02/02/2021

When Hashing Met Matching: Efficient Spatio-Temporal Search for Ridesharing

Chinmoy Dutta

Keywords:

Abstract Paper Similar Papers

Abstract: Shared on-demand mobility holds immense potential for urban transportation. However, finding ride matches in real-time at urban scale is a very difficult combinatorial optimization problem and mostly heuristic approaches are applied. In this work, we introduce a principled approach to this combinatorial problem. Our approach proceeds by constructing suitable representations for rides and driver routes capturing their essential spatio-temporal aspects in an appropriate vector space, and defining a similarity metric in this space that expresses matching utility. This then lets us mathematically model the problem of finding ride matches as that of Near Neighbor Search (NNS). Exploiting this modeling, we devise a novel spatio-temporal search algorithm for finding ride matches based on the theory of Locality Sensitive Hashing (LSH). Apart from being highly efficient, our algorithm enjoys several practically useful properties and extension possibilities. Experiments with large real-world datasets show that our algorithm consistently outperforms state-of-the-art heuristic methods thereby proving its practical applicability.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38947761
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 2
 4:57 
 0
 0
 0
 0
 17:19 
 0
 0
 0
 0
 14:07 
 0
 0
 0
 0
 19:57 
 0
 0
 0
 0
 9:02 
 0
 0
 0
 1
 3:27 
 0
 0
 0
 1
 12:59 
 0
 0
 0
 1
 15:17 
 0
 0
 0
 1
 3:22 
 0
 0
 1
 1
 4:46 
 0
 0
 0
 0
 15:26 
 0
 0
 1
 1
 15:08 
 0
 0
 0
 0
 10:01 
 0
 0
 0
 0
 17:02 
 0
 0
 0
 0
 16:30 
 0
 0
 0
 0
 14:23 
 0
 0
 0
 0
 17:52 
 0
 0
 0
 0
 13:39 
 0
 0
 0
 1
 15:43 
 0
 0
 0
 0
 14:55 
 0
 0
 0
 0
 10:19 
 0
 0
 0
 0
 20:57 
 0
 0
 0
 0
 15:07 
 0
 0
 0
 0
 17:08 
 0
 0
 0
 0
 15:37 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 3:18 
 0
 0
 0
 0
 14:35 
 0
 0
 0
 0
 5:19 
 1
 0
 1
 1
 13:26 
 0
 0
 0
 0
 4:44 
 0
 0
 0
 0
 14:05 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 16:07 
 0
 0
 0
 0
 16:12 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 19:32 
 0
 0
 0
 0
 3:16 
 0
 0
 0
 0
 10:47 
 0
 0
 0
 0
 5:00