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Big data can help get travel moving forward


IN THE field of public transport, SA is battling some unique challenges. These include the legacies of historical urban planning programmes, complex interrelationships of formal and informal travel systems, fragmented and disconnected bus, train and taxi networks, and a section of the population that insists on using private cars.

Overcoming these challenges is proving to be a slow journey, but it is one that could be accelerated by smartly applying technology.

Big data, in particular, has the potential to ease transit flows for millions of daily commuters and unlock huge economic value. Big data can be applied to SA’s public transport in four key areas.

• Planning urban commuter networks: geolocation technology can reveal essential information on where people are, where they’re going and how long it is taking them. That makes it possible to plan public transit systems that match customer needs. Detailed information reveals insights at a very granular level, such as the number of train carriages required at certain times of the day to ensure the optimal allocation of resources.

• Predictive analytics: sensors embedded in physical equipment stream data back to a central hub, giving authorities insights into key metrics such as commuter volumes at certain times of the day or average waiting times. On physical equipment, maintenance-orientated sensors provide early warning of when components are likely to malfunction, which improves uptime and reduces the chances that commuters are left stranded.

• Responding to events: data gathering becomes highly useful when responding to accidents or incidents. Alerts can be automatically dispatched to police and emergency services, which can clear the incident speedily and get traffic moving again. Alternative routes can be suggested to travellers via mobile alerts or digital signage.

• Personalised service: public transport has traditionally been a service that is not-for-profit rather than a revenue-generating effort, but tailored digital marketing is an opportunity for operators to grow new revenue streams. As big data enables a sharper understanding of one’s customers, information alerts can be targeted at only those who would need to know about a specific issue.

For SA, some “quick wins” could be achieved — for example, a taxi system that uses basic sensors, linked to an SMS platform, to keep commuters updated.

For the time being, we need to get to the first level of big data maturity, which is simply surfacing information and presenting it to those who need to know. In the taxi example, this could result in fewer people arriving late for work each day (one of the most basic, persistent problems with our informal transport networks).

In a similar way, rail and bus operators could begin their journey with sensor technology and big data analysis for just the most pressing issues, such as components that fail often or take the longest to fix, causing the most disruption to customers.

With some of the basics in place, we could then turn our attention to the higher-level issues that could be solved, such as pulling together schedules and real-time data from public transport operators — buses, taxis, trains, ride-sharing, car pooling — to present commuters with an integrated view of transit networks.

As businesses, we’ve bought into the benefits of big data for understanding our customers’ needs and tailoring our offerings. But in the realm of public transport, we’ve been slower to see the potential for improving the daily commute for millions of South Africans.

With the decades-long flock of people from rural to urban centres continuing and congestion levels in major metros reaching epic proportions, we have to swiftly and decisively start finding solutions. Big data is central to strategies that work to tackle our transport challenges.

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