Transportation
Transportation mode recognition has always been an important task in trajectory data mining. Trajectories are essentially sequences of trajectory points, so many studies have chosen sequence structures for modeling trajectories. However, sequence models cannot capture the higher-order structural features in trajectory. In this context, we propose a novel graph model Trajectory Feature Graph (TF-Graph) for capturing trajectory features. Core words are usually extracted to express the main meaning of a sentence in the field of Natural Language Processing.
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This dataset comprises ten adapted benchmark instances derived from the classical Prodhon dataset, along with one dataset based on a real-world logistics case study. The data are specifically designed to support the evaluation of a bi-objective Green Location Routing Problem (GLRP) model that aims to optimize both economic cost and environmental impact (carbon emissions).
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The data is derived from 22,898 comments on driverless and human driving obtained by crawler technology on China's Weibo and XiaoHongshu platforms from May 1 to August 31, 2024. The main data formats are xlsx, py, txt, json and so on. The files in py format are script files, which are used to process data. The dataset was eventually used for topic mining, sentiment analysis, and more on Chinese users' comments on driverless and human driving.
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The dataset contains 56 rows and 51 columns, detailing various characteristics of EV charging stations (SPKLU) in the Jadetabek area. Key attributes include station identifiers, transaction metrics (e.g., number of transactions, unique users, average duration), geospatial data (latitude, longitude, easting, northing), administrative divisions, and location criteria. It also captures proximity to infrastructure such as roads, toll gates, and other EV stations, alongside charger counts for different types, population metrics, and income levels.
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217 Views
The U.S. delay dataset is collected from Kaggle(https://www.kaggle.com/datasets/robikscube/flight-delay-dataset-20182022), covering three years of flight data from January 1, 2017, to December 31, 2019. The dataset originally collected includes data from 360 airports. We remove airports with fewer annual flight numbers and select data from 75 medium and large airports for our experiments.
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105 Views
Repeated Route Naturalistic Driving Dataset (R2ND2) is a dual-perspective dataset for driver behavior analysis constituent of vehicular data collected using task-specific CAN decoding sensors using OBD port and external sensors, and (b) gaze-measurements collected using industry-standard multi-camera gaze calibration and collection system. Our experiment is designed to consider the variability associated with driving experience that depends on the time of day and provides valuable insights into the correlation of these additional metrics on driver behavior.
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492 Views
The AIS dataset is provided by the National Oceanic and Atmospheric Administration (NOAA), spanning from January 2020 to December 2020. The trajectories of 140 individual vessels (including tankers and cargo) were collected. Weather and ocean conditions for the same period are obtained from the National Data Buoy Center (NDBC), collected from 15 buoys.
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83 Views
This is a dataset related to spatial crowdsourcing, encompassing data on workers and tasks. The urban spatial data is sourced from an open dataset, and its website link has been provided in the paper.
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53 Views
The datasets are sourced from the Caltrans Performance Measurement System (PeMS) in California, which monitors and collects real-time traffic data from over 39,000 sensors deployed on major highways throughout the state. The PeMS system collects data every 30 seconds and aggregates it into 5-minute interval, with each sensor generating data for 288 time steps daily. Additionally, road network structure data is derived from the connectivity status and actual distances between sensors.
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685 Views
Ground Penetrating Radar (GPR) facilitates the detection and localisation of subsurface structural anomalies in critical transport infrastructure (e.g. tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage - especially of less-accessible structural aspects (e.g. crowns) - alongside unclear visual output of anomaly spatial profiles, both for physical and simulated datasets.
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543 Views