Typical Day demand: the baseline demand matrix for the model, designed to represent trips which take place on an average day of the year.
Median Peak demand: an uplift is applied demand matrix to match the peak demand from the routes. As the peak is represented by the top 5% days, the median of these is taken.
Upper Quartile Peak demand (P75): the upper quartile of the count sites along the route is taken as the uplift for demand matrix.
Lower Quartile Peak demand (P25): the lower quartile of the count sites along the route is taken as the uplift for the demand matrix.
CCC Matrix demand: as a comparison test, the matrix defined by CCC which applies an additional uplift in future years was also used for a limited number of model runs to compare with the Typical Day and Median Peak demand scenarios.
Do nothing: the performance of the existing network (as obtained from the calibration process) is assessed against the various future year demand scenarios.
Carbon Optimisation: the model seeks to add additional devices which will reduce the amount of demand deemed to be 'lost' on the network, i.e. journeys which are carried out in petrol or diesel vehicles because there is a shortage of suitable charging locations.
Developer Optimisation: the model seeks to add additional devices which will maximise the net revenue for developers. This scenario can be considered a representation of how the (commercial) market will develop if left to its own devices.
Drivers Optimisation: the model seeks to add additional devices which will maximise the benefit for drivers (and their passengers) in terms of total time and total costs, combined into a 'generalised cost' by applying a monetary value to driver time. As these measures can always be improved, even fractionally, by the addition of another device, this optimisation scheme is run with defined stopping criteria.
Typical Day demand: the baseline demand matrix for the model, designed to represent trips which take place on an average day of the year.
Median Peak demand: an uplift is applied demand matrix to match the peak demand from the routes. As the peak is represented by the top 5% days, the median of these is taken.
Upper Quartile Peak demand (P75): the upper quartile of the count sites along the route is taken as the uplift for demand matrix.
Lower Quartile Peak demand (P25): the lower quartile of the count sites along the route is taken as the uplift for the demand matrix.
CCC Matrix demand: as a comparison test, the matrix defined by CCC which applies an additional uplift in future years was also used for a limited number of model runs to compare with the Typical Day and Median Peak demand scenarios.
Do nothing: the performance of the existing network (as obtained from the calibration process) is assessed against the various future year demand scenarios.
Carbon Optimisation: the model seeks to add additional devices which will reduce the amount of demand deemed to be 'lost' on the network, i.e. journeys which are carried out in petrol or diesel vehicles because there is a shortage of suitable charging locations.
Developer Optimisation: the model seeks to add additional devices which will maximise the net revenue for developers. This scenario can be considered a representation of how the (commercial) market will develop if left to its own devices.
Drivers Optimisation: the model seeks to add additional devices which will maximise the benefit for drivers (and their passengers) in terms of total time and total costs, combined into a 'generalised cost' by applying a monetary value to driver time. As these measures can always be improved, even fractionally, by the addition of another device, this optimisation scheme is run with defined stopping criteria.
Typical Day demand: the baseline demand matrix for the model, designed to represent trips which take place on an average day of the year.
Median Peak demand: an uplift is applied demand matrix to match the peak demand from the routes. As the peak is represented by the top 5% days, the median of these is taken.
Upper Quartile Peak demand (P75): the upper quartile of the count sites along the route is taken as the uplift for demand matrix.
Lower Quartile Peak demand (P25): the lower quartile of the count sites along the route is taken as the uplift for the demand matrix.
CCC Matrix demand: as a comparison test, the matrix defined by CCC which applies an additional uplift in future years was also used for a limited number of model runs to compare with the Typical Day and Median Peak demand scenarios.
Do nothing: the performance of the existing network (as obtained from the calibration process) is assessed against the various future year demand scenarios.
Carbon Optimisation: the model seeks to add additional devices which will reduce the amount of demand deemed to be 'lost' on the network, i.e. journeys which are carried out in petrol or diesel vehicles because there is a shortage of suitable charging locations.
Developer Optimisation: the model seeks to add additional devices which will maximise the net revenue for developers. This scenario can be considered a representation of how the (commercial) market will develop if left to its own devices.
Drivers Optimisation: the model seeks to add additional devices which will maximise the benefit for drivers (and their passengers) in terms of total time and total costs, combined into a 'generalised cost' by applying a monetary value to driver time. As these measures can always be improved, even fractionally, by the addition of another device, this optimisation scheme is run with defined stopping criteria.
Typical Day demand: the baseline demand matrix for the model, designed to represent trips which take place on an average day of the year.
Median Peak demand: an uplift is applied demand matrix to match the peak demand from the routes. As the peak is represented by the top 5% days, the median of these is taken.
Upper Quartile Peak demand (P75): the upper quartile of the count sites along the route is taken as the uplift for demand matrix.
Lower Quartile Peak demand (P25): the lower quartile of the count sites along the route is taken as the uplift for the demand matrix.
CCC Matrix demand: as a comparison test, the matrix defined by CCC which applies an additional uplift in future years was also used for a limited number of model runs to compare with the Typical Day and Median Peak demand scenarios.
Do nothing: the performance of the existing network (as obtained from the calibration process) is assessed against the various future year demand scenarios.
Carbon Optimisation: the model seeks to add additional devices which will reduce the amount of demand deemed to be 'lost' on the network, i.e. journeys which are carried out in petrol or diesel vehicles because there is a shortage of suitable charging locations.
Developer Optimisation: the model seeks to add additional devices which will maximise the net revenue for developers. This scenario can be considered a representation of how the (commercial) market will develop if left to its own devices.
Drivers Optimisation: the model seeks to add additional devices which will maximise the benefit for drivers (and their passengers) in terms of total time and total costs, combined into a 'generalised cost' by applying a monetary value to driver time. As these measures can always be improved, even fractionally, by the addition of another device, this optimisation scheme is run with defined stopping criteria.
Typical Day demand: the baseline demand matrix for the model, designed to represent trips which take place on an average day of the year.
Median Peak demand: an uplift is applied demand matrix to match the peak demand from the routes. As the peak is represented by the top 5% days, the median of these is taken.
Upper Quartile Peak demand (P75): the upper quartile of the count sites along the route is taken as the uplift for demand matrix.
Lower Quartile Peak demand (P25): the lower quartile of the count sites along the route is taken as the uplift for the demand matrix.
CCC Matrix demand: as a comparison test, the matrix defined by CCC which applies an additional uplift in future years was also used for a limited number of model runs to compare with the Typical Day and Median Peak demand scenarios.
Do nothing: the performance of the existing network (as obtained from the calibration process) is assessed against the various future year demand scenarios.
Carbon Optimisation: the model seeks to add additional devices which will reduce the amount of demand deemed to be 'lost' on the network, i.e. journeys which are carried out in petrol or diesel vehicles because there is a shortage of suitable charging locations.
Developer Optimisation: the model seeks to add additional devices which will maximise the net revenue for developers. This scenario can be considered a representation of how the (commercial) market will develop if left to its own devices.
Drivers Optimisation: the model seeks to add additional devices which will maximise the benefit for drivers (and their passengers) in terms of total time and total costs, combined into a 'generalised cost' by applying a monetary value to driver time. As these measures can always be improved, even fractionally, by the addition of another device, this optimisation scheme is run with defined stopping criteria.