Self-Organization and Multi-Agent Reinforcement Learning for Taxi Dispatch

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

The taxi dispatch problem involves assigning taxis to callers waiting at different locations. An adjacency-based dispatch system currently in use by a major taxi company divides the city(in which the system operates) into regional dispatch areas. Each area has fixed designated adjacent areas hand-coded by human experts. When a local area does not have vacant cabs,the system chooses an adjacent area to search. However, such fixed, hand-coded adjacency of areas is not always a good indicator because it does not take into consideration frequent changes in traffic patterns and road structure. This causes dispatch officials to override the system by manually enforcing movement on taxis. In this thesis, I apply two different methods separately to solve the problem: (1) a multiagent self organization technique to dynamically modify the adjacency of dispatch areas (2) a multiagent reinforcement learning method to optimize the dispatch policy for each area. I compare performance of each method with actual data from,and a simulation of, an operational dispatch system. The multiagent self organization technique decreases the total waiting time by up to 25% in comparison with the real system and increases taxi utilization by 20% in comparison with results of the simulation without self-organization. Interestingly, I also discover that human intervention (by either the taxi-dispatch officials or the taxi drivers) to manually overcome the limitations of the existing dispatch system can be counterproductive when used with a self-organizing system. Furthermore, the proposed multiagent reinforcement learning method decreases the total waiting time by up to 33.5% in comparison with the real system.

روابط وملفات

التعريف والنوع

رقم الوثيقة

03fa56c6-c62e-421e-a28e-4a64c37ab4d6

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2105173_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Aamena Ali Ahmed Omran Alshamsi","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Iyad Rahwan","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Sherief Abdallah","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Self-Organization and Multi-Agent Reinforcement Learning for Taxi Dispatch

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

taxi dispatch problem multiagent self organization reinforcement learning method

إشراف وإعداد

الإشراف

Iyad Rahwan, Sherief Abdallah

الإعداد

Aamena Ali Ahmed Omran Alshamsi

الاقتباسات الببليوغرافية