Abstract
Objective
Until 2016, the KiM Netherlands Institute for Transport Policy Research annually explained the development of train passenger kilometres over the preceding ten years by using the national development of its drivers (such as population growth and train kilometers), as well as the elasticities of demand for those drivers. Since some years this method appeared to be less appropriate for completely explaining the increase in train patronage (+24% from 2005 to 2016). To acquire a complete as possible explanation of patronage development, KiM developed a more thorough method, using the Ministry of Infrastructure and Water Management’s National Model System (LMS), in cooperation with experts of public transport and of LMS. The LMS is a forecasting system for simulating developments in mobility, as based on a spatiotemporal detailed model of the drivers of mobility. The application of this forecasting system for an ex post evaluation can be regarded as innovative.
Method
The LMS generates OD matrices between origin and destination zones for car, public transport and bicycle (trips and kilometers) on an average working day. The output depends on a number of influencing factors such as population, employment, income, car ownership, road congestion, and the public transport service levels and pricing. To explain the development of public transport use from 2005 until 2016 models runs were made of 2004, 2010 and 2014, because much input data were already available for those years . To assess the impact of each influencing factor, we compared a model run with another model run in which only that particular influencing factor was changed, as based on the actual development, while all other factors remained constant. We extrapolated the results to the period 2005-2016, as based on empirical national data.
Results
Based on LMS-analyses, we concluded that the 24% increase in train patronage during the period 2005-2016 was mainly determined by population growth (+5%), train passenger kilometres by students travelling with a student pass card (+4%) and improvements in level of service (+10%). Improvements in level of service consisted of higher frequencies, new rail lines, and better connections between train services. The increase in jobs, income, air traffic, road congestion and fuel price appeared to have small effects. A decreasing impact on train passenger kilometers was caused by the increase of train fares (-5%). The LMS analyses left a growth of 8% unexplained. From other data and analyses it appeared that this 8% increase in train passenger kilometres can be the result of a larger impact from the amount of students (about +1%), improved punctuality (about +1%), and a considerably smaller impact of price changes than -5%, because not all discount offers can be regarded as included in the LMS analyses.
Conclusions
LMS analyses explained the development of train passenger kilometers 2005-2016 better than the previous method, which was based on national developments of influencing factors and elasticities. Cause of the unexplained differences between the monitored developments of public transport use and the explanations based on LMS analyses can be: an insufficient degree of detail of data on patronage and level of service of public transport available for research, as well as insufficient representations in LMS analyses of socioeconomic factors, of pricing and of level of service.
Until 2016, the KiM Netherlands Institute for Transport Policy Research annually explained the development of train passenger kilometres over the preceding ten years by using the national development of its drivers (such as population growth and train kilometers), as well as the elasticities of demand for those drivers. Since some years this method appeared to be less appropriate for completely explaining the increase in train patronage (+24% from 2005 to 2016). To acquire a complete as possible explanation of patronage development, KiM developed a more thorough method, using the Ministry of Infrastructure and Water Management’s National Model System (LMS), in cooperation with experts of public transport and of LMS. The LMS is a forecasting system for simulating developments in mobility, as based on a spatiotemporal detailed model of the drivers of mobility. The application of this forecasting system for an ex post evaluation can be regarded as innovative.
Method
The LMS generates OD matrices between origin and destination zones for car, public transport and bicycle (trips and kilometers) on an average working day. The output depends on a number of influencing factors such as population, employment, income, car ownership, road congestion, and the public transport service levels and pricing. To explain the development of public transport use from 2005 until 2016 models runs were made of 2004, 2010 and 2014, because much input data were already available for those years . To assess the impact of each influencing factor, we compared a model run with another model run in which only that particular influencing factor was changed, as based on the actual development, while all other factors remained constant. We extrapolated the results to the period 2005-2016, as based on empirical national data.
Results
Based on LMS-analyses, we concluded that the 24% increase in train patronage during the period 2005-2016 was mainly determined by population growth (+5%), train passenger kilometres by students travelling with a student pass card (+4%) and improvements in level of service (+10%). Improvements in level of service consisted of higher frequencies, new rail lines, and better connections between train services. The increase in jobs, income, air traffic, road congestion and fuel price appeared to have small effects. A decreasing impact on train passenger kilometers was caused by the increase of train fares (-5%). The LMS analyses left a growth of 8% unexplained. From other data and analyses it appeared that this 8% increase in train passenger kilometres can be the result of a larger impact from the amount of students (about +1%), improved punctuality (about +1%), and a considerably smaller impact of price changes than -5%, because not all discount offers can be regarded as included in the LMS analyses.
Conclusions
LMS analyses explained the development of train passenger kilometers 2005-2016 better than the previous method, which was based on national developments of influencing factors and elasticities. Cause of the unexplained differences between the monitored developments of public transport use and the explanations based on LMS analyses can be: an insufficient degree of detail of data on patronage and level of service of public transport available for research, as well as insufficient representations in LMS analyses of socioeconomic factors, of pricing and of level of service.
| Original language | English |
|---|---|
| Title of host publication | European Transport Conference 2018, Dublin, October 10-12, 2018 |
| Publisher | Association for European Transport (AET) |
| Publication status | Published - 2018 |
| Event | 46th European Transport Conference 2018 - Dublin Castle, Dublin, Ireland Duration: 10 Oct 2018 → 12 Oct 2018 Conference number: 46 https://aetransport.org/en-gb/etc https://aetransport.org/en-gb/etc |
Conference
| Conference | 46th European Transport Conference 2018 |
|---|---|
| Abbreviated title | ETC2018 |
| Country/Territory | Ireland |
| City | Dublin |
| Period | 10/10/18 → 12/10/18 |
| Internet address |