Assessing Transit Pricing Policies by Combining Large Scale Smart-card Data and Surveys



Assessing Transit Pricing Policies by Combining Large Scale Smart-card Data and Surveys

Authors

Zoi Christoforou, Ecole Des Ponts ParisTech / LVMT, Lydia Ladjouge, , Fabien Leurent,

Description

We assess the impact of a major reform in transit fare structures that took place in Paris. The first results show that higher and middle incomes benefit whereas lower incomes don’t.

Abstract

Transit fares directly influence modal share and, thus, mobility patterns and quality of life at all spatial scales going from a neighbourhood to a whole region. The objective of this research is to study and assess the impact of a major change in transit fare structures that took place in the Paris metropolitan region. Since 1975, transit pricing was based on a system of concentric zones. Since September 1st, a flat rate applies across all Paris region for month and annual smart-card holders. The Paris region covers an area of 12,012 km2 and accommodates a population of 11.4 million (OMNIL, 2012) as well as 5.6 million jobs. It is characterized by a dense transit system and a great diversity of transit modes and services: 16 metro lines of over 200 km; 1,500 bus lines of over 25,000 km; 14 train lines of over 1,500 km; 8 tramway lines. It counts over 41 million daily trips while 3.8 million transit users possess month/year smart-cards. To the best of our knowledge, this fare reform is unique for a megacity of the size of Paris and has not been studied so far. The decision-making was largely based upon political and environmental criteria and no ex-ante assessment was made.

In this paper, we present the assessment of the reform from a users’ standpoint and on a specific suburban railway line, the RER A. The RER A is the busiest urban rail line in Europe with over 1 million passengers on a typical working day and a total of 46 stations. The assessment is based on two criteria: mobility gains/losses and monetary gains/losses.

In order to perform the assessment, we developed a novel methodology that combines four sources of heterogeneous data:
1) Household survey data in order to elicit preferences and travel habits of people using the RER A line. This data was provided by the Organizing Authority (Stif).
2) Tap In- Tap Out (TITO) data from smart card validations (both before and after the reform) in order to restore travel patterns within the transit system and to identify eventual changes. This data was provided by the Organizing Authority (Stif).
3) Origin-Destination survey data along the RER A line in order to associate users to exact districts of residence and work (i.e. door-to-door journey reconstruction). This dataset was provided by the RER A operator (RATP).
4) Income distribution data per district in order to associate transit users to specific income levels. This data is openly available by the French National Institute for Statistics and Economic Studies (INSEE).

The combination of these data allows us to finally associate income levels to travel pattern changes before and after the reform. We can thus obtain important insight on the socioeconomic impact of the reform. The first findings show that middle incomes benefit the most as they expand their mobility in terms of both travel length and frequency and, also, pay less. Higher incomes (among public transport users) are also ‘winners’ as they expand their mobility at a slightly higher price that corresponds to a low percentage of the global household expenditure. On the contrary, lower incomes are located to poorly served areas or do not use annual reduction cards. The quantified results can be useful to decisions as they enable them to: (i) define pricing policies in line with their political criteria and (ii) make informed decisions with rigorous socio-economic assessment.

Publisher

Association for European Transport