Predicting spatio-temporal

traffic patterns from sensor data

Felix Kunde

Slides: https://slides.com/fxku/phd

3rd PhD Colloquium of the DGK Section on Geoinformatics and the DGPF Working Group on Geoinformatics

About me

  • Geography / GIS background
  • Worked 3 ys for virtualcitySYSTEMS
  • Research assistant @ Beuth HS
  • Founder of the MAGDa team
  • ♥ using and teaching PostGIS
  • Promoting FOSS4G
  • @FlxKu

About Beuth HS

  • Founded 1971 as Technische Fachhochschule (TFH)
  • Located in wild wild Wedding
  • 12.000 students
  • Widest range of degree courses in applied engineering in Berlin and Brandenburg
  • Hosted the german FOSS4G in 2014

Arbeitskreis Data Science

  • Joint group of Profs from Math, Machine Learning, Computer Vision, Databases, Text Mining, Geotemporal Data Mining
  • Regular PhD Colloquiums and Journal Clubs

PhD Endeavor

  • Start of PhD? - Summer 2016
  • Internal Supervisor - Prof. Dr. Petra Sauer
  • External Supervisor - Not yet
  • Success
    • Participated at SIGSPATIAL GISCUP 2016
    • Talk & Workshop @ FOSS4G Bonn 21.-28.08.16
    • Talk @ WhereCamp, BeuthHS 03.-04.11.16
    • Accepted Short Paper @ BIGQP: 1st International Workshop on Big Geo Data Quality and Privacy - Co-located with EDBT 2017, Venice - 22.-24.03.17
    • Accepted Full Paper @GIS Ostrava 2017 - 22.-24.03.17
    • Accepted Talk @ FOSSGIS 2017, Passau - 22.-24.03.17

             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

               Mobility Stack

               Mobility Stack

  • Processing sensor streams
  • Calculate travel times
  • Predict traffic states
  • Route optimization
  • Smart scheduling

Topic of my talk - TL;DR

  • Context: Transportation research
  • Task: Traffic forecasting
  • Problem: Hardly used in the field
  • General question: What works best?
  • My interest: Spatio-temporal correlations
  • Methods: Machine learning on time series

Context

  • Project data is related to traffic in Dresden
  • Sensor measurements (e.g. inductive loops)
  • Floating Car Data
  • Origin: VAMOS ITS (intelligent transportation system)

Problem definition

  • Many ITS do only traffic monitoring
  • Rule-based systems reacting to current traffic
  • Great uncertainty in traffic messages

Travel time to incident in min

Correctnes of message in %

Pape, S. & Körner M. (2016). Verkehrslageprognose unter Berücksichtigung der dynamischen Kapazitäten an LSA-abhängigen Knotenpunkten zur qualitativen Aufwertung der Verkehrslageinformation im Verkehrsmanagementsystem VAMOS. 25. Verkehrswissenschaftliche Tage 16. und 17. März 2016 in Dresden. (in german)

Tasks

  • Predict future measurements at static sensors
  • Predict travel times for trajectories
  • Predict anomaly patterns (e.g. congestion)

Relation between macroscopic measures

Speed

Flow (Occupancy)

Density

Treiber, & Kesting (2013): Traffic Flow Dynamics - Data, Models and Simulation. Springer. 26.

Theory

Free Flow vs. Congestion

https://www.civil.iitb.ac.in/tvm/1111_nptel/512_FundRel/plain/plain.html

Speed-Flow Diagram

http://www.cl.cam.ac.uk/~rg31/research/

Temporal Dependency

Waterloo, S. (2017): Analyse und Bereinigung unvollständiger und fehlerhafter Messreihen von Verkehrssensoren. Betreute Bachelor-Thesis.

Occupancy

Extracted Season

Temporal Dependency

Waterloo, S. (2017): Analyse und Bereinigung unvollständiger und fehlerhafter Messreihen von Verkehrssensoren. Betreute Bachelor-Thesis.

Autocorrelation

Stationarity

Spatial Dependency

Pfeifer, P.E. & Deutsch, S.J. (1981): Seaosonal Space-Time ARIMA Modeling

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 ...
... ... ... ... ... ... ... ... ... ...

How to do it?

  • Exponential smooting, moving average
  • Autoregressive time series models (e.g. ARIMA)
  • kNN search on historic data
  • Baysian Networks
  • Support Vector Machines (SVM)
  • Artificial Neural Networks (ANN)

What works best?

  • Hard to say. Papers usually favor one approach.
  • How important are non-linear patterns?
  • How is the performance & scalability?
  • Does it work for a whole city?
  • Do we need a combination of different methods?

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

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

  • Take moving average from each sensors as input ...
  • ... to predict a future value for one sensor
  • 5, 10, 15, 30, 45 minutes offset to predict
  • Narrow down to neighbours (sparse input matrix)
  • Feed input vectors into network randomly

Isochrones

Test Setup

  • Network: Feed Forward
  • Layers: 1 input, 1 hidden, 1 output
  • Activation Function: Sigmoid
  • Optimization: Stochastic Gradient Descent
  • Loss Function: RMSE
  • Other Hyperparams:
    • n Neurons = 60 (sensors)
    • Learning rate = 0.01
    • Batch Size = 20
    • Iterations = 10000

Feed Forward NN Inputs

  • FFNNsingle fed only with target sensor
  • FFNNNN fed only with neihbours of target sensor
  • FFNNNN+ fed with neighbours incl. target itself
  • FFNNall fed with values from all sensors

FFNN mimicing RNNs

  • Included sequence information into input by appending the input matrix (idea from Polsen et al. 2015)
  • mFFNNsingle fed only with target sensor
  • mFFNNNN fed only with neihbours of target sensor
  • mFFNNNN+ fed with neighbours incl. target itself
  • mFFNNall fed with values from all sensors

Results

Results

Results

MA = 15 Min; Sequence Size = 10 time steps

Results

MA = 15 Min; Sequence Size = 5 time steps

Not there yet ...

  • Results are a little "too good". Why is that?
  • MA is probably too big. Prediction task is too easy.
  • What can we really say about spatio-temporal patterns?
  • Why do neighbours not have a positive effect?
  • How good is the prediction when anomalies occur?

Roadmap 2017

  • Present existing results and idea DONE
  • Create R package to repair missing data DONE
  • How good are our results really?! WIP
  • Use LSTM NN to learn sequences DONE
  • Use trajectories instead of sensors TODO
  • Let TU Dresden test it with realtime data TODO
  • Create a web-service for ExCELL WIP
  • Concept for continous learning TODO
  • Compare Deep Learning Frameworks DONE
  • Compare with STARIMA WIP
  • Compare with SVM WIP

Thanks

Felix Kunde

Research Assistant, MAGDa

Beuth University of Applied Science

fkunde@beuth-hochschule.de

PhD endeavour

By fxku

PhD endeavour

Predicting spatio-temporal traffic patterns with Deep Learning

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