Real-time planning with Big Data

Expert's column: Creating the perfect framework for seamless mobility is a challenge at city, regional and national levels. How to make the right traffic plan? Big Data can be part of the answer.

By Raul Vibo, Design Manager and Leading Consultant, July 2015.

Creating the perfect framework for seamless mobility is a puzzle. For years urban planning has been a matter of predicting people’s behaviour and adjusting transport systems accordingly. In a city like Abu Dhabi – where I am stationed – however, nobody knows the exact number of inhabitants. So how to make the right traffic plan? Big Data is part of the answer. But only part.

Statistics have to be used intelligently, but the framework of real-time data is setting the stage for an all new way of planning. Big Data can measure people’s real-time behaviour and identify gaps in the existing connections. Traffic planning used to be based on past-time analysis and to some extent assumptions about expected behaviour. It also takes time to conduct a qualitative study of 10,000 people’s transport behaviour.

The new zoning models

Transport modelling with Big Data is based on zoning at different levels, e.g., city, metropolitan area, region, country. An origin-destination model (OD model) can be created accordingly. Conventional transport modelling provides very limited travel data, especially about origins and destinations. Big Data derived from mobile phones provides telecommunication companies with enormous amounts of data.

Location data are associated with all database entries, which for these purposes are, of course, anonymised. As mobility is classified by type, an anchor point model must be established to plot the regular overnight position or positions of every phone.

A second time model provides a basis for periodic statistics and analysis. Finally, the two models are combined and all movements aggregated by movement character (transport mode) and time to the generalised mobility database. This produces the global origin-destination matrices.

These matrices are then validated with different statistical data as well as with manual and automatic traffic counts of roads/streets and public transport passenger counts. Validation helps to improve the models and assess their accuracy.

OD matrices can be used for transport modelling and in any analysis or planning. Highway traffic volumes can be predicted, as can the potential for public transport or even walkability in more densely built-up areas. OD matrices can be further developed with the addition or removal of land use to zones to create prediction models, development scenarios and more.

Utilising mobile positioning techniques has proven its accuracy in several projects, including the Eastern Bypass of Tartu City in Estonia. The modelling results and actual use deviated less than 10%, where conventional transport planning produced a deviation of 20-50%.

Accurate data enable precise prioritisation and ultimately the most cost-efficient and enduring decisions in terms of benefiting society at large.

The expert

Ramboll Group A/S

Ramboll Group A/S
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