Traffic Locality as an Opportunity in the Data Center

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Recent advances in machine learning have dramatically increased model size and computational requirements, increasingly straining computing system capabilities. This tension is particularly acute for resource-constrained edge scenarios, for which careful h ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

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Microscopic traffic flow models can be distinguished in lane-based or lane-free depending on the degree of lane-discipline. This distinction holds true only if motorcycles are neglected in lane-based traffic. In cities, as opposed to highways, this is an o ...

Ridesourcing has driven a lot of attention in recent years with the expansion of companies like Uber, Lift, and many others around the world. Companies use mobile applications connected through the internet to match drivers and their passengers real-time. ...

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We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

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The combination of multimodal interactions, dynamic congestion and lane changing phenomena in an urban environment can have a strong impact on traffic flow dynamics, active road capacity and road safety. However, while there is a vast understanding of micr ...

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A vehicle's steering is a particular system in that it is exposed to individual subjective reviews based on criteria that are hard to assess quantitatively. Haptic design of such systems is a prime concern that has been at the center of industrial developm ...

Traffic Locality as an Opportunity in the Data Center

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