DOI: 10.57760/sciencedb.29423
title: Heterogeneous Spatiotemporal Trajectories
title: Heterogeneous Spatiotemporal Trajectories

description: With the proliferation of location-capturing technologies and location-based services, spatiotemporal entities generate vast amounts of heterogeneous trajectory data. However, it is natural that the spatiotemporal data for a single entity may originate from different sensor types, leading to the trajectories are always asynchronous and discontinuous. This heterogeneity arises because the real-world entities have to interact with multiple sensing infrastructures, yet no single source provides a complete or consistent trajectory, making heterogeneous spatiotemporal trajectories both increasingly critical. However, the lack of heterogeneous spatiotemporal trajectory datasets has significantly hampered progress in this field. To address this gap, we publicly release two novel datasets encompassing both maritime and urban scenarios. Based on the differences in trajectories collection characteristic, we categorize trajectories into two types: active trajectories and passive trajectories.During maritime navigation, we utilizes an Automatic Identification System (AIS) dataset were collected by the U.S. Coast Guard through onboard navigation safety devices that transmit and monitor vessel locations in U.S. and international waters in real time. The AIS dataset (in ais.tax.gz of folder name) includes 67,099,008 active trajectory GPS points (in data1.npy of file name) and 30,044,963 passive trajectory GPS points (in data2.npy of file name), collected from 101,400 vessels over 272 days. Also, we randomly selected 1,000 and 3,000 trajectories (in small_ais.tax.gz of folder name). In the urban road network, T-Drive dataset (in taxi.tax.gz of folder name) is a trajectory dataset for urban safety scenarios, and the dataset contains 7,634,241 GPS points from active trajectories (in data1.npy of file name) and 3,376,737 from passive trajectories (in data2.npy of file name), all generated by 67,380 taxis over 34 days Also, we randomly selected 1,000 and 3,000 trajectories (in small_taxi.tax.gz of folder name).
description: With the proliferation of location-capturing technologies and location-based services, spatiotemporal entities generate vast amounts of heterogeneous trajectory data. However, it is natural that the spatiotemporal data for a single entity may originate from different sensor types, leading to the trajectories are always asynchronous and discontinuous. This heterogeneity arises because the real-world entities have to interact with multiple sensing infrastructures, yet no single source provides a complete or consistent trajectory, making heterogeneous spatiotemporal trajectories both increasingly critical. However, the lack of heterogeneous spatiotemporal trajectory datasets has significantly hampered progress in this field. To address this gap, we publicly release two novel datasets encompassing both maritime and urban scenarios. Based on the differences in trajectories collection characteristic, we categorize trajectories into two types: active trajectories and passive trajectories.During maritime navigation, we utilizes an Automatic Identification System (AIS) dataset were collected by the U.S. Coast Guard through onboard navigation safety devices that transmit and monitor vessel locations in U.S. and international waters in real time. The AIS dataset (in ais.tax.gz of folder name) includes 67,099,008 active trajectory GPS points (in data1.npy of file name) and 30,044,963 passive trajectory GPS points (in data2.npy of file name), collected from 101,400 vessels over 272 days. Also, we randomly selected 1,000 and 3,000 trajectories (in small_ais.tax.gz of folder name). In the urban road network, T-Drive dataset (in taxi.tax.gz of folder name) is a trajectory dataset for urban safety scenarios, and the dataset contains 7,634,241 GPS points from active trajectories (in data1.npy of file name) and 3,376,737 from passive trajectories (in data2.npy of file name), all generated by 67,380 taxis over 34 days Also, we randomly selected 1,000 and 3,000 trajectories (in small_taxi.tax.gz of folder name).