Helsinki shared mobility study – ITF


Executive summary

New types of ride-sharing services have been gaining ground in recent years, especially in urban areas. These services may be precursors to more optimised shared mobility solutions that could deliver better outcomes for citizens. This report examines how the optimised use of new on-demand shared transport modes can change the future of mobility in the Helsinki Metropolitan Area in Finland. To assess the impact of these new modes the entire mobility over the course of one working day was simulated for the Helsinki Metropolitan Area under different scenarios. These included the full replacement of all motorised road modes (car, taxi and buses) and partial adoption of the new shared services by targeting specific trips and users (e.g. only the 20% car trips more likely to shift to shared mobility are replaced). The current rail-based services (rail, metro and tram) were kept operating in all
scenarios while the new shared services can be employed to feed metro and rail.

The analysis comprised three main elements: First, modelling the current personal mobility and transport network of the Helsinki Metropolitan Area; second assessing the openness and preferences of potential users of the new modes via a focus group meeting; and third, a micro-simulation agent-based model where the introduction of the new shared options is simulated. The focus group meeting had two components, a discussion part and a stated preference survey. A total of 20 citizens from the Helsinki region with a balanced mix of socio-demographic and mobility profiles took part in that meeting. The agent-based model reproduced personal daily mobility patterns and the interactions between users and shared mobility. The outputs include a wide array of indicators measuring the new modes performance regarding service quality, productive efficiency and cost competitiveness, plus the impacts on accessibility, rail/metro ridership, required parking space, congestion and CO2 emissions for the Helsinki Metropolitan Area. The simulation assumes current demand patterns, i.e. that there is no induced demand due to any potential accessibility increases or reductions in travel costs.

The ultimate goal of this document is to provide an evidence base for decision makers to weight the opportunities and challenges created by these new services. The leading questions discussed in this study include:

  • Are the citizens of the Helsinki Metropolitan Area open to embracing shared mobility solutions?
  • What are the potential users’ preferences and who will be the first movers?
  • What aspects must be considered when designing policies to promote a shift towards the new modes?
  • To what extent are these new services complementary with existing public transport offer, namely rail and metro?
  • The agent-based micro simulation model was previously tested on a single region, to what degree is it transferable to other metropolitan areas?
  • What insights from the Helsinki Metropolitan Area case study can be generalised and are of potential use to other urban regions?

What we found 

In the simulation, the shared mobility solutions tested delivered significant positive impacts to the Helsinki Metropolitan Area. For the scenarios with lowest car replacement (20% of car trips replaced) the CO2 emissions reduction were in the same order of magnitude as those achievable with impactful measures such as congestion charges. Furthermore, it provides for increases in equitable access, service quality and would mean a relevant shift away from car travel. More ambitious scenarios achieved additional reductions of congestion and increases in freed public space currently needed for private car parking. With an electric fleet of shared vehicles, CO2 emissions would be further reduced.

The simulation results indicated that access to jobs and other services becomes more equitable with the new shared transportation options. Access improved particularly for areas further from the city centre which are less well-serviced by public transport. For trips within the city of Helsinki the public transport offer is already robust. There is also good service for some radial axes connecting the outer areas to the city centre. However, in zones with lower accessibility to the trunk routes of the public transport network, the car is the preferred mode of transport. The same is true for travel patterns that do not fit the radial-axis logic. Here, the simulated shared mobility solutions can provide increased flexibility and comfort that traditional public transport services alone have difficulty to achieve, thereby fostering a modal shift away from cars.

Importantly, shared mobility solutions can act as feeder services and improve access to metro and rail lines. This option combines the new shared modes’ flexibility with the high capacity of rail-based transport. Most focus group participants regarded this combined offer as highly relevant. The simulation results showed that significant increases in metro and rail ridership are possible, particularly in the more ambitious scenarios. In addition, replacing low-occupancy and low-frequency bus services has positive impacts on emissions reduction, which was not the case when other buses were replaced. The positive impacts of the new shared modes are maximised when they replace car travel and are implemented in articulation with public transport.

