Real World Fuel Consumption and Emissions From LDVs and HDVs

Weller, Konstantin; Lipp, Silke; Röck, Martin; Matzer, Claus Uwe; Bittermann, Andreas; Hausberger, Stefan

doi:10.3389/fmech.2019.00045

Cited by 23 publications

(7 citation statements)

References 2 publications

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“…Agents belonging to Ω 0 fleet (non-classified vehicles) and agents not adhering to TWM use the map µ 0 with β 0 i,j = tt i,j that corresponds to the original physical map. Independently of client or server routing modes, the routing agent υ k a calculates the best route R k,m a in Equation (8) or hyperpath for the trip W k a , using the physical map µ 0 or the TWM µ k,m . Best-route calculation algorithms # can be used for this, such as Dijkstra, A* or others:…”

Section: Twm For Urban Traffic Routingmentioning

confidence: 99%

“…Traffic demand in urban environments involves diverse group demands that require multi-objective routing mechanisms [ 1 , 2 ]: the demand can be clustered by emissions labels and power sources [ 7 , 8 ], by their usage (public, taxi services, distribution routes, and shared or private trips), by time and date slots, and other considerations. Usually the “vehicle class” concept refers to physical features such as size, payload, consumption, and emissions, but does not take into account the real value and expectations of the group needs.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.

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Section: Twm For Urban Traffic Routingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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“…The result is obvious. Fuel consumption in "natural" operation is far from that determined in short-term tests [1]. There is no shortage of evidence that this is the case, and the primary one is that the NEDC test has been changed to WLTP [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Operational energy footprint of vehicle

Sitnik¹

2021

Combustion Engines

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One of the many important challenges of the modern world is the greenhouse effect of CO2. It is necessary to implement solutions that will eliminate it. Since the easiest way to eliminate the greenhouse effect is to reduce hydrocarbons fuel consumption, it is important to evaluate the fuel (energy) consumption in long term operation. The article presents a proposal for the use of the so-called vehicle energy footprint. The method of creating such a trace is presented and illustrated with specific results of data analyzes. The data is publicly available, which means that the presented method can be independently verified. It has also been shown that the use of the method not only organizes the analyzes, but also leads to information that cannot be obtained with other methods. It therefore seems that an attractive tool has been developed for future applications.

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“…The amount of emissions from exhaust components, including CO 2 , is estimated based on vehicle test procedures -WLTP (Worldwide Harmonized Light Duty Vehicle Test Procedure), supplemented by road tests (Pavlovic et al 2018;DiPierro et al 2019). Many researchers study real-world emissions by performing various driving cycles on selected routes, not necessarily based on homologation procedures (Weller et al 2019;Gao et al 2022). The estimated emissions are for a speci c vehicle model, but the challenge is to estimate emissions for all vehicle tra c, which consists of many different types of vehicles that run on different fuels and have different emissions-reducing technologies.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous CO2 emission modeling for the Euro 6 start-stop vehicle based on portable emission measurement system and artificial intelligence methods

Mądziel

2023

Preprint

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One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle and gradient of road to predict the emission of CO2. In this paper, 3 methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.

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Real World Fuel Consumption and Emissions From LDVs and HDVs

Cited by 23 publications

References 2 publications

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Operational energy footprint of vehicle

Instantaneous CO2 emission modeling for the Euro 6 start-stop vehicle based on portable emission measurement system and artificial intelligence methods

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