Photos: Inger Grønkjær Ulrik, Andre Neves and Hans Skov-Petersen

www.bikeability.dk

Application of GPS tracking in bicycle research

Hans Skov-Petersen

hsp@life.ku.dk

Geoscience and natural resources

University of Copenhagen

Overview of the presentation

• Basic considerations:... • Sampling locations or sampling individuals

• A base-line framework for analysis of tracking data

• Motivation and field of application for investigations in cyclists’ route choice and and way finding behaviour

• Scopes of spatial cognition and behaviour: a ‘Focal‘ vs ‘Global’ approach

• Data models and spatial domains in spatial behaviour: Fields vs networks

Sampled or comprehensive?

GPS tracking: Analytical framework

Description Inference

Locations only Additional layers

Local Individual points

Where is (x, y)? What is the PDOP of..?

Distance to paths’ and points of interest.

Where do stops occur?

Focal Spatial/temporal ‘window’

How fast?

Stop/go?

How steep?

Speed/slope relations

Choice of ‘next point’ (relative to options)

Zonal Single track/tours/routs

How far?

Round trip?

Average speed? Altitude difference?

Min/max altitude along a track Land cover distribution

Choice of route (relative to options)

Global All tours, for an individual or all respondents

Data mining Spatial/temporal clustering Area of interest

Path pressure Kernel distribution OD distribution

Relation of congested locations

= Map Algebra

(Dana Tomlin)

Path pressure

Description Inference

Locations only Additional layers

Local Individual points

Where is (x, y)? What is the PDOP of..?

Distance to paths’ and points of interest.

Where do stops occur?

Focal Spatial/temporal

How fast?

Stop/go?

How steep?

Speed/slope relations

Choice of ‘next point’ (relative to options)

Zonal Single track/tours/routs

How far?

Round trip?

Average speed? Altitude difference?

Min/max altitude along a track Land cover distribution

Choice of route (relative to options)

Global All tours, for an individual or all respondents

Data mining Spatial/temporal clustering Area of interest

Path pressure Kernel distribution

Relation of congested locations

Path pressure

…. Just an average GIS analysis.

Zonal Statistics

Description Inference

Locations only Additional layers

Local Individual points

Where is (x, y)? What is the PDOP of..?

Distance to paths’ and points of interest.

Where do stops occur?

Focal Spatial/temporal

How fast?

Stop/go?

How steep?

Speed/slope relations

Choice of ‘next point’ (relative to options)

Zonal Single track/tours/routs

How far?

Round trip?

Average speed? Altitude difference?

Min/max altitude along a track Land cover distribution

Choice of route (relative to options)

Global All tours, for an individual or all respondents

Data mining Spatial/temporal clustering Area of interest

Path pressure Kernel distribution

Relation of congested locations

Zonal statistics

Etc, etc….

Bikeability: GPS trip statistics

Number of respondents 179

Number of trips 1292

Avg. dist 5.4 km

Avg. time 22.4 min

Avg. speed 14.4 km/h

Speed/Slope

Description Inference

Locations only Additional layers

Local Individual points

Where is (x, y)? What is the PDOP of..?

Distance to paths’ and points of interest.

Where do stops occur?

Focal Spatial/temporal

How fast?

Stop/go?

How steep?

Speed/slope relations

Choice of ‘next point’ (relative to options)

Zonal Single track/tours/routs

How far?

Round trip?

Average speed? Altitude difference?

Min/max altitude along a track Land cover distribution

Choice of route (relative to options)

Global All tours, for an individual or all respondents

Data mining Spatial/temporal clustering Area of interest

Path pressure Kernel distribution

Relation of congested locations

Speed/Slope dependency 50 summer (hikers and mountainbikers) subtracks

ShapeFile

SubTra

ck

Dis

tance

Fro

mID

ToID

Speed

Slo

pe

trip_000157_20090803.shp 1 103.2752303 1734010 1734030 3.72 3.78

trip_000157_20090803.shp 1 103.3322894 1734011 1734031 3.72 5.71

trip_000157_20090803.shp 1 109.8029544 1734012 1734032 3.95 5.37

trip_000157_20090803.shp 1 108.3478953 1734013 1734032 4.11 6.85

trip_000157_20090803.shp 1 105.0795719 1734014 1734032 4.20 7.06

trip_000157_20090803.shp 1 101.4690836 1734015 1734032 4.30 7.31

trip_000157_20090803.shp 1 100.4480057 1734016 1734032 4.52 7.39

trip_000157_20090803.shp 1 110.8677651 1734017 1734033 4.99 6.69

trip_000157_20090803.shp 1 110.1191898 1734018 1734033 5.29 6.74

trip_000157_20090803.shp 1 109.7932297 1734019 1734033 5.65 6.76

trip_000157_20090803.shp 1 109.4015346 1734020 1734033 6.06 6.78

trip_000157_20090803.shp 1 108.4362564 1734021 1734033 6.51 6.84

trip_000157_20090803.shp 1 107.7804234 1734022 1734033 7.05 6.88

trip_000157_20090803.shp 1 105.8857316 1734023 1734033 7.62 7.01

trip_000157_20090803.shp 1 111.1493209 1734024 1734034 8.00 7.39

trip_000157_20090803.shp 1 102.0558604 1734025 1734034 8.16 6.56

trip_000157_20090803.shp 1 103.5524648 1734026 1734035 8.28 6.46

trip_000157_20090803.shp 1 105.4596282 1734027 1734036 8.44 10.40

trip_000157_20090803.shp 1 110.0411021 1734028 1734037 8.80 9.16

Speed, km/h)