The focus groups showed that users prefer having new mobility services available throughout the metropolitan area, not just the city itself. They particularly welcome them in areas with low public transport performance. Users residing further from the centre are more likely to favour the new modes than those living close to the centre. The evidence suggests that public transport users are more willing than car users to adopt the new shared modes. The same was the case with users over the age of 55. Regardless of the socio-demographic characteristics, potential users showed a pragmatic approach to choosing a transport option, with a high sensitivity to price and service quality.

Generally, both the survey and focus group discussions indicated that citizens of the Helsinki Metropolitan Area have a distinctly positive attitude towards the proposed forms of shared mobility.
They expressed a clear wish to see such services added to the existing offer and an expectation that they can be a tool to improve mobility in the Helsinki Metropolitan Area.

What we recommend

  • Enable implementation of new shared mobility solutions in the Helsinki Metropolitan Area as an additional policy tool
  • Optimised shared mobility can provide significant benefits to the Helsinki region.
  • Replacing private car trips in areas currently not well served by public transport and using flexible, on-demand Taxi-Bus and Shared Taxi as feeder services for existing rail and metro lines would result in better and more equitable access to opportunities, improved service quality and a reduction of CO2 emissions.
  • Emissions could be further reduced if a shared mobility approach is complemented with support for the use of electric vehicles in the fleet.
  • In all cases, transition will require the alignment of other policy tools, such as pricing, regulation, land-use and infrastructure design. Implement new shared mobility solutions at a sufficient scale to boost attractiveness and lower costs 

The benefits of on-demand shared mobility services depend on creating the right market conditions and operational frameworks. Feedback from the focus group indicates that users in the Helsinki metro area are open to such solutions. However,

  • in order to be effective they need to be implemented on a large scale throughout the metropolitan area and not only in parts of it.
  • Sufficient scale is also important for achieving manageable costs.
  • Any business model for shared mobility should be carefully vetted in terms of its potential for innovation, keeping prices for users low and regulation ensuring societal benefits over all.
  • Design shared mobility solutions so they feed rail/metro lines and replace low-frequency, low-occupancy bus services  Existing public transport in the city of Helsinki and on certain axes provides a good level of service in international comparison. But new shared transport modes can complement the existing services and improve the offer for less frequent and low-occupancy bus routes. The study shows that users particularly value shared mobility as a first- or last-mile solution to access metro or rail trips. This provides an opportunity to increase the modal share of metro and rail while contributing to reducing congestion and emissions. 
  • Target shared mobility solutions for sub-urban car users currently not well served by public transport.  Shared mobility services have maximum positive impact when they are adopted by private car users. Policy measures, new services and information campaigns should thus target specifically the potential early adopters among this group: Car users who travel from the outskirts of the metropolitan area along routes not matched by the public transport network. These trips represent a large share of current car travel. If designed well, both price and quality of the shared modes can be attractive for these users. Focusing on this shift would leverage the most out of the added flexibility and comfort provided by shared mobility, which combined with public transport would deliver a transport system that as a whole is more sustainable.
  • Consider improvements in system capacity and access to rail and metro stations A wide-range deployment of shared mobility would significantly reduce the parking space required for private cars and road space taken by congestion. This space would become available for other uses.
  • On the other hand, large-scale use of Shared-Taxis and Taxi-Buses requires drop-off and pick-up zones, especially at rail stations, schools or large employers. The number of boardings in some stations would increase sharply (up to three-fold), requiring operational changes. Dynamic policies may be needed to manage more vehicles in the access links to terminals. The rail and metro network may need additional capacity to cope with higher number of users.


The pace of digitalisation in transport, especially in cities, has accelerated in recent years just as many new technologies have been introduced and as citizens have adopted new behaviours. The conflation of technology, both within and outside of the transport sector, with evolving societal trends and new relationships built around the production and consumption of services has been faster than anticipated by many authorities and has outpaced the speed of regulatory adjustments. These are real challenges for public authorities and it is likely that the kind of disruptions appearing now foreshadow even greater ones that may come about in the future.

The arrival of new types of shared mobility services has recently gained ground, especially in urban areas. These services may be precursors to more optimised shared mobility solutions that could deliver better outcomes for citizens.

