Truck Driver Behavior and Travel Time Effectiveness Using Smart GPS

Yuniar, Dewi; Djakfar, Ludfi; Wicaksono, Achmad; Efendi, Achmad

doi:10.28991/cej-2020-03091504

Cited by 23 publications

(11 citation statements)

References 21 publications

(21 reference statements)

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“…Mining scenario has magnetic field interference, poor GPS signal, and harsh working environment with more dust [36]. Port scenario often encounters rain and wet weather [37]. Airport logistics needs to require high security [38].…”

Section: Scenario Operationmentioning

confidence: 99%

Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles

Yin¹

2023

The Dynamics of Vehicles - Basics, Simulation and Autonomous Systems

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The chapter explains the effects of intelligent autonomous vehicles from future perspectives. The chapter gives readers an overview of the future intelligent autonomous vehicles and promotes the development potential on intelligent. To be specific, the chapter first gives the readers an overview of the development of autonomous vehicles. Then, the chapter introduces the potential of intelligent autonomous vehicles, key technologies that are needed for future intelligent autonomous vehicles, and how intelligent autonomous vehicles affect the future. Finally, the chapter discusses barriers in intelligent autonomous vehicles development. The chapter will be contributed as a start point for people who want to keep working on intelligent autonomous vehicles and help them understand the general condition of future intelligent autonomous vehicles.

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Section: Scenario Operationmentioning

confidence: 99%

Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles

Yin¹

2023

The Dynamics of Vehicles - Basics, Simulation and Autonomous Systems

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“…Social media can also be explored as a means to obtain information about local events as well as driver behavior. For instance, social media has been shown to be a good tool for estimating human behavior [61] and also is a significant predictor of truck drivers' travel time [62]. It is also likely that the use of vehicle information such as the vehicle model and vehicle type can improve predictions, especially in terms of energy consumption.…”

Section: Recommendations and Future Workmentioning

confidence: 99%

Prediction of EV Charging Behavior Using Machine Learning

et al. 2021

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As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.

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“…The components of each group are presented in Table 2. GPS Tracker is a sensor that is installed on a vehicle and collects location data from GPS and vehicle condition data sent from an electrical control unit (ECU) [9]. Application servers become interfaces for both the backend and frontend, the database server stores both raw data and processed data.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment in fleet management system using OCTAVE allegro

Alfarisi

Surantha

2022

Bulletin EEI

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The purpose of this study is to use the OCTAVE allegro methodology to identify risks in fleet management system (FMS), determine prioritized risks to be mitigated, provide mitigation recommendations for these prioritized risks, and shows how effective the recommendation is. The result of this study is expected to become an input for FMS service provider of possible risks in FMS services, and risk mitigation approaches that can be used to handle those risks. This risk assessment has successfully identified 6 critical information assets, 10 risks in total, and 4 risks that need to be mitigated, followed by proposed mitigation approaches for those risks. Some of the recommendation has been applied by the company and contribute to SLA achievement of the system. The result also showed that application and simulation software provide most prominent risks in FMS service, thus securing these two will eliminate most risk in FMS service.

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Truck Driver Behavior and Travel Time Effectiveness Using Smart GPS

Cited by 23 publications

References 21 publications

Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles

Perspective Chapter: Future Perspectives of Intelligent Autonomous Vehicles

Prediction of EV Charging Behavior Using Machine Learning

Risk assessment in fleet management system using OCTAVE allegro

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