Driving with knowledge from the physical world

By Jing Yuan, Yu Zheng, Xing Xie, Guangzhong Sun. Knowledge Discovery and Data Mining, 2011

903 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/d1d8a46a2822424abacd1c567e5fed5d27b60674

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In Automotive Engineering

In Building and Construction

In Transportation

Citations in the corpus

Quantifying the benefits of vehicle pooling with shareability networks (2013, background): “In conventional taxi systems, the problem of dispatching taxis to a trip request is conceptually straightforward: Find the closest empty taxi – where the metric for closeness can involve vehicle velocities, traffic conditions [5], etc.”
Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system (2012, methodology): “Regarding to estimating real time traﬃc information on urban road network, the methodology for estimating the travel time and spatio-temporal traﬃc density on road surfaces was introduced by using GPS trajectory data [13, 14].”
Trajectory Data Mining (2015, methodology): “After the map-matching process, T-Drive regards the top-k road segments frequently traversed by taxicabs as landmark nodes (i.e., the red points shown in Figure 19(a)).”
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners (2018, background): “Route planning has been extensively studied as well [52,53,54,55,56], mainly to compute the fastest, most fuel-efficient, or a customized trajectory to the destination through a road network.”
U-Air: when urban air quality inference meets big data (2013, background): “As a result, the features w.r.t. speeds are reliable [13].”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “Smart Cities [137] T-Drive 2011 826 The dataset tracks 10357 taxi movements in Beijing over the course of one week, from February 2 to February 8, 2008.”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “In order to reaffirm the validity of the proposed approach, we performed the same evaluation in a different dataset: the T-Drive trajectories dataset.”
Traffic prediction in a bike-sharing system (2015, background): “Yuan et al. [23][24] presented a Cloud-based system that computes the quickest driving routes based on traffic conditions and driver behaviors.”
T-share: A large-scale dynamic taxi ridesharing service (2013, methodology): “Our work is also an example of urban computing [25], where a series of research work has been done with taxi trajectories.”
T-Finder: A Recommender System for Finding Passengers and Vacant Taxis (2013, positive): “Then, we feed the knowledge into a probabilistic model that estimates the profit of the candidate locations for a particular driver based on where and when the driver requests the recommendation.”
Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset (2014, background): “The scale is instead much larger in the T-Drive dataset [31], that includes 30000 taxis moving around the same city during three months, although the available refined mobility trace is limited to 10357 cabs and one week of duration.”
Discovering regions of different functions in a city using human mobility and POIs (2012, background): “[19] aims to find the practically fastest driving route to a destination according to a large number of taxi trajectories, and Zheng et al.”
Discovering spatio-temporal causal interactions in traffic data streams (2011, methodology): “Data: We test our algorithms based on a real GPS trajectory dataset generated by 33,000 taxis in a period of 6 months (from 01/03/2009 to 31/08/2009) [25, 24] .”
Discovering Urban Functional Zones Using Latent Activity Trajectories (2015, background): “[19] aimed to find the fastest practical driving route to a destination according to a large number”