Through traffic data analysis and software development, we promote data-driven decision making in road transportation.
Leavitt.ge is a creative development studio based in Tbilisi, Georgia, focusing on bringing new technologies to traffic engineering. We thrive to build digital experiences that transportation engineers, planners, and policymakers love to save lives down the road.
An American astronomer Henrietta Swan Leavitt (1868-1921) unveiled the beautiful Leavitt's Law through years of observation. Just as she sought the hidden rhythms of the cosmos, we, too, cherish curiosity to uncover data woven in everyday life to foster data-driven decisionmaking in the field of transportation.
when we are not drinking coffee, we think about traffic engineering.
We conduct video-based traffic counts with the help of artificial intelligence.
We perform traffic data analysis (e.g., macroscopic and microscopic traffic simulations, safety analysis, etc.) and provide insights to stakeholders.
We do research and publish scientific papers to advance the state of the art in transportation engineering and traffic psychology.
We develop software to accelerate traffic engineers' every-day routine.
I want to reduce the number and severity of crashes through my career. To that end, I am particularly interested in accelerating the use of computers in traffic engineering. This is because I believe that by unleashing the power of computers, we can perform some or all of the tasks that traffic engineers previously did manually—such as copying, pasting, observing, and sensing—more precisely, quickly, and scalably. If you are interested, let's change the world together.
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This Texas research showed that wildlife-vehicle collisions increase by nearly 46% during full moons compared to new moons, especially in rural areas. Non-wildlife collisions showed no difference between lunar phases. Bright moonlight likely affects animal activity, increasing collision rates. Drivers should remain cautious in wildlife-dense areas even on bright nights.
A driving simulator study examined how speed reduction markings on highways affect driving behavior. Results showed these markings reduced speed before horizontal curves, but marking shape had no significant impact. Contrary to expectations, "converging" intervals were not more effective than constant intervals, suggesting the markings primarily function as cognitive alerts rather than visual illusions. Few participants (less than 18%) noticed converging intervals, indicating these markings may create a subconscious perception of acceleration. The study also found potential benefits for lane position maintenance.
Analysis of Greater Houston’s COVID-19 mobility patterns revealed significant income disparities. The study found higher-income residents likely reduced travel distance, radius of gyration, and the number of visited locations much more than lower-income groups. The inflexibility among lower-income populations highlights an equity divide during public health emergencies.
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