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

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].

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). 

During the eye-tracking measurement—using a Tobii Pro eye camera—the participants were asked to watch a video shot inside an autonomous vehicle. The video was compiled from publicly available sequences according to two aspects:

1) Different situations are shown from different positions,

2) The eye movements observed at the beginning and at the end of the video can be compared to some extent; therefore, we selected the first and last sections of the video in such a way that the subjects can watch the ride from the same angle and have relatively more time to become involved in the given situation.

The recording consisted of 5 different sequences, within which we defined 29 areas of interest (AOI) 

In terms of the data collected via eye cameras, four important measures can be defined:

−      Average fixation duration: how long a fixation lasted on average within the given AOI.

−      Fixation count: number of fixations within the given AOI.

−      Total fixation duration: total length of fixations within the given AOI.

−      Pupil diameter (right/left): the change in right and left pupil diameter within the given AOI.

1. This dataset contains all the RAAML files for the architecture design of the urban intersection information physical system proposed in this studyTo open this file, the MagicDraw software mentioned in this article needs to be used.
2. The architecture design file utilizes the RAAML database released by OMG by extending the functionality of UML and SysML. This database includes basic models related to reliability analysis, which have now been imported into the provided dataset. Please refer to OMG Risk Analysis and Assessment Modeling Language, Risk Analysis and Assessment Modeling Language (RAAML) Libraries and profiles for the original file, Version 1.0, OMG Document Number: ptc/21-12-02, http://www.omg.org/spec/RAAML.
3. The data involved in Section 4.6 of this article are all the results obtained in Section 4.5, and the response surface method built-in in Design Expert is used for relevant calculations.

This dataset constains data and codes for determining the VTOL sites.

The OD data in this paper are from the real road network data of Guangdong province. The initial SoC in the paper is randomly generated and follows a normal distribution.

Code and experimental data for "Multi-Level Objectives Control of AVs at A Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach."Codes are written in Python and developed on an open-source tool “Flow”.( https://flow.readthedocs.io/en/latest/index.html)

Total 312 participants in approximately equal proportions of males and females were invited to participate the decision-making experiments for the payoffs that were contingent on their performance. The participants were randomly assigned to 17 groups and they were required to make DTD route choices with no mutual communication. The following eight DTD scenarios with the same origin-destination (OD) pair were set.

• Scenario 1 was the baseline scenario containing a symmetric two-route network.
• Scenarios 2-5 extended Scenario 1 by using asymmetric two-route networks and different cost functions to investigate subject’s route choice behaviors under different cost feedback.
• Scenarios 6-7 employed asymmetric networks with three routes to observe more complicated route choice behavior.          •Scenario 8 extended the configuration to non-linear cost functions and different group sizes (24 subjects per group in Scenario 8, while 16 in others), which would demonstrate the robustness of the proposed theoretical model.

Data used in this study is based on questionnaire distribution in four Indonesian cities about Transport-SuperApps behaviour. 1,051 valid data are used for the analysis. The list of questions that were used in this study is described in the paper. The script and the packages for analysis were disclosed, however, due to funders’ terms and conditions, the data cannot be disclosed.

This report delves into the practical application of Computational Fluid Dynamics (CFD) by simulating viscous channel flow through a sudden contraction in the laminar flow regime. The simulations were conducted using two distinct software platforms: Wolfram Mathematica and COMSOL Multiphysics. The primary goal is to compare the procedures and results obtained from each software, providing a deeper understanding of CFD principles and emphasizing the importance of validating simulation outcomes.

Purpose — Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits if offers. Nevertheless, the adoption of battery electric bus is still impeded by its limited flexibility. The constraint necessitates adjustments to current bus scheduling plans. To this end, this paper aspires to offer a thorough review of articles focused on battery electric bus scheduling.

Design/methodology/approach — We provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain.

There are two folders containing the replication files.

The first folder contains the Voc-Poc tool for transmitting the captured video stream from the Goggles to the local server.

The second folder contains the main code and main results of this manuscript. The main code is for the YOLO + StrongSORT + TMC collection algorithm. The main code is in the track.py file.

Data and code for "Less is more: Lightweight reinforcement learning method for traffic signal control with less observation"

The data consists of normal driving records for dozens of private cars over several months (from June 5, 2015 to June 30, 2016), with a sampling frequency of one minute. The basic specifications of the vehicles are as follows: Roewe E50 is a pure electric vehicle, weighing 1080 kilograms. It is equipped with a 22.4 kWh battery pack, and is reported to have a driving range of 170 kilometers. The raw data has been preprocessed and denoised, resulting in a final dataset containing 10,000 trips. This dataset offers substantial potential for reuse in research and analysis focused on electric vehicle energy consumption. Researchers, engineers, and policy makers can leverage this data to understand patterns, develop optimization algorithms, and inform energy-efficient practices. The dataset adheres to all applicable legal requirements. All sensitive information has been removed, and the data has been preprocessed to ensure confidentiality. There are no known legal or ethical obstacles to its use.

The package includes the codes (Codes.rar) and simulation results (simulation results.rar) related to the manuscript 'Evaluation of platooning configurations for connected and automated vehicles at an isolated roundabout in a mixed traffic environment'. Main codes are written in Python and Matlab, and the simulation results are stored in ‘.xlsx’ format.

The dataset comes from the paper "Markov probabilistic decision making of self-driving cars in a highway with random traffic flow: a simulation study" and mainly consists of three parts:

1. The "policy_training" folder, which includes the code used for training driving strategies in the highway scenario mentioned in the paper. The main function is "main_v3.cpp," and the functionality is implemented in the file "f_define_v3.cpp." Additionally, "to_strategy.m" exports the trained strategy as a state-action table for application, and "FIGURE.me" visualizes this table.

2. The "policy_application" folder contains "test.py" as the main file, which uses "MdpModel.py" to call the state-action table from the "Strategy" folder and visualizes it through "MdpModel.py." The "Map" and "Vehicle" folders store visualization elements.

3. The third part is a video of the visualization results named "simulation_demo.avi."