Derivation of Real-World Driving Cycles Corresponding to Traffic Situation and Driving Style on the Basis of Markov Models and Cluster Analyses

2016 ◽  
Author(s):  
R. Liessner ◽  
A.M. Dietermann ◽  
B. Bäker ◽  
K. Lüpkes
Keyword(s):  
Real World ◽  
Markov Models ◽  
Traffic Situation ◽  
Driving Style ◽  
Cluster Analyses ◽  
Driving Cycles

Modeling the emissions of rural vehicles based on real-world driving cycles

2021 ◽  
pp. 148380
Author(s):  
Yi Li ◽  
Di Peng ◽  
Lei Zu ◽  
Mingliang Fu ◽  
Yao Ma ◽  
...  
Keyword(s):  
Real World ◽  
Driving Cycles

NONLINEAR GATED EXPERTS FOR TIME SERIES: DISCOVERING REGIMES AND AVOIDING OVERFITTING

1995 ◽  
Vol 06 (04) ◽  
pp. 373-399 ◽  
Author(s):  
ANDREAS S. WEIGEND ◽  
MORGAN MANGEAS ◽  
ASHOK N. SRIVASTAVA
Keyword(s):  
Time Series ◽  
Real World ◽  
Markov Models ◽  
Multilayer Perceptrons ◽  
Time Step ◽  
Local Complexity ◽  
Previous State ◽  
Segmentation Task ◽  
Gating Network ◽  
Update Rules

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes; (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state); and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are: (1) the gating network correctly discovers the different regimes of the process; (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes); and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Comparative study of real-world driving cycles, energy consumption, and CO2 emissions of electric and gasoline motorcycles driving in a congested urban corridor

2019 ◽  
Vol 45 ◽  
pp. 619-627 ◽  
Author(s):  
Triluck Koossalapeerom ◽  
Thaned Satiennam ◽  
Wichuda Satiennam ◽  
Watis Leelapatra ◽  
Atthapol Seedam ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Comparative Study ◽  
Co2 Emissions ◽  
Real World ◽  
Driving Cycles ◽  
Urban Corridor

Assessment and design of real world driving cycles targeted to the calibration of vehicles with electrified powertrain

2021 ◽  
pp. 146808742110387
Author(s):  
Stylianos Doulgeris ◽  
Zisimos Toumasatos ◽  
Maria Vittoria Prati ◽  
Carlo Beatrice ◽  
Zissis Samaras
Keyword(s):  
Real World ◽  
Simulation Models ◽  
Cost Effective ◽  
Operational Parameters ◽  
Virtual Design ◽  
Passenger Cars ◽  
Real World Data ◽  
Vehicle Simulation ◽  
Driving Cycles ◽  
The Impact

Vehicles’ powertrain electrification is one of the key measures adopted by manufacturers in order to develop low emissions vehicles and reduce the CO2 emissions from passenger cars. High complexity of electrified powertrains increases the demand of cost-effective tools that can be used during the design of such powertrain architectures. Objective of the study is the proposal of a series of real-world velocity profiles that can be used during virtual design. To that aim, using three state of the art plug-in hybrid vehicles, a combined experimental, and simulation approach is followed to derive generic real-world cycles that can be used for the evaluation of the overall energy efficiency of electrified powertrains. The vehicles were tested under standard real driving emissions routes, real-world routes with reversed order (compared to a standard real driving emissions route) of urban, rural, motorway, and routes with high slope variation. To enhance the experimental activities, additional virtual mission profiles simulated using vehicle simulation models. Outcome of the study consists of specific driving cycles, designed based on standard real-world route, and a methodology for real-world data analysis and evaluation, along with the results from the assessment of the impact of different operational parameters on the total electrified powertrain.

scholarly journals Developing Markov Models From Real-World Data: A Case Study of Heart Failure Modeling Using Administrative Data

2020 ◽  
Vol 23 (6) ◽  
pp. 743-750
Author(s):  
Praveen Thokala ◽  
Peter Dodd ◽  
Hassan Baalbaki ◽  
Alan Brennan ◽  
Simon Dixon ◽  
...  
Keyword(s):  
Heart Failure ◽  
Administrative Data ◽  
Real World ◽  
Markov Models ◽  
Real World Data ◽  
World Data ◽  
Failure Modeling

A framework for development of real-world motorcycle driving cycle in India

2020 ◽  
pp. 095440702097753
Author(s):  
Masilamani Sithananthan ◽  
Ravindra Kumar
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
State Of The Art ◽  
Emission Factors ◽  
Driving Cycle ◽  
Driving Cycles ◽  
Traffic Scenario

This paper proposed a framework for development of real-world driving cycle in India after a thorough review and comparison of motorcycle driving cycles used in different countries. A limited state-of-the art work for the development of driving cycles for motorcycles is available. The motorcycle driving cycles developed by different countries differ from each other in terms of their driving cycle characteristics, emission factors, and fuel economy. This paper reviewed the parameters of real-world driving cycles of motorcycles and compares the same with legislative cycles concerning their characteristics and emissions. The parameters of real-world driving cycles and Indian legislative cycle (IDC) deviate significantly from other legislative cycles in the range of −97% to +1172% and −74% to 284% respectively. The emission factors of the legislative cycle do not match with the realistic emissions measured by real-world driving cycles. This is due to the reason that the legislative cycles do not represent the current traffic scenario and hence need to be revised. A framework is proposed to develop a real-world driving cycle in India.

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