Previous reports at the International Transport Forum at the OECD have looked at the potential impacts of new shared urban mobility solutions leveraged by digital connectivity in the city of Lisbon (ITF, 2015; 2016; 2017). The results of these simulations are extremely promising in terms of a strong reduction of the required vehicle fleet, parking space, emissions and congestion while improving equity of access. The ITF Shared Mobility Model simulates daily travel for a hypothetical shared mobility system. However, results from one city are never fully and directly transferable to another city and the ITF set out to test the transferability of the model to other cities around the world. The city and metropolitan area of Helsinki are the case study approached in this report.
Looking at the potential of new mobility services and technologies is precisely one of the key vectors of the on-going Helsinki Region Transport System Plan (HLJ). The plan is promoted by several
stakeholders invested in the further development of the Helsinki region – 14 Municipalities, Helsinki Regional Transport (HSL) and Government. This regional co-operation is carried out in order to strive for regional goals. In addition to providing flexible long-term strategic guidelines, concrete actionable measures for the near future are also expected outcomes from the planning process. Currently the biggest challenge is to decrease CO2 emissions by 50% by 2030. The HLJ planning process has been developing gradually and is currently done in conjunction with land-use and housing plans (MAL 2019).

Traditionally the transport plan has focused mostly on infrastructure projects. Although this is still of concern, the current approach aims for a broader set of tools introducing digitalisation, automation and regulatory changes to the plan. 

Indeed, the Finnish capital has already experimented with innovative services that share some similarities to the Taxi-Bus services described below, namely the on-demand buses known as Kutsuplus (HSL, 2016a). At the moment there are several experiments underway concerning new technologies, such as testing automated vehicles or the use of real time data for traffic monitoring. Steps are being taken to implement Mobility as a Service (MaaS), where the whole transport system is user- and service-oriented.

This report examines how the optimised use of new shared modes can change the future of mobility in the Helsinki Metropolitan Area (HMA). To assess this change the entire mobility of the HMA is simulated for one working day, including the current modes and different adoption rates of the new shared services. The simulation provides a detailed array of indicators that allow the measurement of:

Impacts on the city and the transportation system, such as decreases in CO2 emissions, required parking space, car use, congestion, changes to accessibility and the extent of modal shift.

New shared services performance, both from a user perspective (travel times, waiting times, access times, number of transfers) and operator or production side (number of vehicles, occupancy, depot location and sizes, costs).

The indicators produced for different scenarios, together with the built up knowledge obtained from previous reports, allow for an increased understanding of the policy implications that come from the emergence of these new services. The ultimate goal of this document is to provide governments and other public officials with meaningful advice regarding the challenges and opportunities brought by these new services. Some of the questions discussed are:

  • Are the people of HMA open to embracing shared mobility solutions in such a large scale? What are the potential users’ preferences and who will be the first movers? Which factors should be
    taken into account when managing the transition to these new services?
  • What should be considered when designing policies promoting the shift towards new modes? Which parameters can help balance the sometimes conflicting aims of quality of service,
    emission reductions, political feasibility and operational/cost performance?
  • To what extent are these services complementary to the existing offer of public transport, namely rail and metro? In the cases of increased ridership for the latter, will changes in the infrastructure, namely stations, be required?
  • The shared mobility model has been employed for a single test case, Lisbon. What new insights can the HMA case study bring towards the model transferability and generalisation of previous findings?

In order to get to the above outputs and insights a combination of qualitative and quantitative approaches were employed. The main steps of the analysis include modelling the current personal
mobility and transportation network, a focus group meeting with potential users and a micro-simulation agent-based model where the new modes were introduced.

To model the current personal mobility a “synthetic population” is generated which represents the entire population in the metropolitan area and their respective trips. For each person all the daily trips are recorded along with their characteristics like origin-destination, departure time, trip duration and length, mode taken, distances for each mode and if it is the case (for public transport trips) waiting, access and on board times, plus the number of transfers. This “synthetic population” is generated by an algorithm that combines information from the travel survey with the land uses, transportation network, set of all possible mode alternatives between each origin and destination, plus a discrete choice model developed for the current situation.

The focus group meeting has two components, a discussion part and a stated preference survey. This allows exploring the HMA transport users’ preferences regarding the proposed shared modes and comparing them to the existing urban and metropolitan transport options. It includes identifying and quantifying the new modes most relevant attributes that together with the users’ socio-demographic characteristics influence their mode choice. This provides a profile for users that are potential early adopters of the new services. It also assists in the design of the shared modes so that they are better tailored to the potential users’ needs, thus facilitating the desired modal shift.

