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Artificial intelligence is growing in almost every field in the modern world. I chose transportation because every little improvement, whether it's algorithmically or physically, can benefit the lives of everyday people. With fewer crashes, renewable energy, and shorter routes, who would complain about such a huge win when it comes to integrating AI with transportation? This field is interesting to me because it is where theory mixes with the physical aspect of the world in real time under real conditions. Constantly using a feedback loop to progress and change the field positively.

On this Padlet, I will cover a few things regarding how AI has shaped the world of transportation. For example, autonomous vehicles that use computers and deep learning to identify changes in the environment, perceive roads, and react to real-time situations safely. Secondly, AI-assisted traffic management in major cities can use predictive analytics to quickly spot incidents and prevent them from happening. Lastly, logistics optimization, where AI will be used to determine the most cost-efficient method for getting from point A to point B. This will have a positive impact as it reduces fuel, emissions, and cost for an entire network. For each topic above, I will give a summary of how this AI-integrated technology works and the benefits it offers.

Why does this matter to me? I live in NYC, where transportation is a daily necessity to get to places. Everyone wants to maximize their time, whether it's getting to work or visiting a friend. Transportation is at the center of every person’s mind when getting from point A to point B. Finding a better way to get around such a crowded city would be so beneficial for everyone, which is why AI in this industry is compelling.

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Transportation - AI in Autonomous Vehicles (WAYMO)

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Transportation - AI in Autonomous Vehicles (WAYMO)

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Waymo, also known as robotaxis, needs to process large streams of data from cameras, LiDAR, and radar, which is then used to predict the presence of surrounding cars and safely react to real-time environments. Waymo has AI that heavily relies on deep learning to perceive the vehicles surrounding it, determine what to do based on the actions of those cars, and safely execute the maneuver. Due to the AI needing all this information, high-bandwidth DRAM and storage are necessary to give these data networks at a fast pace while taking in new information and learning from it. Some benefits from AI would be faster reaction times and consistent updates on unnatural conditions that can continuously be fixed because the AI learns from itself. However, there are challenges that come with the integration of AI. This includes the amount of thermal and power limits within the vehicle, the cost of maintaining the lifecycle of the car, and, of course, the routes that need to be learned so it will be easier to adapt locally compared to crowded cities. I think autonomous vehicles would be really cool to implement, as AVs will never drive distracted and are always on alert to reduce human-error collisions. For people who don't have access to a car, they can drive and don't want to go through the process of an Uber, they can easily access a ride for all.

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Why You Should Want Driverless Cars On Roads Now

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Transportation - AI Traffic Control (INRIX)

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Transportation - AI Traffic Control (INRIX)

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When it comes to transportation, one thing everyone can agree on hating is dealing with traffic. Using signal optimization and citywide prediction, INRIX can help big cities run smoothly and safely using AI in traffic management. INRIX uses machine learning with the help of probe vehicle sensors and signal timing, by predicting areas of congestion and adjusting traffic lights based on data, they can cut the average delay for drivers, cyclists, and public buses. In addition, emergency response teams can get real-time alerts, and AI can make some routes less congested with traffic light adjusting, which can help first responders reach the targeted destination at an earlier time. This would also contribute to the environment in a positive way because less idling means lower CO2 emissions in general. Some challenges that arise are obviously getting the required data access because of privacy/governance concerns, and it might not work as efficiently if the environmental conditions are far from perfect. Storms or heavy rainfall can negatively affect the coordination of the AI to continue controlling traffic. I believe AI being incorporated like this would be a great thing because, seeing how much traffic is in NYC daily, it would benefit everyone if traffic were a little less hectic, and first responders would be able to reach the person in need.

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Improving Traffic Flow, Safety and Sustainability with AI and 5G

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AI has already made such a big impact outside this industry, so making a big impact in transportation shouldn’t be questioned. Have you ever ordered something online? If so, AI already plays a role in determining how they arrive, the most optimal routes, and so much more. Everyone believes that AI would cause instant job loss because it might replace human workers in the Uber, Lyft industry by using fully automated cars, which is most definitely a possibility how quickly AI learns. In addition, privacy concerns can affect society because all these sensors and cameras needed to capture the data that allows the machine to learn can reveal personal information. How much information are we, as a society, willing to give for an easier lifestyle?

Where we deploy this type of technology also has a different societal impact. For example, if we only apply traffic management in marginalized neighborhoods where only a small portion of the population lives, it wouldn’t benefit society compared to a working system in a metropolis that benefits way more people. AI should expand access instead of being directed to only a small community. Like I said before, I agree with AI-integrated systems as long as there is evidence that proves the condition of the equipment is working well. If not, they should take more time to perfect it and then bring it to the public. Overall, I think AI will one day become a major factor in everyone’s lives, from autonomous cars to robots taking care of housework to AI in hospitals.

