Data in mobility policy and Innovation

Mobility data can come from conventional counters or surveys[1]. Big data, however, brings with it many new sources which offer exploitable location and travel data. These include smartphones, social media[2], TomTom, Vaze, Google, or other geolocation-based services. Other new sources include the public or private transport providers responsible for car-sharing stations, e-car charging points, bike-sharing stations, or car-pooling services. Data on activities such as cycling and walking are easier to collect than in the past, etc.

All of this new data can be sorted into categories, including, at least:

• Cartographic, weather data;
• Personal location data;
• Data on: transport schedules, fares and prices, network disruptions, planned events, real-time network capacity for people, vehicles & goods;
• Vehicle location data;
• Performance evaluation data from service users and non-users; third party service usage data; and payment/transaction data[3];
• Information about disabled access, lifts, etc. for improved inclusivity[4].

Data is used to inform (short and long-term) infrastructure/service planning and day-to-day traffic management by identifying patterns of mobility demand and by helping planners to understand a transport network’s traffic flows and status. Data analytics can reveal unsuspected links and connections in mobility patterns, it can match weather patterns with traffic fluctuations, and can predict the traffic flows likely to be generated by changes in the weather or by large-scale events, such as a football game or a concert. Data analytics can give insights into accidents or predict how voyagers might be affected by them. It can also predict air quality, etc.

Data is the essential ingredient in MaaS apps aimed at citizens although technological developments are also key to enabling these information services. In this regard, API[1] technology is making it easier to merge information from different mobility services and modes of transport and other relevant sources (weather, travel alerts, etc.).

The ‘true potential’ of data to support mobility is still ‘largely unknown’[2]. Nonetheless, the role of data in the emerging transport landscape is being hailed as a ‘revolution’[3]. ICT enabling technologies are in general enabling unprecedented B2B, B2C, and C2C services. Industry experts believe that, in the field of mobility, open data (OD) platforms will be indispensable to intelligent multimodal transport and in particular MaaS[4], placing importance - for cities - on establishing mechanisms for data sharing and ensuring their mastery of ICT enabling technologies, for example.

Data platforms (aka portals) can support applications which can then easily make data available for specific uses, thus providing value to the city and voyagers[5]. Apps can include dynamic journey planning (optimum or ‘most enjoyable’ route generation) for example and can reduce travel times for voyagers. As part of an integrated approach, it is believed that data has the potential to disrupt the car-dominated transport model and to help reinvent transport services. For many industry observers, data is set to become ‘nervous system’ of future sustainable urban mobility systems[6], enabling cities, among others, to:

• Roll out Mobility as a Service (MaaS) with single ticket journeys across multiple modes;
• Contribute to city planning (land use), and generally better inform city development policies;
• Optimise real-time traffic and parking management;
• Optimise the regulation, operation, and control of smart city infrastructure (multimodal platforms, public transport, including avoiding congestion caused by road works, etc.);
• Drive a modal shift in commuting/traveling towards lower-impact vehicles/alternative soft modes, thereby reducing pollution, pollution peaks, and increasing road safety;
• Help cities to deploy new access rules for private and public transportation and identify new streams of revenue;
• Enable the future integration of connected self-driving vehicles in multi-modal networks;
• Help to combat the spread of diseases (pandemic response);
• Service evaluation. As travel experience is becoming a decisive factor in citizens’ decisions to opt for one transport mode over another, it is important that cities gather user satisfaction data. Key Performance Indicators (KPIs) can support a people-centred approach to mobility. For example, the SMP 2.0 Sustainable Mobility Indicator set, developed by the WBCSD, includes soft indicators on journey experience, such as on ‘comfort and pleasure’. In another example, the EU the Urban Agenda (2019) Partnership on Urban Mobility – Action 3 – highlights the need for a new ‘simple walking indicator’ for improving urban accessibility. Rather than simply focus on journey time or walking distance to the nearest bus stop, consumer satisfaction should also be measured. As the collection of qualitative data is easy with sensors and smart phone applications, KPI indicators can generate feed-back on travel experience. Such feedback also helps service providers to understand the reasons why customers may be unhappy with a service, enabling them to address issues and improve their services.

