اليكم الان From Strangers to Travel Companions: How AI is Fixing Our Daily Ridesharing Trips والان إلى التفاصيل من المصدر وهج الخليج
Wagulf-Muscat
Step into a world where your daily ride is no longer a stressful chore or an anonymous ride with strangers, but a personalized experience. In our latest research at Hasselt University in Belgium, this study represents a first-of-its-kind investigation into profiling shared mobility users. we look at how the socially-structured vanpooling in regions like Oman can be transformed by merging artificial intelligence with advanced optimization. Before designing better vans, we first had to understand the people riding them. By analyzing a dataset of over 3,615 riders, our machine learning models successfully uncovered five distinct behavioral profiles that can be described as follows: 1) Reserved Students: Mostly young female students seeking basic campus and family trips with riders in similar ages and interests. 2) Independent Workers: Typically higher-income working males who own cars and rarely share rides unless traveling long distances outside the city. 3) Dependent Workers: Working women who does not have driving licenses and mainly rely on shared vans for safe, quiet trips to work. 4) Unemployed / Job Seekers: Individuals looking to transform simple trips into comfortable, social opportunities. 5) Broad-Minded Students: Highly flexible riders open to diverse travel purposes and companions. Furthermore, the classification models we used were able to predict a new rider‘s profile with an impressive 91% accuracy which reflects the high performance of the used model.
Knowing the social preferences of the riders allowed us to tackle the next big puzzle: how to build smarter vanpooling. Instead of relying on traditional geographic matching that ignores socialpreferences, we developed a two-stage system that first groups people by location and time and then uses the profiles we described earlier to maximize real social harmony inside an 8-seater van. We tested this framework in a digital simulation of Salalah city and the results were incredible. Introducing our profile-aware method more than doubled users’ satisfactioncompared to traditional methods. However, this massive boost in comfort came with only a minor 3.38% increase in travel distance and operational costs.
Looking to the future of smart cities here in Oman, it’s not to just think about the roads we built , concrete and expanding highways, but the people traveling on them. By focusing on AI-powered shared vanpooling tailored to what riders actually need, and understand the social preferences of each rider, we can build smart mobility that truly understand the riders.
Amal AL Murfadi
Omani PhD Researcher,
Hasselt University, Belgium
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