Towards mobility-based clustering

Liu, Siyuan; Liu, Yunhuai; Ni, Lionel M.; Fan, Jianping; Li, Minglu

doi:10.1145/1835804.1835920

Cited by 158 publications

(70 citation statements)

References 27 publications

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“…Thus, we can unite the historical pick-up information to explore high-demand areas and analyze the distribution patterns of pick-up hot spots. Clustering is a feasible and meaningful approach to identify hot spots of moving vehicles in an urban area [9]. In particular, using a density-based clustering method, we can discover clusters with arbitrary shapes and avoid the adverse impacts of noise and unusual points.…”

Section: Cluster Of Pick-up Locationsmentioning

confidence: 99%

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Yin

et al. 2017

IJGI

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Abstract:Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.

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Section: Cluster Of Pick-up Locationsmentioning

confidence: 99%

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Yin

et al. 2017

IJGI

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“…It has little chance to be the real destination when the region has few frequencies. Moreover, existing studies ignore the characteristics of destination distribution with regional distribution [22][23][24]. In other words, the vast majority of destinations are distributed in several hot spots.…”

Section: Ignoring Destination Distribution Characteristicsmentioning

confidence: 99%

A Real-Time Taxicab Recommendation System Using Big Trajectories Data

Peng-peng

Gao

et al. 2017

Wireless Communications and Mobile Computing

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Carpooling is becoming a more and more significant traffic choice, because it can provide additional service options, ease traffic congestion, and reduce total vehicle exhaust emissions. Although some recommendation systems have proposed taxicab carpooling services recently, they cannot fully utilize and understand the known information and essence of carpooling. This study proposes a novel recommendation algorithm, which provides either a vacant or an occupied taxicab in response to a passenger's request, called VOT. VOT recommends the closest vacant taxicab to passengers. Otherwise, VOT infers destinations of occupied taxicabs by similarity comparison and clustering algorithms and then recommends the occupied taxicab heading to a close destination to passengers. Using an efficient large data-processing framework, Spark, we greatly improve the efficiency of large data processing. This study evaluates VOT with a real-world dataset that contains 14747 taxicabs' GPS data. Results show that the ratio of range (between forecasted and actual destinations) of less than 900 M can reach 90.29%. The total mileage to deliver all passengers is significantly reduced (47.84% on average). Specifically, the reduced total mileage of nonrush hours outperforms other systems by 35%. VOT and others have similar performances in actual detour ratio, even better in rush hours.

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“…Since errors and missing records are common in the raw data (Liu et al, 2010), we first remove the drift records. Fig.…”

Section: Data Cleaning and Processingmentioning

confidence: 99%

A Web-Based Platform for Visualizing Spatiotemporal Dynamics of Big Taxi Data

Xiong

Chen

Gui

2017

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

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With more and more vehicles equipped with Global Positioning System (GPS), access to large-scale taxi trajectory data has become increasingly easy. Taxis are valuable sensors and information associated with taxi trajectory can provide unprecedented insight into many aspects of city life. But analysing these data presents many challenges. Visualization of taxi data is an efficient way to represent its distributions and structures and reveal hidden patterns in the data. However, Most of the existing visualization systems have some shortcomings. On the one hand, the passenger loading status and speed information cannot be expressed. On the other hand, mono-visualization form limits the information presentation. In view of these problems, this paper designs and implements a visualization system in which we use colour and shape to indicate passenger loading status and speed information and integrate various forms of taxi visualization. The main work as follows: 1. Pre-processing and storing the taxi data into MongoDB database. 2. Visualization of hotspots for taxi pickup points. Through DBSCAN clustering algorithm, we cluster the extracted taxi passenger's pickup locations to produce passenger hotspots. 3. Visualizing the dynamic of taxi moving trajectory using interactive animation. We use a thinning algorithm to reduce the amount of data and design a preloading strategyto load the data smoothly. Colour and shape are used to visualize the taxi trajectory data.

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Towards mobility-based clustering

Cited by 158 publications

References 27 publications

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

A Real-Time Taxicab Recommendation System Using Big Trajectories Data

A Web-Based Platform for Visualizing Spatiotemporal Dynamics of Big Taxi Data

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