What impact will IoT have on the transport industry?

What are the most important types of data that can be gathered by IoT devices?

The IoT data needs to be high quality with very high accuracy and integrity, along with a safe and appropriate handling focused to customers in order to trust the information being provided.
The most important types of data that can be gathered by IoT devices to improve the management of urban road networks include:

Data related to the vehicle position, heading and/or route using the most suitable communication channel: in this case, traffic light status and time to change for the next intersection(s) according to direction (route), traffic flow on parking areas, vehicle control performance and duration of the parking session

Data related to the platoon assembly, platooning driving performance (e.g. in terms of safety and comfort), detection and prediction capability of legacy vehicle manoeuvres, predicted and actual travel times of vehicles, data about the user comfort (e.g. waiting time), data from the sensors (e.g. parking cameras)

Data related to the behaviour of traffic (including vulnerable road users), as well as a functional data performance of current AD systems and IoT‑extended AD systems in real traffic conditions.

Eusebiu envisages IoT leading to things such as automated valet parking in years to come.

How important is it for transport authorities to be able to visualise this data to be able to glean insights from it?

IoT services and applications are important for any transport authority, especially in three emerging areas where automated mobility is set to play a key role and data is critical: MaaS (Mobility-as-a-Service) with services such as real-time car sharing and city chauffeur services; smart mobility, which will benefit from the services related to the urban driving and automated valet parking use cases; and the EU’s digital priorities for cross-border corridors, where platooning and services such as electronic driving license, dynamic eHorizon and HD maps for automated driving have the potential to deliver real benefits.

For example, the analysis of the data generated by the accelerometer and gyroscope sensors in the vehicles make it possible to predict the formation of potholes and communicate the information to cities, communities or public authorities responsible for road maintenance and safety, helping authorities to better judge their parking guidance and traffic management actions.

Another example is the next-generation autonomous positioning data. This represents an innovative data positioning solution allowing a vehicle to localise itself in the surrounding environment with the benefit of minimising the number of sensors on the ground. A further example is advanced driver assistance data, which implements a collision warning system to support the driver in detecting and recognising obstacles around the vehicle close to the track line.

What are the primary benefits of better connectivity to transport in urban environments?

Connectivity is helping cities to improve the local urban ecosystem in terms of traffic safety and city liveability. It does this by implementing, for example, IoT applications related to platooning from one city to another, driverless car rebalancing services, as well as automated valet parking and car-sharing services. In addition, connectivity can enable speed optimisation for road networks with multiple intersections, as well as prevent dangerous interactions with vulnerable road users.

Taking the above into consideration, the concept of the Internet of Things can be used to obtain and exchange additional and redundant information that can improve integrity levels. The benefits of better connectivity of transport in urban environments include information for the interaction of platooning services, traffic light status and request handling. Traffic status (roadworks, weather, accidents, road conditions, etc) could also be relayed more effectively in a better connected system, as could the expected occupancy of buses in bus lanes.

Other benefits include:

• Position, velocity (etc) of legacy traffic and planned routes, intent to enter/exit motorways via on/off ramps from:
• Camera-based traffic monitoring systems
• Smartphone traces and user navigation functions
• IoT-equipped cars
• Information aiming for (redundant) localisation, for example:
• Real-time HD maps
• Position error corrections through RTK DGPS service.

The concept of IoT could also be used for connecting probabilistic and historical data not yet available in automated driving vehicles for urban driving using several different sources in order to improve the world model of an automated vehicle.

It could use schedule data to adapt probabilistic models in the AV’s world model and statistically predict the probability of large amount of vulnerable road users. Therefore, the data could help decide when and when not to drive and adapt the vehicle’s driving behaviour.

Furthermore, IoT could enable actual weather and daylight information to be received by the vehicle from the internet, and thus reconfigure sensors to better perform under various weather and daylight conditions.

Car-sharing services are based on several IoT sources and are used to improve routing and vehicle-to-customer matching. In particular, they make choosing and delivering passengers more efficient, enable traffic management to assess the need to allow the use of emergency lanes, as well as avoiding delays by garnering real-time traffic information and reducing waiting time by knowing bus or flight schedules.

Which sector of the transport industry do you think stands to benefit most from IoT?

With regards to future benefits, an IoT transport system is a modular system useful for future market uptake, mainly on motorways and urban road network.

Public transport is usually run by municipal transport authorities, and the IoT transport concepts are intrinsically linked to those authorities achieving the transport goals they have set, e.g. campus mobility services, automated shuttle services, last-mile passenger transport and last-mile freight delivery.

As public transport is considered key for large-scale market deployment, future IoT-enhanced automated mobility services have to be implemented into public transport business models in the context of MaaS or other payment models. This type of alignment and IoT service integration needs further research, policy support and business analysis.

Automated driving is expected to progress significantly towards higher levels of automation (e.g. SAE Level 4) once there is a reliable amount of data from external roadside and in-vehicle sensors. Public transport operators were found to be closer to mid-term market deployment than the automotive passenger car industry, especially with regards to the public infrastructure required when implementing IoT-enhanced automated mobility.

At the same time, the human-machine dialogue involved in automated driving requires new components and solutions – such as cluster intelligence – formed from the vehicle fleet on the road, which will allow highly-automated vehicles to literally see around corners. These large quantities of data must be transmitted extremely reliably inside and outside the vehicles.

As automated vehicles are producers and consumers of IoT data, European projects – such as Autopilot and SHOW – can be considered as the first step towards a connected automated mobility data marketplace for interoperable IoT platforms and devices in a smart city and MaaS context.  

Biography 

Eusebiu Catana
Eusebiu joined ERTICO in March 2017 and is an expert in ITS platforms, vehicle telematics, computer vision, IoT and 3D driver simulators. He was awarded the international best XML award in Europe for ITS development in 2002 from the IT/automotive industry. He holds PMI, PMBOK and PMP certifications, a Master’s degree in Automotive Engineering, a Postgraduate Master’s in Industrial Management, a Postgraduate Master’s in modelling and simulation dynamic ITS systems, operations research, distributing computer programming, logistics models, data modelling, and a PhD in vehicle telematics.