The micro-simulation model reproduces the daily mobility patterns and the interactions between users and shared mobility modes in a transport network for a metropolitan context. The agent-based simulation enables a dynamic optimisation that matches supply and demand, minimising detour distances and travel times. The simulation provides a wide array of indicators measuring the new modes performance regarding quality of service, productive efficiency and cost competitiveness, plus the impacts on accessibility, current public transportation, required parking space, congestion and emissions for the Helsinki Metropolitan Area. The results are achieved assuming current demand patterns, i.e. that there is no induced demand due to potential accessibility increases and travel cost reduction.

The model allows testing different transport scenarios for the same time-space mobility patterns, while preserving the citizens’ current behavioural preferences. The scenarios tested include full
replacement of road based motorised modes and partial adoption of the new shared services. In the former the currently existing motorised transport alternatives (car, taxi, and bus) are completely substituted by the shared modes, either from start to finish or as feeder services to heavy modes (rail, metro). For the partial-adoption scenarios only certain motorised modes trips are substituted, depending on utility value associated with car trip for a given user, trip location, or bus trip/services characteristics. In all cases the rail-based modes (rail, metro and tram) are kept. The partial-adoption scenarios are particularly relevant for investigating the impact of a gradual deployment of the services and obtaining insights concerning different transition strategies and their feasibility.

The new shared mobility services considered are: Shared Taxi and Taxi-Bus (see Table 1).

Table 1. Shared mobility services

Mode Booking Access Vehicle type

Shared Taxi Real time Door-to-door Minivan currently seating 8
rearranged to seat only 6,
providing easy entry and exit

Taxi-Bus 30 minutes in advance
Boarding and alighting up to 400 m away from door
Minibuses with 8 or 16 seats.
No standing places

These services mostly provide full start-to-finish trips that replace current motorised road transport alternatives (car, taxi and bus), but they are also employed as feeders to heavy transport modes (metro, rail). Both the Shared Taxi and Taxi-Bus services are on-demand and dynamically dispatched. Shared Taxi operates a door-to-door service in spacious vehicles for up to six people. They move along real-time optimised trajectories with small detours for boarding and alighting passengers.

Taxi-Bus is a street corner-to-street corner service that requires a 30-minute advanced reservation, providing transfer-less trips in a minibus of 8-16 people along dynamically defined routes. The corners where the Taxi-Bus can stop belong to a predefined set of locations across the region (see Annex 1). They are, as much as possible, near existing bus stops. Hence, when the user reserves a service and receives a notification it comes with information on which Taxi-Bus stop to go to and that stop – which is near a street corner and existing bus stop – is at a given physical location that is identified.

The two services specifications are designed to minimise public transport’s negative features compared to private car travel. The aim is to have services that offer users levels of flexibility, comfort
and availability closer to car travel than the current public transport offer. The report is structured as follows. First the simulation modelling framework is introduced. Then the case study is broadly described. Next it is explained how current travel is modelled. This is followed by the presentation of the study performed in order to better understand the potential users’ preferences.
Afterward the scenarios tested in the simulation are explained. The simulation results and respective findings are discussed, first considering their overall impacts in the Helsinki Metropolitan Area and then the performance of the shared mobility services. The report ends with a summary of the key insights and further topics of interest that are not explicitly addressed by the model, as well as other effects that go beyond the transportation field.


This report examined how the optimised use of new shared modes can change the future of mobility in the Helsinki Metropolitan Area. To assess this change the entire mobility of the HMA was simulated for one working day. The agent-based model employed allowed testing different transport scenarios for current demand patterns. The scenarios explored include full replacement of road motorised modes (car, taxi and buses) and partial adoption of the new shared services by targeting specific trips and users. In all cases the rail-based modes (rail, metro and tram) are kept and the new shared modes can be employed to feed metro and rail.

The ultimate goal of this document is to provide governments and other public officials with meaningful advice regarding the challenges and opportunities brought by these new services. The key
findings and further research needs are discussed below.