In many EU cities, the two types of data gathering methodologies, conventional and ‘new data’, are used in parallel. While the future of large-scale representative surveys and travel counts is uncertain, the two methodologies can be complementary. In many cases, the digitally sourced data can fill in the time gaps for conventional survey methods or provide more frequent feedback, while traditional counting methods can verify and refine data collected through sensors by providing the demographic background behind this data (automatically generated data sets may miss invisible users). One of the biggest challenges facing cities is how and to what extent to switch from conventional methods to work with digitally created data (real time/ big data, sensor based). Is it possible to fully rely on new data analytics methods or will it only provide information on specific segments of travel behaviour?

Data governance, i.e., designing surveys and selecting the right mix of survey methods and data acquisition for a project (what data, what for?) is part of a wider mobility planning process, which implies that cities must possess the relevant expertise and planning capability. Veeneman et al (2018)[7] call this a ‘governance challenge’. Cities’ ability to work with digital data varies and to master big data many will need to build capacity and develop data governance strategies, with cross-departmental working at a minimum. Air quality (CO2), travel times, user satisfaction, road safety, congestion, land use, etc., are all expressed through data.

Working with big data (new sources and types of data) may imply infrastructure/equipment costs, including investment in sensors and/or software, as well as an ability to enter into agreements with data owners. All of this may require the creation of in-house teams with the relevant technical skills, as well as the presence of and ability to comply with legal frameworks governing data gathering, storage, and use, etc.

For cities such as Lahti and Toulouse, both implementing UIA mobility projects, a reliable data management system is an essential first step. This implies:

• establishing data gathering systems (conventional, in-house, bought in, big data, etc.);
• coordinating the different streams of information;
• building up models from the analysis of very large amounts of complex and geographically distributed sources of data (citizens, traffic, vehicles, city infrastructures, IoT devices, etc.) collected in heterogeneous formats (need for metadata, linked to the city’s goals). Then, visualising the information in a format which enhances understanding and allows better decision-making, including enabling stakeholders to (1) raise useful questions (exploiting the synergies of cross-departmental and external collaboration) and (2) build a shared vision of mobility needs and solutions.

Many transport organisations are now routinely providing data that enables Mobility-as-a-Service applications to provide real-time information to customers. The data can also be used to create centralised data platforms which can then support communication between operators and voyagers via apps. There is a no definitive checklist on the uses of data, and, with reference to the content of projects implemented by the five UIA cities, specific uses tend to be linked to specific local objectives.

Table 1 (below) presents insights from the 5 UIA cities on the emerging use of new data techniques (greatest challenges/opportunities/data sets used/data control/in-house processing/processing needs).

A survey questionnaire asked the five IA cities to provide feedback on the benefits and difficulties of using data and the types of data they are using in their projects/city mobility planning. The following paragraphs summarise, for each city, the key points in their feedback.

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In addition to biannual traffic and speed surveys, Albertslund is seeking to integrate data from new sources, including automatic sources, such as sensors in traffic lights and is also testing drones to obtain data. This data is helping the city with its different mobility goals including reducing congestion, improving maintenance and road safety, and reducing pollution and CO2 emissions. Albertslund also conducts surveys to obtain data from citizens, especially from schoolchildren and cyclists and the LINC project uses a co-creation approach which will exploit user feedback during trials to ensure it meets their needs (co-creation). The local DOLL[1] initiative should also enhance data gathering capabilities there are (ongoing tests for dynamic traffic detection, intelligent parking, and connected autonomous vehicles).

Albertslund established a data governance structure across all its city departments, including mobility, in order to ensure full (public) control of how data is monitored and stored. The city is currently working on its data management and reports challenges of a technical nature relating to the switch from using conventional data to using big data. The city reports that it needs more skills in data management, where there is current a capacity gap. However, it does make its data available to third-party service providers.

