Methodology and Learnings from Calculating the Cost of the

Causes of Congestion

David Johnston, Intelligent Transport ServicesKath Johnston, QLD Transport and Main Roads

27 July 2016

Project ObjectiveTo produce a congestion pie for TMR similar to the FHWA example, but with the following causes of excessive congestion:

• Recurring congestion• Traffic Incidents• Roadworks• Inclement Weather• Special Events/Other

Steps in Methodology

A) Import dataB) Generate benchmarks of

link performance (i.e. ‘Normal’)C) Generate congestion cost components

(delay, fuel use, pollutants)D) Generate abnormal congestion footprintsE) Map causes onto abnormal congestion footprintsF) Produce reports

Start

(A) Import datafor processing

(B) Generate benchmark link

performance profiles

(C) GenerateCongestion Cost

measures

End

(E) Map causes onto abnormal

congestion footprints

(F) Producereports

(D) Generate abnormal

congestion footprints

Import Data

• NPI Link Data – Speed & Volume• STREAMS Transport Network model – links, intersections,

movements, NPI Links• Weather data (30 minute rainfall observations)• SIMS data – incidents, roadworks, planned events.• 131940 data (traffic information line)• Fleet data - % by vehicle type, % business / private use• Unit cost data – delay (ABS wages), fuel, pollution

Step B: Benchmark ‘Normal’ Traffic

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

‘Normal’ Profiles

A profile defines what is ‘normal’ for an NPI Link and each 15-minute period• Profile holds mean & standard deviation of volume & speed

across days selected for profile• Multiple profiles across the days in a data set• Key question: How do you select which days to include in a

profile?

Day Types in the Calendar

The following attributes are identified in the calendar for each day:• Weekday (Sat and Sun will normally be different to Mon – Fri)• Season (More travel to & from the beach during summer)• Public Holidays• School Holidays• School Fringe (e.g. November when grade 10-12 out, private

schools)• Late night shopping (Thursdays plus week before Christmas)

Break types associated with DaysFurther intelligence required for public holidays near weekends• Each weekend is a 2-day “break”• If Friday is a public holiday, Thursday traffic will be more like a normal

Friday• If Thursday is a public holiday, Wednesday will be like a normal Friday

and Friday will be much quieter than normal.• To ‘learn’ these, the calendar identifies each day as one of:

a) Day not in “break”;b) Day before “break”;c) First day of “break”;d) Day inside “break”;

e) ‘Normal’ day during “break”;f) Last day of break; org) Day after “break”

Step C: Generate Congestion Cost Components

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

10 |

Daily cost of congestion for Brisbane state-controlled roads

(Network & Performance Team E&T Road Operations Feb 2016)

Allocation of Costs Excessively Congested

(as per ARRB formula)Not Excessively Congested

(as per ARRB formula)Less than Normal Congestion All congestion cost attributed to

Recurring Excessive Congestion.

No cost of excessive congestion to allocate.

Normal Congestion

Greater than Normal Congestion

Any ‘normal’ congestion cost attributed to Recurring Excessive Congestion.All excessive congestion cost attributed to one or more causes.

12 |

Congestion – without incident

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1040

2

4

6

8

10

12

14

16

18

20Weekday freeway speeds, 5:30pm

Speed, km/h

Excessive congestion< 70% of posted speed

Posted speed100 km/h

13 |

Congestion – without incident

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1040

2

4

6

8

10

12

14

16

18

20Weekday freeway speeds, 5:30pm

Speed, km/h

Average speed53 km/h Normal range

14 |

Congestion – without incident

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1040

2

4

6

8

10

12

14

16

18

20Weekday freeway speeds, 5:30pm

Speed, km/h

Normal recurring

44%

Step D: Generating AbnormalCongestion Footprints

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

Determine spatial extent of abnormal congestion

Merging Abnormal Congestion Footprints

• Where separating link is excessively congested and this is normal, merge the abnormal congestion footprints.

• NPI Link X meets this condition. NPI Link Y does not.

Step E: Map Causes ontoAbnormal Congestion Footprints

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

19 |

Separating the causes of excessive congestion

Excessive Congestion

Normal

Infrastructure bottlenecks

Abnormal

Incidents Weather Roadworks Special events

20 |

Congestion – during incident

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1040

2

4

6

8

10

12

14

16

18

20Weekday freeway speeds, 5:30pm

Speed, km/h

21 |

Congestion – during incident

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 1040

2

4

6

8

10

12

14

16

18

20Weekday freeway speeds, 5:30pm

Speed, km/h

Abnormal recurring

34%

Incidents9%

Step F: Report Results

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

23 |

Causes of congestion 2014,Brisbane State-controlled roads

Normal recurring $112,302,783

Abnormal recur-ring $86,874,208

Incidents $22,533,228

Roadworks, $535,967

Special, $7,368Other, $302,107

Weather, $7,189,360

Unknown $24,204,211

Limitations & Opportunities Identified

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

25 |

Data limitations

Missing data

Stationary vehicles

Truck cost excludes value

of goods

Congestion inside 15 min

periodsOther modes

Additional Opportunities Arising• ‘Normal’ profiles could be used to:

– improve detector monitoring, improve incident detection– input to traffic models, better understanding of what is ‘normal’ when– calculate actual operational capacity of each link in real time & where

there is spare capacity• Calculate impact of individual weather or incident events: cost,

VKT affected, VKT lost, actual start time & duration, etc. and save with SIMS or 131940 record

• Improve traffic management methods by analysis of cost data to target specific causes

• Visualisation of congestion events (see example)

The authors wish to acknowledge the support of QLD Transport and Main Roads and thank Kelvin Marrett, Miranda Blogg

and Frans Dekker for their contributions to this project.

METHODOLOGY AND LEARNINGSFROM CALCULATING THE COST

OF THE CAUSES OF CONGESTION

Visualisation:Traffic Data,

Incident Data,Weather Data,

Bus Service Data.

Thanks to netBI for data integration & visualisation.