Key findings

The shared mobility solutions tested deliver significant positive impacts to the Helsinki Metropolitan Area. For the lowest car replacement scenarios – e.g. with 20% of car trips replaced – the CO2 emissions reduction are in line with what can be expected from introducing influential measures such as congestion charges. Furthermore, it provides for increases in accessibility and quality of service, and it would signify a relevant modal shift away from car. For more ambitious scenarios there can be additional gains in decreased congestion and the ,release of public space currently used for private car parking. With an electric fleet, CO2 emissions could be further reduced.

The new shared services should be implemented at a sufficient scale in order to deliver relevant benefits to the city and be provided at manageable costs. Previous studies show that for low uptakes there are no significant impacts to the city and there might even be increases in congestion. For the scenarios tested the price at which the new services can be offered is competitive for their respective segments, but under the condition that there is a relevant adoption of shared mobility – e.g. 20% of car trips shift to the new modes. In addition, fleets of this dimension provide economies of scale that might be used as entry points for emerging technologies such as electric powered vehicles or driverless cars. The HMA has a robust public transport offer which is why – although relevant – the shared mobility impacts in this case are lower than for previous case studies. This is particularly true for the Helsinki city centre. There is also a good service in the radial axis connecting the outer areas to the core. However, in zones with less accessibility to the trunk PT network, or for travel patterns that do not fit the radial-axis logic, car is the preferred mode. The increased flexibility and comfort provided by the shared mobility solutions are particularly suited to attract this type of trips. Additionally, the increased level of service can also attract car users in areas where the PT offer is good but some of its features are still a barrier – e.g. lack of comfort/seated place or fixed timetables. Moreover, improved access to the heavy modes network can be provided through feeder services; this option combines the flexibility of the new modes with the high capacity of rail-based modes. Most survey respondents regarded this combined offer as highly relevant and welcomed it in the focus group discussion. The simulation results suggest that significant increases in metro and rail ridership are possible, particularly in more ambitious scenarios.

Transport users in the HMA have a very positive attitude towards shared mobility. They are rather familiar with digital age technologies and the already existing transportation services based on mobile apps. Hence, potential users’ perceptions and knowledge of the tools required to use shared mobility do not constitute barriers to its implementation. If anything, there is a clear wish to see these new services added to the existing offer and an expectation that they can be a tool to improve mobility in the metropolitan area. Indeed, users mentioned that the attractiveness of these services would be very much related to how much they are present throughout the entire HMA. For instance, in the focus group almost a third of car owners (27%) were willing to sell their cars but only if shared mobility is provided in the entire HMA. Notwithstanding the users’ demographic characteristics that favour adoption of the new modes – living far from the centre, being a PT user and a senior – quality of service and price play a decisive role in mode choice.

The positive impacts of shared mobility services on the HMA are maximised by targeting private car users who are currently not well covered by public transport. Policy measures, new services and information campaigns should target these potential early adopters, i.e. who live far from the city centre and are carrying out trips from the outskirts of the metropolitan area with trip patterns not aligned with existing public transport offer. These are the car users more likely to be first attracted to the new modes and their trips represent a large share of current car passenger-km. In addition, replacing low occupancy and frequency bus services delivers positive impacts emissions wise. But the simulation of several scenarios indicates this does not happen when other bus types are replaced. Special care is needed when designing the services to target these users and trips. Evidence from the focus group suggests that PT users are more prone to adopt the new modes. Price wise the new modes are more competitive versus private car for shorter trips. Regulation and some sort of guidance is likely to be necessary in order to ensure that most trips replaced are car-based and that high prices in areas further from the centre are not a barrier to modal shift from car

A wide-range deployment of shared mobility services would result in a significant reduction in required parking places. Together with congestion relief this would free space for other uses. However, new mobility services will need to be accompanied by improvements in drop-off and pick-up zones especially at rail or metro stations and at final destinations with a concentration of opportunities (such as major employers or schools). Our results show a sharp increase in the number of boarding’s in some stations and this implies operational changes in order to cope with increased demand.

Additional system capacity may also be required in heavy modes (particularly rail) in order to maintain current service levels due to increased ridership. Electric vehicles can be used to provide the new shared services. This would entail an increase of approximately 10% to the fleet size, along with the required charging stations at depots. In this case reductions of CO2 emissions of close to 20% can be obtained in a scenario where 20% of car trips and all bus feeder trips are replaced. This increases to a 25% reduction when there is no car travel inside the city centre and 97% reductions are achieved with the full replacement of current car and bus trips.

