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 providesdecision 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 & scenariosand 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: 

  • It was designed to support decision-makers of cities & regions, as well as public transport authorities and operators.

  • It is accessible freely online and can be used by decision-makers and/or any organizations interested in the topic.

  • 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.

  • It was designed to be usable in several European cities.

  • 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


The users can estimate the impact of CCAM on their cities.  

Steps of the Forecasting Analysis 

  1. Select one or two policy interventions​ 
  2. Select the CCAMdeployment scenario​ 
  3. Define the policy intensity and policy effectivenessthrough the years 2020-2050 
  4. Adjust theinitial PST values of the parameters and impacts 
  5. 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 
  6. Receive theresults, 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​ 


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 

  1. Selection of target year between 2020-2050​
  2. Selection of CCAM deployment scenario​
  3. 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​
  4. Adjust the initial PST values of the parameters and impacts​
  5. 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 


Vehicle operating cost 


Freight transport cost 


Access to travel 

6 out of 10 

Amount of travel 



50 s/veh-km 

Modal split of travel using public transport 


Modal split of travel using active travel 


Shared mobility rate 


Vehicle utilization rate 


Vehicle occupancy 


Parking space 


Energy efficiency 


NOX due to vehicles 


CO2 due to vehicles 


PM10 due to vehicles 


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 

Other supporting materials can be found here.