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Transportation - Logistics Optimization (ORION)

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Transportation - Logistics Optimization (ORION)

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UPS’s ORION logistics optimization is, in short, route planning at a large scale to cover a variety of networks. ORION uses AI to plan delivery and pickup routes on a daily routine spanning thousands a stops. Being able to maximize the potential of AI when it comes to updating the delivery plans throughout the day while minimizing the travel distance and cost would cut costs by a mile. This would be done through machine-learning vehicle routing problem solvers, which basically means they can optimize vehicle routes and supply chains when certain variables that aren’t seen by us play a role. Dealing with constraint programming and real-time telemetry data to update the system consistently. This, in return, means lower mileage, less fuel, and a total lower CO2 emission into the environment while performance is improved. Some obstacles would be a model drift if the conditions are unusual, or integrating time windows, so no extra time is wasted for perfect execution. I find this interesting because using AI to create an algorithm that would always update based on real-time data for delivering people from place to place or groceries to doorsteps can definitely help me because I sometimes need groceries from far places on time.

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Route Optimization In Python - Starbucks Example & Simulation

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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I believe AI in the field of transportation will expand exponentially. Shifting from a previous separation from AI to being heavily incorporated in everyday transportation. Meaning city-scale systems, automated cars that are objectively safer to commute in than your present Uber, and efficient time, cost management when it comes to getting around a city. AI is definitely going to be a huge part of any industry if companies want to keep improving. Using the examples of autonomous vehicles, soon enough, the car will be able to “see” around corners, traffic signals will be automated in real-time based on data, and everything will be coordinated simultaneously in a more efficient way.

However, ethically speaking, safety comes first. The technology and equipment that AI will be integrated with should be tested and fully confirmed to be working before it is accessed by the public. Especially with automated taxis that need to be reliable and safe for people to trust and use them daily. There should be default safe states for all AI-infused technology, as well as post-incident transparency in case an accident does occur.

Therefore, before incorporating AI into the field of transportation, trial outcomes need to be satisfactory with progress being permitted by evidence. There should be only a little deployment of this type of innovation before fully integrating autonomous cars and traffic management all around the globe. Little by little, we can definitely fully rely on the use of AI, but it will take time before AI transportation indicates safer streets, reliable mobility, and a cleaner environment.

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Micron Technology. (2018, May). How do self-driving cars work? Self-driving cars will rely on AI and innovative memory. Www.micron.com. https://www.micron.com/about/blog/applications/automotive/self-driving-cars-will-rely-on-ai-innovative-memory [Rationale: This blog explains how memory hardware helps AI perception systems in autonomous vehicles.]

Waymo. (2024). Waymo Driver. Waymo. https://waymo.com/waymo-driver/ [Rationale: The website provides details from Waymo about how its AI-driven autonomous driving system functions.]

Imagination. (2023, July 12). Artificial Intelligence in Autonomous Vehicles - Imagination. Imagination. https://www.imaginationtech.com/future-of-automotive/how-do-autonomous-cars-work/artificial-intelligence-in-autonomous-vehicles/

[Rationale: The article describes the role of AI in perception and decision-making systems in self-driving cars.]

‌INRIX. (2020). Transportation Agencies. INRIX. https://inrix.com/industries/public-sector/transportation-agencies/

[Rationale: This website discusses how INRIX uses AI and data analytics to optimize traffic flow in major cities.]

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Isarsoft. (2025, April 7). AI in Traffic Management. Isarsoft.com; Isarsoft. https://www.isarsoft.com/article/ai-in-traffic-management

[Rationale: This article examines how AI-powered systems predict congestion and enhance urban traffic management.]

Shahbazian, R., Di, L., Guerriero, F., & Macrina, G. (2024). Integrating Machine Learning Into Vehicle Routing Problem: Methods and Applications. IEEE Access, 12, 93087–93115. https://doi.org/10.1109/access.2024.3422479

[Rationale: This journal gives insights into technical research on AI algorithms that improve logistics and routing efficiency.]

Roundtrip Systems Inc. (2016). EasyRoutes Local Delivery. Roundtrip.ai. https://www.roundtrip.ai/articles/ups-route-optimization-software

[Rationale: This article explains how UPS’s real-world use of AI minimizes delivery distances, costs, and emissions.]

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After researching AI in the field of transportation, I got more intrigued with how AI works and how a machine can learn and come up with decisions after feeding it information. This is all very new to me because I didn’t expect AI to have that much of an impact as it currently does on transportation. There weren’t any major challenges in finding the articles on AI, but there are many impacts of AI, in my opinion. Everyone thinks AI is only going to be the downfall of humanity, but they don’t see the positive outcomes AI comes with. Who wouldn’t want an easier lifestyle, whether it's transportation-wise or just in general? AI can definitely make roads a safer space in the near future, and I’m counting on it. This research influenced my understanding because I used to be the type to believe AI would take over jobs instead of creating new opportunities, but now I think the opposite.