The following is an excerpt from ”Future of Urban Transport: Learning from Success and Weakness: Light Rail” a study by Carmen Hass-Klau & Graham Crampton.
Influences on success
The available data allowed the effects of eleven different influences to be examined, using correlation and multivariate regression methods:
- the average light rail speed,
- population density 300m light rail corridors, following the lines,
- monthly fare relative to the country’s GDP/Capita,
- percentage of new light rail vehicles,
- peak headway in minutes of light rail service,
- park and ride spaces per light rail track/km,
- pedestrian street length per city population,
- % of passengers using travel cards,
- light rail network density, number of public parking spaces in the city centre according to city centre size,
- other suburban rail provision.
The four factors in bold are those which, on first analysis, seem to have statically significant effect on the overall indicator of success on their own, before considering their combined effect with other vehicles. The three strongest of these – travel card use, length of pedestrianized streets and corridor density, have positive effects, i.e. they improve the likelihood of the system scoring well in the combined measure of success. High levels of fares worked in the opposite direction.