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Artificial intelligence as we know it is reshaping transportation end-to-end: how vehicles perceive the road, how cities manage traffic, and how logistics firms plan the “last mile.” I chose transportation because mobility sits at the intersection of my interests-cars, and real-world systems that affect everyday life. Ive been taking the MTA busses and trains for years and am a hybrid car enthusiast so I'm very interested in the vision of transportation in the future. AI already powers computer-vision stacks in robotaxis, predictive analytics that tune traffic lights, and optimization engines that cut millions of miles from delivery routes. In this Padlet I’ll cover 3 concrete applications: (1) autonomous ride-hailing, (2) AI traffic management for cities, and (3) route optimization in parcel logistics. For each, I’ll outline the core tech (deep learning perception, sensor fusion, reinforcement learning, and operations research.etc.), the wins we’re seeing (safety, time, cost, emissions), and the real and current limitations (edge-case safety, accountability, privacy, job impacts).

Why this matters to me: mobility is freedom, it's accessibility is power, and its efficiency is core to the efficiency of a community. When AI makes travel safer and more efficient, it can widen access to work, school, healthcare etc. and reduce the stress and waste of congestion. But the same tools raise hard questions: whose data powers these systems, who is accountable when algorithms err, and who bears the disruption when tasks become automated? My Padlet will highlight both the promise and the tradeoffs so I’m not just cheering the tech for the culture of it (Tesla.....) I’m evaluating it. I’ll close with future trends (e.g. safe driverless scaling, connected-vehicle data for signals, and greener logistics) plus ethical and societal impacts I think are the most urgent to address.

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Waymo operates driverless robotaxi services using AI perception (deep learning on lidar/radar/cameras), prediction, and planning to navigate complex urban traffic. The company publishes safety analyses comparing its “Waymo Driver” to human benchmarks and runs fully driverless rides in Phoenix and San Francisco.

Benefits: potential crash reduction, always-alert driving, infallible to the breadth of human error (just the limits of human programming).

Limits: rare but high-stakes failures, unclear ticketing/accountability (if there is no driver -who is liable?), and evolving regulation. Recent incidents (e.g. illegal U-turn stop) show why transparent data and oversight matter.

Why I find it interesting: it tests whether software can be safer than humans ..without losing public trust. Many have imagined a world have imagined a world of self driving cars in the past but do we want them now that they are here? Its made me realize the Waymos I see also could have been jobs for real taxis.

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Cities use INRIX analytics, powered by connected-vehicle data and AI, to monitor speeds, delays, and incidents in real time, and to optimize signals and corridors. Reports like the Global Traffic Scorecard quantify congestion costs and trends; Signal Analytics helps fix underperforming lights without fieldwork.

Benefits: shorter travel times, lower emissions.

Limits: data governance, representativeness of connected-car data, and equity (who benefits first).

My take: great example of AI as infrastructure-quietly improving everyone’s daily commute. On the other hand I'm really not a fan of the new age of mass data collection. Especially when we are nowhere close to true data rights or transparency.

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3) Route Optimization in Parcel Logistics (UPS ORION)

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3) Route Optimization in Parcel Logistics (UPS ORION)

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UPS’s ORION (On-Road Integrated Optimization & Navigation) applies advanced analytics/AI to reorder stops and minimize miles. At full scale, ORION has been credited with eliminating ~100M miles and saving ~10M gallons of fuel per year, with large cost and CO₂ reductions-now extended with Dynamic ORION for on-the-fly re-routing. Benefits: efficiency and emissions cuts at national scale. Limits: workforce adaptation and algorithm-human alignment.

Why I like it as well as thoughts: Elegant math making the real world greener and faster. Im curious how it affects r and d tho and if it is as efficient as they say. Orion can't yet track what is being delivered and how much space is taken in the back of the truck or how efficient delivery is in a school zone at school pickup time.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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Near-term trends:

(1) Scaled driverless operations in geofenced (harder to access) areas with richer safety datasets and updated state rules. NHTSA’s Standing General Order continues to collect ADS crash data for oversight.

(2) Vehicle-to-everything (V2X) + connected analytics feed smarter signal timing and corridor management beyond pilot corridors- with limits!

(3) AI-first logistics and continuous optimization across planning, loading, and last-mile, compounded with fleet electrification (kind of scary when you think about it). Longer-term, models will better handle rare edge cases through simulation, self-supervision, and improved calibration. Tho -not as good at handling optimization as localized and dynamic as a experienced human driver for example.

Ethics: Safety and accountability remain core-who is liable when an ADS breaks the law or injures someone? There is no human-as is the point and the issue! Recent enforcement gaps (e.g. ticketing a driverless vehicle) show policy must keep pace. Privacy & surveillance risks arise as mobility data is getting more precise; strong minimization, anonymization, and purpose limits are essential. Bias & fairness can appear if models underperform on vulnerable road users (pedestrians, cyclists, wheelchair users..etc.) or certain environments. Transparency-publishing comparable safety metrics and opening methods for outside scrutiny-builds trust.

