Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency

2011 ◽  
Vol 2237 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Satish Ukkusuri ◽  
Samiul Hasan ◽  
H. M. Abdul Aziz
Keyword(s):  
Built Environment ◽  
Random Parameter ◽  
Crash Frequency ◽  
Random Parameter Model

A calibrateable site index model for Pinus kesiya plantations in southeastern Africa

2002 ◽  
Vol 32 (11) ◽  
pp. 1916-1928 ◽  
Author(s):  
Kalle Eerikäinen ◽  
Danaza Mabvurira ◽  
Ladislaus Nshubemuki ◽  
Jussi Saramäki
Keyword(s):  
Random Parameter ◽  
Site Index ◽  
Stand Age ◽  
Random Parameters ◽  
Index Model ◽  
Forest Inventories ◽  
Pinus Kesiya ◽  
Dominant Height ◽  
Random Parameter Model ◽  
Site Index Model

The aim of the study was to develop a site index model for Pinus kesiya Royle ex Gordon plantations in southeastern Africa based on the relationship between the dominant height and stand age. Conversely, analysis of dominant height and age data showed that the growth patterns of plantations were different. In addition, the asymptotes and forms of standwise dominant height curves varied within plantations. In developing a common site index model, instead of using the more common approach of estimating separate dominant height–age models for different plantations or sites, a mean curve approach based on a linear random parameter model with fixed and random parameters was applied. The random parameter model of this study was calibrated by predicting random parameters for the plantation and stand effects, in accordance with the standard linear prediction theory. The analyses showed that the calibration of the dominant height model was an efficient method to obtain reliable dominant height predictions of a stand, particularly when several dominant height–age observations from different stands of a plantation and at least one measured dominant height and stand age of a target stand are available. This is the case in many forest inventories based on temporary samples, i.e., cross-sectional data. The new site index model is a useful tool for use in different mensurational applications, and its properties can efficiently be utilized for example in forest inventories of P. kesiya plantations in southeastern Africa.

scholarly journals POSITIONING VIETNAM’S PANGASIUS CATFISH IN THE FRENCH MARKET USING DISCRETE CHOICE MODEL

2014 ◽  
Vol 17 (4) ◽  
pp. 96-111
Author(s):  
Thong Tien Nguyen ◽  
Hung Manh Nguyen
Keyword(s):  
Discrete Choice ◽  
Low Income ◽  
Latent Class ◽  
Choice Model ◽  
Latent Class Model ◽  
Random Parameter ◽  
Discrete Choice Model ◽  
Class Model ◽  
Additional Information ◽  
Random Parameter Model

The study used discrete choice model to investigate the position of Vietnam’s Pangasius catfish in the French market. Data was collected via a choice experiment designed for 12 aquaculture species familiar to French consumers. The random parameter model was estimated and used to calculate the share elasticity. The market position of the aquaculture products in this study was calculated based on the competitive clout, vulnerability scores, and ranked-order implicit values. The results show that Vietnam’s Pangasius has a low competitive clout, high vulnarability score, and low ranked-order implicit value. A latent class model was also estimated for comparison and acquisition of additional information. A strong segment of Pangasius (11.9%) is described by low income and education consumers, women at mid-age dominated, and family with children. To improve the Pangasius position and image in the international market, Vietnam needs promotional and marketing campaigns at global level for the product.

scholarly journals A Random-Parameter Height-Dbh Model for Cherrybark Oak

2005 ◽  
Vol 29 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Thomas B. Lynch ◽  
A. Gordon Holley ◽  
Douglas J. Stevenson
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Random Parameter ◽  
Parameter Estimates ◽  
Quercus Pagoda ◽  
Random Parameter Model ◽  
Natural Range ◽  
Height Prediction ◽  
Effect Parameter ◽  
Height Predictions

