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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