析下界，并提出了参数化估计方法，对按需交通和物流服务（如拼车、定制公交）有参考价值。
This paper seeks an analytical estimate of the expected distance for visiting an arbitrary subset of independently and uniformly distributed random points within a compact region. This problem has many real-world application contexts such as the emerging on-demand transportation and logistics services (e.g., ridesharing, customized buses). The lower bounds of the expected optimal tour length are analytically derived by considering a so-called “trapping effect”, which explicitly addresses probabilistically the situation that some of the tour legs must connect points that are not neighbors. A parametric approach is developed to estimate the expected optimal tour length for both Euclidean and rectilinear metrics. Numerical experiments demonstrate the validity of these bounds, as well as the closeness of the proposed estimator to simulated results.