Over the years, information and communication technology (ICT) solutions have helped transform the transportation industry’s operations. These solutions have facilitated the development of next-generation intelligent transportation systems that have the potential to deliver high throughput services in the existing transportation infrastructure. For instance, many airports have deployed intelligent sensors to automate the entire process chain, including check-ins, airport baggage handling and loading in the aircraft. Further, enterprises in the transportation industry have taken many customer-facing initiatives such as launching websites, mobile applications and payment solutions to enhance business efficiency. The industry has also started adopting new-age technology solutions such as cloud computing, internet of things (IoT), artificial intelligence (AI) and data analytics to improve decision-making, enable real-time monitoring and simplify traffic management.
A look at the key ICT solutions being leveraged by transportation enterprises and the way ahead in terms of technology adoption…
Leveraging IoT for transforming operations
The use of IoT solutions can enable transportation enterprises to optimise costs, increase fleet productivity and improve customer satisfaction. As per industry estimates, the IoT market in India is estimated to grow at a compound annual growth rate of 62 per cent to reach a valuation of $9 billion by 2020. The transport sector will play a key role in driving this growth. The segments in the transportation industry that will increase the use of IoT solutions are fleet telematics and management, transport logistics, guidance and control, inventory and supply chain management, passenger entertainment and commerce, smart vehicles, reservation, toll and ticketing systems, peer-to-peer services such as car sharing, and security and surveillance.
Further, IoT devices equipped with GPS-enabled sensors can provide valuable information such as travel time, directions to the destination, vehicle volume and traffic movements. It can also ensure effective fleet management by enabling intelligent tracking and monitoring of vehicles. Moreover, IoT can be used for automating toll collection and implementing dynamic pricing of toll charges. It can also help in smart parking by giving real-time information about parking status, thereby enabling organised parking and increasing the utilisation of parking spaces. By leveraging IoT, city administrations can construct smart roads. The sensors installed on roads can be used to determine the number of cars in each lane and operate traffic lights based on this information. These sensors can also be used to redirect traffic in response to an accident or other type of hazard.
Going forward, IoT is expected to play a significant role in the autonomous vehicles segment by enabling operations such as in-vehicle infotainment, predictive maintenance, real-time monitoring, and surveillance and safety.
AI makes inroads in the sector
All segments of the transportation ecosystem have started leveraging AI technology. Some potential applications of AI in the transportation domain include autonomous fleets for ride sharing, semi-autonomous features such as driver assistance, predictive engine monitoring and maintenance, autonomous trucking and delivery, and improved traffic management. Further, AI is being used to design self-driving cars with advanced active safety capabilities such as autobraking, understanding of road signs and lane deviation alerts.
AI-based intelligent transport management systems have also gained uptake in the sector. These systems include sensors, CCTV cameras, automatic number plate recognition cameras, speed detection cameras, signalised pedestrian crossings and stop line violation detection systems. They help in making real-time dynamic decisions based on lane monitoring, access to exits and toll pricing; allocating right of way to public transport vehicles; and enforcing traffic regulations through smart ticketing.
Further, smart freight locomotives equipped with sensors are being developed for improving cargo transportation. The data from these sensors is fed into machine learning-based analytics applications to enable real-time decision-making. In the field of traffic management, AI solutions are being used to convert traffic sensors into intelligent sensors, thereby helping predict and mitigate accidents.
Cloud as a medium of data storage
The transportation industry has started leveraging cloud computing to store data pertaining to ridership, traffic status and toll collection on a simple and secure dashboard. The data can be collated and used to generate reports on fare collection, passenger count, etc. Since this data can be accessed on a real-time basis, enterprises can make quick adjustments to ensure better service delivery. Cloud technology is also helping transportation companies in enhancing their business performance by improving operational efficiencies, and creating innovative revenue and business models. The deployment of cloud solutions enables the industry to save a considerable amount of time, which is otherwise spent on planning, procuring and deploying IT infrastructure to connect the embedded transportation devices, as in the case of traditional computing networks.
Using big data to facilitate decision-making
Technologies like big data analytics enable enterprises to convert the enormous amounts of data generated through connected devices and sensors into actionable insights. For instance, predictive analysis and data mining can be leveraged to draw insights and patterns from the vast pool of data pertaining to transport and traffic conditions in particular areas. AI-powered systems can be used to plot the fastest routes for commuters based on traffic patterns. Predictive analytics can also help alert drivers and passengers about bottlenecks and congestion on the route, thus reducing travel time.
Besides, analytics can be used to improve public transport by forecasting weather conditions and determining the arrival time of buses. Big data can play a significant role in gaining an accurate understanding of customer demand on different routes. In addition to determining the frequency and size of vehicles on existing routes, transport authorities can use this data to efficiently plan future routes. This will help reduce customer wait time, leading to increased ridership. The authorities can map customer journeys across multiple modes of transportation and plan additional services. In addition, big data analytics can help in predictive maintenance by enabling transport authorities to forecast the maintenance requirements of vehicles.
Challenges and the way ahead
While enterprises in the transportation domain are stepping up their efforts to incorporate ICT solutions, several deployment- and management- related challenges continue to hamper their adoption. The two key challenges relate to the integration of these solutions with the existing IT infrastructure and training the existing workforce to use new-age ICT solutions. Further, cybersecurity has emerged as a key area of concern with regard to technology adoption in the transportation industry. As a growing number of transportation systems such as traffic lights, road sensors, mass transit rail or bus, ports and airport vehicles are getting connected to the network, cybercriminals are finding more opportunities to attack not only the IT infrastructure, but also the operational technology infrastructure that runs the signalling and control systems. In this scenario, enterprises need to implement effective security measures to prevent the misuse of data.
Challenges notwithstanding, ICT adoption in the transportation industry is poised to grow considerably in the coming years as the country moves towards a smart transport medium such as autonomous vehicles. To bring in the desired change and establish a smart transportation ecosystem, there is a need to adopt cutting-edge technology solutions, implement strong policies and regulations, attract public and private sector investments, and create public awareness regarding smart transportation.
Kuhu Singh Abbhi