Having the Helsinki centre (inside ring road I) free of car travel delivers significant benefits to the HMA and efficient operations at a good service level. Although in this case the shared mobility offer is concentrated in the city centre, around 54% of all car trips in the HMA are impacted. This includes commuting trips that occur between the outer regions and areas inside the ring road I which represent 36% of the total car trips in HMA. Nonetheless, it implies a sharp increase in P&R capacity whose feasibility was not studied. Special attention should be given to the design of these facilities and respective accesses which might otherwise become bottlenecks.

There is a degree of uncertainty associated with any modelling exercise. In this case the main sources of uncertainty and error are:

  • A total of 20 people took part in the focus group. They represented a balanced mix of socio-demographic and mobility profiles representative of the overall HMA population. Nonetheless, this is a small sample from which to calibrate a discrete choice model. A larger sample would be necessary to quantify with more confidence the impact on modal choice of factors like price or place of residence.
  • The road network employed in the simulation was sparse in some of the more remote areas of the HMA. These are areas with low trip density and this issue will not have a relevant impact on the aggregate values. But for a more spatially disaggregated analysis this can create a bias in the results leading to increased VMT/vkm and travel times for the new services compared to what would happen had a network more detailed in those areas been employed in the simulation.
  • It is assumed that the metro/rail stations and other high trip density destinations are ready to cope with the increase in number of vehicles performing pick-up and drop-off maneuvers. It is also assumed that the heavy modes are able absorb the increased ridership and in scenario 4 that park and ride facilities can be made with sufficient parking. The congestion results presented are sensible to the number and type of vehicles on the links. They indeed signal increases in traffic in some of the accesses to these potential bottlenecks. But they do not take into account potential additional turbulences caused by vehicles stopping in circulation lanes to pick-up/drop-off passengers or unable to find parking. If these assumptions are not met there will also be a drop in service levels.

Beyond these issues there are topics of relevance to the implementation of shared mobility not addressed in the simulation. The introduction of shared mobility at the scale studied in this report implies changes to travel behaviour and the overall transportation system that are hard to grasp by any single model or study. In addition, there are impacts to other areas beyond transportation. For instance, the modelling framework assumes static demand patterns. The simulation employed provides a very detailed analysis of several scenarios, but it does not take into account changes induced to travel behaviour by a wide adoption of these services. Beyond transportation this can affect land use and value. The increases in accessibility for currently more remote areas can increase their commercial attractiveness and even foster urban sprawling to a certain degree.

Another example is the modelling of soft modes which have a considerable share of the HMA modal distribution (measured in trips, not pkm). Walking and biking were considered in both discrete choice models developed and some insights were obtained. The bicycle utility function coefficients indicate that the absence of dedicated cycling lanes hinders the adoption of this alternative. The space freed by the release of on-street parking and decreased congestion could be employed to this end.  Nonetheless, in the simulation there is no dynamic analysis to the effects shared mobility might have on the soft modes.

Other policy issues raised in connection with the studied changes to mobility include the modifications introduced to parking and road use. Significant decreases in parking spots open these spaces to new uses, but it will also imply a loss of revenues. While parking requirements will drop, curb use and spots for short term pick-up and drop-off will be in higher demand. This will require changes to the infrastructure but also to parking policies or more broadly to public surface use. A more dynamic and flexible mobility offer will have to be accompanied by equally flexible public space policies. Sharp decreases in congestion might also open road lanes to different use, from the already mentioned bicycle paths to lanes dedicated to autonomous vehicles.

Finally there are a host of issues related with the organising and business models of the new services. The required level of funding and unprecedented scale of deployment of these services points to a collaborative effort that can involve other PT operators, ride services and taxis, vehicle manufactures and other institutions. Apart from that, several concerns need to be balanced, for instance: service costs and price for the end user, innovation in service provision and regulation required to avoid unintended societal consequences (e.g. increases in congestion). All of these can have far reaching consequences in questions such as the number and quality of employment provided. In the end a complete toolbox including economic, infrastructure, regulatory and procurement tools is required to manage the transition to digital age mobility services.