In addition to this, and in the context of establishing PPP arrangements involving private companies, Albertslund suggests that companies could also bring in not only data, but also alternative sources of funding through their own investments and that, in their view, there is a need for good practice models in this area. Albertslund suggested the example of investment in e-charging stations, for which it is currently envisaging a PPP collaborative format.

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Ghent collects conventional data through a triennial survey for modal split data and uses ad hoc surveys. It also gathers multiple mode and real-time data, including buying in data from private suppliers, which is exemplified in Ghent’s UIA project. The Ghent TMaaS project is centred on the use of data and data management/use technologies to enhance traffic management through the creation of an affordable ‘traffic centre’ data platform (TMaaS stands for Traffic Management as a Service). The platform automatically monitors traffic and mobility for the city of Ghent, providing traffic operators and end-users insight in mobility and notifying them with useful transit related data. It can incorporate different data sources to achieve this. From a data point of view it is not just the incorporation of data sets which characterises the project, but the essential wide partnership needed to deliver the platform, which has required Ghent to prepare agreements with companies, which cover data use.

Ghent has also begun to use feedback from citizens. As in Lahti, this has enabled Ghent to better understand usage and make improvements. As in Lahti the feedback comes from cyclists for the purpose of improving routes. In Ghent, the feedback informs route planners, which go one better than standard planners in that they identify the qualitatively ‘better’ routes rather than the shortest identified by private services. The approach takes a more psychologically sophisticated approach to understanding choices between modes and to changing behaviours.

Ghent reports several types of data management challenge. These include: multiple data sources, large volumes of data, a lack of meta data (i.e., consistent data categories), leading to data storage problems (data lake), and data security issues. In terms of delivering transport policy, Ghent uses a mobility company, although it also indicates there remain cross-departmental barriers hindering data sharing.

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Lahti’s CitiCAP project has different strands to its sustainable mobility goals, with mobility data at the heart of design and delivery. The general purpose of the data is to inform and incentivise the use of green mobility and reduce the use of private cars. CitiCAP is part of the city’s ambitious goal that by 2030 more than 50% of all trips will be made using sustainable modes of transport.

Lahti’s PCT Scheme uses data on CO2 emissions added to financial incentives to encourage citizens to switch to green modes. Moreover, usage/demand data is gathered and is indeed generated, for example on spatial aspects of green mobility, with this data – knowledge – then used to inform the city’s SUMP process. SUMP development is a participative approach, which encourages stakeholder buy-in and enhances the prospects of success by tailoring solutions to needs. Lahti’s PCT scheme uses this approach ‒ so data gathering, data generation, and communication informed by relevant data, is at the heart of this approach (again, as with Albertslund LINC project we can see how a data flow is central to co-creation).

In terms of data gathering, Lahti’s use of new data techniques can be seen against the background of a conventional approach based on 6 yearly surveys. Like the other UIA city projects, Lahti reports that the switch from conventional to new data brings with it a series of data management challenges. These include the sheer volume of different sources and types of data available, which is technically difficult to exploit, although new data sources can be less expensive and provide higher frequency insights than conventional methods. The new data is also indispensable to developing green policy alternatives because it reveals change in use and can reveal user preferences (are buses comfortable, for example), and therefore provides useful data about demand dynamics. Another generic issue reported by Lahti, and which is also present in several of the UIA projects (Ghent, Toulouse), is that data management, from collection to visualisation, requires specific technical skill-sets.

The extensive incorporation of data in mobility solutions is a case in point. Developing online applications (Journey planners, PCT, and so on) implies integrating associated ICT technologies such as API, which enables devices to communicate. Journey planners inform citizens about the multimodal services available to them, while PCT informs and incentivises a modal shift. Both of these strategies require data and sophisticated technologies to communicate that data.

To help in the development of mobility apps, Lahti is moving towards an open data policy and is seeking to integrate new sources of data (such as real-time data from traffic light sensors or from buses), all of which implies mastering API technology. The city is requiring service providers to share their data too, as a condition for market access (e.g., e-scooters). And the city makes its data available to service providers, including Google’s Transit Trip Planner, a national app, + others (e.g., Moovit, Nysse, TripGo).

