Avoiding bumps in the road when designing AI-powered traffic-management systems

Since its introduction in the 1950s, artificial intelligence (AI) software has transcended its theoretical existence in research labs to become omnipresent in our lives. This early AI research software has not only fueled an explosion in efficiency, but it has also opened the door to entirely new opportunities in business, education and government. Most of us use AI software daily in e-commerce, banking, health care and insurance.

In this article, we focus on an industrial sector where AI is showing great promise: transportation system management. Artificial intelligence can provide traffic system managers with real-time, predictive insights about traffic flow that can lessen congestion and improve safety. However, as explained here, a rugged hardware network must be developed and a software system in place to connect devices and transmit data.

AI in transportation management

Source: IEEE — Smart Town Traffic Management System Using LoRa and Machine Learning Mechanism

Artificial intelligence and machine learning (ML) techniques have gained traction in transportation system management as a key part of Smart City and Intelligent Transportation Systems (ITS) projects and initiatives. Some of the most significant and recent advancements in AI-based transportation networks happened with the integration of sophisticated ML algorithms into ITS technologies. ML algorithms “learn” from studying traffic patterns, pedestrian behaviors and other experiences so they are constantly improving their models and by extension, improving the safety of roadways.

Traffic management is highly complex. The only possibility of dealing with its tsunami of data is to abandon traditional traffic management approaches and turn control over to AI. Artificial intelligence will analyze, summarize and finally relay back to an administrator actionable insight that can reduce congestion, and possibly save lives. Sequenced follow-up actions can range from dispatching emergency services to adjusting traffic signal timing system controls. In most cases, responses are automated without human interaction.

AI’s use in ITS has been heralded as a new era of mobility, one characterized by improved driver safety and comfort, reduced traffic congestion, lowered carbon emissions, and greater speed and efficiency in supply chain management tasks. That’s not to say AI is without challenges. Infrastructure expense, managing competing priorities among city leaders and coordinating the multiple parties and technologies involved in projects have slowed adoption, as have public concerns over privacy and security related to sensing. Despite these challenges, the future of AI in transportation holds immense potential for startups and private companies to pitch their solutions to authorities.

Practical uses of AI in transportation
Artificial intelligence integrated with ITS creates a context-aware solution that merges real-time data from connected road infrastructure with predictive analytics to effectively coordinate traffic across key city arteries. Today, we are on the cusp of a proliferation of traffic applications, technologies and services where AI integration takes center stage, namely:

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