多城市物流运输时间预测数据

该数据在多城市物流运输时间预测中具有重要的应用价值。能够提供高精度路线时效分析，更精确地估算城市间货物运输所需时间，帮助物流调度员进行智能路径规划与任务分配。在城市物流运输领域具有广泛的应用场景，特别是城市配送优化、干线运输时效评估和应急物流响应，能够提高运输调度效率，降低运输延误风险，提供精准的预计送达时长参考。数据收集：
该模型的数据来源包括城市路网结构（来源于地图API）、历史运输订单记录（提取“起点节点编号”、“终点节点编号”、“路网平均速度”以及“实际运输时间”）。所有样本数据通过订单ID进行编号，确保样本唯一性。

数据预处理：
首先根据“起点节点编号”和“终点节点编号”构建邻接矩阵，表示路网中节点之间的连通关系；再对“路网平均速度”进行归一化处理，使其落入[0,1]区间。图结构使用节点特征矩阵X表示，每个节点包含其平均速度等统计特征。样本数据分批构建训练集和验证集，去除异常值如速度为0或运输时间超过3倍均值的记录。

模型构建：
使用图卷积神经网络（GCN）结合运输路径的图结构信息进行建模，预测“实际运输时间”。模型主要包括图嵌入提取层、时间预测回归层。

模型核心公式如下：节点嵌入计算：H = ReLU(A × X × W)。其中，A为标准化邻接矩阵，X为节点特征矩阵（含路网平均速度），W为可训练的权重矩阵，H为图中所有节点的嵌入表示。运输时间预测：T̂ = f(H_s, H_d)。其中，H_s表示起点节点的嵌入，H_d表示终点节点的嵌入，T̂为预测的“实际运输时间”，f为回归函数（如多层感知机），输出预测时间值。

模型使用均方误差（MSE）和平均绝对误差（MAE）作为性能评估指标，对预测时间与实际运输时间进行评估。最终模型能够根据历史运输数据和实时交通状态，准确预测任意起止点之间的运输所需时长，为智能物流提供核心决策依据。

This dataset holds significant application value in multi-city logistics transportation time prediction. It enables high-precision route timeliness analysis, more accurate estimation of cargo transportation time between cities, and assists logistics schedulers in intelligent path planning and task allocation. It has a wide range of application scenarios in the urban logistics transportation field, especially in urban distribution optimization, trunk transportation timeliness assessment, and emergency logistics response, which can improve transportation scheduling efficiency, reduce the risk of transportation delays, and provide accurate estimated arrival time references.

Data collection:
The data sources of this model include urban road network structure (derived from map APIs) and historical transportation order records (extracting "starting node number", "ending node number", "average road network speed", and "actual transportation time"). All sample data is numbered with order IDs to ensure sample uniqueness.

Data preprocessing:
First, an adjacency matrix is constructed based on the "starting node number" and "ending node number" to represent the connectivity relationship between nodes in the road network; then, the "average road network speed" is normalized to fall within the [0, 1] interval. The graph structure is represented by a node feature matrix X, where each node contains statistical features such as its average speed. Sample data is divided into training sets and validation sets in batches, and outliers such as records with a speed of 0 or transportation time exceeding 3 times the mean are removed.

Model construction:
A graph convolutional neural network (GCN) is used to model combined with the graph structure information of transportation routes to predict the "actual transportation time". The model mainly includes a graph embedding extraction layer and a time prediction regression layer.

The core formulas of the model are as follows:
Node embedding calculation: H = ReLU(A × X × W). Among them, A is the standardized adjacency matrix, X is the node feature matrix (including the average road network speed), W is the trainable weight matrix, and H is the embedding representation of all nodes in the graph.
Transportation time prediction: T̂ = f(H_s, H_d). Among them, H_s represents the embedding of the starting node, H_d represents the embedding of the ending node, T̂ is the predicted "actual transportation time", and f is the regression function (such as a multi-layer perceptron) that outputs the predicted time value.

The model uses mean squared error (MSE) and mean absolute error (MAE) as performance evaluation metrics to evaluate the predicted time and the actual transportation time. The final model can accurately predict the transportation time required between any start and end points based on historical transportation data and real-time traffic conditions, providing a core decision-making basis for intelligent logistics.