In addition, in some cases, such as the PCT Scheme, which collects data from a range of transport modes, there can be specific management challenges, such as GDPR constraints, which imply a need for legal expertise.

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To know more about this project, click here!

Szeged’s conventional mobility data collection includes a 3-year modal split survey, with a focus on public transport. Szeged has attempted to control high-levels of private car use, and the pollution/CO2 this generates, by investing in its public transport and cycling infrastructures to give citizens access to attractive alternative sustainable mobility options. To convince citizens to board a tram or a bus or to use a bicycle, the city has developed a set of outreach approaches, targeting employers in particular, as they have the power to influence commuting dynamics. The city believes that, along with improved services, if stakeholders and users better understand mobility options, then they can reduce the entrenched reliance on private cars. Until SASMob, efforts to increase cycling had had some success, albeit at the expense of public transport.

The data-based approach under SASMob involves mobility tracking, using smartphones, to better understand mobility needs, which then can inform bespoke solutions. This tracking has been possible thanks to the participation of the Szeged Technical University, which has patented a novel workaround to the issue of data privacy (GDPR), essentially anonymising the data collected. This is important because understanding flows and therefore the needs of specific populations is fundamental to the strategy of targeting these population with behavioural change schemes. The approach is epitomised by the SASMob Pledge, which identifies sustainable mobility solutions for employees, with employer support, to reduce commuting. The approach is interesting because it does not see users as a homogenous group, but as distinctive groups with distinctive needs, which can be targeted.

Szeged is seeking to integrate new forms of data and types of data to enhance its mobility policies. This includes gathering data on public transport use ‒ with a view to optimising service provision and quality (comfort), including asking citizens to report any vandalism. It also includes real-time tracking to analyse, for example, congestion caused by refuse vehicles. The data issues Szeged sees as challenging include: storage, due to the diverse nature of the types of data implied by the different aspect of mobility; and integrating data from the private sector. Szeged is also sharing its mobility data with private companies.

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COMMUTE’s goal is to alleviate heavy congestion leading to and from its airport’s business and industrial areas.

The City chose a collaborative governance model for the project, involving public and private mobility stakeholders.

This approach made it possible to share data between public and private stakeholders, enabling the partnership to carry out joint assessments, design strategies, deploy action plans, evaluate their impact and amend their strategies and action plans. The city reports that, although such collaboration is not easy, it is a necessary platform for the emergence of a shared vision.

As for several of the above city experiences with data, the city of Toulouse points to the need for a reliable data management system (in COMMUTE this takes the form of a digital platform) to support policy development and also the importance of anticipating the technical challenge of progressing from the mere gathering of heterogenous data sets to visualising data in a way that is useful for strategy development.

The COMMUTE strategy involves behaviour change more than building hard infrastructures (with the exception of autonomous shuttles). The project’s outreach component relies heavily on mobility data including static and real time data managed through the digital platform (journeys, flows, journey times, including third parties such as VéloToulouse etc.) as a basis for informing targeted schemes on car sharing, ride sharing, co-working spaces, teleworking, flexible timetables, carpooling, autonomous shuttles, etc.).

One of the core uses for data was in the development of KPIs, which required close collaboration with company human resources departments, which could provide the mobility data needed. This link between partnership and access to data is important, and COMMUTE highlights the importance of collaborative working to accessing the ‘data behind the data’. Toulouse also collects mobility data from citizens through various schemes (e.g., Je Participe, & VILAGIL integrated mobility project), while respecting GDPR rules. Toulouse also shares its data extensively with Google, Tom Tom, Waze, etc.

Toulouse, like several other UIA cities sees data management as a new profession and API a technical challenge. Toulouse recruited engineers specialised in the processing of big data and believes that the personnel responsible for analysing data must come from the transport professions as they must understand mobility issues to produce robust analyses.

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