Redesigning the Transport Ecosystem
From traffic analysis, facial recognition at metro stations, robotaxis, to self-driving vehicles in the logistics industry. The smart transportation market has wide applications and plays a critical role in the construction of a smart city. In 2018, China’s Ministry of Transportation formally introduced the integration of smart transportation to the value chain of the transportation ecosystem by proposing 6 key focuses: infrastructure digitization, road transport integration vehicle-road collaboration, Beidou high-precision positioning integrated application, integrated management of road network based on big data, "Internet" road network integration, and new national traffic control networks. Since then, China’s smart transportation industry has been booming, extending beyond individual vehicles to include nation-wide interconnectivity and public transportation. According to AskCI Consulting, it is projected that the Chinese smart transportation industry will exceed 140 billion RMB in 2023. Here are some examples of smart transportation applications in China.
Self-driving cars are not a new concept and there exist multiple autonomous driving companies in China such as WeRide, MiniEye, ZhiChe Auto etc. WeRide, being one of the earliest self-driving car pilot companies, has successively picked up more than 3,000 passengers and commuted a total distance exceeding 70,000km. Apart from autonomous vehicles, the automatic assisted parking technology is relatively mature and popular among drivers too.
Shared robotaxis are increasing in popularity, due to its convenience, easy accessibility, and increasing preference for shared cars / taxis over private cars. Users are able to book a vehicle through the mobile application and the nearest vehicle is able to self-drive to the user’s exact location. Once the passenger has been dropped off, the self-driving vehicle can then automatically return to the nearest parking spot, or immediately respond to another user’s booking, promoting efficiency.
Self-driving trucks in logistics
Unmanned heavy trucks used for long-distance trucking or in-city delivery could potentially result in workforce cost savings, reduce energy consumption and decrease the number of accidents associated with long-distance driving. Furthermore, with artificial intelligence such as remote sensing, calibration and optimisation, the truck’s stability can be improved. In China, Suning, a Chinese retail company, have successfully tested their L4-class unmanned heavy trucks.
Major chinese cities often face a congestion issue as a result of inefficiencies on the road. By leveraging on AI through analysis the big data from urban traffic and real-time traffic conditions through surveillance cameras, the traffic signal timing could be optimised to eliminate any inefficiencies in the signalling algorithm. For example, Huawei’s “TrafficGo” is the leading innovation in improving traffic efficiency. In its pilot test at Shangdi, Beijing, “the average delay in the main direction of Shangdi’s third street decreased by 25.2%, which led to the average delay of surrounding roads decreased by 10-20%” as stated on the “TrafficGo” official website.
Ticket free train stations
Instead of commuters spending time buying a ticket or searching through their wallets for the train card, AI face recognition technology is able to eliminate this inefficiency. Commuters are able to enter the station once they have been verified using AI and the ticket fare will be charged directly to the bank account linked to their ID. Such a system will be extremely useful in ensuring a more seamless transport journey, especially in a bustling city like Shanghai.
To conclude, for smart transportation to be executed on a nation-wide scale, it requires deep integration of new technologies such as big data, AI and supercomputing within the transportation industry. Simultaneously, R&D of intelligent networked vehicles has to be strengthened, forming a complete industrial chain.
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