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Markov-based redistribution policy model for future urban mobility networks

Mikhail Volkov, Javed Aslam, Daniela Rus
2012 2012 15th International IEEE Conference on Intelligent Transportation Systems  
In this paper we present a Markov-based urban transportation model that captures the operation of a fleet of taxis in response to incident customer arrivals throughout the city.  ...  We consider three different evaluation criteria: (1) minimizing the number of transportation resources for urban planning; (2) minimizing fuel consumption for the drivers; and (3) minimizing customer waiting  ...  We present a model for a real urban mobility network, and discuss the steps taken to ensure that the model is realistic while still complying with the Markov framework.  ... 
doi:10.1109/itsc.2012.6338848 dblp:conf/itsc/VolkovAR12 fatcat:gqloutd5brb7tloefszqkxm4ju

Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises [article]

Chao Fan, Xiangqi Jiang, Ali Mostafavi
2020 arXiv   pre-print
The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience  ...  planning to reduce the impacts of crises on urban mobility.  ...  The authors also would like to thank INRIX for providing the data for this research. Jan Gerston provided editorial services.  ... 
arXiv:2009.01359v1 fatcat:tjo3b45u5zhorcxikpsg4h7i6y

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems Empowered by AI Technologies

Seung-Hyun Kong, Yisheng Lv, Hai L. Vu, Juan-Carlos Cano, Jun-Won Choi, Dongsuk Kum, Brendan Tran Morris
2019 IEEE transactions on intelligent transportation systems (Print)  
The last three articles introduce studies using AI techniques for other various problems in ITS, such as transit network design, prediction of pedestrian walking behavior, and representing locations in  ...  This paper proposes a dynamic electric vehicle routing problem (D-EVRP) model to plan the itinerary for goods delivery with the utilization of EVs in logistics industry.  ...  Specifically, a scalable framework, based on big data clustering and Markov models, is developed for vehicle trajectory prediction which is suitable for a large number of overlapping trajectories in a  ... 
doi:10.1109/tits.2019.2940856 fatcat:wrvsx6pgyfbjxlgnfekr7g7ovu

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

Alessandro Crivellari, Euro Beinat
2020 Sustainability  
The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited  ...  To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory.  ...  Acknowledgments: The authors would like to thank Vodafone Italia for providing the dataset for the case study, and the Austrian Science Fund (FWF) for the Open Access Funding.  ... 
doi:10.3390/su12010349 fatcat:pcxpqgjv7zauvo6anhu3wh62ce

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Nicola Bui, Matteo Cesana, S. Amir Hosseini, Qi Liao, Ilaria Malanchini, Joerg Widmer
2017 IEEE Communications Surveys and Tutorials  
A growing trend for information technology is to not AQ1 1 just react to changes, but anticipate them as much as possible. 2 This paradigm made modern solutions, such as recommendation 3 systems, a ubiquitous  ...  network performance.  ...  A Markov model is implemented in order to fore-595 cast future channel conditions.  ... 
doi:10.1109/comst.2017.2694140 fatcat:xdpdceqnwfgprdlpmbeuhufl4u

Special Issue on Deep Reinforcement Learning for Emerging IoT Systems

Jia Hu, Peng Liu, Hong Liu, Obinna Anya, Yan Zhang
2020 IEEE Internet of Things Journal  
By analyzing real-time urban flood data and weather data collected by the IoT system, the model of the urban flood can be constructed and refined.  ...  DRL is a particular type of RL, with deep neural networks used for state representation or function approximation of value function, policy, transition model, and reward function.  ... 
doi:10.1109/jiot.2020.2998256 fatcat:rct75tsesbh7lkjmhuyogfi4ym

Guest Editorial: Special Issue on Internet of UAVs Over Cellular Networks

Merouane Debbah, Hongliang Zhang, Walid Saad, Lingyang Song
2021 IEEE Internet of Things Journal  
We hope that this special issue will serve as a useful reference for researchers, scientists, engineers, and academics in the field of the Internet of UAVs over cellular networks.  ...  We would like to express our sincere thanks to all the authors for submitting their papers and to the reviewers for their valuable comments and suggestions that significantly enhanced the quality of these  ...  A partial observable Markov decision process (POMDP) model is utilized to formulate a joint optimization of flight cruise control and data collection schedule to minimize the network data loss.  ... 
doi:10.1109/jiot.2021.3074777 fatcat:sqtmbg2djvfcdjbiu4eoomjgxy

A Closed Queueing Maintenance Network with Two Batch Policies [article]

