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Published on: 2025-09-19

Multimodal Network Data for Privacy-Preserving Traffic Assignment

Guoyang Qin, Shidi Deng, Qi Luo, Jian Sun
 

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

Traffic assignmentTransportation network
Category: Road transport data (passenger), Transport infrastructure data
DOI: 10.26599/ETSD.2025.9190062
CSTR: 32009.11.ETSD.2025.9190062
POSITIN North America, United States
Published on: 2025-09-19

Traffic congestion data in Alameda County in the San Francisco Bay Area, California

Dan Zhu, Chisin Ng, Litian Xie, Yang Liu

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

Traffic flowCity
Category: Road transport data (passenger), Traffic flow data
DOI: 10.26599/ETSD.2025.9190061
CSTR: 32009.11.ETSD.2025.9190061
POSITIN North America, United States
Published on: 2025-09-16

Machine learning-based real-time crash risk forecasting for

pedestrians

Fizza Hussain

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

TrajectoryTraffic speed
Category: Road transport data (passenger), Driving behavior data
DOI: 10.26599/ETSD.2025.9190060
CSTR: 32009.11.ETSD.2025.9190060
POSITIN Oceania, Australia, brisbane
Published on: 2025-09-16

Replication Package for “What patterns contribute to autonomous vehicle crashes?” (L2 & L4, 2014–2024)

Hongliang Ding, Sicong Wang, Yang Cao, Xiaowen Fu, Hanlong Fu, Quan Yuan, Tiantian Chen

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

Autonomous vehiclesDriving behavior
Category: Road transport data (passenger), Driving behavior data
DOI: 10.26599/ETSD.2025.9190059
CSTR: 32009.11.ETSD.2025.9190059
POSITIN North America, United States, California (statewide) POSITIN North America, United States, United States (nationwide
Published on: 2025-09-16

Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment

Leizhen Wang, Peibo Duan, Cheng Lyu, Zewen Wang, Zhiqiang He, Nan Zheng, Zhenliang Ma

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

Traffic assignmentDemand
Category: Road transport data (passenger), Travel demand data
DOI: 10.26599/ETSD.2025.9190058
CSTR: 32009.11.ETSD.2025.9190058
POSITIN Global
Published on: 2025-09-10

Efficient and Stable Ride-Pooling through a Multi-Level Coalition Formation Game

Yaotian Tan

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.

Ride-sourcingRide-hailing
Category: Road transport data (passenger), Trip transaction data
DOI: 10.26599/ETSD.2025.9190057
CSTR: 32009.11.ETSD.2025.9190057
POSITIN Asia, China, NingxiaPOSITIN Asia, China, ChengduPOSITIN Asia, China, Haikou
Published on: 2025-09-07

Manuscript: Quantitative assessment of mid-air collision probability in urban air mobility: A safety barrier-based framework for integrated operations

Jinpeng Zhang, Yan Xu, Kaiquan Cai, Victor Gordo, Gokhan Inalhan

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).  

TrajectoryDroneEnvironment parameter
Category: Aviation transport data, Others
DOI: 10.26599/ETSD.2025.9190056
CSTR: 32009.11.ETSD.2025.9190056
POSITIN Global
Published on: 2025-09-06Updated on: 2025-09-07

Humanoid Cognition-Based Approach: Lane-Changing Decision Making and Dynamic Trajectory Planning for Autonomous Driving

Lingshu Zhong

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.

Autonomous vehiclesLane-changingTrajectory planningHuman-like decisionDriving behavior
Category: Road transport data (passenger), Vehicle dynamics data
DOI: 10.26599/ETSD.2025.9190055
CSTR: 32009.11.ETSD.2025.9190055.V2
POSITIN Global
Published on: 2025-08-31

Manuscript: Deep Reinforcement Learning-Based Eco-Driving: Bridging Short-Term Energy-Efficient and Long-Term Battery-Health-Aware Strategy in Dynamic Driving Cycles

Hao Qi, Shiqi(Shawn) Ou, Ya-Hui Jia, Zhixia Li, Yuan Lin

This is the code and datasets for the potential publication "Deep Reinforcement Learning-Based Eco-Driving: Bridging Short-Term Energy-Efficient and Long-Term Battery-Health-Aware Strategy in Dynamic Driving Cycles".

