SHOW POLICY SUPPORT TOOL FOR CITIES
TheLEVITATE policy support tool (PST) is an open-access, online-based system that provides users with access to the LEVITATE methodologies and results.
The LEVITATE PST provides decision support on CCAM-related interventions, of City Authorities, transport experts, and interested citizens.
This freely accessible tool provides the possibility of interactive use by comparing several different parameters & scenarios and reducing uncertainty during the decision-making process.
Role in SHOW
The LEVITATE PST has been selected because it already contains some key features, such as:
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It was designed to support decision-makers of cities & regions, as well as public transport authorities and operators.
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It is accessible freely online and can be used by decision-makers and/or any organizations interested in the topic.
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It includes several degrees of complexity and personalization which allows to obtain results with a variable level of precisions, depending on the quality and amount of the input data.
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It was designed to be usable in several European cities.
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It was developed by SHOW partners in the framework of other EU-funded projects, namely NTUA (LEVITATE).
As mentioned in D17.1, the LEVITATE Policy Support Tool appears as a good basis and source of inspiration since it focuses on CCAM policies in urban areas. The PST provides a solid basis to develop a system able to guide policymakers on the relevance of their CCAM-related policy measures – especially when it comes to cities that wish to include CCAM into their respective sustainable urban mobility plans (SUMPs).
Please find the necessary information for using the tool below
Forecasting
The users can estimate the impact of CCAM on their cities.
Steps of the Forecasting Analysis
- Select one or two policy interventions
- Select the CCAM deployment scenario
- Define the policy intensity and policy effectiveness through the years 2020-2050
- Adjust the initial PST values of the parameters and impacts
- Provide input in terms of temporal implementation of the measure(s) for the system to take into account by adjusting the response curves of the impacts
- Receive the results, in form of table with analytical results and curves presenting both results for the baseline scenario (no intervention) and for the selected policy intervention(s)
Forecasting info before using the tool
Based on your local climate objectives/SUMPs, please gather the data needed for the exercise:
Table 1: Parameters for the forecasting tool
Parameters | Unit of Measurement | Default Initial Value (can be changed by user) |
GDP per capita | € | 17,000 |
Annual GDP per capita change | % | 1.50% |
Inflation | % | 1.00% |
City Population | million persons | 3.000 |
Annual City Population change | % | 0.50% |
Urban shuttle fleet size | no. of vehicles | 300 |
Freight vehicles fleet size | no. of vehicles | 100 |
Average load per freight vehicle | tones | 3 |
Average annual freight transport demand | million tones | 1.5 |
Fuel cost | € / lt | 2.50 |
Electricity cost | € / KWh | 40 |
Fuel consumption | lt / 100Km | 6.00 |
Electricity consumption | KWh / 100Km | 15.00 |
VRU Reference Speed (Typical on Urban Road) | km/h | 40.00 |
VRU at-Fault accident share | km/h | 30.00 |
Table 2: Indicators for impact in the forecasting tool
Impacts | Description/measurement | Unit of Measurement | Default Initial Value (can be changed by user) |
Travel time | Average duration of a 5Km trip inside the city centre | min | 15 |
Vehicle operating cost | Direct outlays for operating a vehicle per kilometre of travel | €/Km | 0.35 |
Freight transport cost* | Direct outlays for transporting a tonne of goods per kilometre of travel | €/tonne.Km | 1 |
Access to travel | The opportunity of taking a trip whenever and wherever wanted (10 points Likert scale) | - | 5 |
Amount of travel | Person kilometres of travel per year in an area | person-km | 15000 |
Congestion | Average delays to traffic (seconds per vehicle-kilometer) as a result of high traffic volume | s/veh-km | 60 |
Modal split of travel using public transport | % of trip distance made using public transportation | % | 20.00% |
Modal split of travel using active travel | % of trip distance made using active transportation (walking, cycling) | % | 3.00% |
Shared mobility rate | % of trips made sharing a vehicle with others | % | 4.00% |
Vehicle utilisation rate | % of time a vehicle is in motion (not parked) | % | 5.00% |
Vehicle occupancy | average % of seats in use (pass. cars feature 5 seats) | % | 25.00% |
Parking space | Required parking space in the city centre per person | m2/person | 0.9 |
Energy efficiency | Average rate (over the vehicle fleet) at which propulsion energy is converted to movement | % | 25.00% |
NOX due to vehicles | Concentration of NOx pollutants as grams per vehicle-kilometer (due to road transport only) | g/veh-km | 0.2 |
CO2 due to vehicles | Concentration of CO2 pollutants as grams per vehicle-kilometer (due to road transport only) | g/veh-km | 150.00 |
PM10 due to vehicles | Concentration of PM10 pollutants as grams per vehicle-kilometer (due to road transport only) | g/veh-km | 0.05 |
Public health | Subjective rating of public health state, related to transport (10 points Likert scale) | - | 5 |
Inequality in transport | To which degree are transport services used by socially disadvantaged and vulnerable groups, including people with disabilities (10 points Likert scale) | - | 5 |
Commuting distances | Average length of trips to and from work (added together) | Km | 20 |
Unmotorized VRU crash rates | Injury crashes with unmotorized VRUs per vehicle-kilometer driven | injury-crashes/veh-km | 2.20 |
Road safety motorized | Number of crashes per vehicle-kilometer driven | crashes/veh-km | 1.40 |
Road safety total effect | Road safety effects when accounting for VRU and modal split | crashes/veh-km | 0.86 |
Backcasting
The users can find the most appropriate combination of CCAM technologies and measures to provide specific policy objectives – which could be relevant to defining their sustainable urban mobility plans (SUMPs).
Steps of the Backcasting Analysis
- Selection of target year between 2020-2050
- Selection of CCAM deployment scenario
- Definition of the desired policy vision described in terms of desired values in 1 (minimum) to 5 (maximum) impacts as well as the desired values for each of the selected impacts
- Adjust the initial PST values of the parameters and impacts
- Receive the results, in the form of a table where all policy interventions are presented with the characterization “true” or “false”, based on the potential to reach the desired policy vision
Backcasting info before using the tool
Based on your local climate objectives/SUMPs, please gather the data needed for the exercise:
Table 3: Indicators for target impact in the backcasting tool
Target Impact |
What kind of value |
Travel time |
20min/5km |
Vehicle operating cost |
0.30€/Km |
Freight transport cost |
1.1€/tonne.Km |
Access to travel |
6 out of 10 |
Amount of travel |
16000person-km/year |
Congestion |
50 s/veh-km |
Modal split of travel using public transport |
40% |
Modal split of travel using active travel |
5% |
Shared mobility rate |
3% |
Vehicle utilization rate |
4% |
Vehicle occupancy |
50% |
Parking space |
2m²/person |
Energy efficiency |
30% |
NOX due to vehicles |
0.1g/veh-km |
CO2 due to vehicles |
100g/veh-km |
PM10 due to vehicles |
0.04g/veh-km |
Public Health |
7 out of 10 |
Accessibility in transport |
4 out of 10 |
Commuting distances |
15 km |
Unmotorized VRU crash rates |
1 injury-crashes/million veh-km |
Road safety motorized |
3 crashes/ million veh-km |