Comparing Driving Cycle Development Methods Based on Markov Chains

Abstract

The transportation sector is a major contributor to greenhouse gas (GHG) emissions, accounting for 14% of global emissions in 2010 according to the United States Environmental Protection Agency. In Quebec, this share amounts to 43%, of which 80% is caused by road transport according to the MinistÉre de l’Environnement et de la Lutte contre les changements climatiques of QuÅbec. It is therefore essential to support the actions taken to reduce GHGs emissions from this sector and to quantify the impact of these actions. To do so, accurate and reliable emission models are needed. Driving cycles are defined as speed profiles over time and they are a key element of emission models. They represent driving behaviors specific to various road types in each region. The most widely used method to construct driving cycles is based on Markov chains and consists of concatenating small sections of speed profiles, called microtrips, following a transition matrix. Two of the main steps involved in the development of driving cycles are microtrip segmentation and microtrip classification. In this study, several combinations of segmentation and clustering methods are compared to generate the most reliable driving cycle. Results show that segmentation of microtrips with a fixed distance of 250 m and clustering of the microtrips by applying a principal component analysis on many key parameters related to their speed and acceleration provide the most accurate driving cycles.

Examining the Intended and Unintended Impacts of Raising a Minimum Legal Drinking Age on Primary and Secondary Societal Harm and Violence from a Contextual Policy Perspective: A Scoping Review

“…Twenty-six studies were found which reported information (26 units of information) on the impacts of an increase in the MLDA on secondary societal harm and violence with the bridging variable (i.e., with a consideration of drinking patterns in methodology or analyses) [ 35 , 36 , 44 , 45 , 46 , 49 , 55 , 62 , 64 , 67 , 69 , 80 , 82 , 100 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 ]. Twenty-four studies were conducted in the United States, one in Canada and one in Belgium.…”

“…The bridging variable included in the analyses is based on: self-reported and (to be) convicted drink-driving behaviour measured on an individual level [ 44 , 45 , 49 , 57 , 62 , 75 , 100 , 119 , 120 , 123 , 125 ]; criteria for alcohol abuse or dependence (and other illegal substances) on an individual level [ 117 ]; the incidence of hospital-based health service use (diagnostic codes) linked to alcohol use on an individual level [ 69 ]; mean Blood Alcohol Concentration measures (BAC) mostly on an individual level [ 35 , 82 , 116 , 122 ]; controlling and/or mediating for drink-driving behaviour mostly on a population level [ 36 , 46 , 59 , 80 , 115 , 118 , 121 , 124 , 126 ]. …”

“…self-reported and (to be) convicted drink-driving behaviour measured on an individual level [ 44 , 45 , 49 , 57 , 62 , 75 , 100 , 119 , 120 , 123 , 125 ];…”

“…Out of 26 studies, 8 (30.8%) found no significant or relevant secondary societal harm and violence associated with an increase in the MLDA [ 35 , 45 , 57 , 64 , 80 , 100 , 123 , 124 ]. Four of these studies [ 45 , 57 , 80 , 124 ] did find significant or relevant primary societal impacts, with a decrease in drinking patterns in the target population associated with an increase in the MLDA.…”

“…Some reasons for not finding an impact were the additional landmark improvements in the accident avoidance and crash protection of cars, advances in medical technology [ 62 ] and a postponement in accidents by youth in the 18–20 category until they reached the age of 21 or older (i.e., postponing traffic deaths, not avoiding them) [ 124 ]. Three studies measured arrest data of DUI-offenders [ 45 , 100 , 123 ]. Two of them identified reasons for not finding an impact on the inability of measuring sensitive changes in the larger context which influences drink-driving behaviour, precluding simple before–after comparisons [ 45 , 100 ].…”