Urban computing with taxicabs

By Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie. Ubiquitous Computing, 2011

555 citations recorded by the source corpus.

Subfields: Automotive Engineering, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4f9bf8f664a85c4d7c7c9512a4579d17a9767a2f

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Transportation

In Signal Processing

Citations in the corpus

Revealing daily travel patterns and city structure with taxi trip data (2013, background): “…do not involve privacy issues, they are now applied in many related research fields, such as traffic prediction (Gao et al. 2013), urban planning (Zheng et al. 2011, Veloso, Phithakkitnukoon and Bento 2011), land use analysis (Yuan, Zheng and Xie 2012, Liu et al. 2012b, Qi et al. 2011) and…”
Trajectory Data Mining (2015, methodology): “…P into a sequence of meaningful places S , therefore facilitating a diversity of applications, such as travel recommendations [Zheng and Xie 2011b; Zheng et al. 2011c], destination prediction [Ye et al. 2009], taxi recommendation [Yuan et al. 2011b, 2013b], and gas consumption estimation [Zhang…”
Accelerometer-based transportation mode detection on smartphones (2013, background): “This in turn would enable improving urban planning [38], monitoring and addressing the spread of diseases and other potential hazards, as well as providing emergency responders information of the fastest route to aid the lost or injured [29].”
Prediction of urban human mobility using large-scale taxi traces and its applications (2012, methodology): “[15] detected flawed urban planning using the GPS trajectories of taxicabs traveling in urban areas.”
Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system (2012, methodology): “The research conducted by Zheng et al. [15] is also based on GPS trajectories data from urban taxis, whereas it focused on the aspect of urban computing and application.”
Crowd sensing of traffic anomalies based on human mobility and social media (2013, methodology): “In this section, we deﬁne the terminologies used throughout the paper and give an overview of the system.”
Discovering regions of different functions in a city using human mobility and POIs (2012, unknown): “[21] glean the problematic urban planning in a city using the corresponding taxi trajectories.”
Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “Index Terms—Big Data, cross-domain datamining, data fusion, multi-modality data representation, deep neural networks, multi-view learning, matrix factorization, probabilistic graphical models, transfer learning, urban computing Ç”
Inferring human mobility patterns from taxicab location traces (2013, methodology): “A class of such applications [7, 16, 22] rely on being able to obtain human mobility patterns (typically obtained from location sensors on mobile phones) and are used for urban planning [22], carpooling [20], traffic forecasting, and the spread of biological and mobile viruses [7].”
Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card (2016, background): “More recently, several studies have used the mobility data for regional development [5], urban planning [6], and policymaking [7].”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “, ranging from simple home preheating and sending dinner coupons to epidemic control [221], [222], urban planning [223]–[225], traffic forecasting systems [226]–[228], resource management of mobile communications [3], [229], [230]).”
Exploring human mobility with multi-source data at extremely large metropolitan scales (2014, background): “But based on our empirical results in Section 3, an approach based on data from one type of transit has a bias against residents using other types of transit.”
Understanding intra-urban trip patterns from taxi trajectory data (2012, background): “Recently, GPSenabled floating cars2 have provided an alternative approach to gathering large volumes of individual trajectories and studying individuals’ behaviors and urban dynamics (Jiang et al. 2009; Liu et al. 2010; Li et al. 2011; Qi et al. 2011; Zheng et al. 2011).”
U-Air: when urban air quality inference meets big data (2013, background): “As taxis generate about 20% of traffic flow on road surfaces of Beijing [14], the dataset is big enough to represent the traffic patterns there.”