Simulation of Traffic Congestion as Complex Behaviour

Society of Cartographers Annual Conference – 3rd September 2012

Ed Manley Department of Civil, Environmental and Geomatic Engineering

University College London

Today’s Talk The Complexity of Road Congestion

• Behaviour and Complexity in

the City

• Agent-based Modelling of

Choice Behaviours

• Analysis of Taxi Driver Route

Selection Data

Urban Complexity A Product of Human Behaviour

• The function and nature of the city is defined by its the

choices of its citizens

• Choices influence how we interact

• This accumulation of behaviours lead to the patterns of

movement we see everyday

• Understanding and modelling these patterns requires a

fundamental understanding of human behaviour

Urban Complexity Road Congestion

• Road congestion is an excellent example of how human

behaviour influences urban dynamics

• People unilaterally pick their route and proceed towards

their target, they remain reactive to problems

• Competition for limited space at a given time results in

emergence of congestion

• Following shocks to the system, the influence of

individual responses is of greatest significance

Urban Complexity Understanding Individual Movement

• We examine the individual behaviours that contribute

towards the formation and spread of congestion

• How do drivers really choose a route?

• What areas of the city do they know best?

• How do they use information to aid them?

• What is the heterogeneity in behaviour across the

population?

• These behaviours are incorporated within an agent-

based model of the urban road system

Agent-based Modelling From Micro to Macro

• Agent-based Modelling allows us to link individual

behaviour with the macroscopic evolution of the system

• Individuals are represented distinctly, enabling

incorporation of population heterogeneity

• Individuals are autonomous and independent

• Interactions between agents may lead to emergence of

macroscopic phenomena

Case Study Investigating the Influence of Behaviour

• Aim to identify how different definitions of route selection

behaviour alter resulting road network patterns

• A range of individual route selection behaviours are

incorporated into agent-based model

Route Selection

Least Distance

Least Time

Least Angular

Least Turns

Spatial Knowledge

500m Area

1000m Area

Around OD Locations

Agent Behaviour Design

Driver agents independently choose route through city

Model Test Area Central London

Location: Central London

All road links

Road regulations and capacities integrated

30 minutes during AM peak

Agents: ~15000 driver agents

AM peak OD distribution

from TfL Trip Matrix

Model: Developed using Java +

Repast Simphony 1.2

© OpenStreetMap 2012

The Base Case

Base Case Path: Shortest Distance

Knowledge: Complete

1 0 0.5

mile

The Influence of Route Choice

Least Time Path: Least Time

Knowledge: Complete

Faster, main routes

Reduced on subsidiaries

Stronger influence in West

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

1 0 0.5

mile

Least Angular Path: Least Angular

Knowledge: Complete

Greater redistribution

Towards straighter sections

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

1 0 0.5

mile

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Least Turns Path: Least Turns (Distance Constrained)

Knowledge: Complete

Effect not as strong

Influenced by distance

But, highlights straighter sections

1 0 0.5

mile

The Influence of Spatial Knowledge

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Partial Knowledge Path: Shortest Distance

Knowledge: Reduced to 500m

Movement away from subsidiaries

Greater reliance on main routes

1 0 0.5

mile

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Partial Knowledge Path: Shortest Distance

