Transportation

Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios.

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The odometric model is simulated herein. We described the trajectory of such one odometric model, with the delta of the heading angle given as one parameter of the simulation. The iterations show that the trajectory is well in the continuity of the variations of the heading angle. Moreover the distance in X and in Y are shown for the vehicle to be driven in the trajectory of the odometric model. Moreover I have displayed the linear approximation then. This is a tiny approximation made based upon the equations of the odometric model.

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The rawdata.csv profile indicates the traffic analysis based mobility patterns. we extract human trips from Call Records Detail data. Combining traffic analysis zone dataset, we map each trip record to the zones with the same origin zones and destination zones. After  this, we can obtain this dataset. This dataset stores the hourly number of departure and arrival trips in each traffic analysis zone.

The POI-importance.csv profile indicates the term frequency-inverse doument frequency(TF-IDF) of each category of poi the in each traffic analysis zone.

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This dataset used in the experiment of paper "Bus Ridesharing Scheduling Problem". This is a real-world bus ridesharing scheduling problem of Chengdu city in China, which includes 10 depots, 2,000 trips.

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This is the dataset used in the experiment of paper "Bus Pooling: A Large-Scale Bus Ridesharing Service". The dataset contains 60,822,634 trajectory data from 11,922 Shanghai taxis from one day (Apr 1, 2018). The 100 groups of coordinate sets containing three coordinates as experimental samples are used to compare the effectiveness and efficiency of location-allocation algorithms.

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The "Dynamic Scenes" Dataset is provided for testing visual loop closure detection algorithms in highly dynamic scenes. It has a strong background in some crucial applications such as autonomous driving systems.

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This dataset corresponds to a paper published in IEE Transactions on Power Systems entitled: "High-Speed 2x25kV Traction System Model and Solver for Extensive Network Simulations"

 

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when iter time =50 ,we  can obtain this picture

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This data is the real-time Estimated time of departure in the airspace of China in Feb 10 2019.  

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