In this article, we discuss what are the methods used to analyse mobile positioning data for transportation, traffic and spatial planning. Our team has summarised the research behind these methods, validation and success case studies of the use of MPD in this field.

What Is Mobile Positioning Data and Can It Be Used for Transportation Planning?

Mobile positioning data (MPD) is a powerful data source retrieved from mobile network operators (MNOs). Wherever phones connect to nearby cell towers, this data is stored within the premises of the MNO. In urban areas, cell towers tend to be within a few hundred meters from each other. The MNOs store data regarding which subscribers connect to which cell towers, along with precise time stamps. This means that each mobile phone record has coordinates attached to it.

This is useful for transportation planning as it allows processing and analysing how various subscribers move through space. Because nearly everyone uses mobile phones, it allows on a large scale to measure movement flows across neighbourhoods, cities and countries. MPD allows for hourly or even more frequent updates.

To learn more about Mobile Positioning Data, read our article 'Mobile Positioning Data FAQ: MPD Basics'

How Is MPD Used for Transportation Planning and What Are the Methods Used?

There are various methods that can be used for transportation planning with MPD. A common approach is to develop MPD-based origin-destination (OD) matrices. On the most basic level, trips can be categorised as stay or move sections. For example, if a subscriber appears to be located in one place for at least 15 minutes, this can constitute a stay section between trips.

Would you like to learn more? Sign up for our webinar 'Using big data in transportation planning'

Positium also has complex methods to determine home and work ‘anchor points’ for subscribers depending on metrics such as time spent in a certain location and the hours of the day typically spent there. A subscriber could be deemed to have a first and secondary home anchor, as well as up to two work anchors and secondary anchors. These methods have been found to have a high overall accuracy. OD-matrices are then constructed to show movements between home and work anchor points (commuting behaviour) as well as movements between other locations. Read about how Positium’s home detection algorithms have been validated here.

What Are the Benefits of Using MPD?

MPD is powerful in that it captures all mobilities in the district, city or other area of interest. This is in contrast to transit ticket data, which only captures public transportation users. If the goal is to improve the public transportation system, then it is also important to know movement flows that happen outside the transit system. Another often-used data source is road censors, which only capture movements on specific road segments and measure vehicles instead of people. Additionally, the cost and resources necessary to measure mobility with MPD are much less than with road censors. MPD can also be combined with these and other datasets to get a more complete overview of movement flows and types.

What Are the Limitations of MPD and How Can They Be Overcome?

Because the spatial accuracy is on the cell tower level, it is typically not possible to determine which specific roads subscribers move on. To counter this, Positium uses probabilistic spatial interpolation methods to increase the spatial accuracy. Positium uses reference data (roads, buildings, land use etc.) to develop probabilities that a subscriber is on a specific road segment within the coverage area. For example, highways will have higher probabilities attached to them than smaller side-roads.

Another limitation of MPD is that it cannot distinguish between travel modes. Studies have been conducted trying to distinguish travel modes based on speed, where slower moving subscribers are deemed pedestrians and faster ones cars or buses. This field however requires further research. For one, it requires signalling data instead of CDR (call detail records). In the case of signalling data, the data is continuously collected whenever the phone has cell coverage. CDR only collects data when phone calls are made, SMS messages are sent or phone internet data is used. As such, signalling data has a higher frequency of data points, allowing to better measure distances travelled in a given amount of time. Secondly, MPD does not distinguish between vehicles that have similar speeds, such as cars from buses. As such, the best way so far to distinguish between travel modes is to combine MPD with other data sources.

How to Deal With Privacy Issues?

Positium follows international guidelines and regulations (such as GDPR in the European Union) to ensure privacy protection to a high standard. Data regarding mobile phone numbers and names is never collected from MNOs. Instead, randomised subscriber IDs are used that cannot be traced back to specific accounts. Additionally, Positium is only interested in group movements, not individuals. Therefore, any movements that fall beneath a certain threshold (e.g. less than 10 subscribers) is removed from the data to ensure that individuals cannot be identified based on their movement patterns.

What Are Successful Examples of MPD in Transport Planning?

The city of Tartu, Estonia used MPD for redrawing their bus network. MPD was used to understand where people live and work, as well as commuting movements between these locations. This gave a better understanding of which locations in Tartu people are actually moving between. The bus network was reduced from 27 lines to 13, but the frequency was increased and the new lines reflected where people needed to move better. This resulted in higher ridership and satisfaction with the network. 

To learn more about this project, read the case study 'Case study: building a smart city with data-driven solutions, inclusivity and innovation in Tartu'.

Tallinn, Estonia used MPD to map out the full network of mobilities in the district of Northern Tallinn. This was done in order to better plan projects in a district that was going through rapid change. With MPD, they were able to map out home and work locations, commuting within the district as well as from and to other parts of Tallinn. The results were considered a huge success, with many benefits over traditional sources such as surveys. 

Read more about this project in the case study 'Mobile Positioning Data Helps City of Tallinn Understand the Origin and Destination of Movements'

Would you be interested in learning more about mobile big data and Positium? Let's talk about what mobile big data can do to help your organisation make decisions based on population mobility.

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