택시호출 간 기대수익 조정을 통한 택시 수급불일치 완화방안 연구
A Study on the Mitigation of Taxi Supply and Demand Discrepancy by Adjusting Expected Revenues of Platform Taxi Calls
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스마트폰 보급과 ICT 기술을 발전에 따라 택시영업의 형태는 배회영업에서 플랫폼 기반 영업으로 변화해왔다. 이는 이용자의 이동성 및 접근성을 향상시키는 장점을 갖고 있지만 반대로 단거리 및 첨두수요 시간대의 간접 승차거부 등의 문제를 지속적으로 발생시키고 있다. 간접승차거부는 호출이 발생했을 때 이를 무시하고 수락하지 않는 경우를 의미하며 이를 개선할필요가 있다. 이에 본 연구에서는 택시 운행 데이터를 통해 강화 학습 기반 호출 간 기대수익조정 시뮬레이션을 수행하여 택시 수급의 불일치 완화 방안을 도출하고자 한다. 분석 결과 운행 완료율에 따라 인센티브 지급을 할 경우 평균 운행 완료율이 50.29%에서 54.24% 수준까지증가함을 확인하였으며 5,000원 미만 단거리 구간에서 5.86%의 개선 효과를 도출하였다. 운행완료율의 개선으로 운전자에게는 수익성 개선, 승객에게는 대기시간 감소의 편익을 줄 수 있을 것으로 기대되며, 택시 서비스 전반의 만족도 향상이 나타날 것으로 사료된다.
Abstract
As smartphones spread and ICT technologies develop, taxi services have changed from hovering to platform-based calls and reservations. This has improved the mobility and accessibility of taxi users but caused problems, such as digital observing (no-responses to calls) for either short-distance services or services during the peak-demand periods. Digital Observing means ignoring and not accepting calls when they occur, which require improvement. Therefore, this study aims to derive measures to mitigate discrepancies in taxi supply and demand by adjusting the expected revenue of each taxi service using reinforcement learning based on the Taxi operation data. The results confirmed that the average complete response rate to calls would increase from 50.29% to 54.24% when incentives are applied, and an improvement of 5.86% can be achieved in short-distance sections of less than 5,000 won incentives. It is expected that the improvement will increase profitability for drivers, reduce the waiting time for passengers, and improve satisfaction with taxi services overall.
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