Transportation
My dataset
- Categories:
99 Views
This directory contains data on over 4.5 million Uber pickups in New York City from April to September 2014, and 14.3 million more Uber pickups from January to June 2015. Trip-level data on 10 other for-hire vehicle (FHV) companies, as well as aggregated data for 329 FHV companies, is also included. All the files are as they were received on August 3, Sept. 15 and Sept. 22, 2015.
- Categories:
1160 Views
Figures of Data
- Categories:
111 Views
The dataset contains drone measurements of traffic at highway entrances/exits in Austria and Italy. Main features:
- Categories:
391 Views
This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
- Categories:
9752 Views
This dataset is gathered for quadrotor tail-sitter UAV system identification and modeling by PolyU MAV/UAV Lab. Various operating conditions are recorded during the UAV hovering phase.
- Categories:
524 Views
The dataset contains 1704 publications and 49,426 references in autonomous vehicles from the core collection of the Web of Science.
- Categories:
57 Views
The experiment of fixing the velocity of both sprockets of UTV proves that the tracked vehicle slips considerably when steering on a slope, making it impossible for the vehicle to follow the desired trajectory precisely, and it is necessary to propose an effective and feasible solution specifically for the motion planning of UTV slope-steering.
- Categories:
81 Views
Results for CTPS 2 are listed in CTPS2.docx.
- Categories:
153 Views
Experiments about satisfaction from ridesharing, from mturk
The first two experiemtns asked which explanations are likely to increase user satisfaction
The third experiment ask for satisfaction (1-7) given a scenario and some explanations. It's divided to three:
- pbe: explanations are all known info
- random: explanatiitons are random subset of knwon info
- axis: smart choosing of subset of the known info
- Categories:
122 Views