Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data

Tang, Luliang; Sun, Fei; Kan, Zihan; Ren, Chang; Cheng, Luling

doi:10.3390/ijgi6050134

Cited by 22 publications

(15 citation statements)

References 27 publications

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“…We divide the urban rail transit service area into grid cells and split the time axis into several time slots using a 2-h interval. We consider the service scope of the rail station [29] and the effect of granularity of grid cell on algorithm accuracy [30] when defining the scale of the spatial analysis unit. Since the time regularity of the travel demand hinges on the rhythm of residents' daily lives, the time interval in this study was determined by referring to previous research [28].…”

Section: ) Construction Of Travel Demand Matrixmentioning

confidence: 99%

A Cooperative Data Mining Approach for Potential Urban Rail Transit Demand Using Probe Vehicle Trajectories

Cheng

Huang

et al. 2020

IEEE Access

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Promoting the use of public transportation is an important approach to develop sustainable mobility. However, lots of potential users of public transportation chose taxi, a semi-private mode for convenience. In this study, we first define this potential urban rail transit demand based on its spatiotemporal features. Then a novel data mining method is proposed to ascertain the potential urban rail transit demand from taxi trajectory data through considering spatial and temporal constraints simultaneously. Two features of the potential demand, i.e., the zero rates and volatility, are obtained by the combination of statistical and feature extraction (local neighbor descriptive pattern, LNDP) techniques. They are used to classify the urban rail transit stations into different categories which need different improvement measures to promote the attraction to the potential users. The effectiveness of the proposed method is tested using the GPS trajectory data of Shanghai collected from over 10,000 taxis in 12 consecutive days. We find that most urban rail transit stations have the potential to absorb the regular part of taxi ridership. Moreover, obvious imbalances exist between access and egress potential travel demands at these stations. The results show that metro stations can be classified into six groups according to the time-varying laws of potential travel demand, four of which need urgent measures. These findings provide useful insights for developing more effective and targeted strategies to encourage travelers to shift to public transportation. The estimated method of potential demand is the prerequisite for further optimization models. INDEX TERMS Public transportation, taxi GPS trajectory data, travel spatiotemporal pattern, urban rail transit station.

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Section: ) Construction Of Travel Demand Matrixmentioning

confidence: 99%

A Cooperative Data Mining Approach for Potential Urban Rail Transit Demand Using Probe Vehicle Trajectories

Cheng

Huang

et al. 2020

IEEE Access

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“…[11] fit an econometric model of driver income with five strategic factors that may explain income difference, and found that the significant factors are (in descending order of contribution) delivery speed, search distance, supply-demand ratio, and trip fare, while search intensity is not statistically significant. [12] found that taxis with high single-trip efficiency, defined as the average income rate between two consecutive pickups, usually avoid traffic and seek locations with high passenger demand. Among studies of this kind, [13] is perhaps the largest in scale to date.…”

Section: Introductionmentioning

confidence: 99%

Drivers learn city-scale dynamic equilibrium

Zhang

Ghanem

2021

Preprint

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Understanding driver behavior in on-demand mobility services is crucial for designing efficient and sustainable transport models. Drivers' delivery strategy is well understood, but their search strategy and learning process still lack an empirically validated model. Here we provide a game-theoretic model of driver search strategy and learning dynamics, interpret the collective outcome in a thermodynamic framework, and verify its various implications empirically. We capture driver search strategies in a multi-market oligopoly model, which has a unique Nash equilibrium and is globally asymptotically stable. The equilibrium can therefore be obtained via heuristic learning rules where drivers pursue the incentive gradient or simply imitate others. To help understand city-scale phenomena, we offer a macroscopic view with the laws of thermodynamics. With 870 million trips of over 50k drivers in New York City, we show that the equilibrium well explains the spatiotemporal patterns of driver search behavior, and estimate an empirical constitutive relation. We find that new drivers learn the equilibrium within a year, and those who stay longer learn better. The collective response to new competition is also as predicted. Among empirical studies of driver strategy in on-demand services, our work examines the longest period, the most trips, and is the largest for taxi industry.

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“…For example, Li et al (2019) used the vehicle trajectory data to extract coach operation information such as coach stations, routes and timetables, which provided data support for China's national road passenger transportation ticketing platform. Some studies used taxi trajectory data to conduct passenger-finding strategies, spatiotemporal analysis of public transportation, road networks update and other studies to optimize urban traffic (Wu et al, 2016;Tang et al, 2017;Tu et al, 2018). Scholars also used mobile phone traces to study residents' mobility laws to assist scientific and smart city planning (Jiang et al, 2013;Chen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Trajectory Data Management and Analysis Technology Framework Based on Spatiotemporal Grid Model

Liu

Mei

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The trajectory data generated by various position-aware devices is widely used in various fields of society, but its conventional vector representation and various analysis algorithms based on it have high computational complexity. This makes it difficult to meet the application requirements of real-time or near real-time management and analysis of large-scale trajectory data. In view of the above challenges, this paper proposes a trajectory data management and analysis technology framework based on the Spatiotemporal Grid Model (STGM). First, the trajectory data is represented by spatiotemporal grid encoding instead of vector coordinates, and it can achieve dimensionality reduction and integrated management of high-dimensional heterogeneous trajectory data. Second, the trajectory computing and analysis methods based on STGM are introduced, which reduce the computing complexity of algorithms. Furthermore, various types of trajectory mining and applications are realized on the basis of high-performance computing technologies. Finally, a trajectory data management and analysis prototype system based on the STGM is developed, and experimental results verify the reliability and effectiveness of the proposed technology framework.

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Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data

Cited by 22 publications

References 27 publications

A Cooperative Data Mining Approach for Potential Urban Rail Transit Demand Using Probe Vehicle Trajectories

A Cooperative Data Mining Approach for Potential Urban Rail Transit Demand Using Probe Vehicle Trajectories

Drivers learn city-scale dynamic equilibrium

Design and Implementation of Trajectory Data Management and Analysis Technology Framework Based on Spatiotemporal Grid Model

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