Deep Q-Network Based Route Scheduling for Transportation Network Company Vehicles

Shi, Dian; Ding, Jiahao; Errapotu, Sai Mounika; Xu, Wenjun; Zhou, Xiangwei; Pan, Miao

doi:10.1109/glocom.2018.8647546

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…Another issue for transportation network companies is route scheduling for their drivers to pick up passengers in order to minimize passenger waiting time as well as cost for the driver and company. Shi et al (2018) has suggested a DRL model aiming to give drivers the best route. This paper considers different factors such as the current location of vehicles, time of day, and competition between drivers, resulting in a significantly shorter search time and more long-term revenue for drivers.…”

Section: Ride Sharing and Public Transportationmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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Section: Ride Sharing and Public Transportationmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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“…The application of machine learning to improve driver revenue by reducing idle time has been studied before (Han et al, 2016;Verma et al, 2017;Shi et al, 2018). Based on existing work, a Deep Reinforcement Learning (DRL) scheme is demonstrated to provide good quality improvement in driver revenues (Shi et al, 2018). However, these approaches assume the presence of a centralized coordinator to steer the RL process.…”

Section: Case Study: Improving Taxi Driver Revenue With Bafflementioning

confidence: 99%

“…A central repository of ride information presents several privacy issues which have been successfully exploited to de-anonymize passenger information (Douriez et al, 2016). The work done in (Shi et al, 2019) as an extension of (Shi et al, 2018) introduces privacy preserving features and distributed computation as a means to improve driver revenue. However, (Shi et al, 2019) assumes a hierarchical computational setup that prevents all the benefits of decentralized computations from being realized in their entirety.…”

Section: Case Study: Improving Taxi Driver Revenue With Bafflementioning

confidence: 99%

BAFFLE : Blockchain Based Aggregator Free Federated Learning

Ramanan

Nakayama²

2019

Preprint

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A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to select and integrate models from various user devices. However, infeasibility of an aggregator due to a variety of operational constraints could prevent FL from being widely adopted. In this paper, we introduce BAFFLE, an aggregator free FL environment. Being powered by the blockchain, BAFFLE is inherently decentralized and successfully eliminates the constraints associated with an aggregator based FL framework. Our results indicate that BAFFLE provides superior performance while circumventing critical computational bottlenecks associated with the blockchain.

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“…Deep Q-networks (DQN) are a fundamental component of reinforcement learning that utilize Qlearning and deep neural networks. DQNs are applied to areas as diverse as game playing [1], portfolio management [2], scheduling [3], industrial control [4], robotics [5] and intrusion detection [6]. If a DQN is trained with samples from a problem space, they can leverage Qlearning theory to learn by trial and error.…”

Section: Introductionmentioning

confidence: 99%

Computer Science and Machine Learning Trends 2023

2023

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Mnih's seminal deep reinforcement learning paper that applied a Deep Q-network to Atari video games demonstrated the importance of a replay buffer and a target network. Though the pair were required for convergence, the use of the replay buffer came at a significant computational cost. With each new sample generated by the system, the targets in the mini batch buffer were continually recomputed. We propose an alternative that eliminates the target recomputation called TAO-DQN (Target Accelerated Optimization-DQN). Our approach focuses on a new replay buffer algorithm that lowers the computational burden. We implemented this new approach on three experiments involving environments from the OpenAI gym. This resulted in convergence to better policies in fewer episodes and less time. Furthermore, we offer a mathematical justification for our improved convergence rate.

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Deep Q-Network Based Route Scheduling for Transportation Network Company Vehicles

Cited by 12 publications

References 9 publications

Applications of Deep Learning in Intelligent Transportation Systems

Applications of Deep Learning in Intelligent Transportation Systems

BAFFLE : Blockchain Based Aggregator Free Federated Learning

Computer Science and Machine Learning Trends 2023

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