Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply — demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique.


This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” ( In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.


<p>This is&nbsp;<span style="font-family: Verdana, Arial, Helvetica, sans-serif;">Charlotte street netowrk data.</span></p>


This dataset contains road network information of Chengdu with travel time data during four time slots: weekday peak hour, weekday off-peak hour, weekend peak hour and weekend off-peak hour.


This is the dataset provided and collected while "Car Hacking: Attack & Defense Challenge" in 2020. We are the main organizer of the competition along with Culture Makers and Korea Internet & Security Agency. We are very proud of releasing these valuable datasets for all security researchers for free.

The competition aimed to develop attack and detection techniques of Controller Area Network (CAN), a widely used standard of in-vehicle network. The target vehicle of competition was Hyundai Avante CN7.


1. Description

RoundTypeDescription# Normal# Attack# Rows
PreliminaryTrainingNormal and four types of attacks dataset with class3,372,743299,4083,672,151
SubmissionNormal and four types of attacks dataset with class
(during the competition, without class)
FinalSubmissionNormal and five attacks (4 spoofings, 1 fuzzing) dataset with class
(during the competition, without class)
  • Preliminary round contains two status of the vehicle -- S: Stationary, D: Driving.
    In final round, only stationary status traffic was collected for safety reason.

  • All csv files have same headers: Timestamp (logging time), Arbitration_ID (CAN identifier), DLC (data length code), Data (CAN data field), Class (Normal or Attack), and SubClass (attack type) of each CAN message.


2. Class

Normal: Normal traffic in CAN bus.

Attack: Attack traffic injected. Four types of attacks are included -- Flooding, Spoofing, Replay, Fuzzing.

  • Flooding: Flooding attack aims to consume CAN bus bandwidth by sending a massive number of messages.

  • Spoofing: CAN messages are injected to control certain desired function.

  • Replay: Replay attack is to extract normal traffic at a specific time and replay (inject) it into the CAN bus.

  • Fuzzing: Random messages are injected to cause unexpected behavior of the vehicle.


3. Acknowledgement

This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00866, Challenges for next generation security R&D).


During the five-month surveying period, 459 individuals are recruited either on site or via Internet, more than 17 million GPS tracking points over 3,766 days are obtained. A total of 318 respondents’ socio-economic attributes, demographic information, and frequently visited locations are also collected via web-based survey. Among them, 267 volunteers completed more than 5 days of continuous smartphone-based GPS tracking, and participated in the PR survey to provide activity chain information corresponding to the GPS data.


This dataset consists of GPS trajectories of vehicles at fifteen road junctions and maps of road junctions with annotations of slope sections.


Please see in the root directory.


We disclose a traffic landmark dataset for detection.The dataset generated with our framework includes about 150,000 images and annotations of about 470,000 traffic landmarks.Our dataset was collected in an urban area of Seoul and suburban areas of Suwon, Hwaseong, Yongin, and Seongnam in South Korea at different times of the day.Images taken in the morning or evening included a large number of saturated areas due to exposure to direct sunlight.Most images taken under the light condition of the late evening was low-contrast.The images taken at noon included the reflection of the windshield


BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.


If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.


A criticality analysis dataset of the phenomenon occlusion on a T-intersection. Simulation was done using CARLA.