scholarly journals Identification of Factors Affecting the Crash Severity and Safety Countermeasures Toward Safer Work Zone Traffic Management

2016 ◽  
Vol 34 (4) ◽  
pp. 354-372
Author(s):  
Seok Min YOON ◽  
Cheol OH ◽  
Hyun Jin PARK ◽  
Bong Jo CHUNG
Keyword(s):  
Traffic Management ◽  
Work Zone ◽  
Crash Severity ◽  
Factors Affecting ◽  
Safety Countermeasures

CLUSTERING-NEURAL NETWORK MODELS FOR FREEWAY WORK ZONE CAPACITY ESTIMATION

2004 ◽  
Vol 14 (03) ◽  
pp. 147-163 ◽  
Author(s):  
XIAOMO JIANG ◽  
HOJJAT ADELI
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Network Models ◽  
Work Zone ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Factors Affecting ◽  
Management Plans ◽  
Parametric Studies ◽  
Clustering Approach

Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attractive characteristics of training stability, accuracy, and quick convergence. The results of validation indicate that the work zone capacity can be estimated by clustering-neural network models in general with an error of less than 10%, even with limited data available to train the models. The clustering-RBFNN model is used to study several main factors affecting work zone capacity. The results of such parametric studies can assist work zone engineers and highway agencies to create effective traffic management plans (TMP) for work zones quantitatively and objectively.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

scholarly journals The Effects of Picking Up Primary School Pupils on Surrounding Street’s Traffic: A Case Study in Hanoi

2020 ◽  
Vol 14 (1) ◽  
pp. 237-250
Author(s):  
Dinh Hiep ◽  
Vu V. Huy ◽  
Teppei Kato ◽  
Aya Kojima ◽  
Hisashi Kubota
Keyword(s):  
Primary School ◽  
Traffic Safety ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Primary Schools ◽  
Traffic Accidents ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Almost All

Introduction: One of the significant characteristics of schools in Vietnam is that almost all parents send their children to school and/or pick up their children from school using private vehicles (motorcycles). The parents usually stop and park their vehicle on streets outside the school gates, which can lead to serious congestion and increases the likelihood of traffic accidents. Methods: The objective of this study is to find out factors affecting the picking up of pupils at primary school by evaluating the typical primary schools in Hanoi city. A binary logistic regression model was used to determine factors that influence the decision of picking up pupils and the waiting duration of parents. The behavior of motorcyclists during the process of picking up pupils at the primary school gate has been identified and analyzed in detail by the Kinovea software. Results and Discussion: The study showed that, on the way back home, almost all parents use motorbikes (89.15%) to pick up their children. During their waiting time (8.48 minutes in average), they made a lot of illegal parking actions on the street there by, causing a lot of “cognitive” errors and “crash” points surrounding in front of the primary school entrance gate. Risky picking-up behaviors were significantly observed, i.e. picking-up on opposite side of the school, making a U-turn, backing-up dangerously, parking on the middle of street, and parking on the street next to sidewalk). Conclusion: Based on the analyzed results, several traffic management measures have been suggested to enhance traffic safety and reduce traffic congestion in front of school gates. In addition, the results of the study will provide a useful reference for policymakers and authorities.

scholarly journals Collective Risk Ranking of Highway Segments on the Basis of Severity-Weighted Crash Rates

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Dawei Li ◽  
Mustafa F. M. Al-Mahamda
Keyword(s):  
Empirical Bayes ◽  
Safety Information ◽  
Crash Severity ◽  
Regression To The Mean ◽  
Factors Affecting ◽  
Crash Data ◽  
Crash Rates ◽  
Risk Ranking ◽  
Collective Risk ◽  
Major Factors

This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each model besides the abstraction of new features to check if there are unobserved correlations affecting the independent variables, such as accounting for the number and weight of crashes within 1 km2 area by implementing the Getis-Ord Gi∗ index. Moreover, to calculate the collective risk (CR) score, crash rates were adjusted to incorporate crash severity weights (cost per severity type) and regression-to-the-mean (RTM) bias via Empirical Bayes (EB) method. Finally, segments were ranked according to their CR score.

scholarly journals PLANNING FACTORS AFFECTING METROPOLITAN TRAFFIC IMPROVEMENT (CASE STUDY: METROPOLIS OF TABRIZ)

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Sina Darban Khales
Keyword(s):  
Urban Planning ◽  
Traffic Control ◽  
Traffic Management ◽  
Reduction Rate ◽  
Field Research ◽  
Essential Elements ◽  
Urban Traffic ◽  
Main Concern ◽  
Factors Affecting

