Machine Leaning is expected to play a curucial role across diversified segments such as traffic management, optimising routes as well as make autonomous vehicles more robust
Have you ever used virtual personalised assistance from Alexa? Or checked recommended videos on Youtube? Switched in your mobile’s GPS to navigate towards your next holiday destination? Then you have been using Machine Learning and this usage is only expected to increase in future.
As technology continues to penetrate into newer segments, machines are learning new horizons and have been learning faster than never before. Advancements in computer processing and cloud computing have enabled collection and analysis of enormous amounts of data that has led to vehicles becoming smarter by the day. Given the massive amounts of data trucking industry generates, Machine Learning has now penetrated deep into the segment that has electronic logging devices, sensors being an integral part under connected vehicles. There are already areas such as route optimisation, predictive maintenance, driver development as well as back office automation where Machine Learning is being applied up to a large extent.
However, this is just the tip of an iceberg. Machine Learning is playing a crucial role in the development of key futuristic technologies such as digital load platforms and platooning. According to Volvo Trucks, Machine Learning can significantly reduce number of empty miles travelled by a truck as it can predict and match the cluster deliveries with different types of vehicle arriving and freight as per the destination and geographical locations. Such technologies can lead to better fleet utilisation as well as cut down on emissions significantly by up to 30 % and 25% on delivery costs, says Jonas Lindholm, VP of Productivity Services at Volvo Trucks. Empty miles account for 20 % of road freight traffic in Europe, which can almost double up in countries like China.
There is often shortage of sufficient information available for city planners and other critical decision makers entrusted with responsibilities to plan traffic patterns. This is increasingly becoming a pain especially with urban cities facing traffic congestion. Machine Learning can analyse data sources such as satellite imagery, GPS navigation as well as social media to make recommendations and predictions. Automated traffic signals that operate on data collected from cameras, satellite as well as sensors can be optimised for decongesting city roads. Alibaba’s City Brain project is already testing grounds across the city of Hangzhou where 1,000 road signals are working in coordination for preventing gridlocks in the city.
Notably, autonomous vehicles continue to be an area of concern as with regards to their security while operating in real environment. Machine Learning will also play a crucial role in making autonomous vehicles more robust as it continuously renders surrounding environment around such vehicles and predict changes to optimise costs, productivity and mitigate challenges.
Technology today is an integral part of our lives and Machine Learning is often talked about, particularly in unrealistic futuristic context. As we move towards a cost effective future of mobility, Machine Learning will keep on evolving to serve better predictions across increasingly complex environments. However, it will be crucial for a transporter’s your business to adapt and incorporate digital technologies in to their everyday operations for a cost optimised future of mobility.