Slo

pe (

%)

Speed/slope (Zonal: Entire tours)

Alpha Beta R2

Fast (> 6 km/h) 9.95 -0.22 0.11

Slow 4.01 -0.02 0.02

Speed/slope (Entire tours – Actual activities)

Studying wayfinding behaviour Motivation and potential fields of application

Preference estimation and evaluation

• Investigation of the relative importance of characteristics to the bicycle infrastructure

Route finding

• Preset impedance parameters

• Incremental, personalized parameters (web 2.0 style)

Accessibility modeling

• Assessment of anticipated effects of planned infrastructures

Behavior simulation

• Agent-based modeling

Revealed Preference Look at what people do!

To reveal preferences,

behaviour has to be

investigated in terms of

possibilities

... So it is all about choices

made among alternative

options

1: Do we have a perfect, ’mental map’ to base out choices on?

2: Do we apply knowledge that can be perceived from our immediate surroundings?

? ?

... A ’focal’ or ’node’ scope (locomotion)

... A ’global’ or ’route’ scope (wayfinding)

?

How do bicyclists navigate – investigation strategies

The ‘global’ choice experiment Map matching and choice set generation

?

Strategies for generation of alternative routes

Based on OSM (with moderations) chosen route was compared to …

• Shortest path

• A random selection of alternatives

• Based on a modified labeling algorithm

• Max overhead distance over chosen route: 25%

• Max distance from chosen route: 1000 m

• Max 20 alternatives

• Two approaches:

• Including Path Size (a measure of internal overlap)

• Max25: Allowing only member with less overlap than 25% with any other alternative in the set

?

GPS data handling: Map matching and local choice set generation

? ?

The Route models

Parameter Path Size Max25% Shortest

path

Length -0.00433 *** -0.00254 *** 0.06005 ***

Number of left turns -0.18323 *** -0.21797 *** 0.33594 ***

Number of right turns -0.0738 ** -0.12617 ** 4.22133 ***

% of route with Cycle track 4.66329 *** 4.68672 *** 141.683 ***

Cycle lane 5.86333 *** 7.82885 *** 66.2823 ***

Designated cycle track 6.20337 *** 8.72841 *** 182.773 ***

Shared track 2.17781 *** 2.79813 *** 270.278 ***

% of route with Artery road -2.34578 ** -4.04166 ** -88.9948 ***

Minor road 0.80765 0.43654 52.9103 ***

Other road (road type not specified) 0.43071 -0.63882 84.0751 ***

Road with multy story housing -0.78488 -1.42363 108.121 ***

Shopping street -9.5252 -16.99 204.692 ***

Log Likelihood Function -852 -309 -917

Routes are compared to a standard situation with no

bicycle facilities on a main road

The Focal model

Quite early results….

Parameter Coefficient

Significans level

Directions Relative angle to destination 0.0005317 ***

Left turn -1.181548 ***

Right turn -1.48063 ***

Uturn -1.747325 ***

Bicycle facilities Track 0.7465979 ***

Lane 0.9427726 ***

Designated track 1.140476 ***

Shared track 0.5860569 ***

Green Environment -0.0608228 ***

Road type Artery road 0.7905237 ***

Minor road 0.8029141 ***

Other road (road type not specified) 0.5247407 ***

Road with multi storyed housing 0.0150856 *

Shopping street 0.2214325 ***

The route model vs the focal model: The known vs unknown areaal

A route is stated to be in a ‘unknown area’ if more than 50% of its points where more than 250m from poinst on any other route taken by the same respondent

Pseudo R2 Route model

Max25% Focal model

All n=1291

0.7523 0.3679

Known area n=1092

0.7811 0.3672

Unknown area n=199

0.6046 0.3743

The Focal model

The first and the last 25% of of a trip was defined as ‘start’ and ‘end’

Pseudo R2 Focal model

All (unfortunately not ‘Middle’) n=192,370

0.3679

Start n=50,953

0.3130

End n=42,424

0.3618

Way forward..

• Further analysis has to be performed

• Focal vs Global scope for different cyclist types and different cycling situations

• Refinements of parameters

• Reassessment of the estimates to support probabilistic locomotion in Agent Based Models

• We are aiming at four papers from the study:

• Cyclists’ wayfinding and route choice (GPS/RP)

• Cyclists’ wayfinding and route choice (SP)

• A typology of Danish cyclists, based on mobility styles

• Cyclist types applied to wayfinding

Spatial domains in revealed spatial choice experiments

Restricted spatial domain (network)

Unrestricted spatial domain (field/raster)

Focal, locomotion

Global, way finding

?

? ?

Revealed Choice experiment Unrestricted

A single point

It’s alternatives

All points and alternatives

Spatial domains in revealed spatial choice experiments

Restricted spatial domain (network)

Unrestricted spatial domain (field/raster)

Focal, locomotion

Global, way finding

?

? ?

?

That’s it Thanks for now

Hans Skov-Petersen – hsp@life.ku.dk

Jette Bredahl Jacobsen

Bernhard Snizek

Suzanne Elisabeth Vedel

Skov & Landskab, LIFE/KU

Bernhard Barkow, creativeyes.at

Bikeability – cities for zero-emission cities and public health