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Paper Analysis. Presented by Team 8. Kartik Sunku Manasa Valli Mantripragada Surya Bheema raju Pasumarthi. Outline. Motivation Problem Definition Different Approaches Used Description of Papers Comparison Conclusion References. Motivation.

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Paper analysis

Paper Analysis

Presented by

Team 8

Kartik Sunku

ManasaValliMantripragada

Surya Bheemaraju Pasumarthi


Outline

Outline

  • Motivation

  • Problem Definition

  • Different Approaches Used

  • Description of Papers

  • Comparison

  • Conclusion

  • References


Motivation

Motivation

  • Motivation for these papers comes from the fact that nowadays with availability of huge spatiotemporal information like GPS data collected from taxi networks and with mining spatiotemporal data we can reduce high energy costs, maximize taxi utilization, improve performance delivery and some other predictions like traffic prediction, urban design, identify mobility pattern of passengers caused by cruising taxi cabs.


Problem definition

Problem Definition

  • Predicting Customers / Finding vacant Taxis

  • HUNTS: A Trajectory Recommendation System for Effective and Efficient hunting of Taxi Passengers.

  • Hunting or Waiting? Discovering Passenger-Finding Strategies from a Large-scale Real-world Taxi Dataset.

  • T-Finder: A Recommender System for Finding Passengers and Vacant Taxis


Different approaches used

Different Approaches Used


Description of paper 1

Description of Paper (1)

T Finder

  • mobility patterns of passengers learned from a large number of taxi trajectories and pick-up behaviors of high-profit taxi drivers are collected.

  • For the taxi recommender, a range query according to the location of the taxi is performed.

  • For the passenger recommender, a range query is performed to obtain a region, which is within a walking distance of the passenger.


Frame work

Frame work

  • Develop an approach to detect the parking places from GPS trajectories

  • Segment the GPS trajectories according to timestamp, latitude, longitude and state.

  • Map-match the GPS trajectories to road networks using the IVMM

  • Algorithm.

  • Utilize the detected parking places and the mapped trajectories to

  • learn the time-dependent statistical results based on a probabilistic model.


Model description

Model description

  • Taxi recommender

  • Passenger recommender

  • Road segment clustering

  • Online recommendation


Description of paper c ontd 1

Description of Paper contd… (1)

  • Test data

  • the GPS trajectory recorded by over 12,000 taxis in a period of 110 days in the year of 2010 to validate the system.

  • PROS

  • accurately predicts the time-varying queue length at parking places.

  • effectively provides the high-profit parking places.

  • the passenger recommender successfully suggests the road segments where users can easily find vacant taxis.

  • CONS

  • Only for high-profit taxi drivers.


Description of paper 2

Description of Paper (2)

HUNTS

How it works?

  • Develop a dynamic scoring system which evaluates each road segment in different time periods.

  • Introduce a method called trajectory sewing to produce optimal Trajectory.

  • Update the score of each road segment through an online handler.


Description of paper c ontd 2

Description of Paper contd… (2)

Test Data

Large scale data of 15,000 taxis in a city serves as test data for validating our system.

Pros

  • Recommendation System considers both the picking – up rate and average income .

  • Hunting Trajectory is global – optimal .

  • Produces Recommendations in real time.

    Cons

  • Picking up rate could be lower because all the taxis choose the same route at the same

  • Experiments show that our recommendations work much better than the traditional POI (Place of Interest) recommendations and regular taxi hunts.


Description of paper 3

Description of Paper (3)

Data collected from 5350 taxis for one year in a large city of china used for analysis.

  • This analysis helps to identify passenger-finding strategies.

  • Symbolizing these strategies into a collection of feature patterns represented by triplets (time-location-strategy).

  • Different combinations of triplet to study drivers behavior by counting the times that a taxi falls in each pattern.

  • The feature selection tool ( L1-Norm SVM) to select salient patterns for discriminating top and ordinary performance taxis and to discover hidden features. The selected features are useful for efficient- passenger finding strategies.

  • The taxi performance predictor built on the selected features helps for the prediction accuracy.


Comparison

Comparison

  • Paper 1 is dealing with mobility patterns of passengers learned from trajectory paths and pick-up behaviors of drivers.

  • Paper2 is dealing with connected trajectory pathbuilt by recommender system based on spatiotemporal data for hunting of taxi-passengers.

  • Paper3 is dealing with the taxi-drivers behavior to depict the passenger finding strategies.


Conclusion

Conclusion

  • In first paper, the T-finder save the time for finding a taxicab and reduce unnecessary traffic flows as well as energy consumptions caused by cruising taxicabs

  • The second paper made recommendations based on the approximate global optimal trajectories obtained by trajectory sewing rather than several place of interests (POIs).

  • The third paper determines taxi-patterns using data mining algorithm, analyzing these patterns reveal hidden facts and in this the taxi drivers behavior before picking up and after dropping off passengers was considered but not driving trajectories for predicting passengers.


References

References

  • Hunting or Waiting? Discovering Passenger-Finding Strategies from a Large-scale Real-world Taxi Dataset

    Bin Li ; Daqing Zhang ; Lin Sun ; Chao Chen ; Shijian Li ;Guande Qi ; Qiang Yang2011 IEEE International Conference on Digital Object Identifier: 10.1109/PERCOMW.2011.5766967 Publication Year: 2011 , Page(s): 63 - 68 Cited by:  Papers (7)

  • HUNTS: A Trajectory Recommendation System for Effective and Efficient Hunting of Taxi Passengers

    Ye Ding ; Siyuan Liu ; JiansuPu ; Ni, L.M.Mobile Data Management (MDM), 2013 IEEE 14th International Conference on Volume: 1 Digital Object Identifier: 10.1109/MDM.2013.21 Publication Year: 2013 , Page(s): 107 – 116

  • T-Finder: A Recommender System for Finding Passengers and Vacant Taxis

    Yuan, N.J. ; Yu Zheng ; Liuhang Zhang ; Xing XieKnowledge and Data Engineering, IEEE Transactions on Volume: 25 , Issue: 10 Digital Object Identifier: 10.1109/TKDE.2012.153 Publication Year: 2013 , Page(s): 2390 - 2403  Cited by:  Papers (1)


Paper analysis

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