Understanding intra-urban trip patterns from taxi trajectory data

By Yu Liu, Chaogui Kang, Song Gao, Yu Xiao. Journal of Geographical Systems, 2012

337 citations recorded by the source corpus.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/4ea599889da761c591c1c57d6a26d9790db067c9

How researchers see this paper

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

In Transportation

1. Yu Liu
2. Li Gong
3. Zhengwei Sui
4. Cynthia Chen
5. Xi Liu
6. Menglin Wang
7. Yu Liu
8. Yusak O. Susilo

Citations in the corpus

• Online Factorization and Partition of Complex Networks From Random Walks (2017, positive): “Each trip can be viewed as a fragmented sample path realized from a city-wide Markov chain that characterizes the traffic dynamics [11, 3].”
• The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, background): “Examples include the use of taxi data that is mostly suitable for studying drivers’ patterns in searching for passengers (Jiang et al., 2009; Liu et al., 2012), use of transit smart card data that only captures the use of transit modes (Long and Thill, 2015), or the use of social media data whose sp”
• Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “They include mobile phone call records [7,30], GPS (Global Positioning System) enabled taxi trajectories [8,9,31], smart card records in public transportation systems [32], and check-in data [2,3].”
• 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].”
• Revealing daily travel patterns and city structure with taxi trip data (2013, methodology): “Previous study used an exponentially truncated power law distribution to fit the distance distribution of taxi trips in Shanghai (Liu et al. 2012a).”
• An online primal-dual method for discounted Markov decision processes (2016, background): “A randomly selected passenger has a random destination, where the length of trip follows a power law distribution (as is commonly assumed in the literature [14]).”
• Generalized bathtub model of network trip flows (2019, background): “Empirically, the distribution of trip distances is neither time-independent nor exponential (Liu et al., 2012; Thomas and Tutert, 2013).”