ALWAES: an Automatic Outdoor Location-Aware Correction System for Online Delivery Platforms

For an online delivery platform, accurate physical locations of merchants are essential for delivery scheduling. It is challenging to maintain tens of thousands of merchant locations accurately because of potential errors introduced by merchants for profits (e.g., potential fraud). In practice, a platform periodically sends a dedicated crew to survey limited locations due to high workforce costs, leaving many potential location errors. In this paper, we design and implement ALWAES, a system that automatically identifies and corrects location errors based on fundamental tradeoffs of five measurement strategies from manual, physical, and virtual data collection infrastructures for online delivery platforms. ALWAES explores delivery data already collected by platform infrastructures to measure the travel time of couriers between merchants and verify all merchants' locations by cross-validation automatically. We explore tradeoffs between performance and cost of different measurement approaches. By comparing with the manually-collected ground truth, the experimental results show that ALWAES outperforms three other baselines by 32.2%, 41.8%, and 47.2%, respectively. More importantly, ALWAES saves 3,846 hours of the delivery time of 35,005 orders in a month and finds new erroneous locations that initially were not in the ground truth but are verified by our field study later, accounting for 3% of all merchants with erroneous locations.

CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods.