Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models

2016 ◽  
Vol 97 ◽  
pp. 28-37 ◽  
Author(s):  
Ahmed Osama ◽  
Tarek Sayed
Keyword(s):  
Prediction Models ◽  
Macro Level ◽  
Collision Prediction ◽  
The Impact

Macro-level collision prediction models for evaluating neighbourhood traffic safety

2006 ◽  
Vol 33 (5) ◽  
pp. 609-621 ◽  
Author(s):  
Gordon R Lovegrove ◽  
Tarek Sayed
Keyword(s):  
Linear Regression ◽  
Traffic Safety ◽  
Road Safety ◽  
Prediction Models ◽  
Demand Management ◽  
Macro Level ◽  
Safety Planning ◽  
Transportation Demand ◽  
Regression Modelling ◽  
Collision Prediction

This study describes the development of macro-level (i.e., neighbourhood or traffic zone level) collision prediction models using data from 577 neighbourhoods across the Greater Vancouver Regional District. The objective is to provide a safety planning decision-support tool that facilitates a proactive approach to community planning which addresses road safety before problems emerge. The models are developed using the generalized linear regression modelling (GLM) technique assuming a negative binomial error structure. The resulting models relate traffic collisions to neighbourhood characteristics such as traffic volume, demographics, network shape, and transportation demand management. Several models are presented for total or severe collisions in rural or urban zones using measured and (or) modelled data. It is hoped that quantifying a predictive traffic safety – neighbourhood planning relationship will facilitate improved decisions by community planners and engineers and, ultimately, facilitate improved neighbourhood traffic safety for residents and other road users.Key words: neighbourhood safety, macro-level collision prediction models, road safety, safety planning, transportation demand management, sociodemographic, generalized linear regression modelling.

Macro-Level Collision Prediction Models Related to Bicycle Use

ICTIS 2011 ◽  
2011 ◽  
Author(s):  
Feng Wei ◽  
Ahsan Alam ◽  
Gordon Lovegrove
Keyword(s):  
Prediction Models ◽  
Macro Level ◽  
Collision Prediction

scholarly journals Macro-spatial approach for evaluating the impact of socio-economics, land use, built environment, and road facility on pedestrian safety

2017 ◽  
Vol 44 (12) ◽  
pp. 1036-1044 ◽  
Author(s):  
Ahmed Osama ◽  
Tarek Sayed
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Prediction Models ◽  
Pedestrian Safety ◽  
Macro Level ◽  
Spatial Effects ◽  
Severe Injuries ◽  
Traffic Exposure ◽  
Safety Models ◽  
The Impact

With the increasing demand for sustainability, walking is being encouraged as a main active mode of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes, which can discourage road users from walking. Therefore, studying the factors that affect the safety of pedestrians is important. This paper investigates the relationship between pedestrian-vehicle crashes and various zone characteristics in the city of Vancouver. The goal is to assess the impact of socio-economics, land use, built environment, and road facility on pedestrian safety using macro-level collision prediction models. The models were developed using generalized linear regression and full Bayesian techniques. Both walking trips and vehicle kilometres travelled were used as the main traffic exposure variables in the models. The safety models showed that pedestrian-motorist crashes were non-linearly positively associated with the increase in traffic exposure. The crashes were also found positively associated with the socio-economic variables (i.e., employment and household densities), some built environment variables (transit stop, traffic signal, and light pole densities), commercial area density, and arterial-collector roads proportion. On the other hand, the models revealed a decline in the pedestrian-motorist crashes associated with the increase in the proportions of pedestrian-actuated signals and local roads, as well as the increase in the recreational and residential areas’ densities. The spatial effects were accounted for in the full Bayes models and were found significant, which imply the importance of considering spatial correlation when developing macro-level pedestrian safety models.

Investigating Impact of Speed on Traffic Safety using Collision Prediction Model

2013 ◽  
Vol 779-780 ◽  
pp. 482-485
Author(s):  
Zhun Tian
Keyword(s):  
Traffic Safety ◽  
Urban Areas ◽  
Prediction Models ◽  
Motor Vehicle ◽  
Collision Frequency ◽  
High Ratio ◽  
Vehicle Speed ◽  
Rural Roads ◽  
Collision Prediction ◽  
The Impact

Motor vehicle speed is a risk factor contributing to many road accidents which result in significant social and economic costs. Although a review of previous research shows that the literature is extensive on the impact of speed on traffic safety, the majority of previous researches mainly focused on rural roads while rarely on urban roads. It is crucial that the relationship between speed and road collisions should be investigated in urban areas because of the fact that a high ratio of collisions is occurred on urban roads. The objective of this study is to examine the influence of motor vehicle speed on road collisions on urban road sections. This objective is achieved by developing collision prediction models which quantitatively correlate collision frequency to speed characteristics. It is found that both mean speed and speed standard deviation are positively related to collision frequency. Both of them are risk factors in traffic safety.

scholarly journals GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Phenotypic Expression ◽  
Genetic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Korean Cattle ◽  
Evaluation Programs ◽  
The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

scholarly journals Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neel Patel ◽  
William S. Bush
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Prediction Models ◽  
Long Distance ◽  
Chromatin Looping ◽  
Gene Regulatory ◽  
The Impact

Abstract Background Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features. Results We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression. Conclusions Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

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