In modern urban planning, traffic management and planning are considered essential elements in urban planning and design. In fact, there is an increasing need to consider and apply certain policies for traffic system improvement in the modern urban planning and management due to the important role of urban roads and their direct relationship with population growth in cities. The urbanization phenomenon in Iran and the increasing number of automobiles in its cities have led to an exponential growth in urban traffic, which is the main concern of urban managers in Tabriz Metropolis in this country. Considering the current conditions in Tabriz, this study attempts to evaluate the traffic management and planning strategies in this city. Desk and field research methods were used for data collection. The related data was then analyzed using SPSS through the one-sample t-test and regression analysis. The mean value of the studied indicators for traffic planning improvement is 4.56, which is higher than the average and indicates the important role of the factors affecting traffic control planning improvement in Tabriz. Finally, CORSIM was used to examine the high-traffic areas of Tabriz and evaluate the traffic volume reduction rate in this city.

scholarly journals Crash severity analysis of nighttime and daytime highway work zone crashes

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0221128 ◽  
Author(s):  
Kairan Zhang ◽  
Mohamed Hassan
Keyword(s):  
Work Zone ◽  
Crash Severity

scholarly journals Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

2020 ◽  
Vol 10 (5) ◽  
pp. 1675 ◽  
Author(s):  
Ciyun Lin ◽  
Dayong Wu ◽  
Hongchao Liu ◽  
Xueting Xia ◽  
Nischal Bhattarai
Keyword(s):  
Machine Learning ◽  
Traffic Safety ◽  
Rural Areas ◽  
Computational Cost ◽  
The United States ◽  
Crash Severity ◽  
Rural Roads ◽  
Factors Affecting ◽  
West Texas ◽  
Teen Drivers

Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.

scholarly journals Modeling the Traffic Flow of the Urban Signalized Intersection with a Straddling Work Zone

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Liling Zhu ◽  
Bingmei Jia ◽  
Da Yang ◽  
Yuezhu Wu ◽  
Guo Yang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Negative Impact ◽  
Work Zone ◽  
Signalized Intersection ◽  
Specific Work ◽  
Social Force ◽  
Irregular Boundary ◽  
Factors Affecting ◽  
Boundary Characteristics

Work zones widely exist on urban roads in many countries and have a significant negative impact on traffic. Few studies have focused on modeling the traffic flow of the work zone on the urban arterials, especially on the work zone at the intersections. In this paper, a microscopic model based on the social force theory for the traffic flow of the intersection with a specific work zone, called straddling work zone, is proposed. The model can capture the no lane division and irregular boundary characteristics of the traffic of the intersection with a straddling work zone and also can reflect the interaction of the intersection traffic flows from the two opposite directions. The proposed model is calibrated and validated using the real work zone data, and the results display that the MARE values are all less than 10%. The factors affecting the traffic flow in the straddling work zone are analyzed through simulation. Our study reveals that the distance from the lower edge of the work zone to the median divider of the road and the proportion of large vehicles in the work zone have the greatest impact on the signalized intersection, which provides a reference for the future traffic control at the intersection with the straddling work zone.

Work Zone Optimization for Two-Lane Highway Resurfacing Projects with an Alternate Route

2005 ◽  
Vol 1911 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Chun-Hung (Peter) Chen ◽  
Paul Schonfeld ◽  
Jawad Paracha
Keyword(s):  
Simulated Annealing ◽  
Traffic Management ◽  
Agency Cost ◽  
Work Zone ◽  
Time Dependent ◽  
User Cost ◽  
Total Cost ◽  
Management Plans ◽  
Delay Cost ◽  
Alternate Routes

Pavements on two-lane two-way highways are usually resurfaced by closing one lane at a time. Vehicles then travel in the remaining lane along the work zone, alternating directions within each control cycle. In an earlier work, Chen and Schonfeld developed a work zone optimization model for two-lane highways with time-dependent inflows and no detours, based on simulated annealing. In this paper, several alternatives are evaluated, defined by the number of closed lanes and fractions of traffic diverted to alternate routes. The algorithm referred to as SAUASD (simulated annealing for uniform alternatives with a single detour) is developed to find the best single alternative within a resurfacing project. The algorithm referred to as SAMASD (simulated annealing for mixed alternatives with a single detour) is developed to search through possible mixed alternatives and their diverted fractions, to minimize total cost, further including agency cost (resurfacing cost and idling cost) and user cost (user delay cost and accident cost). Thus, traffic management plans are developed with uniform or mixed alternatives within a two-lane highway resurfacing project.

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