Electric vehicleDriver behaviourVehicle dynamics
Category: Road transport data (passenger), Vehicle dynamics data
DOI: 10.26599/ETSD.2025.9190054
CSTR: 32009.11.ETSD.2025.9190054
POSITIN Africa, China, GuangzhouPOSITIN North America, United States, Los AngelesPOSITIN North America, United States, New England
Published on: 2025-08-22 Associated article: https://doi.org/10.1016/j.commtr.2025.100203

Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control

Xiaocai Zhang

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.

交叉口Demand
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2025.9190053
CSTR: 32009.11.ETSD.2025.9190053
POSITIN Oceania, Australia, Melbourne
Published on: 2025-08-01Updated on: 2025-08-22

Beyond Conventional Vision: RGB-Event Fusion for Robust Object Detection in Dynamic Traffic Scenarios

Zhanwen Liu, Yujing Sun, Yang Wang, Nan Yang, Shengbo Eben Li, Xiangmo Zhao

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

Multimodal transportAutonomous vehiclesEvent
Category: Road transport data (passenger), Computer vision data
DOI: 10.26599/ETSD.2025.9190049
CSTR: 32009.11.ETSD.2025.9190049.V2
POSITIN Global
Published on: 2025-08-17

 Can combined virtual-real testing speed up autonomous vehicle testing? Findings from AEB field experiments

Meng Zhang, Jiatong Xu, Ying Gao, Dandan Shen, Zhigang Xu

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.

Autonomous vehiclesVehicle dynamics
Category: Road transport data (passenger), Vehicle dynamics data
DOI: 10.26599/ETSD.2025.9190052
CSTR: 32009.11.ETSD.2025.9190052
POSITIN Asia, China
Published on: 2025-08-09 Associated article: https://doi.org/10.1016/j.commtr.2023.100116

The dataset of the paper "MoTIF: An end-to-end Multimodal Road Traffic Scene Understanding Foundation Model"

changxin chen

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。

City交叉口
Category: Road transport data (freight), Computer vision data
DOI: 10.26599/ETSD.2025.9190051
CSTR: 32009.11.ETSD.2025.9190051
POSITIN Asia, China, 天津
Published on: 2025-08-01

The Fundamental Diagram of Autonomous Vehicles: Traffic State Estimation and Evidence from Vehicle Trajectories

Michail A. Makridis, Shaimaa El-Baklish, Anastasios Kouvelas, Jorge Laval

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

Connected and automated vehiclesTraffic flow
Category: Road transport data (passenger), Traffic flow data
DOI: 10.26599/ETSD.2025.9190050
CSTR: 32009.11.ETSD.2025.9190050
POSITIN Europe, SwedenPOSITIN North America, United States
Published on: 2025-07-31

Optimal speed limit under multi-class user equilibrium: A prescriptive approach using mathematical programming

Xiao Lin, Ludovic Leclearcq, Lorant Tavasszy

The replication package contains the matlab code and data to replicate the performed experiments described in the manuscript.

Traffic speedMatlabTraffic assignment
Category: Road transport data (freight), Traffic flow data
DOI: 10.26599/ETSD.2025.9190048
CSTR: 32009.11.ETSD.2025.9190048
POSITIN Europe, Netherlands
Published on: 2025-06-18

Original data used in the paper Privacy-preserving Personalized Pricing and Matching for Ride-hailing Platforms

Bing Song

This dataset is the original data used in the paper Privacy-preserving Personalized Pricing and Matching for Ride-hailing Platforms. Based on the real demand information contained in this dataset, we generated the dataset used in our study. The generation process is detailed in the numerical experiments section of the paper.

