The Tampere Tram service started as planned on Monday 9 August 2021. We analysed the success of the first day of tramway traffic based on open data published by Nysse (Tampere Regional Transport). It appears that in addition to the many other benefits, the tramway is both fast and punctual!
Fast and on time
The design principle for the tramway from the start has been that the tram will not stop in a traffic jam during rush hours. For most of the way, the tram moves along its own routes, and it is given right of way at junctions wherever possible.
The design principle is reflected clearly in the punctuality of traffic: the tram is considerably better at keeping to the schedule than buses. Nearly 60% of trams arrived at stops fully on schedule (less than one minute late), approximately 80% no more than two minutes late, and nearly 90% no more than three minutes late. For buses, the corresponding figures are as follows: 50% on schedule, 60% no more than two minutes late and 70% no more than three minutes late.
However, perhaps the most important observation was that the tramway hardly suffers from long delays at all: only in 4% of cases, the tram arrived at a stop more than five minutes late, and never more than ten minutes late. At the same time, more than 20% of buses were more than five minutes late, and there were even several delays of more than 15 minutes.
Figure: Classification of the punctuality of trams (red bars) and buses (blue bars) at stops into different categories of delay. Arrivals on schedule or only a little late are shown on the left, and later arrivals are shown on the right. The measure used is the percentage of arrivals at stops.
Tampere Tram is also very fast: its average speed is 19–22 km/h, while the speed of the tramway in Helsinki is only 14 km/h on average and as low as 12 km/h on some lines. In practice, the difference in speed is even greater than the figures indicate. To put it bluntly, the tramway in Helsinki can be compared to a bus or even walking, while Tampere Tram offers a service level comparable to a local train.
Smooth traffic even during rush hours
The greatest challenge for public transport – and for any other traffic system – is rush hours. A passenger on the way to work or school does not appreciate the average punctuality of traffic much if their own rush-hour bus is always a quarter of an hour late. It seems the tramway can tackle this challenge as well: because the tram does not suffer from the other traffic, it runs punctually even during the morning and afternoon rush hours.
This is also clearly visible in the data for Monday: during the afternoon rush (from 4 to 5 p.m.), the share of buses more than ten minutes late increases to nearly 20%. At the same time, there were no delays for the tram of more than ten minutes at all during rush hour, and the share of delays of 5–10 minutes only increased to approximately 5%.
Figure: Classification of the punctuality of trams (on the left) and buses (on the right) at stops in the middle of the day (from noon to 1 p.m.) and during rush hour (from 4 to 5 p.m.). The measure (colour) indicates the percentage of different categories of delay among arrivals at stops.
Fast connection from Hervanta to the city centre
What does all this mean to a regular passenger? Nysse does not yet publish numbers of passengers in real time. However, it can be assumed that one of the busiest passenger flows is from Hervanta to Hämeenkatu in the city centre. For people travelling along this route, the tramway is a godsend: when buses were still the only option, the trip took a painfully long time, especially during rush hours – well over half an hour. The same journey can now be taken much faster by tram – based on the data for Monday, in 19.5 minutes (median). This is a very competitive speed, even against a car!
Some development needs found
From the punctuality perspective, the tramway seems to deliver on the promises. However, some items for development could be found based on the first day of traffic. Most stop-specific delays occurred on Line 3 from Hervanta towards Pyynikintori. The median delay on the line remains about one minute at most up to the start of Sammonkatu. On Sammonkatu, the median delay begins to slightly increase, and at the end of Itsenäisyydenkatu and on Hämeenkatu, the trams have been running approximately two minutes later than scheduled. These issues can certainly be improved by studying the data from different perspectives.
Figure: Line 3 from Hervanta in the Pyynikintori direction: (difference from the schedule). Blue indicates delays of less than 1.5 minutes; orange indicates delays longer than this.
Making the best possible decisions based on data
Analysis of the tramway’s first day of service already revealed a lot: overall, it seems that this form of traffic, new to people living in Tampere, is meeting the expectations in terms of speed and punctuality. On the other hand, some room for improvement can also be found.
In future, it would be very interesting to study the smoothness of traffic over a longer period, e.g. for different lines, stops, times and individual fleet unit. It would be particularly interesting to combine the punctuality data with passenger volumes and the start and end points of trips. Indeed, this will be possible in the future, as the trams have the option of passenger counting based on advanced pattern recognition.
For the overall traffic system, it would also be important to study how the start of tramway traffic was reflected in the punctuality of buses – after all, the whole public transport network was redesigned with the introduction of the tramway. It should also be noted that with the tramway, a considerable number of trips with public transport will involve a transfer connection to a bus at the start or end of the trip, or both. So from the passengers’ point of view, it is important to evaluate the smoothness of the whole transport chain, not just the tramway.
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Solita creates data solutions for the future, boldly combining expertise of different types, from business skills to the use of open-source code and container-based technologies. For this blog post, we built a real-time solution for refining data. Scalable edge and cloud computing on the AWS cloud platform offered the possibility to quickly create a fully automated solution without any servers. The tramway traffic data was obtained as open data provided by the City of Tampere. Providing open data is an important part of cities’ services and the transparency of their activities. It is great to see that Tampere is also spearheading this development!
The solution for analysing tramway traffic data was implemented at Solita’s Data Academy, where we train the star data analysts of the future. Read more here and see all our open positions.