(1) Basic Elements of the Traffic Network
The basic elements of the traffic network include nodes (intersections) and edges (road segments).
(1) Function
Given the topology of all paths and road segments, this algorithm provides the minimum number of active (AVI) detectors needed to observe all paths and their positions. For specific model formulas, please refer to the section "7.1 Full Route Observability Problem" in my paper "Route Flow Estimation Based on the Fusion of Probe Vehicle Trajectory and Automated Vehicle Identification Data."
(2) Description
route_link_indicator is a 0-1 sparse matrix indicating whether path r includes road segment a (1 for yes, 0 for no). This is primarily constructed based on the df_route and df_link dataframes. Although constructing these two input dataframes can be complex, the code can be modified in specific applications to simplify this process.
Code path: model_param.py, function get_indicator.
(3) Others
Code path: observability_model.py, function full_observability.
For specific case applications, please refer to sensor_location_model_flow_observability.ipynb.
(1) Function
Unlike Algorithm 1, this algorithm maximizes the number of observable paths given a fixed number of detectors. For specific model formulas, please refer to the section "7.2 Budget-Constrained Maximum Route Flow Observability Problem" in my paper "Route Flow Estimation Based on the Fusion of Probe Vehicle Trajectory and Automated Vehicle Identification Data."
(2) Others
Code path: observability_model.py, function partial_observability.
For specific case applications, please refer to sensor_location_optimization_eg1.ipynb.
For specific case applications, please refer to sensor_location_model_PFE.ipynb.
The following is the Chinese version.
（1）交通网络基本要素
交通网络基本要素包括节点（交叉口）、边（路段）
（1）功能
在已知所有路径和路段拓扑结构的前提下，给出所有路径可观测的最少需要主动型（AVI）检测器的数量及其位置。具体模型公式，请参考本人论文“Route Flow Estimation Based on the Fusion of Probe Vehicle Trajectory and Automated Vehicle Identification Data”的7.1 Full Route Observability Problem这一章节。
（2）说明
route_link_indicator是一个0-1稀疏矩阵，表示路径r是包含路段a，1表示是，0表示否。其构建方法主要基于df_route、df_link两个dataframe数据开展。虽然构建这两个输入数据的条件有点复杂，但自己的应用中是可以修改代码规避的。
代码路径：model_param.py中的get_indicator函数。
（3）其它
代码路径：observability_model.py内的full_observability函数。
具体案例应用，请参考sensor_location_model_flow_observability.ipynb。
（1）功能
区别于前面算法1，在给定检测器数量的前提下，尽可能最大化可观测路径数量。具体模型公式，请参考本人论文“Route Flow Estimation Based on the Fusion of Probe Vehicle Trajectory and Automated Vehicle Identification Data”的7.2 Budget-Constrained Maximum Route Flow Observability Problem这一章节。
（2）其它
代码路径：observability_model.py内的partial_observability函数。
具体案例应用，请参考sensor_location_optimization_eg1.ipynb。
具体案例应用，请参考sensor_location_model_PFE.ipynb。