The data are stored in Excel files, all in tabular format as structured data. Specifically: timetable data (train diagram data), routing data (train service plan data), running time (train travel duration), distance (inter-station distances), line net (network connectivity between lines), and position (actual geographical positions of stations). All aforementioned datasets are sourced from publicly available data provided by Beijing Subway or Baidu Map.

The highD dataset is a new dataset of naturalistic vehicle trajectories recorded on German highways. Using a drone, typical limitations of established traffic data collection methods such as occlusions are overcome by the aerial perspective. Traffic was recorded at six different locations and includes more than 110 500 vehicles. Each vehicle’s trajectory, including vehicle type, size and manoeuvres, is automatically extracted. Using state-of-the-art computer vision algorithms, the positioning error is typically less than ten centimeters. Although the dataset was created for the safety validation of highly automated vehicles, it is also suitable for many other tasks such as the analysis of traffic patterns or the parameterization of driver models.The dataset is extracted from the HighD dataset and pertains to the car type data, presented in the form of a tabular CSV file, which includes parameters such as the speed, acceleration, and longitudinal position of the ego vehicle, as well as the speed, acceleration, and longitudinal position of the preceding vehicle, among others. It can be utilized for studying driver following behavior.

Overview
It is a Python-based application aimed at designing and optimizing air corridors for efficient air traffic management. This project leverages various third-party risks to create safe and efficient air routes.

Features
- Route Optimization
- Safety Analysis
- Data Visualization

Installation
To get started with the Air Corridor Design project, follow these steps:

1. Download the data set.
2. Navigate to the project directory:
    cd ./
3. Install the required dependencies:
    pip install -r requirements.txt

Usage
To run the application, use the following command:
python ./Air_Corridor_Design/main.py

Project Structure
- README.md: This file.
- requirements.txt: List of dependencies required for the project.
- setup.py: Script for completing setup.
- Air_Corridor_Design/: Directory containing the source code.
- Data/: Directory containing data files used by the application.
- Experiments/: Directory containing experimental results.
- Scripts/: Directory containing some scripts for testing.

Contact
For any questions or suggestions, please contact [zhouk23@mails.tsinghua.edu.cn].

The files is the replication for study of "Should Autonomous Vehicles Be Subsidized to Reduce Parking Fees? A Productivity Perspective". The readers can replicate the study using GAMS to solve the non-linear program.

Replication Package for "Compensation scheme and split delivery in a collaborative passenger-parcel transportation system"

Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle’s perception using the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model’s ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin, while also reducing deployment costs. 

‘NodeSet_2D.mat’ and ‘LinkSet_2D.mat’ describe the node set and link set in Wangjing road network, and ‘ODset.mat’ describe the O-D pairs of demand for UAVs.

This study utilizes datasets from the original baselines, including Los-loop & SZ-taxi (https://github.com/lehaifeng/T-GCN), PEMS-BAY and METR-LA (https://github.com/liyaguang/DCRNN), and PeMSD4 and PeMSD8 (https://github.com/wanhuaiyu/ASTGCN#datasets). The code is available at https://github.com/Chengfeng-Jia/SAFER-Predictor. 

All the materials source coding and data of the Paper titled a multi-agent social interaction model for autonomous vehicle testing

Replication Package for "Towards robust motion control in multi-source uncertain scenarios by robust policy iteration"

This study proposes a Safety Assurance Adaptive Model Predictive Control (SAAMPC) framework to achieve distributed docking/undocking operations for MAVs in uncertain environments. The SAAMPC framework integrates a Model Predictive Control (MPC) controller for trajectory optimization, an adaptive module for dynamic adjustment of control parameters with disturbance, and an adaptive safety assurance module with longitudinal and lateral Control Barrier Functions to ensure safe operation during risky and uncertain conditions. The effectiveness of the proposed approach is validated through simulations in Simulink and field tests on a reduced-scale MAV platform. Experimental results validate that the SAAMPC framework successfully ensures smooth and safe vehicle following and robust execution of docking/undocking operations under uncertainties.

Replication Package for "Enhanced trajectory reconstruction from sparse and noisy GPS data: A progressive chunked transformer approach"

All the materials source coding and results coding  of the Paper titled a trajectory planning and tracking method based on deep hierarchical reinforcement learning

All the materials source coding and results coding  of Enhancing Driver Emotion Recognition Through Deep Ensemble Classification.

The replication package dataset (OpenstreetMap study network, GPS raw data, and intersection locations) used for the study. 

This repository supports the research on mitigating traffic oscillations using a Physics-Enhanced Residual Learning (PERL)-based predictive control approach. It contains all necessary components related to both the prediction and control aspects of the study. The prediction module includes the pre-processed NGSIM dataset, prediction models, and the resulting predictions, which focus on forecasting the behavior of preceding vehicles, including speed fluctuations, to allow timely responses. The control module implements a Model Predictive Control (MPC) approach that uses the prediction results to control connected and automated vehicles (CAVs), enhancing safety and comfort in mixed traffic environments. All code, data, and results are included to ensure that users can replicate the experiments and validate the findings effectively.

The Layered Bidirectional Q-Learning (LBQ) algorithm is designed for path planning, tackling the complexities inherent in multilayer path planning during the recycling process. This approach incorporates a bidirectional update mechanism that minimizes the unpredictability associated with initial exploration phases. Additionally, the algorithm employs a hierarchical reinforcement learning strategy, which breaks down intricate tasks into more manageable subtasks. Through the strategic design of reward functions that address various constraints, the LBQ algorithm successfully optimizes paths under multiple conditions.

The replication file contains data used in the paper "Collaborative electric vehicle routing with meet points" published in COMMTR. In this paper, we use real-world locations of grocery stores in Gothenburg, Sweden. The original data is the real addresses. Additionally, we use some test location data to evaluate large-scale instance performance, with customer locations randomly generated within a 25 km × 25 km region. The x and y coordinates range from 0 to 25 and the unit is kilometer. In addition to customer locations, we have included meet point and depot locations in the file. The type of each location and its associated company are noted. This data has been prepared for replication purposes. 
The code for the proposed algorithms is currently being used in another paper, which is under review. We will upload the code once the other paper is published. 

This repository provides the system dynamics simulation model to the paper titled "Bridging the gap: towards a holistic understanding of shared micromobility fleet development dynamics", submitted to the Journal Communications in Transportation Research. The simulation model is made available as a Vensim Packaged Model in the file format vpmx. The free software Vensim Model Reader is necessary to view and simulate the model (https://vensim.com/free-downloads/#Model_Reader). The Model Reader allows read-only access to models created with Vensim. Policies and policy combinations can be defined and simulated using the slider controls. All equations and parameter values can be inspected using the “Document” and “Document all” tools. It furthermore includes the complete simulation results for all parameters and variables for ten different policy packages in the file format csv (Comma Separates Values).