Traffic Prediction in a Bike-Sharing System Team 1 Jiaqi Liu, - - PowerPoint PPT Presentation

traffic prediction in a bike sharing system
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Traffic Prediction in a Bike-Sharing System Team 1 Jiaqi Liu, - - PowerPoint PPT Presentation

Traffic Prediction in a Bike-Sharing System Team 1 Jiaqi Liu, Shaowei Gong, Qiuyi Hong, Zhenhua Li, Caidan Liu 1 Background Worcester Polytechnic Institute Solution Overview Worcester Polytechnic Institute Framework Worcester Polytechnic


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Traffic Prediction in a Bike-Sharing System

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Team 1

Jiaqi Liu, Shaowei Gong, Qiuyi Hong, Zhenhua Li, Caidan Liu

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Worcester Polytechnic Institute

Background

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Worcester Polytechnic Institute

Solution Overview

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Worcester Polytechnic Institute

Framework

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Worcester Polytechnic Institute

Bipartite Station Clustering

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  • Group individual station into clusters according to their

geographical location and transition patterns. ○ a single station’s traffic seems too chaotic to predict. ○ It is not necessary to predict the check-out/in

  • f each individual

station.

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Worcester Polytechnic Institute

Bipartite Station Clustering

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K1=4 K1=4

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Entire Traffic Learning

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  • In hierarchical prediction model, the traffic in the higher

level is predicted first.

  • Time features

○ the hour of the day ○ the day of the week

  • Meteorology features

○ weather ○ temperature ○ wind speed

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Cluster Check-out Proportion Learning

  • 8
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Cluster Check-out Proportion Learning

  • Insights

○ ○ λ ○ λ ○

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Worcester Polytechnic Institute

Cluster Check-out Proportion Learning

  • Methodology

○ … ○ … ○ … ○

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Worcester Polytechnic Institute

Cluster Check-out Proportion Learning

  • Methodology

■ ■ ■

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Worcester Polytechnic Institute

Cluster Check-out Proportion Learning

  • Methodology

■ ■

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Worcester Polytechnic Institute

Cluster Check-out Proportion Learning

  • Methodology

■ ■ ■

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Worcester Polytechnic Institute

Cluster Check-out Proportion Learning

  • Methodology

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Worcester Polytechnic Institute

Cluster Check-out Proportion Learning

  • Methodology

○ ○ − − − ̂− − …

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Inter-cluster Transition Learning

  • We predict each cluster’s check-in based on

their check-out

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Inter-cluster Transition Learning

  • The inter-cluster

transition matrix describe the transition probability between clusters

  • Using

multi-similarity-based inference model to predict the matrix

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Worcester Polytechnic Institute

Trip Duration Learning

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  • In bike traffic, jam is no

longer an important factor that affects trip duration

  • It is mainly determined

by the locations of bike stations

  • Duration does not

change too much

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Worcester Polytechnic Institute

Trip Duration Learning

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  • According to NYC’s bike

data, the trip duration between each pair of cluster

  • By maximum likelihood

estimation, we obtain symmetric matrix, describing the trip duration between cluster Ci and Cj

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Worcester Polytechnic Institute

Online Prediction Process

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  • Check-out Inference

─ Entire traffic Prediction Et ─ Check-out proportion prediction Pt

  • Calculation

─ Check-out of each cluster Ci is

O = Et * Pt

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Worcester Polytechnic Institute

Online Prediction Process

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  • Check-in Inference For Common Scenarios

─ use the same model as calculating check-out

▪ Entire traffic Prediction Et ▪ Check-in proportion prediction Pt

  • Check-in Inference For anomalous Scenarios

─ Update the prediction of target cluster in real time For a bike,

  • Original Cluster Ci
  • Check out time
  • Inter-cluster transition matrix and trip duration

▪ Get the expectation number of bikes on their way which are going to check in this cluster

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Worcester Polytechnic Institute

Experiments

Data Source: New York

We use the data of Citi Bike system, which is in NYC, from 1st Apr. to 30th

  • Sep. in 2014 as the bike data. We use the meteorology data of NYC, from 1st,
  • Apr. to 30th, Sep.

D.C

We use the data of Capital Bikeshare system, which is mainly in D.C., from 1st Apr. to 30th Sep. in 2014 as the bike data. we use the meteorology data in D.C., from 1st, Apr. to 30th, Sep., 2014

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Baseline & Metric

Methodologies: HA, ARMA, GBRT, HP-KNN, GC, Metric: RMLSE,ER

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Results

Result of clustering

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Results (cont.)

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Worcester Polytechnic Institute

Conclusion

Our model is better and applicable to different bike-sharing systems

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Worcester Polytechnic Institute

Thank you!