There are three parts in the replication package:

• Nowcasting

The Nowcasting folder contains the complete pipeline to train the CNN model (Python Code) with images of different vehicle classes and subsequently use the trained model to classify vehicles from video data and estimate their emissions. 

• ODME

The ODME folder includes the software DTALite as well as its input data for estimating OD demand. 

• Simulation

The simulation folder contains the raw demand data extracted from Dynameq initially calibrated by City of Stockholm for the entire Stockholm area and the scripts to transform the demand first into MATSim format and subsequently into SUMO format to realize the hybrid simulation format. Ultimately following this pipeline, the emission for the study area in Kista can be realized for given demand of the current scenario and alternative future scenarios.

To address the limited extensibility of standardized message format this study proposes a modular, edge-intelligent framework — The mobility Operating System (mOS) — integrated with a mixed-reality testbed for realistic validation of infrastructure-guided autonomous vehicle coordination. We analyzed to verify the feasibility of the C-V2X Framework for autonomous vehicle guidance in real-time at the physical testbed. The dataset was collected from the testbed experiment to analyze framework performance (speed profiles, post-encroachment time, and numerical error) and service performance (latency, jitter, and packet loss).

This replication package includes all materials necessary for readers to understand and reproduce the analyses presented in the manuscript. As this study is a review paper that does not involve empirical datasets, experimental code, or algorithmic simulations, the materials provided focus on enabling transparent replication of the literature search and bibliometric procedures used in the study. All literature used in the analysis is fully cited within the review paper, ensuring complete traceability of the sources.

This paper introduces a novel deep learning framework that enhances vehicle re-identification (ReID) accuracy by integrating visual and temporal data. Vehicle ReID, which identifies target vehicles from large volumes of traffic data, is essential for continuous tracking in large-scale monitoring scenarios involving multiple Unmanned Aerial Vehicles (UAVs). UAV-based monitoring, while offering a comprehensive bird’s-eye view (BEV), faces key challenges: loss of uniquely identifiable features and reliance on visual data, which struggles with vehicles of similar appearance. To overcome these issues, our approach incorporates traffic-oriented features based on shockwave theory to model predictable vehicle travel times. Methods have been tested with data from one of the largest drone experiments with 10 drones monitoring 20 intersections for one week in the city of Songdo in Seoul Area. Experimental results demonstrate a 36.8\% improvement in ReID accuracy over traditional methods, highlighting the potential of UAV-based solutions for robust and scalable traffic monitoring.

The replication package containing data and code to reproduce this study' s results contains:
 • code generated or adapted via GPT-based prompt methods,
 • detailed evaluation scripts (Python) and analysis notebooks,
 • extensive persona prompts used in the guided LLM methodology,
 • supplementary hints and strategy documentation for persona modelling, and
 • an extracted subset of the dataset corresponding to the study' s tables and results.

Dataset: MiD2017 Tabellenband Deutschland

This article presents FollowGen, a conditional diffusion model for vehicle trajectory prediction in car-following scenarios. Unlike existing diffusion-based approaches that introduce conditions only during the denoising stage, FollowGen incorporates a scaled noise conditioning mechanism in the forward process to embed historical motion features, and employs a cross-attention transformer in the reverse process to explicitly model interactions between leading and following vehicles. Experiments demonstrate that FollowGen consistently outperforms state-of-the-art baselines, achieving higher accuracy and robustness in diverse car-following environments. 

This repository contains all necessary assets to replicate the study "Multimodal traffic assignment from privacy-protected OD data."

📁 Data

Location: /data
The dataset includes the following components:

• Network Link Data: Parameters (cost, capacity, travel time) for each network link.

• Route Sets: Pre-defined sequences of links (routes) analyzed in the case studies.

💻 Code

Implementation: The full PPTA model, privacy mechanism, and evaluation framework.

• Language: Python 3.8+

• Dependencies: Install required libraries:

  pip install numpy pandas matplotlib scipy cvxpy

  Note: The Mosek solver requires a separate license and installation.

• Execution: Navigate to the PrivacyPreservingTrafficAssignment directory and run:

  python main_ppta_new.py

  This will execute the main script and reproduce the key results from the paper.

📄 Documentation

For a detailed explanation of the methodology, file descriptions, and step-by-step instructions, refer to:
replication-explanatory-file-commtr-PPTA.docx

‘final_df.csv’ includes travel time index (TTI) and all potential  influential factors for pre-lockdown, lockdown, and post-lockdown periods.

This package contains R codes and python code as well as model input data to conduct replication. An explanatory file has also been provided. 

This replication package accompanies the manuscript “What patterns contribute to autonomous vehicle crashes?  A study of Levels 2 and 4 automation using association rule analysis.”
It provides: (i) code to harmonize California DMV AV crash reports and NHTSA SGO crash reports into a unified, analysis-ready table; and (ii) scripts to run Apriori association rule mining with support/confidence/lift threshold

This is the code and datasets for the potential publication "Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment".

This replication package contains the source code, configuration files, input data, and result samples for the paper “Efficient and stable ride-pooling through a multi-level coalition formation game”. The package allows users to reproduce all experiments reported in the article. Instructions for environment setup and execution are provided in the included README and configuration files.

This is the code and datasets for the paper "Quantitative assessment of mid-air collision probability in urban air mobility: A safety barrier-based framework for integrated operations". This research proposes a method to systematically quantify the mid-air collision (MAC) risk for different operation types in urban air mobility (UAM).  

This study proposes a lane-changing decision and trajectory planning algorithm for intelligent vehicles on highways that takes driver behavior into consideration. A co-simulation model based on Prescan and Simulink was built to validate the designed trajectory planning algorithm. The code and data related to the proposed algorithm and its verification are also provided.

The is the code for paper "Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control". Run train.py for deep reinforcement learning model training. Run test.py for testing.

The code is used in the paper:"Beyond Conventional Vision: RGB-Event Fusion for Robust Object Detection in Dynamic Traffic Scenarios"

This replication package includes the complete code and dataset used in our study titled “Can Combined Virtual-Real Testing Speed Up Autonomous Vehicle Testing? Findings from AEB Field Experiments." The materials provided are essential for researchers and practitioners interested in replicating our experiments and validating the findings.

该数据集包括 2023 年 10 月城市十字路口四个方向的监控视频。 这些视频的分辨率为 3840×2160，帧速率为每秒 30 帧 （fps），场景理解标注涵盖交通拥堵程度、行人和车辆数量、行为意图等正常场景。

This repository includes the replication code for utilizing the PFD method for traffic state estimation applications.