Knowledge: Reduced to 1000m

Less deviation from base case

Reduction in use of subsidiaries

Due to greater all around knowledge

1 0 0.5

mile

Modelling Cities The Need for a Realistic Model of Behaviour

• Models demonstrate strong importance of establishing a

realistic representation of behaviour

• Small changes in behaviour definition lead to big

changes in city level patterns

• Establishing this model of behaviour represents an

important research goal

• In respect to route choice, we have been analysing route

trace data from minicab firm in London

Route Analysis Private Hire Cab Routes

• Dataset of 700k processed routes through London from

Addison Lee taxi company

• Not Black Cab drivers, but will have generally better

knowledge and may use navigation devices

• Analysis compared each route against a range of

optimal paths – here we will focus mainly on distance

• This work still in its early stages…

Taxi Driver Data Total Flows

Route Analysis Comparison to Alternatives – Averages

• For each whole route,

percentage of path

matched against range of

alternatives

• Average match taken for

each alternative

Choice Alternative Percentage

Matched

Least Distance 39.83

Least Time 38.21

Least Angular Deviation 27.37

Least Angular Deviation constrained by distance 33.06

Least Angular Deviation constrained by time 32.86

Least turns constrained by distance 42.48

Least right turns constrained by distance 39.48

Lowest descriptor term score constrained by distance 41.52

Lowest descriptor term score constrained by time 38.24

Lowest descriptor term score constrained by angle 28.58

Maximise number of lanes constraining distance 38.97

Maximise number of lanes constraining time 35.20

Maximise number of lanes constraining angle 25.47

Least turns constrained by time 39.50

Least right turns constrained by time 38.45

No strong stand out

artificial representation

of behaviour

Route Analysis Comparison to Alternatives – Good Matches

• Count of paths where

alternative matches over

75% of real journey

• Only journeys over 1km

in distance considered

Choice Alternative Percentage

Achieving 75%

Least Distance 13.1

Least Time 12.4

Least Angular Deviation 6.1

Least Angular Deviation constrained by distance 8.4

Least Angular Deviation constrained by time 8.8

Least turns constrained by distance 16.1

Least right turns constrained by distance 12.6

Lowest descriptor term score constrained by distance 15.9

Lowest descriptor term score constrained by time 13.2

Lowest descriptor term score constrained by angle 7.4

Maximise number of lanes constraining distance 12.8

Maximise number of lanes constraining time 10.7

Maximise number of lanes constraining angle 5.8

Least turns constrained by time 14.1

Least right turns constrained by time 12.7

Poor performance

by each measure of

prediction

WHY?

Route Analysis Spatial Distribution

• No complete routing algorithms provide an adequate

representation of reality

• This finding goes against assumptions within many

conventional models of traffic simulation

• So, which parts of these journeys are a good match

against optimal routes?

• We looked at deviations in route patterns across space,

by direction of travel, against optimal distance journeys

East to West London Journeys

Difference in flows between 7576 actual and

optimal distance routes

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Std. Dev. = 137.2

Mean = 4.1

Maximum = 1991

Minimum = -2365

1 0 0.5

mile

West to East London Journeys

Difference in flows between 9850 actual and

optimal distance routes

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Std. Dev. = 143.9

Mean = 4.5

Maximum = 1553

Minimum = -3018

1 0 0.5

mile

SE16 to W London Journeys

Difference in flows between 522 actual and

optimal distance routes

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Std. Dev. = 18.2

Mean = 1.3

Maximum = 130

Minimum = -176

1 0 0.5

mile

W to SE16 London Journeys

Difference in flows between 704 actual and

optimal distance routes

> 2.5 Std. Dev.

1.5 to 2.5 Std. Dev.

0.5 to 1.5 Std. Dev.

0.5 to -0.5 Std. Dev.

-0.5 to -1.5 Std. Dev.

-1.5 to -2.5 Std. Dev.

< -2.5 Std. Dev.

Std. Dev. = 27.4

Mean = 1.0

Maximum = 184

Minimum = -381

1 0 0.5

mile

Route Analysis Spatial Distribution

• Differences seem to indicate an attraction and

repulsion of certain parts of the road network

• Apparent preference for straight, longer sections,

possibly with greater salience or perception of travel time

• Route choice appears to not consist of a single route

selection, but a phase-based process of selection

• But does this mean distance plays no role at all? That

doesn’t appear to be quite the case…

Route Analysis Distance Minimisation

Route Analysis Choice Heterogeneity

• Indications are that route selection is a heuristic process,

probably involving minimisation of distance and route

complexity

• There is also a heterogeneity in decision-making –

Perhaps variation in knowledge? Location of decision?

• Analysing collections of paths between discrete locations

reveal that both of these factors may further contribute

E14 to Kings Cross Journeys

Flows of 521 routes between origin and

destination

1 0 0.5

mile

SE16 to W Journeys

Flows of 522 routes between

origin and destination

1 0 0.5

mile

W to SE16 Journeys

Flows of 704 routes between

origin and destination

1 0 0.5

mile

Route Analysis Decision Points

• Visualisations also allow us to identify locations of

significant splits in flow - decision points

• These areas of high activity are likely to be more salient

in an individual’s mind, on which choices made

• Decision points identified where inflow is split between

more than one outflow route (10% minimum)

• Could be used as foundation for decision making

process within model

E14 to Kings Cross Journeys

Decision Points origin and destination

Size indicates volume of traffic flow

through point

1 0 0.5

mile

Conclusions Summary of Research

• The definition of behaviour is clearly highly influential

in determining global patterns of movement

• Getting this representation right is key – requires full

examination of population heterogeneity

• Initial route analysis has highlighted some interesting

trends with relation to established assumptions

• Route choice appears to take place in phases

• Minimisation of distance and route complexity,

attraction to salient features appear important

Thank you

Ed Manley

Edward.Manley.09@ucl.ac.uk

Blog: http://UrbanMovements.posterous.com

Project: http://standard.cege.ucl.ac.uk

Twitter: @EdThink