Big data has become a valuable asset, their competent use will bring benefit to all participants in the transport chain. Experts from Simetra tell how big data is applied to transport today.
What Big Transport Data Looks Like
From the semi-canonical definitions of big data is what is accumulated almost by itself, potentially limitless, but subject to analysis. Most often transport big data means data that make it possible to characterize movements. For example, data of mobile operators, helping to understand the main directions of movement of people. Mobile operators are increasingly offering the market samples of such data. And based on them some experience has been gained in solving very specific problems. For example, selecting locations for stores or service objects. For transport planning purposes, they can be also used, but too large size of “cells” and several other technical aspects contribute to their limited scope of application. When working at the city level, cellular data play a more supportive role. However, to get a quality result, data from other sources, including sociological surveys, should also be used.
Second layer, a narrower one, but also more appreciative in terms of efficiency and cost, is fare data, both in cash (registered through the online cash register), and especially by validating electronic tickets and bank cards. This data, along with precise reference to time and geolocation, can be used for calibration at modelling of traffic flows, to assess the economy of a particular project in passenger transportation. And also, for retrospective analysis — as deep, as their collection period and homogeneity of possible samples allow.
Third data set, rather heterogeneous in source, but very large and actively growing one, is tracking of movement of various vehicles. This includes, for example, order information and taxi operators travel tracks, freight information, relevant information of control bodies, generalized data on traffic rates and volumes by network segments from Google and Yandex online services and, of course, data accumulated and processed by global transport navigation players — TomTom and Here. Such data is widely used in various aspects of transport planning: from the creation of large and small models to carrier enterprises and operational services.
Separately, data supporting the development of alternative modes of transport can be distinguished, from bicycle to scooter and other means of individual mobility. For example, Strava service data, where users share their type, location and duration of sports activity, is used to design bicycle paths. Active development of various sharing services also allows to improve understanding of which routes are the most popular and in which direction the infrastructure should be developed.
How BigData changes transport
Big Data serves transport a great service — it helps identify non-obvious communications, assess throughput and carrying capacity, demand and quantity of routes, optimality of modes of transport and size of rolling stock, find gaps in urban system, decide whether network configuration changes are necessary and define modes of transport for their maintenance.
All these types of data are used in a particular volume in transport planning, although when updating public transport routes. Therefore, certain “big data” were applied in no less than half of the transport projects implemented. At least half — solely because quite a lot in this area for various reasons is still being done in the old fashioned way.
Passenger traffic and specifically cellular data were applied at least when calculating railway projects of the Moscow region and developing a new bus network in Nizhnevartovsk.
Despite some skepticism about implementing “digital transformation” in many sectors of modern life, in the transport sector it really happens. We face it in the first place in passenger transportation in aviation, railway transport, in taxis. However, urban transport systems are “covered” by big data very heterogeneously: from almost complete penetration in the Moscow region and the Tver region to zero in Astrakhan. The same applies to intercity bus transportation and water transportation transport, where by law everything is left to carriers’ will at low or comparatively low level of regulator control.
Therefore, it is important to talk about deepening and expanding the penetration of big data into “lagging” transport industries. It is necessary to do this, even though not always and not all players in the market are interested in the openness promoted by big data.
By the way, it was thanks to digitalization that the implementation of the MaaS (mobility as a service) concept became possible, whose foundation is the use of big data. In Russia so far, it is being implemented in a noticeable volume only in Moscow, that is logical — the capital sets the tone for implementing digital solutions in transport. But the success of the implementation of this concept for any region will play a key role in big data.
What’s next?
To fully extract all the opportunities from big data, new technologies are needed. And it is far from “space,” because for all projects where they have already found application, the capacity of modern computers is enough. Math for processing available datasets is also enough: in fact, they are sets of tables, and table processing is a technique, well mastered in programming. The problems of legislation of any countries should also not create special obstacles, the only potential barrier to their use is — protection of personal data. But this issue is still being resolved at the level of suppliers of initial data — by depersonalizing them.
The global practices of using big data are no different from Russian ones, their focus and results are the same. We can safely say that Russian know how’s are not inferior to the world. Yes, there is an actual backlog in part of the territory and depth of coverage with big data solutions. But this is due to the absence of a system for such work and the organization of follow-up support and results’ updates. The existing contract system, and — to an even greater extent — a shortage of specialists are largely “guilty” of this.
Authors: Vsevolod Morozov, Director of Analytics and Planning, Simetra,
Vladimir Valdin, Public Transport Solutions Director, Simetra