There are three certainties in life – death, taxes and traffic jams. We can’t prevent the first two, but traffic jams are definitely something we can tackle, and it doesn’t necessarily mean building more roads, writes Tal Ater, CTO, DAV.
IoT has promised to revolutionise every aspect of life, and transportation is not immune from this. In the last couple of years alone, we have seen the onslaught of apps and open data transform our ability to hail a cab, know when the next bus is coming, plan a train trip and compensate for traffic during a car journey. Yet, all this is focused on the individual.
Bigger projects are delivering IoT-enriched transportation at a higher level, using sensors, data analytics, real-time information and algorithms to pre-empt traffic and demand. This so-called internet of transportation is ensuring that roads, rail and any other form of transport in major cities can meet demand.
Sensing traffic and demand
Most major cities are experiencing a boom in construction to cope with the growth of population. More homes and more people mean greater pressure on everything from motorways to urban railways.
Congestion impacts the daily lives of commuters and the local economy, disrupting businesses and inconveniencing visitors to the city. To meet this challenge many city planners are looking to smart transport solutions to cut congestion and optimise the use of public transport.
For example, the combination of smart sensors in road surfaces and motion sensors on lampposts, combined with data from CCTV cameras can be used to monitor increases in the number or road users and decreases in average traffic speeds. It can pinpoint accidents through voids in traffic flows and locate parking spaces.
By taking a sensor approach, slowing traffic can be spotted faster and countermeasures can be implemented automatically and more efficiently. Measures include repurposing hard shoulders as temporary extra lanes to clear congestion, rephasing traffic lights, automatic adjustment of variable road speed signs, automated lane closure (rather than waiting for a human with cones to block off a lane with a stranded car in it) and smart control of street lighting so that money is not wasted on excessive lighting on busy roads.
This is in addition to the most obvious function – feeding data to mobile apps, display screens at bus and train stops, locations for cycle hire, as well as to vehicles themselves to better inform individual users.
Data can also be collected from decentralised sources such as the vehicles themselves. As we move closer to autonomous vehicles, these cars will rely on a regular, decentralised and localised data feed in order to fully anticipate road conditions and incidents. The same decentralised network of vehicles can also feed data back to smart city interfaces and databases, as well as to other vehicles, to GPS device networks and so on.
Smartening up city transportation
Exeter claims to have the most advanced implementation of smart transport in the UK. The Exeter Engaged Smart Transport project is a collaboration between Exeter City Council and Devon County Council. It uses real-time data from traffic and weather sensors, combining that with eyewitness and behavioural information to reveal where there is congestion, work out the cause and take action to solve the problems.
The city aims to deliver 12,000 new homes and 60 hectares of new business land by 2026, which will put additional pressure on the city’s infrastructure and public transport. Using existing roads and public transport services such as buses more effectively will be essential to achieving that aim.
Outside of cities, smart motorway projects for the M3 and M4 motorways are already making use of surface sensors and dynamic lane and speed reassignment, providing temporary increases in capacity and better flow management at peak times.
Meanwhile, bicycle hire service ofo has expanded from China to the UK, using GPS data from trackers on its bikes to offer a decentralised, city-wide cycle hire service without needing expensive, fixed cycle docking stations. Anywhere can become a pick-up and drop-off location for a bike and logistics staff can redeploy stock to areas reporting the most demand for them. When temporary surges occur, such as a public transport failure, an event or a spell of good weather, smart transport data can be used to facilitate maximum accessibility and minimise unused capacity.
With greater access to smart transport data, ride hailing from autonomous vehicles becomes just as practical as making any street corner a bike rental location.
The author of this blog is Tal Ater, CTO, DAV