The public transport service frequency is usually one of the worst-rated topics by passengers. According to a survey carried out by the OCU, major complaints users of Madrid underground have are directly related to low frequency:


– 66% of users rarely find somewhere to sit.
– 60% often feel overwhelmed because the wagon is full.


The most relevant thing is that these problems occur during peak hours but are even more remarkable in off-peak hours: 60% of the agglomerations registered occur during regular time periods.


Dissatisfaction appears to be reasonable: according to the company Moovit, Madrid users spend an average of 21 minutes waiting for the public transport, which accounts for 24.4% of time spent using this service. Meanwhile, in Barcelona the average is 18 minutes, which represents 28.6% of the total. These amounts are similar to European cities: in Rome represents 31.4%; Milan 25.7%; Paris 28.2%; London and 29.9%


However, the planning of public transport systems is essential for cities and urban decision makers. The problem is that the key for a successful service, which is the knowledge of user demand, is usually unknown. Traditionally, this knowledge has been collected through mobility surveys which provide super valuable indicators about passengers behavior: where do they get on, where do they get off, which transfers do they do, how often do they use the service, etc.


In order to make these surveys reliable enough and compare them among years, they normally are done during months in which there are no special days (christmas, easter, summer holidays or other festivities).


However, at the end of the year we see that actually there are only 2 or 3 months with no special days. So, if it is that hard to find normal days it may have no sense to even think about normal days.


And besides holidays or festivities, there are actually many other factors that can really affect passengers behavior and, therefore, surveys results: weather, technical incidents in other lines that bring me more passengers than what was planned, car crashes that collapse the road and other events: marathons, fairs,…


All this makes public transport being planned according to those supposed normal days, while actual demand varies much more. So public transport is often oversized with respect to real passengers with its corresponding extra costs, both in terms of economy and environment, and other times it is undersized causing queues, agglomerations and, basically, passengers anger.


If we could have information about passengers behavior along the whole year, we could come to understand how real demand fluctuates along the time and we could identify which external factors affect the most and to what extend. So we could identify patterns and predict behavior and, therefore, we could design a more efficient public transport system.


The good news is that this is now possible: today it is possible to provide massive, constantly updated and accurate data about passengers behavior. You will find here how people counting technology can help to design a more efficient public transport system.