Creating smart cities and digital workplaces means connecting infrastructure and digitizing transport systems, particularly in the taxi industry. Can you imagine what South Africa roads would looks like in 10-years-time, if taxis were connected?
According to Statistics SA’s 2013 Household Survey, taxi operators transport over 15 million commuters daily. Around 200,000 minibus taxis, across 2 600 taxi ranks, provide the main mode of transport for 50% of SA’s population earning less than R3 000 per month.
The impact of the taxi industry on the daily lives of South Africans is huge, research by Transaction Capital, a financial services provider in the taxi industry revealed. An estimated 70% of people who attend educational institutions make use of taxis, 69% of all South African households use taxis in their transport mix, and a staggering 68% of all public transport trips to work are in taxis. Plus, minibus taxis reach remote places other forms of public transport don’t – the average South African lives within a 5-minute walk of a minibus taxi.
Sadly, the industry is still faced with challenges when it comes to road congestion, accidents and safety, and with drivers often forced by financial needs to work long hours. But a future where taxis can operate efficiently and profitably, while improving safety and providing a more convenient customer and employee experience, is possible. But it requires a digital business transformation.
Our cities need to start connecting infrastructure and piloting these digital experiences now. Globally, there will be 380 million connected vehicles on the roads by 2020, but that is only half the battle. The first step toward making the frictionless commute a reality is for local governments to begin investing in technology architectures and physical infrastructure to accelerate connected transportation systems and create workplace innovation.
On the strategic side, transportation officials can begin by identifying best practice. It is best to first pinpoint a problem that is unique to a city or region. For example, a city with notorious traffic congestion might want to start integrating smart sensors on roadways to alert drivers and connected vehicles in real-time of potential hazards, and possibly prevent accidents before they happen.
How would that look in practice? Let’s take the example of Sipho Ngwenya, a fictional character, from Zola in Soweto, one of the 600 000 people employed in the industry.
He gets up at 4am everyday to get to the taxi rank where he parks his mini bus overnight. Sipho hopes to be one of the first drivers there to ensure he fills his taxi with commuters, who travel to the northern suburbs of Johannesburg for work and school.
The earlier he starts transporting people, the better chance he has of generating the daily “rental fee” he pays his boss – the owner of the minibus. If Sipho is even 10 minutes late, the queue of people at the rank may have halved. If his taxi is the last one in the queue, it may not fill up, and he may need to drive around the block to find more commuters. The delay means longer hours for him, his conductor-cum-assistant (guardjie) will have to spend more time calculating and collecting fares, and it will increase his costs – he’ll spend more money on fuel.
Fast forward six-months later, when the Joburg metro area would have implemented the Cisco Connected Mass Transit technology solution to connect the taxi industry. Sipho’s alarm goes off at 4am. He grabs his phone and logs onto the Cisco platform before he jumps out of bed: the weather is clear but there’s been an accident overnight on his route to the rank – he’ll have to take a detour. He checks once again just as he leaves home, and sees that he has time to grab breakfast on his way.
He is the first driver to arrive at the rank that morning – stress-free and ready to start. The rest of the minibuses are stuck behind the accident. He loads commuters and manages to get all of them to their destinations 10 minutes early, by checking the best routes. Payments are no longer collected in person – there is now an easy mobile payment option that customers love, especially the young ones. And Sipho no longer needs to search for commuters – they stop his minibus on the road because it is marked as a ‘connected minibus’. This is a smart workplace.
These digital solutions are real and available to the SA taxi world. There are some caveats, though: Cisco’s international experience shows that these solutions are best implemented alongside awareness campaigns for commuters and government incentives to drive adoption, as well as ensuring the regulatory environment is conducive. Luckily, technology itself isn’t too much of a problem: the solutions work with existing IT systems local governments have installed.
Imagine South Africa in a decade. Now imagine a South Africa where traffic congestion is a thing of the past.
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.