Experience

Wang, Guang; Chen, Xiuyuan; Zhang, Fan; Wang, Yang; Stankovic, John A.

doi:10.1145/3300061.3300132

Cited by 51 publications

(8 citation statements)

References 50 publications

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“…This data format is typical trajectory data, and the data source is Shenzhen, China, which is one of the first cities in China to popularize ETs and is representative and typical in this regard. Moreover, from the findings of previous research such as Wang et al, 54 the hourly proportional trend of charging events closely resembles the trends described in this study. The relatively stable trend of four charging peaks per day between 2013 and 2017 indicates that the hourly trend of charging incidents in a city can remain stable for a considerable period.…”

Section: Discussion and Limitationssupporting

confidence: 90%

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

Zhang,

Tan

2023

SIMULATION

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The development and popularization of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimize the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviors of electric taxis (ETs). In the case study of Shenzhen, China, geographical positioning system (GPS) trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a geographic information system (GIS) context of an urban road network with traveling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviors of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximizing the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimization technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation.

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Section: Discussion and Limitationssupporting

confidence: 90%

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

Zhang,

Tan

2023

SIMULATION

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“…The ETTD dataset [28] contains trajectories of 377 electric taxis with 1,155,654 GPS records and speed profiles collected on Oct 22, 2014. The driving conditions range from approximately all scenarios city-wide.…”

Section: Data Descriptionmentioning

confidence: 99%

A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

Lai

Zhang

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. However, the energy consumption of each vehicle may diverge widely due to the personalized driving behavior under varying travel contexts. To this end, this paper proposes a preference-aware meta-optimization framework (Meta-Pec) for personalized vehicle energy consumption estimation. Specifically, we first propose a spatiotemporal behavior learning module to capture the latent driver preference hidden in historical trips. Moreover, based on the memorization of driver preference, we devise a selection-based driving behavior prediction module to infer driver-specific driving patterns on a given route, which provides additional basis and supervision signals for VEC estimation. Besides, a driver-specific meta-optimization scheme is proposed to enable fast model adaption by learning and sharing transferable knowledge globally. Extensive experiments on two real-world datasets show the superiority of our proposed framework against ten numerical and datadriven machine learning baselines. The source code is available at https://github.com/usail-hkust/Meta-Pec. CCS CONCEPTS• Information systems → Spatial-temporal systems.

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“…As shown in Fig. 5, we use four trajectory collections sampled from the dataset in [45] to demonstrate the effectiveness of movement prediction of AVs based on the current location and historical routes. We select the LSTM model as the movement prediction model.…”

Section: A Experimental Setupsmentioning

confidence: 99%

Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality

Niyato

Kang

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multidimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively.

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Experience

Cited by 51 publications

References 50 publications

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality

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