The advantages of connected vehicles, especially in traffic safety, rely on the real-time micro high-resolution traffic data (HRMTD) of all road users. The existing traffic sensors are not able to provide the required data when unconnected vehicles are on the road. Roadside LiDAR deployment provides a way to obtain the HRMTD of unconnected road users for the connected-vehicle road network.This research developed algorithms to process raw roadside LiDAR data. A single roadside LiDAR sensor has many limitations considering the scant coverage and the difficulty of handling object occlusion issues. The deployment of multiple roadside LiDAR sensors can provide a larger coverage and eliminate object occlusion issues encountered in single roadside LiDAR deployment. To combine different LiDAR sensors, it is necessary to integrate the point clouds into the same coordinate system. The existing point cloud registration methods serving mapping scans or autonomous sensing systems could not be directly used for roadside LiDAR sensors considering the different features of point clouds and the spare points in cost-effective LiDAR sensors. This paper developed a novel approach for roadside LiDAR data integration (point registration). The developed point aggregation-based particle iterative closest point algorithm (PA-PICP) is a semi-automatic data integration method, which contains two major parts: XY data registration and Z adjustment. A semi-automatic key points selection method was introduced. The partial iterative closest point (PICP) was applied to minimize the difference of triangles between different LiDARs. The intersection of the ground surface between different LiDARs was used for Z-axis adjustment. The performance of the developed process was evaluated with field-collected LiDAR data. The effectiveness and accuracy of PA-PICP were greatly improved compared to the traditional ICP. The case studies showed that the occlusion issue can be fixed after PA-PICP point registration. Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data. It can significantly reduce the data processing time and improve the vehicle/pedestrian identification accuracy. An algorithm is proposed in this research, based on the spatial distribution of laser points, which filters both static and moving background efficiently. Lane identification is an important and necessary step in LiDAR data processing to track the position of each road user accurately. The current lane identification algorithms are mainly developed for autonomous vehicles, which could not be directly used to process roadside LiDAR data. This research provides an innovative algorithm to automatically identify traffic lanes based on the foreground. The lane identification algorithm introduced in this paper only needs three input parameters: road geometry (intersection or non-intersection), number of lanes, and lane width. With these parameters, the algorithm can automatically detect the boundary of each traffic lane using traversal search. The case studies show that this algorithm can provide accurate boundaries of traffic lanes.This research presented a new approach for vehicle classification using the roadside LiDAR sensor. Six features (one feature—Object Height Profile—contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distances to the LiDAR et al.). The Naïve Bayes (NB), K-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were applied for vehicle classification. The results showed that RF has the best performance among those investigated methods. The overall accuracy of RF is about 76%, which is limited by the similar features among some vehicle types (e.g., pickup and SUV). Some types were merged together to serve different types of users. This research also provided the distribution of the accuracy of RF along the distance to LiDAR. The users can set up the proper location of the roadside LiDAR, based on their own requirements of the classification accuracy. The HRMTD of all road users can be extracted using the algorithms developed in this research. The HRMTD can benefit other transportation applications other than connected vehicles. This research provides one applications-deer crossing roads detection using HRMTD.Deer crossing roads is a major concern of highway safety in rural and suburban areas. The current static deer crossing sign deployed on the road is not effective in warning drivers. The flashing sign triggered by real-time deer crossing detection is a solution to reduce deer-vehicle conflicts. The major challenge in the development of the real-time deer crossing system is how to detect the deer crossing in advance with high accuracy. This paper provided an innovative approach to detecting deer crossing at highways using 3D Light Detection and Ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, and object classification. An automatic background filtering method based on the point distribution was applied to exclude background but keep the deer (and road users if they exist) in the space. Adaptive searching parameters were applied in the vertical direction and horizontal direction to cluster the points. The cluster groups were further classified into three groups—deer, pedestrians, and vehicles—using three different algorithms: naïve Bayes (NB), random forest (RF) and k-nearest neighbor (KNN). The testing results showed that RF can provide the highest accuracy for classification among the three algorithms. The deer crossing information can warn drivers about risks of deer-vehicle crashes in real time. The roadside LiDAR can be considered as auxiliary equipment working together with cameras for deer detection. Considering the challenge of tracking blocked deer, this paper did not track the individual deer.