The Stockholm trials – Emme/2 as a tool for designing a congestion

charges system

1. The trials and the congestion charges system

2. Observed effects

3. Transportation forecast results compared to observed effects

4. The referendum

The Stockholm trials

Extended public transport

More park-and-ridefacilities

Implementation of acongestion tax

Objectives

• Reduce traffic volumes on the busiest roads during peak hours by 10-15%

• Improve the flow of traffic on streets and roads

• Reduce emissions of pollutants harmful to human health and of carbon dioxide

• Improve the urban environment as perceived by Stockholm residents

The congestion charges system

10 SEK = 1,06 Euro, = 1,33 USD

The congestion charges system

”Weekdays 6:30 am – 6:30 pm”

• SEK 10, 15 or 20 for passage into and out of the inner city

• No charges on evenings, nights, saturdays, sundays public holidays and the day before a public holiday

• Maximum charge of SEK 60 per day and vehicle

Percentage change in traffic flows in and out of the congestion charge zone during the charge period (6.30

am – 6.30 pm)

Traffic passing in and out of the inner city on an average day in spring 2005 compared with

spring 2006N

umbe

r of

veh

icle

s pe

r ho

ur

Time

No charge

15 SEK

20 SEK

10 SEK(1,06 Euro, 1,33 USD)

Difference in journey time along various monitoring routes, 2005-2006

Increase

Unchanged

Reduction

Big reduction

Transportation forecasts – the purpose

To supply:

• basic data for decision about the design of the congestion charges system

• basic data as input to other actors planning activities because of the Stockholm trial (for example Stockholm Transport (SL))

Transportation forecasts – analyzed scenarios

• Different price structures

• Different number of charging zones

• With and without congestion charges on Essingeleden

• With and without congestion charges for residents in Lidingö

Transportation forecasts – the forecast model

Sampers:

• Trip frequency

• Mode split (car, public transport, walk, cykle)

• Destination choice

Emme/2:

• Auto assignment (auto volumes on road network)

• Transit assignment (passenger volumes on transit lines)

Transportation forecasts – model features

• Traffic during the average weekday

• Traffic during peak period and between peak periods

• Different time values for different categories of people

• Choice of departure time

Percentage change in traffic flows in and out of the congestion charge zone during the charge

period

Observed effect = -22 %

Transportationforecast = -25 %

Forecasted number of vehicles passing in and out of the inner city on an average day

0

2000

4000

6000

8000

10000

12000

06.0

0-06

.15

06.4

5-07

.00

07.3

0-07

.45

08.1

5-08

.30

09.0

0-09

.15

09.4

5-10

.00

10.3

0-10

.45

11.1

5-11

.30

12.0

0-12

.15

12.4

5-13

.00

13.3

0-13

.45

14.1

5-14

.30

15.0

0-15

.15

15.4

5-16

.00

16.3

0-16

.45

17.1

5-17

.30

18.0

0-18

.15

18.4

5-19

.00

Utan avg

Med avg

Num

ber

of v

ehic

les

per

15 m

inut

es

Time

Without charges

With charges

Observed number of vehicles passing in and out of the inner city on an average day

0

1000

2000

3000

4000

5000

6000

7000

8000

90000

0:0

0

01

:15

02

:30

03

:45

05

:00

06

:15

07

:30

08

:45

10

:00

11

:15

12

:30

13

:45

15

:00

16

:15

17

:30

18

:45

20

:00

21

:15

22

:30

23

:45

höstvardag 2005

jan 06

feb 06

Autumn 2005January 2006February 2006

Num

ber

of v

ehic

les

per

15 m

inut

es

Time

Number of vehicles on different parts on E4-Essingeleden during the charge period (6.30 am –

6.30 pm)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

jan feb mar apr maj jun jul

Observed increase = 4-5 %

Forecast = +7 %

Essingeleden

Frösunda

Midsommar-kransen

2006

2005

Num

ver

of v

ehic

les

Month

What’s the results?• Percentage differences in traffic flows during an average weekday were

forecasted with relative good results

– The increase of traffic flow on Essingeleden were slightly overestimated

– The decrease of traffic flow across the zone boundary were slightly overestimated

• Incorrect distribution of the effects on morning peak period, between peaks and afternoon peak period

• The forecasts missed the decrease in evening traffic

• The effects of time departure choices were overestimated

• Underestimated time values and underestimated travel time effects => more people opted to travel through the city than expected

• Shortages in the model of time distribution functions and neglecting “turn and return thinking” => the real effects were bigger during afternoon peak period and between peaks and smaller during morning peak period

The referendum

No referendumReferendum

Total60,2%

39,8%

0,0%

10,0%20,0%

30,0%

40,0%

50,0%60,0%

70,0%

Yes No

Yes

No