Pennsylvania State University (Penn State) researchers analyzing a large dataset of ride requests to Didi Chuxing, a Chinese car-hailing company, found computers may be better at forecasting demand for taxi and ride-sharing services than current models.
The researchers used two types of neural networks to extract patterns of taxi demand, and then to predict the demand patterns with significantly better accuracy than current technology, explains Penn State's Huaxiu Yao.
When users need a ride they make a request via a computer application, and the researchers think tapping these requests, instead of relying on ride data only, reflects overall demand better. With the historical data, which includes the request's time and location, the computer could anticipate how the demand will change over time, and the researchers were able to visualize how that demand evolved by plotting it on a map.
"Basically, we used a very complicated neural net to simulate how people digest information, in this case, the image of the traffic patterns," notes Penn State professor Jessie Li.
From Penn State News
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