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