The dataset consists of tracking data of over 23,000 vehicles travelling though five different roundabouts in Sydney, Australia. This data was collected by a vehicle outfitted with a ibeo.HAD Feature Fusion detection and tracking system. This system uses 6 ibeo LUX 4 beam, 25 Hz Lidar scanners to identify road users at a range of up to 200m, and has an on-board computer for classification and tracking, in real time.
The paper deals with fractional-slot permanent magnet synchronous machines
(FSPMSMs) equipped with phases made up of one coil parallel branches, with emphasis on their
faculty to reject the harmonic currents circulating in the loops yielded by the phase parallel branches.
These exhibit attractive potentialities, especially their enhanced open-circuit fault tolerance capability.
Furthermore, these topologies are suitably-adapted for low-voltage power supply that makes them
Car-hailing order data are a rich source to study the human mobility patterns, which could contribute to transporation planning and policy-making. In general, a orginal car-hailing order record includes information such as origin, destination, pick-up time, drop-off time, and travel distance. Beijing car-hailing order dataset stored the discretized order data at a traffic analysis zone(TAZ) scale, including the dataset for training and test.
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.
These datasets include the results from the comparison of different traffic-free path planning strategies presented in the work entitled "A primitive comparison for traffic-free path planning", Antonio Artuñedo, Jorge Godoy, Jorge Villagra. https://doi.org/10.1109/ACCESS.2018.2839884
Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living).
Making use of a specifically designed SW tool, the authors here presents the results of an activity for the evaluation of energy consumption of buses for urban applications. Both conventional and innovative transport means are considered to obtain interesting comparative conclusions. The SW tool simulates the dynamical behaviour of the vehicles on really measured paths making it possible to evaluate their energetic performances on a Tank to Wheel (TTW) basis. Those data, on such a wide and comparable range were still unavailable in literature.