OdRide-hailing
Category: Road transport data (passenger), Travel demand data
DOI: 10.26599/ETSD.2025.9190047
CSTR: 32009.11.ETSD.2025.9190047
POSITIN Asia, China, Haikou
Published on: 2025-06-18

Safety-critical scenario test for intelligent vehicles via hybrid participations of natural and adversarial agents

wang yong

Safety-critical scenario test for intelligent vehicles via hybrid participations of natural and adversarial agents

Automated vehicleAutonomous vehicles
Category: Road transport data (passenger), Driving behavior data
DOI: 10.26599/ETSD.2025.9190046
CSTR: 32009.11.ETSD.2025.9190046
POSITIN Global
Published on: 2025-06-18

A knowledge-informed deep learning paradigm for generalizable and stability-optimized car-following models

Chengming Wang, Dongyao Jia, Wei Wang, Dong Ngoduy, Bei Peng, Jianping Wang

This repository contains the official code and datasets for A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models.

Driver behaviourTraffic flow
Category: Road transport data (passenger), Traffic flow data
DOI: 10.26599/ETSD.2025.9190045
CSTR: 32009.11.ETSD.2025.9190045
POSITIN North America, United States
Published on: 2025-06-03 Associated article: https://doi.org/100185

DBSCAN-Natural Break Hybrid Clustering for Traffic State Classification

Guojun CHEN, Wei Huang, Dalin Tang, Xin Qiao

This project implements a hybrid clustering approach combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Natural Breaks (Jenks) to classify traffic states. The algorithm is designed to extract meaningful traffic patterns from speed data across road segments by determining relative speed thresholds for various traffic states and analyzing the inter-segment relationships in traffic behavior.

The main functions include:

  • Determining relative speed thresholds for traffic state classification using hybrid clustering.
  • Computing statistical metrics (mean, standard deviation, etc.) of relative speeds in each cluster.
  • Analyzing the correlation of traffic states between adjacent road segments, including:
    • Linear regression slope and intercept.
    • Pearson correlation coefficient (R).
    • p-value of statistical significance.
    • Standard error of regression coefficients.
Traffic flowCity
Category: Road transport data (freight), Traffic flow data
DOI: 10.26599/ETSD.2025.9190044
CSTR: 32009.11.ETSD.2025.9190044
POSITIN Asia, China, Beijing
Published on: 2025-06-02

code and data of A Bidirectional-Search-Based Hybrid A* Path Planning Method for Enhancing Passenger Comfort in Adjacent Vehicle Deviation Scenarios

Xiaoxiao Lv, Wenrui Jin, Xiangping Qiu, Fan Mo, Min Fang, Jiaxue Li

The deviation of the FPS of adjacent vehicles will significantly reduce the CPBA of the target vehicle, and may even render it infeasible for passages to board or alight the vehicle. To mitigate this negative impact, this paper proposes a BHA* path planning method for non-standard FPS. In this method, the FPS optimization strikes a balance between the safety distance and the allowed OAVD to enhance the CPBA. Then, the BHA* algorithm rapidly generate paths for optimized FPS without FPS planning errors. The total time for FPS optimization and path planning is within 2.5 seconds, which meets the real - time requirements of practical applications. Simulation experiments based on the parameters of real vehicles and parking scenarios, as well as comparative experiments with the HA* algorithm, were conducted to verify the effectiveness and superiority of the proposed method. This research pioneers the integration of considerations regarding the CPBA into automatic parking path planning. This database contains the implementation code and supporting data for the proposed method

Autonomous vehiclesPath planning
Category: Road transport data (passenger), Driving behavior data
DOI: 10.26599/ETSD.2025.9190043
CSTR: 32009.11.ETSD.2025.9190043
POSITIN Asia, China, Shanghai
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