DS504/CS586: Big Data Analytics --Presentation Example Prof. Yanhua - - PowerPoint PPT Presentation

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DS504/CS586: Big Data Analytics --Presentation Example Prof. Yanhua - - PowerPoint PPT Presentation

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Welcome to DS504/CS586: Big Data Analytics --Presentation Example Prof. Yanhua Li Time: 6:00pm 8:50pm R. Location: AK233 Spring 2018 Project1 Timeline and Evaluation Start: Week 2, 1/18 R Proposal: Week 3, 1/26 F

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DS504/CS586: Big Data Analytics

  • -Presentation Example
  • Prof. Yanhua Li

Welcome to

Time: 6:00pm –8:50pm R. Location: AK233 Spring 2018

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SLIDE 2

2

Project1

  • Timeline and Evaluation

– Start: Week 2, 1/18 R – Proposal: Week 3, 1/26 F – Methodology Week 4, 2/1 R – Empirical Results: Week 5, 2/8 R – Introduction, Conclusion, Abstract: Week 6, 2/15 R (No class on 1/15 R) – Final Report :Week 7, 2/22 R – In-class Presentation: Week 8, 3/1 R

  • Discussions (Scheduling meetings with me.)
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Growing Charging Station Networks with Trajectory Data Analytics

Yanhua Li1, Jun Luo2, Chi-Yin Chow3, Kam-Lam Chan3, Ye Ding4, and Fan Zhang2

1WPI, CAS2, CityU3, HKUST4

Contact: yli15@wpi.edu

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Growth of Electric Vehicles

http://www.energyandcapital.com/articles/electric-car-market-growth-soars-in-2013/4173

1k 170k

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Charging Station Deployment

Gasoline Car Electric Car Refueling Time 3~5 minutes 1.5~2 hours Kilometers Around 600km Around 200km Number of cars 2.5 million 2,000 (780 EV taxis) Gas Stations Charging Stations Number of stations 270 25 Seeking time 2 minutes 4 minutes

  • Electric Vehicles:
  • Green transportation:
  • Switching to EVs, 42% reduction in CO2 emissions
  • Cost efficiency:
  • Fuel (electricity) costs are much lower
  • Statistics in Shenzhen, China: (by 2013/11)
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Current Station Geo-Distribution

How to deploy charging stations to meet the increasing needs? Challenges

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Optimal Charging Station Deployment (OCSD)

Trajectory Road Map Charging Stations Gridded Road Map Seeking Sub-Trajectory Charging Sub-Trajectory Traveling Sub-Trajectory Optimal Charging Station Placement K Optimal Charging Point Assignment M Side length Average Travel Time btw Grids

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Input Data Description

  • EV Trajectory Data:
  • Source: EV taxi GPS in Shenzhen
  • Duration: November 1st–30th, 2013.
  • Size: 23,967,501 GPS records of 490 EV taxis
  • Sampling Frequency: 40 seconds.
  • Format: Taxi ID, time, latitude, longitude, load
  • Road Map and Charging Station Information:
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Stage 1: Road Map Gridding

  • Given a side length s=0.01o
  • 1508 grids are obtained
  • 760 grids are strongly connected by road network
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Stage 2: Extracting sub-trajectories

  • Traveling sub-trajectory
  • Seeking sub-trajectory
  • Charging sub-trajectory
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Stage 2: Extracting sub-trajectories

  • The spatial distribution of seeking events:
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Stage 3: Optimal Station Deployment

  • Problem definition:
  • Given: L existing stations, Seeking event set,

K new charging stations, M new charging points

  • How to deploy: Minimize the average time of an EV to find and

wait at a charging station

  • Two Components:
  • Optimal Charging Station Placement (OCSP)
  • Goal: Minimize the average seeking time
  • Optimal Charging Point Assignment (OCPA)
  • Goal: Minimize the average utilization of charging points
  • (proportion of time each charging point is occupied)
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Stage 3-I: OCSP

  • K-median Problem with Initial medians
  • Assumption: Going to the nearest charging station
  • NP-Hard Problem
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Stage 3-I: OCSP

  • Formulation:
  • Approximation Alg:
  • (1) LP-Relaxation
  • (2) Rounding
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Stage 3-II: OCPA

  • Formulation:
  • Each charging station is an queue.
  • Arriving rate

: average # of per hour seeking events

  • Serving rate

: average # of per hour served EVs

  • Charging point utilization
  • Optimal Solution:
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Evaluation

  • Charging station placement
  • Baselines
  • Rand-SP: Random station placement
  • Top: Top seeking events
  • OCSP algorithm
  • Charging point assignment
  • Baselines
  • Rand-PA: Random point assignment
  • Aver.: Average charging point assignment
  • OCPA algorithm
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Average Seeking & Waiting Time

  • Average Seeking Time:

26%–94% reduction rate Average Waiting Time: 2.5 to 25 times reduction 26% 94% 2.5 25

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Redeployment Current Geo-Distribution Ave Seeking Time: 213s Ave Seeking Time: 110s Ave Waiting Time: 928s (15min) Ave Waiting Time: 11s

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Questions?

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Logistics 20

Next Class: Data Acquisition and Measurement

v Do assigned readings before class

v

Be prepared, read and review required readings on your own in advance!

v

Do literature survey: find and read related papers if any

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Bring your questions to the class and look for answers during the class.

v Submit reviews/critiques

v

In Canvas before class

v

Bring 1 hardcopy to the class

Review Writing: http://users.wpi.edu/~yli15/courses/DS504Fall17/Critiques.html

v Attend in-class discussions

v

Please ask and answer questions in (and out of) class!

v

Let’s try to make the class interactive and fun!