2017/01/15 by Liyi Lin, Lin, Li-Yi
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Optimization and Search Problems #Other Computer Science (cs.OH) #Transportation and Mobility Innovations
paper · pdf · doi:10.48550/arxiv.1701.04126
openalex publication_date 2017/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In case of a severe accident, the key to saving lives is the time between the incident and when the victim receives treatment from the first-responders. In areas with well designed emergency medical systems, the time for an ambulance to arrive at the accident location is often not too long. However, in many low and middle income countries, it usually takes much longer for an ambulance to arrive at the accident location due to lack of proper services. On the other hand, with ubiquitous wireless connectivity, and emergence of radio based taxis, it seems feasible to build a low-cost emergency response system based on taxi service. In this report, we explore one such solution for deployment of a taxi-based emergency response systems using reinforcement learning.