I’m most concerned with meaningful public auditability (standardized safety reporting, independent assessments) and especially data governance (clear rules on who can collect, share, and monetize mobility data). If we get those right, the benefits of fewer crashes, smoother trips, and lower emissions are enormous.

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AI in transportation could materially reduce crashes and congestion while shrinking carbon footprints, but impacts are uneven without policy design.

Employment: automation may shift work from driving to fleet operations, remote assistance, maintenance, mapping, and AI safety requiring reskilling and fair transition plans. Privacy: citywide analytics and connected-vehicle telemetry create powerful datasets for better and worse; strong governance is needed to prevent misuse or re-identification. Equity: smarter corridors tend to land first where ROI (return on investment) is highest; policymakers should target safety improvements for high-injury networks and underserved neighborhoods.

Public safety: oversight matters; federal data collection on ADS and investigations (automated-vehicle crashes..etc.) aim to surface risks early.

Environment: optimization like ORION shows how algorithms can cut fuel and emissions at scale; pairing AI with electrification can multiply gains.

Personally, I’m optimistic but highly highly cautious. I want the mobility benefits: fewer crashes, less wasted time, and cleaner air for pedestrians and transit riders. But trust hinges on accountability, not marketing: continuous, comparable safety data; clear liability; and privacy protections by default. Done right, AI can make everyday mobility feel calmer, fairer, and more reliable. :)

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Researching AI in transportation showed me how “invisible” algorithms shape daily mobility-often more than headline-grabbing self-driving cars (Tho these other systems hover more over our communal information). My biggest challenge was balancing company claims with neutral sources; using government pages and long-running programs (NHTSA crash reporting, INRIX Scorecard, and ORION’s multi-year track record- tho all bias isn't avoidable) helped me stay grounded. I learned that optimization alone (like ORION) can deliver massive sustainability gains even before full autonomy arrives. I’m leaving more optimistic about safety and emissions but also more fervently on transparent metrics, clear liability, and privacy protections so communities can trust these systems and it can be a all round societal benefit for everyone not just share-holders...

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National Highway Traffic Safety Administration. (n.d.). Standing general order on crash reporting. https://www.nhtsa.gov/laws-regulations/standing-general-order-crash-reporting

Rationale: Establishes official U.S. crash-reporting requirements for ADS/ADAS—key for oversight.

National Highway Traffic Safety Administration. (n.d.). Automated vehicle safety. https://www.nhtsa.gov/vehicle-safety/automated-vehicles-safety

Rationale: Neutral overview of ADS potential and current regulatory posture.

Waymo. (2024, Sept. 5). New Data Hub shows how Waymo improves road safety. https://waymo.com/blog/2024/09/safety-data-hub

Rationale: Company safety data you can cite and critique; includes methodology claims.

Waymo. (n.d.). Safety: Our approach and impact. https://waymo.com/safety/impact/

Rationale: Details on safety practices and published comparative injury/airbag metrics.

INRIX. (2024). Global Traffic Scorecard. https://inrix.com/scorecard/

Rationale: Widely cited, current congestion analytics that ground societal-impact claims.

INRIX. (n.d.). AI Traffic: Real-time traffic data product page. https://inrix.com/products/ai-traffic/

Rationale: Explains how connected-vehicle data and AI feed city traffic management.

INFORMS. (2015). Optimizing delivery routes (UPS ORION success story). https://www.informs.org/Impact/O.R.-Analytics-Success-Stories/Optimizing-Delivery-Routes

Rationale: Independent operations-research case study documenting ORION’s fuel/mile savings.

UPS. (2018, Dec. 4). UPS deploys purpose-built navigation (ORION + UPSNav). https://about.ups.com/us/en/newsroom/press-releases/innovation-driven/ups-deploys-purpose-built-navigation-for-ups-service-personnel.html

Rationale: Primary source on ORION’s evolution and capabilities.

UPS. (2020, Jan. 29). UPS announces Dynamic ORION rollout. https://about.ups.com/us/en/newsroom/press-releases/customer-first/ups-announces-numerous-products-and-innovative-technology-programs-to-help-smbs-grow-and-compete.html

Rationale: Confirms on-the-fly, dynamic route optimization in production.

Business Insider. (2025, Sept.). Police pull over a driverless Waymo… https://www.businessinsider.com/san-bruno-police-pull-over-waymo-illegal-turn-autonomous-vehicle-2025-9

Rationale: Illustrative, recent incident that surfaces accountability and policy gaps.

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due to padlet having a 2 minute limit.