AI can increase use of alternative modes by matching commuters based on data about their unique habits and preferences. Machine learning and AI are emerging as hot topics in transportation as technology’s increasing sophistication continues to open doors to new possibilities.
RideAmigos Director of Innovation, Corey Tucker, recently appeared on the Your AI Injection podcast to discuss the growing intersections of artificial intelligence, machine learning, and how Pave Commute integrates these to help everyone choose a smarter commute. Here are some of the key topics she discussed:
Recommendation Engines
Previous generations of employer-based rideshare programs were limited in their matching capabilities. Commuters generally had limited options and had to work within the confines of inflexible schedules. These factors combined to depress adoption and historical usage rates.
As Corey discussed, emerging TDM recommendation engines are far more dynamic. Users can now connect with a much broader set of potential matches based on factors including not only travel preferences and scheduling, but also according to their own commuting habits.
The Monday-Friday, 9-to-5 world is rapidly disappearing. AI and smart commuting can track individualized data regarding a person’s unique habits and trends, and use them to generate intelligent matches. This holds the potential to dramatically increase rideshare participation rates, as it removes many of the barriers that problematized legacy approaches.
Public Transit Traffic Information
Corey acknowledged that public transit has historically lagged behind in its ability to accrue and distribute traffic-related public transit system information in real time. This is particularly true of light rapid transit (LRT) and subway networks, as road-based forms of traffic like municipal buses are well-covered by the crowdsourced traffic data collected by platforms like Google Maps and Waze. Rural areas that are served by only a very small number of transit lines also tend to offer limited levels of data-based insight.
In addition, Corey touched on some of the challenges associated with data collection. Some transit agencies readily supply data related to system performance, while others do not. Thus, it can be difficult to build accurate data repositories for cross-jurisdictional trips.
AI technologies have the potential to address some of these issues. Smart technologies that facilitate comprehensive more accurate and complete real-time views of transit conditions continue to make inroads in cities. As companion technologies that interpret and distribute this data develop, this is an area of AI and smart commuting that could see major improvement in years to come.
Optimization of Road Networks
Car-based commuting is universally recognized as a major source of greenhouse gas emissions, and policymakers continue to seek ways to mitigate its climate change impacts. In this regard, the concept of optimizing existing road networks has emerged as a point of discussion in AI and smart commuting circles.
As Corey noted, building more roads tends to have the effect of incentivizing the continuation of existing commuter behaviors with regard to driving. When new road networks are built and existing road networks are widened, commuting by car gets reinforced as a normalized behavior.
AI and advanced data collection holds the potential to disrupt that status quo. Corey argues that instead of building new roads, policymakers should focus on optimizing the use of existing roads. Advanced forms of AI-powered data analysis could yield powerful new clues regarding usage trends and habits.
Applying those insights toward spreading commuters out across various modes to ease congestion at peak periods could make a positive impact. It could also address the negative messaging around car use that alienates some segments of the commuter population. Instead of telling people “don’t drive,” messaging could shift toward “you’ll have a better driving experience if you travel using these routes or during these times.”
Additional AI and Smart Commuting Use Cases
Corey also discussed some established and emerging use cases for AI technology in the smart commuting arena. Key examples include:
- Compiling data regarding the commuting modes people tend to use, when they tend to use them, and which routes they tend to use them on
- Mode density: how many people are in a given vehicle?
- The distribution of transportation benefits and incentives
With regard to transportation benefits and incentives, Corey emphasized that many such perks are tied to high-density modes. As more powerful technological tools emerge, their ability to recognize higher-density commutes will continue to improve. That opens the door to a new generation of more appealing and larger-scale incentive programs.
TDM professionals have long recognized that incentive and benefits programs rank among the most effective ways to encourage long-term shifts to more sustainable modes of transportation. In other words, “the carrot” has historically been shown to be more effective than “the stick.” With AI and smart commuting, incentive programs could reach an entirely new level and enact real, lasting change on a mass scale.
For more fascinating insights, be sure to check out the full podcast.
Pave Commute Guides Clients to the Cutting Edge of AI and Smart Commuting
Pave Commute offers an app-based commuter program that businesses can use to make daily commutes less painful for their people teams. At a time when employees are demanding a better work-life balance and more empathy for the challenges they face on a daily basis, effective commuter programs function as a low-cost, high-impact benefit that can help businesses attract and retain higher-quality talent.
The Pave Commute app combines advanced, AI-driven mobile technology with behavioral science to promote lasting change on an organizational level. Clients can launch a new Pave Commute-powered program with just a few clicks. To learn more, or for a demonstration of our platform’s capabilities, please get in touch with us.
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