Smart City Gnosys
Smart city article details
| Title | Towards Optimized Dynamic Ridesharing System Through Multi-Objective Reinforcement Learning |
|---|---|
| ID_Doc | 58246 |
| Authors | Abdelmoumene H.; Boussahoul S. |
| Year | 2024 |
| Published | 2024 IEEE International Multi-Conference on Smart Systems and Green Process, IMC-SSGP 2024 |
| DOI | http://dx.doi.org/10.1109/IMC-SSGP63352.2024.10919539 |
| Abstract | As cities grow, addressing traffic congestion, environmental impact, and transportation efficiency becomes crucial. Dynamic ridesharing is a new paradigm for smart cities that aims to reduce traffic congestion and emissions by dynamically forming passengers' trips at short notice. It involves matching drivers and passengers in real-time. The utilization of ridesharing is highly volatile and undergoes significant changes over time, making it important to model and forecast its potential ahead of time. This work presents a novel approach to enhance ridesharing systems within the context of smart cities, where an intelligent and adaptive transportation infrastructure is paramount. Unlike conventional ridesharing platforms that typically prioritize either a singular objective or multiple objectives that are aligned, our approach focuses on the optimization of conflicting objectives by leveraging Multi-Objective Reinforcement Learning (MORL) principles. Firstly, it targets minimizing passengers' waiting times, which is crucial for enhancing user experience and satisfaction. Secondly, it focuses on reducing drivers' detour times, thus optimizing their efficiency and reducing operational costs. Thirdly, the system aims to maximize vehicle utilization, ensuring that resources are utilized optimally. By leveraging MORL, the ridesharing platform adapts to real-time demands of the urban environment, accounting for spatio-temporal constraints and diverse user preferences. The results obtained from our experiments demonstrate the system's effectiveness, robustness, and adaptability to various constraints. © 2024 IEEE. |
| Author Keywords | Dynamic matching; Dynamic ridesharing system; Multi-objective optimization; Multi-Objective Reinforcement Learning (MORL) |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
11602  | 0.885 | Li J.; Allan V.H. | Balancing Taxi Distribution In A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based On Demand Learning | 2020 IEEE International Smart Cities Conference, ISC2 2020 (2020) |
| 46636 | 0.884 | Seng K.P.; Ang L.-M.; Ngharamike E.; Peter E. | Ridesharing And Crowdsourcing For Smart Cities: Technologies, Paradigms And Use Cases | IEEE Access, 11 (2023) |
| 37504 | 0.879 | Altshuler T.; Altshuler Y.; Katoshevski R.; Shiftan Y. | Modeling And Prediction Of Ride Sharing Utilization Dynamics | Applied Swarm Intelligence (2024) |
| 50634 | 0.876 | Ahmadi K.; Allan V.H. | Smart City: Application Of Multi-Agent Reinforcement Learning Systems In Adaptive Traffic Management | 2021 IEEE International Smart Cities Conference, ISC2 2021 (2021) |
| 1880 | 0.872 | Magsino E.R.; Ching G.R.C.; Espiritu F.M.M.; Go K.D. | A Game Theory-Based Pricing Technique For Ridesharing Pairings | 1st International Conference in Advanced Innovation on Smart City, ICAISC 2023 - Proceedings (2023) |
| 38873 | 0.87 | Al-Kaff A. | Navigating The Future: Ai Innovations For Intelligent Mobility In Smart Cities | SAE Technical Papers (2023) |
| 52545 | 0.869 | He S.; Shin K.G. | Spatio-Temporal Capsule-Based Reinforcement Learning For Mobility-On-Demand Network Coordination | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 (2019) |
| 54026 | 0.867 | Louati A.; Louati H.; Kariri E.; Neifar W.; Hassan M.K.; Khairi M.H.H.; Farahat M.A.; El-Hoseny H.M. | Sustainable Smart Cities Through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles | Sustainability (Switzerland) , 16, 5 (2024) |
| 9772 | 0.867 | Xiao X.; Xu Y. | Application Of A Path-Based Ridesharing User Equilibrium Model Of Drivers Matching Passengers From Multiple Od Pairs To Smart City | Proceedings of SPIE - The International Society for Optical Engineering, 13224 (2024) |
| 8894 | 0.867 | Chen Z.; Li P.; Xiao J.; Nie L.; Liu Y. | An Order Dispatch System Based On Reinforcement Learning For Ride Sharing Services | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020) |