Predicting spatio-temporal traffic patterns with Deep Learning

Felix Kunde

Alex Hartenstein

 

Slides: https://slides.com/fxku/traffic-dl-wherecamp

About me

  • Working here as a research assistant
  • Team MAGDa
  • Geography / GIS background
  • ♥ using and teaching PostGIS
  • Promoting FOSS4G
  • @FlxKu

               Project

  • Funded by German government and DLR
  • Goal: Build up a mobility service plattform
  • Addressing small companies
  • Crowdsource traffic related data
  • Explore / extend spatial data mining tools

So what is Deep Learning?

  • It's Machine Learning with neural networks
  • "Deep" reflects the anatomy of these nets
  • Mostly supervised learning
  • Ideal for fuzzy data (images, audio, text)
  • Outperfoms other approaches
  • New easy-to-use frameworks for DL

Yes, it's a hype

Images

Convolutional Nets

#neuralnetworkzoo

(c) Fjodor Van Veen, Asimov Institute

Input Cell

Kernel

Convolutional

Hidden Cell

Output Cell

Text Mining

Text

Text

Memory Networks

=

The entire net repeated and interconnected

Sequences?

Single values (many-to-one)

Sequences (many-to-many)

Daytime?   Weekday?   Holiday?   Weather?   Events?

Location?

Spatio-temporal traffic forecasts with neural networks

Test Data

  • ~ 130 inductive loops (speed, occupancy etc.)
  • 9 years of data (atm testing only 1 month of 2014)
  • 80% training, 20% test

Time Series

Workflow

  • Define a time window (e.g. 15 min of historical data)
  • Take moving average from each sensors as input ...
  • ... to predict a future value for one sensor (e.g. 15 min ahaed)
  • Narrow down to neighbours
  • Feed input vectors into network
  • Use RMSE as loss function

Spatial Correlation?

pos. correlation

neg. correlation

Isochrones

Adjacency Matrix

s1 s2 s3 s4 s5 s6 s7 s8 ...
s1 1 1 0 0 1 1 0 0 ...
s2 1 1 1 0 0 0 0 0 ...
s3 0 1 1 0 0 0 0 0 ...
s4 0 0 1 1 0 1 0 1 ...
s5 1 0 0 0 1 0 1 0 ...
s6 1 1 0 1 0 1 0 1 ...
s7 0 0 0 0 1 0 1 0 ...
s8 0 0 0 1 0 1 0 1 ...
... ... ... ... ... ... ... ... ... ...

Python Code (Tensor Flow)

@tf_attributeLock
def prediction(self):
    c.debugInfo(__name__,"Adding Prediction nodes to the graph")
    with tf.name_scope('layer1'):
        weights = tf.Variable(tf.truncated_normal(
                (self.n_input,self.n_hidden),stddev=0.1
            ), name="lay1_weights")
        bias = tf.Variable(tf.constant(
                0.1,shape=[self.n_hidden]
            ), name = "lay1_bias")
        out_layer1 = tf.nn.sigmoid(tf.matmul(
                self.data,weights
            )+bias, name = "lay1_output")
    with tf.name_scope('layer2'):
        weights = tf.Variable(tf.truncated_normal(
                (self.n_hidden,self.n_output),stddev=0.1
            ), name="lay2_weights")
        bias = tf.Variable(tf.constant(
                0.1,shape=[self.n_output]
            ), name="lay2_bias")
        out_layer2 = tf.nn.sigmoid(tf.matmul(
                out_layer1,weights
            )+bias, name = "lay2_output")
    return out_layer2

Visual Representation

Results

  • Absolute error of 0.3 for occupancy
  • Plans: Extend the input dataset to see differences between months

How it can be used

  • Travel time propagation for road network links
  • Create more accurate messages on traffic signs
  • Recommendation of alternative routes
  • Improve traffic simulation software

Caveats?

  • It needs A LOT of training data
  • Computationally intense = waste of energy?
  • DKWGO, but it works!?
  • So many hyperparameters to tune
  • How often do I need to train the net?

Alternatives?

  • Autoregressive methods (STARIMA etc.)
  • Support-Vector Machines

Thanks

Felix Kunde

Research Assistant, MAGDa

Beuth University of Applied Science

fkunde@beuth-hochschule.de

Talk @ Where Camp 2016

By fxku

Talk @ Where Camp 2016

Predicting spatio-temporal traffic patterns with Deep Learning

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