Future Of World Smart Transportation Market

The epoch of the smart transportation market has been remarkable so far. It gets more engaging as its vendors venture to experiment with deep learning.

The smart transportation market experience

Remember those long ticketing queues, and those hours of wait before you could get to know the availability status alone? A couple of decades ago, automatic fare collection and ticket vending machines was the future plan. That future, is now. We live in a generation, where embedded devices in machines enable automatic fare collection and simultaneous data entry. Travel authorities can keep a constant check on mode and route of travel at multiple nodes. In addition, the authorities ensure that passengers are kept informed through appropriate guidance through the course of travel. With enough knowledge of moving traffic over route, a general estimation is drawn for the design of parking. A driver can confidently drive-in at a facility be rest assured of ample parking space, whereas, the facility owner has a detailed profile of who and what is passing through. An integrated supervision system and traffic management system adds convenience in today’s fast-moving lives. Collectively, the standalone installations feed inputs to multiple back-end computers in a distributed or central network. Once, big data analytics and deep learning arrives in the picture, the smart transportation industry exhibits immense future scope for getting even smarter.

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Smart transportation market: Need of the hour

An extraordinary degree of technological advancements has been incorporated in the transport segment over previous decade. Global smart transportation market has evolved significantly; from basic traffic and vehicular support to more complex integrated supervision systems. Advanced applications, rule the mindset of current population. An intelligent transport system (ITS) is what wins the trust of any consumer looking forward to logistic solutions. It is quite obvious, if one thinks of all extended support that accompanies an integrated traveling environment. It becomes a lot easier to know what is being transferred, in what amount, where all it has left its footprints, and the events that occur midway. Although, a vast amount of back support is required for developing these systems. The solution vendors range from small local players and mid-sized enterprises to huge corporate giants, which manage their services right across the face of the globe.

Right after the European Union (EU) declared its directives binding the European operations within a legal framework, entire global smart transportation market took the hint. It adapted accordingly, supporting the idea of a more efficient network that functions within a cleaner and safer environment. Objective of the EU directive match the requirements which led to the emergence of smart transportation market. A correct structure enhances the driving and traveling experience despite the booming increase in on-road vehicles. Regional administration offices are equipped with real-time data which helps them reduce the risks of traffic blocks. Automatic rerouting instructions are carried out, as soon as any sort of congestion is detected. Adherence to safety measures is easily monitored, effectively penalized or rewarded, and hence, consequently lower the numbers of accidents. The use of green fuels, as an alternative to the conventional, is encouraged under an intelligent transport system. Several concessions and privileges are provided to stimulate its adoption rates among the non-users.

The smart transportation market we know

The ITS industry involves systems that comprise cutting-edge devices, networks, and applications. Original Design Manufacturers (ODMs) focus on developing highly defined cameras and sensory equipment that are compatible to existing hardware and easy to configure. Intermediate wireless or wired communication is attended to by network providers. Interactive panels deliver real-time instructions based on vehicle’s internal and surrounding parameters.

Smart transportation market segments render business, professional, and cloud services to its end-consumers. Professional sector generates highest revenue on the global scale, while industry experts bet on the growth potential of cloud service segment. On the basis of types, the transport industry classifies its options on the basis of fuels used also. Analysts project that out of the smart fuels, or green fueling sources available, namely, bio-gas, ethanol, hydrogen, wood-gas, and solar-cell, ethanol has the maximum revenue share. The case is evident from its extensive role in biofuel stabilizing application for gasoline to make it more environment friendly. Moreover, research activities are on the rise for developing the solar-cell technology as an alternative for driving vehicles. Considering the geographical patterns of market expansion, the European region is expected to report highest revenue, as a result of EU’s initiative to implement ‘ITS’. While, the Asia-Pacific region currently exhibits optimum potential to emerge with the highest figures for purchase volumes, considering the rate of regional economic progress.

As of now, the consumer group has limited bargain options because vendors are scarce. On the contrary, vendors benefit from this situation and have full control over pricing.

Deep learning and its role in impending growth

Earlier, we had discussed about the involvement of big data analytics and deep learning in the smart transportation market. The flood of data from the integrated devices, standalone systems, communication channels, and data portals, should put into perspective the inevitable need of big data analytics. Deep learning arrives in style, and at a great cost, to improvise present systems where there is no scope for human error. It comprises advanced artificial neural network and parallel computing procedures that help overcome human limitations associated to data entry, processing, and analyzing. Beyond that, artificial intelligence (AI) enabled devices can detect similarities and anomalies in data inputs – whether audio, visual, or textual.  Virtual bots for assistance, along with the automation of vehicle and traffic management components, adds on to the capabilities of existing vehicles. Cloud-based crowdsourcing helps gather huge sets of data from diverse sources. The ‘deepens’ the learning for machines, owing to the rich source of variable data that goes into their training. 

Standard deep learning frameworks, when commissioned for machine vision and embedded systems, is bound to construct Mobility as a Service (MaaS) models beyond consumer expectations. Configuring over the air (OTA) updates and ensuring sufficient cyber security will be the critical grounds that need attention. If regional economic restraints are dealt with in an efficacious manner, global population will soon be able to experience autonomous smart transportation mediums, in exchange for an affordable price.