TARIFICATION 3: How to Manage Peaks and Falls in Transport Demand?
2 August 2021

TARIFICATION 3: How to Manage Peaks and Falls in Transport Demand?

Our series of 5 articles on pricing continues with the analysis of Sabina KAUARK, Civil Engineer at SYSTRA Brazil, in collaboration with Joana NICOLINI, Luiza MACIEL and Emília GUERRA . This week they analyze how to manage peaks and falls in public transport demand, based on their experience in Latin America.

Mobility as a Service, referred to as Maas, cannot be developed without addressing the issue of transport pricing, or mobility in general. This is not a new issue, but it is even more pertinent in the context of the health crisis, with operators hard hit, user behaviour becoming both more demanding and unpredictable, and mobility less routine. So what fare policy should be adopted? How can profitability and efficiency, flexibility and sustainability be reconciled? Can a pricing policy be a lever for managing crowds in transport networks? Can pricing have an impact on urbanisation and the shape of the city?

We asked our experts these questions. From Australia to Brazil, via Asia, the UK and France, our international network of consultants worked together to answer them. For a month, we shed light on the subject, informed by local experiences.


Dealing with variations in demand is challenging, especially when we consider that the main purposes of journeys are work and study. It is known that journeys tend to be concentrated at peak periods, which are usually in the early morning and late afternoon, varying according to local aspects and the profile of residents. The high concentration of demand for transport at peak hours generates congestion and overloads the transport systems and infrastructure.

Moreover, although there is a large portion of passengers that travel during a specific short range of time, public transport systems should be able to run all day long, providing travel possibilities to all.

Therefore, the key challenge lies in matching the supply of transport to the demand of the system, providing as many journeys as needed at different times of the day, but not so many that the system becomes idle and costly, and not so few that passengers must squeeze themselves into a vehicle or wait for many hours for the next vehicle. 


In this context, fare policy instruments can be used to improve the operational, economic-environmental, and social efficiency of public transport services. Price discrimination aims to promote migration of part of the peak demand to less demanded times, as well as from individual to collective transport mode. This is driven by the technological evolution of charging systems, such as electronic ticketing, on-demand applications, and other ITS traffic control systems. 

Time-based pricing is a kind of price discrimination based on demand-price elasticity, in which the tariff can vary according to the start of the journeys, reason, income, and age of the user. Users with more rigid work schedules are not affected by this measure, unless there are additional strategies such as staggered work hours, flexible work hours, compressed work weeks, or even home office, to be implemented in a coordinated manner with employers. 

Price discrimination instruments like time-based pricing are related to the Transport Demand Management (TDM) strategies to influence behaviour change. TDM measures in Latin American Cities were previously studied by SYSTRA and published by AFD, CAF, and the European Union in a report entitled Medidas de gestion de la demanda de transporte en ciudades de América Latina. As it is resource intensive to expand public transport capacity to meet increases in travel demand, cities are turning these measures to help spread peak hour travel demand and reduce overcrowding. [1] This is the case in some South American cities such as Santiago, in Chile, and Fortaleza, in Brazil:  

  • Fortaleza, Brazil: the variation was adopted in the bus and van system in 2011. The fare reduction occurs in off-peak hours, from 9am to 10am and from 3pm to 4pm, so that the user pays a fare 7.27% of the amount charged during peak periods (full fare). In addition, the system also has the ‘Social Fare’, which occurs on Sundays and special days. The results of the study were not conclusive, as other situations happened in parallel, such as the tariff increase, which may have influenced the system’s demand. [2]
  • Santiago, Chile: the time-based pricing was implemented in the city’s metro and buses in three time slots: peak, off-peak, and low demand. Thus, the metro fare has a discount of 10.81% and 17.57%, in the off-peak and in low demand period, respectively. [3].  A model predicted that there was a decrease in demand for morning and evening peaks varying from 8% to 3.5% for two Metro lines. Moreover, the decrease in the demand for morning peak is higher compared to evening peak [4].  However, validation with real data showed a slightly greater decrease in demand for evening peak compared to morning peak. 

Moreover, apart from tariff policies, changing schools and universities schedules can be a strategy to manage peaks. A European example is the case of Villejean-Université Station, in Rennes, France. Previously, students used to crowd the metro trains in the morning peak, which made the journey uncomfortable for them as well as for the workers who used the metro line at the same time.

The solution was simple: start the master’s and third-year undergraduate classes at 8:15, while the first and second-year undergraduate classes started at 8:30. These short 15 minutes made all the difference to offer more comfort to the users and for the system to be able to meet the demand.


As an evolution towards a broader price discrimination, we highlight the most recent possibilities of dynamic tariff charging applications within the MaaS concept, a tool that should be used to determine tariff prices based on costs or on supply/demand. MaaS could enable cities and providers to manage peaks and falls by increasing travel options, providing a mechanism to influence journeys behaviour through incentives to travel off-peak or encouraging mode-shift during peak times. Such systems aim to help users to plan and book journeys using several transport modes, making first and last-mile connections simpler. [5]

The table below summarizes the tariff policies measures related to TDM to manage peaks and falls and presents some places where such measures have been implemented.


For the implementation of these measures, local realities and restrictions must be considered. In Brazil, the main users of public transport have less flexibility to change their schedule, because they are in a more rigid market condition. Most people who commute during the peak period for work reasons are financed by their employer, with a discount of up to 6% on their salary.

Therefore, the decision of changing work schedules can directly benefit the employer, who is the one who would benefit the most from the fare discount. Another aspect is the informality of the Brazilian labour market, which can promote a random commuting pattern.

Finally, the financing of these measures is one of the main obstacles to the implementation of solutions to encourage changes in habits. Fare discounts, implementation of bus services on demand, and last mile improvements may require a government subsidy and there must be political support for this.

[1] Gwee, E., Currie, G., 2013. Review of Time-Based Public Transport Fare Pricing. Journeys. Halvorsen, A., Koutsopoulos, H.N., Ma, Z., Zhao, J., 2019. Demand management of congested public transport systems: a conceptual framework and application using smart card data. Transportation 1–29.

[2] ETUFOR, 2016.

[3] Rabay, L., & Andrade, N. P. (2019). O uso de diferentes valores de tarifa como estratégia de transferência de demanda em sistemas de transporte público urbano. urbe. Revista Brasileira de Gestão Urbana, 11, e20180024.

[4] Bianchi, R.; Jara-Dı́az, S.R; Ortúzar, J de D. (1998) Modelling new pricing strategies for the Santiago Metro, Transport Policy, Volume 5, Issue 4, Pages 223-232, ISSN 0967-070X.

[5] Durand, A, L Harms and S Hoogendoorn-Lanser (2018) Mobility-as-a-service and changes in travel preferences and travel behaviour: a systematic literature revie., Amersfoort: sn.

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