Preliminary Investigation of the Effectiveness of High-Visibility Crosswalks on Pedestrian Safety Using Crash Surrogates

2017 ◽  
Vol 2659 (1) ◽  
pp. 182-191 ◽  
Author(s):  
M. Tawfiq Sarwar ◽  
Grigorios Fountas ◽  
Courtney Bentley ◽  
Panagiotis C. Anastasopoulos ◽  
Alan Blatt ◽  
...  
Keyword(s):  
New York ◽  
Unobserved Heterogeneity ◽  
Preliminary Investigation ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Pedestrian Safety ◽  
Test Site ◽  
Driving Behavior ◽  
Preliminary Evaluation ◽  
Linear Regression Models

This paper, with the use of data from the SHRP 2 naturalistic driving study, provides a preliminary evaluation of the effectiveness of high-visibility crosswalks (HVCs) in improving pedestrian safety at un-controlled locations. This evaluation was accomplished by analyzing the driving behavior of SHRP 2 participants at three uncontrolled locations at the Erie County, New York, test site. In this context, crash surrogates (i.e., speed, acceleration, throttle pedal actuation, and brake application) were used to evaluate the participants’ driving behavior, primarily on the basis of data from before and after the HVC installation. The before–after analysis allowed the assessment of HVC effectiveness in driver behavior modification. Mixed logit and random parameters linear regression models were estimated, and panel effects and unobserved heterogeneity were accounted for. Several factors were explored and controlled for (e.g., vehicle and driver characteristics, roadside environment, weather conditions), and the preliminary exploratory results show that HVCs can improve pedestrian safety and positively modify driving behavior.

scholarly journals The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Changxi Ma ◽  
Wei Hao ◽  
Wang Xiang ◽  
Wei Yan
Keyword(s):  
Open Space ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Driving Behaviors ◽  
Grade Crossings ◽  
High Level ◽  
The Impact

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

scholarly journals Ensemble-Based Storm Surge Forecasting Models

2017 ◽  
Vol 32 (5) ◽  
pp. 1921-1936 ◽  
Author(s):  
Amin Salighehdar ◽  
Ziwen Ye ◽  
Mingzhe Liu ◽  
Ionut Florescu ◽  
Alan F. Blumberg
Keyword(s):  
New York ◽  
Storm Surge ◽  
Weather Conditions ◽  
Difficult Problem ◽  
Forecasting Models ◽  
Bay Area ◽  
Temperature Levels ◽  
The Individual ◽  
Specific Sense ◽  
Stated Purpose

Abstract Accurate prediction of storm surge is a difficult problem. Most forecast systems produce multiple possible forecasts depending on the variability in weather conditions, possible temperature levels, winds, etc. Ensemble modeling techniques have been developed with the stated purpose of obtaining the best forecast (in some specific sense) from the individual forecasts. In this work a statistical methodology of evaluating the performance of multiple ensemble forecasting models is developed. The methodology is applied to predicting storm surge in the New York Harbor area. Data from three hurricane events collected from multiple locations in the New York Bay area are used. The methodology produces three key findings for the particular test data used. First, it is found that even the simplest possible way of creating an ensemble produces results superior to those of any single forecast. Second, for the data used and the events under study the methodology did not interact with any event at any location studied. Third, based on the methodology results for the data studied selecting the best-performing ensemble models for each specific location may be possible.

scholarly journals Highly Productive Pine Forests in a Changing Climate

2021 ◽  
pp. 9-23
Author(s):  
Nina F. Kuznetsova ◽  
◽  
Elena S. Klushevskaya ◽  
Elena Yu. Amineva
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Genetic Material ◽  
Weather Conditions ◽  
Test Site ◽  
Pine Forests ◽  
Soil Drought ◽  
Forest Steppe ◽  
Changing Climate ◽  
Vital State

Forest steppe of the Central Chernozem Region (CCR) of Russia belongs to the zone of highly productive pine forests. In 2015, for the first time a partial destabilization of Scots pine (Pinus sylvestris L.) was recorded within the territory of the CCR. It affected the population, organism and cellular levels of Scots pine (Pinus sylvestris L.). The destabilization was caused by the 8-year heatwave of 2007–2014 followed by a sharp drop in the water table and four severe droughts (2007, 2010, 2012, and 2014). The analysis was carried out on two sites of pine forest plantations growing in the environmentally sound region: the Stupino test site (Voronezh region, typical plantation for the CCR) and the Usman site (Lipetsk region, lands with elevated groundwater level). The results of morphological, cytogenetic and biochemical studies of model trees of the Stupino test site during the following periods are presented: 4 optimal years in terms of weather conditions, 2014 drought year and 2015 destabilization year. It was found that prolonged hydrothermal stress resulted in the transition of pine from the basic equilibrium state to a slightly nonequilibrium state. The trigger mechanism for changing their vital state was a severe autumn soil drought in 2014, after which the plants became weakened right before winter. A decrease in cone bioproductivity by the traits of seed fullness and the total number of seeds per cone, a change in population sampling structure, an increase in the number of mitosis pathologies, and an increase in proline content in needles were observed despite optimal weather conditions in 2015. The recovery of species was studied for three subsequent optimal years on the example of the Stupino and Usman populations. Experimental data indicate that the processes of vital state normalization involve profound changes in metabolism and require certain energy expenditures. It took the Stupino population longer to return to the regional norm, which indicates a different depth of destabilization of the tree genetic material of the studied populations. For citation: Kuznetsova N.F., Klushevskaya E.S, Amineva E.Yu. Highly Productive Pine Forests in a Changing Climate. Lesnoy Zhurnal [Russian Forestry Journal], 2021, no. 6, pp. 9–23. DOI: 10.37482/0536-1036-2021-6-9-23

Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (17) ◽  
pp. 83-95 ◽  
Author(s):  
Bashar Dhahir ◽  
Yasser Hassan
Keyword(s):  
Data Collection ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Study Data ◽  
Output Analysis ◽  
Horizontal Curves ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Design Consistency ◽  
Favorable Weather

Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

Analysis of compensatory driving behavior under fog weather conditions

2021 ◽  
pp. 662-668
Author(s):  
Yu Zhang ◽  
Zhongyin Guo ◽  
Bencheng Zhu ◽  
Zhaodong Fan ◽  
Hankun Zhang
Keyword(s):  
Weather Conditions ◽  
Driving Behavior

Near Optimal Model Predictive Control of Thermal Energy Storage

2020 ◽  
Author(s):  
O. A. Qureshi ◽  
P. R. Armstrong
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
High Reliability ◽  
Operating Cost ◽  
Weather Conditions ◽  
Preliminary Evaluation ◽  
Novel Algorithm

Abstract Efficient plant operation can be achieved by properly loading and sequencing available chillers to charge a thermal energy storage (TES) reservoir. TES charging sequences are often determined by heuristic rules that typically aim to reduce utility costs under time of use rates. However, such rules of thumb are in most cases far from optimal even for this task. Rigorous optimization, on the other hand, is computationally expensive and can be unreliable as well if not carefully implemented. Model-predictive control (MPC) that is reliable, as well as effective, in TES application must be developed. The goal is to develop an algorithm that can reach ∼80% of achievable energy efficiency and peak shifting capacity with very high reliability. A novel algorithm is developed to reliably achieve near optimal control for charging cool storage in chiller plants. Algorithm provides a constant COP (or cost per ton-hour) for 24-hr dispatch plan at which plant operates during most favorable weather conditions. Preliminary evaluation of this novel algorithm has indicated up to 6% improvement in plant annual operating cost relative to the same plant operating without TES. TOU rate used in both cases charges 7.4cents/kWh during off peak hours and 9.8cents/kWh during peak hours (Peak hours are 10 am to 10 pm).

Insights from Integrated Geo-Location Data for Pedestrian Crashes, Demographics, and Land Uses

2020 ◽  
Vol 2674 (8) ◽  
pp. 720-731
Author(s):  
Rui Guo ◽  
Zhiqiang Wu ◽  
Yu Zhang ◽  
Pei-Sung Lin ◽  
Zhenyu Wang
Keyword(s):  
Low Income ◽  
Fast Food ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Pedestrian Safety ◽  
Marginal Effect ◽  
Land Uses ◽  
Crash Frequency ◽  
Location Data ◽  
Pedestrian Crashes

This study investigates the effects of demographics and land uses on pedestrian crash frequency by integrating the contextual geo-location data. To address the issue of heterogeneity, three negative binomial models (with fixed parameters, with observed heterogeneity, and with both observed and unobserved heterogeneities) were examined. The best fit with the data was obtained by explicitly incorporating the observed and unobserved heterogeneity into the model. This highlights the need to accommodate both observed heterogeneity across neighborhood characteristics and unobserved heterogeneity in pedestrian crash frequency modeling. The marginal effect results imply that some land-use types (e.g., discount department stores and fast-food restaurants) could be candidate locations for the education campaigns to improve pedestrian safety. The observed heterogeneity of the area indicator suggests that priority should be given to more populated low-income areas for pedestrian safety, but attention is also needed for the higher-income areas with larger densities of bus stops and hotels. Moreover, three normally distributed random parameters (proportion of older adults, proportion of lower-speed roads, and density of convenience stores in the area) were identified as having random effects on the probability of pedestrian crash occurrences. Finally, the identification of pedestrian crash hot zone provides practitioners with prioritized neighborhoods (e.g., a list of areas) for developing effective pedestrian safety countermeasures.

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