Abstract A random-parameter model was used to relate total height to diameter at breast height (dbh) for cherrybark oak (Quercus pagoda Raf.). Data were obtained from 561 trees located in 50 stands occurring on bottomland hardwood sites in East Texas, near the western extent of the cherrybarkoak natural range. Mixed-model estimation techniques were used to fit fixed-effects parameters to the height-dbh relationship for cherrybark oak, with random-effects parameters representing sample stands from which tree data were obtained. The fixed-effect parameter estimates can be used topredict average cherrybark oak height for a given dbh in the region from which the data were obtained. Because random parameters associated with stands were used in the data-fitting process, the models can be calibrated to fit new stands by obtaining measurements to fit appropriate randomparameters for that stand. This calibration improves height predictions for individual stands while requiring less data than would the development of a completely new height prediction model for that stand. South. J. Appl. For. 29(1):22–26.

scholarly journals Extracting Useful Information from Basic Safety Message Data: An Empirical Study of Driving Volatility Measures and Crash Frequency at Intersections

2018 ◽  
Vol 2672 (38) ◽  
pp. 290-301 ◽  
Author(s):  
Mohsen Kamrani ◽  
Ramin Arvin ◽  
Asad J. Khattak
Keyword(s):  
Random Parameter ◽  
Crash Risk ◽  
Crash Frequency ◽  
Safety Message ◽  
Vehicle Data ◽  
Combining Data ◽  
Data Points ◽  
Pass Through ◽  
Safety Countermeasures ◽  
Surrogate Measures

With the emergence of high-frequency connected and automated vehicle data, analysts can extract useful information from them. To this end, the concept of “driving volatility” is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles’ instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. For each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Given that volatility reflects the degree to which vehicles move, erratic movements are expected to increase crash risk. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities. A deeper analysis of these intersections can be undertaken, and proactive safety countermeasures considered to enhance safety.

scholarly journals Development of Safety Performance Functions: Incorporating Unobserved Heterogeneity and Functional Form Analysis

2018 ◽  
Vol 2672 (30) ◽  
pp. 9-20 ◽  
Author(s):  
Behram Wali ◽  
Asad J. Khattak ◽  
Jim Waters ◽  
Deo Chimba ◽  
Xiaobing Li
Keyword(s):  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Safety Performance ◽  
Validation Dataset ◽  
Segment Length ◽  
Significant Heterogeneity ◽  
Crash Frequency ◽  
Form Analysis ◽  
Safety Performance Functions ◽  
Functional Forms

To improve transportation safety, this study applies Highway Safety Manual (HSM) procedures to roadways while accounting for unobserved heterogeneity and exploring alternative functional forms for Safety Performance Functions (SPFs). Specifically, several functional forms are considered in Poisson and Poisson-gamma modeling frameworks. Using 5 years (2011–2015) of crash, traffic, and road inventory data for two-way, two-lane roads in Tennessee, fixed- and random-parameter count data models are calibrated. The models account for important methodological concerns of unobserved heterogeneity and omitted variable bias. With a validation dataset, the calibrated and uncalibrated HSM SPFs and eight new Tennessee-specific SPFs are compared for prediction accuracy. The results show that the statewide calibration factor is 2.48, suggesting rural two-lane, two-way road segment crashes are at least 1.48 times greater than what HSM SPF predicts. Significant variation in four different regions in Tennessee is observed with calibration factors ranging between 2.02 and 2.77. Among all the SPFs considered, fully specified Tennessee-specific random parameter Poisson SPF outperformed all competing SPFs in predicting out-of-sample crashes on these road segments. The best-fit random parameter SPF specification for crash frequency includes the following variables: annual average daily traffic, segment length, shoulder width, lane width, speed limit, and the presence of passing lanes. Significant heterogeneity is observed in the effects of traffic exposure-related variables on crash frequency. The study shows how heterogeneity-based models can be specified and used by practitioners for obtaining accurate crash predictions.

Built environment effects on bike crash frequency and risk in Beijing

2018 ◽  
Vol 64 ◽  
pp. 135-143 ◽  
Author(s):  
Peng Chen ◽  
Feiyang Sun ◽  
Zhenbo Wang ◽  
Xu Gao ◽  
Junfeng Jiao ◽  
...  
Keyword(s):  
Built Environment ◽  
Crash Frequency ◽  
Environment Effects
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