Rui-Na Fan, Quan-Lin Li, Xiaole Wu, Zhe George Zhang
2022 arXiv   pre-print
This paper discusses a maintenance network with failed items that can be removed, repaired, redistributed, and reused under two batch policies: one for removing the failed items from each base to a maintenance  ...  shop and the other for redistributing the repaired items from the maintenance shop to bases.  ...  Therefore, the aim of this paper is to develop a stochastic network model to discuss the bikes' failure, removing, repair, redistribution and reuse processes under two batch policies, and to further evaluate  ... 
arXiv:1910.02276v3 fatcat:j25mb3iptnazdo2y54dtfurq2i

Vehicle Redistribution in Ride-Sourcing Markets using Convex Minimum Cost Flows [article]

Renos Karamanis, Eleftherios Anastasiadis, Marc Stettler, Panagiotis Angeloudis
2020 arXiv   pre-print
We demonstrate that this model can have a convex domain, and we introduce an edge splitting algorithm to solve a transformed convex minimum cost flow problem for vehicle redistribution.  ...  Ride-sourcing platforms often face imbalances in the demand and supply of rides across areas in their operating road-networks.  ...  A fluid-based optimization problem on a queuing network was used in [18] to identify an optimal routing policy with an upper bound for empty car routing in ride-sharing systems.  ... 
arXiv:2006.07919v2 fatcat:qbsikklzu5dhfjgekhe2fwpihy

Data Science for the Internet of Things

Francesco Piccialli, Salvatore Cuomo, Nik Bessis, Yuji Yoshimura
2020 IEEE Internet of Things Journal  
., Treviso, Italy, an innovative startup company whose mission is the development of innovative systems and services based on IoT technology for cultural heritage.  ...  He has been involved in research and development projects in the research areas of the Internet of Things, smart environments, data science, and mobile applications. Dr.  ...  The article titled "Multiuser multivariate multiorder Markovbased multimodal user mobility pattern prediction" presents a multiuser multivariate multiorder Markov model including the influence model of  ... 
doi:10.1109/jiot.2020.2985598 fatcat:6rmpgcitjbbmdkctvhirzxxk3q

Design and Management of Vehicle Sharing Systems: A Survey of Algorithmic Approaches [article]

Damianos Gavalas, Charalampos Konstantopoulos, Grammati Pantziou
2015 arXiv   pre-print
solutions to schedule operator-based repositioning of bicycles/cars (by employees explicitly enrolled in vehicle relocation) based on the current and future (predicted) demand patterns (operator-based  ...  This chapter offers a comprehensive review of algorithmic approaches for the design and management of vehicle sharing systems.  ...  Introduction Sustainable principles in urban mobility urge the consideration of emerging transportation schemes including vehicle sharing as well as the use of electro-mobility and the combination of vehicle  ... 
arXiv:1510.01158v1 fatcat:qx62cmfv4zbxziuzbc6cltda3a

Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2—Applications in Transportation, Industries, Communications and Networking and More Topics

Xuanchen Xiang, Simon Foo, Huanyu Zang
2021 Machine Learning and Knowledge Extraction  
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems.  ...  The first part of the overview introduces Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language  ...  [33] presented a DQN-based approach for navigation in the urban environment, and Isele et al. [34] used a DQN-based method for navigating in occluded intersections.  ... 
doi:10.3390/make3040043 doaj:45bf00de595c44d186fa3d200589c1c5 fatcat:qx4srh7qabgjvd5l6lj6nulhxa

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Marios Avgeris, Dimitrios Spatharakis, Dimitrios Dechouniotis, Aris Leivadeas, Vasileios Karyotis, Symeon Papavassiliou
2022 Sensors  
To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism  ...  for the distribution of the excess workload.  ...  Utilizing neural networks and Markov chains, Labriji et al. [18] presented a mobility prediction algorithm to proactively and online migrate computation services (VMs) for vehicular 5G networks.  ... 
doi:10.3390/s22020660 pmid:35062619 pmcid:PMC8778252 fatcat:5gybixt7gzgjbaoleno6pwqwga

Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review

Vitória Albuquerque, Miguel Sales Dias, Fernando Bacao
2021 ISPRS International Journal of Geo-Information  
published between 2015 and 2019, creating an outline for future research.  ...  Cities are moving towards new mobility strategies to tackle smart cities' challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on  ...  Acknowledgments: We wish to thank Vitor Duarte Santos and Maria Anastasiadou for their help in the PRISMA methodology.  ... 
doi:10.3390/ijgi10020062 fatcat:uagkcbtxe5gitb4cyx5n64nnhq

Enhanced Mobility with Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems [article]

Liuhui Zhao, Andreas A. Malikopoulos
2019 arXiv   pre-print
Shared mobility can provide access to transportation on a custom basis without vehicle ownership.  ...  The advent of connected and automated vehicle technologies can further enhance the potential benefits of shared mobility systems.  ...  For example, based on the same network in New Jersey as in [43] , Zachariah et al.  ... 
arXiv:1905.12602v1 fatcat:mtgl3pv2mjdmjnudpv2eamiqhi
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