Usage of SUMO Computer Modeling Software for Road Traffic Control System Validation

Ivanov, Alexey; Abu-Abed, Fares

doi:10.35940/ijrte.b3396.078219

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Simultaneous and rapid advances in computer science and informatics have led to remarkable technological progress, which affects logistics as a scientific management discipline, as an activity and as an industry. Professional truck drivers employed within the transport logistics sector are facing major changes as a result of emerging technologies, such as platooning (Jia et al , 2016), autonomous or self-driving vehicle technologies (Collingwood, 2018), intelligent transportation systems (Hu and Deng, 2019) and intelligent road traffic control systems (Ivanov and Abu-Abed, 2019). Although automation and assistance technologies are increasingly changing the work systems of blue-collar workers, humans will remain essential elements of logistics systems.…”

Section: Introductionmentioning

confidence: 99%

Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics

Loske

Klumpp

2021

IJLM

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PurposeTechnological advances regarding artificial intelligence (AI) are affecting the transport sector. Although fully autonomous delivery, or self-driving trucks, are not operating currently, various AI applications have become fixed components of cargo vehicles. Since many research approaches primarily concentrate on the technical aspects of assistance systems (ASs), the economic question of how to improve efficiency is seldom addressed. Therefore, the purpose of this paper is to apply an efficiency analysis to measure the performance of truck drivers supplying retail stores.Design/methodology/approachFor this comparative study, 90 professional truck drivers in three groups are compared with (1) trucks without AS, (2) trucks with AS that cannot be turned off and (3) trucks with AS that can be turned off. First, we build a model investigating the impact of performance expectation, effort expectation, social influence and facilitating conditions on the behavioural intention to use AS. Second, we explore the impact of truck drivers' behavioural intention on actual technology use, misuse and disuse; operationalize these constructs; and merge them with our behavioural constructs to create one econometric model.FindingsThe human–AI system was found to be the most efficient. Additionally, behavioural intention to use ASs did not lead to actual usage in the AI-alone observation group, but did in the human–AI group. Several in-depth analyses showed that the AI-alone group used AS at a higher level than the human–AI group, but manipulations through, for example, kickdowns or manual break operations led to conscious overriding of the cruise control system and, consequently, to higher diesel consumption, higher variable costs and lower efficiency of transport logistical operations.Research limitations/implicationsEfficiency analysis with data envelopment analysis is, by design, limited by the applied input and output factors.Originality/valueThis study represents one of the first quantitative efficiency analyses of the impact of digitalization on transport performance (i.e. truck driver efficiency). Furthermore, we build an econometric model combining behavioural aspects with actual technology usage in a real application scenario.

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Section: Introductionmentioning

confidence: 99%

Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics

Loske

Klumpp

2021

IJLM

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Simulation Tools for Transport Monitoring Systems in the Mining Industry

Abu-Abed

2021

E3S Web Conf.

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This paper presents a computer modeling approach for monitoring a fleet of mining machines based on a software solution for traffic modeling. Computer simulations can help reduce prototyping costs and reduce the risk of initial launch failure by analyzing and tuning a prototype to test the most appropriate options. Using a computer modeling approach, we show in the first part of the article that the resulting vehicle monitoring metrics can be tested during the modeling process, instead of adding equipment to vehicles during the prototyping phase. Using real equipment in the prototype phase increases fleet downtime and decreases productivity. Using modern solutions for storing time series, we show how easy it is to analyze the data obtained as a result of modeling. In the second part of the article, we propose a workflow for integrating SUMO with a time series data warehouse through a software interface (API) called TraCI, which allows you to aggregate and visualize vehicle fleet data over time. At the end of this work, we discuss the measurement methodology and propose a potential solution for efficient data transmission.

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Methods for integrating and executing programs in the Java language when developing an Android application on the unreal engine 4 game engine

Abu-Abed¹,

Volkov²

2023

Modern Approaches in Engineering and Natural Sciences: Maens-2021

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Usage of SUMO Computer Modeling Software for Road Traffic Control System Validation

Cited by 3 publications

References 5 publications

Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics

Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics

Simulation Tools for Transport Monitoring Systems in the Mining Industry

Methods for integrating and executing programs in the Java language when developing an Android application on the unreal engine 4 game engine

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