Traffic Accident Locations Research Articles

KLASIFIKASI KARAKTERISTIK KECELAKAAN LALU LINTAS DI KOTA DENPASAR DENGAN PENDEKATAN CLASSIFICATION AND REGRESSION TREES (CART)

The aim of this research is to determine the classification characteristics of traffic accidents in Denpasar city in January-July 2014 by using Classification And Regression Trees (CART). Then, for determine the explanatory variables into the main classifier of CART. The result showed that optimum CART generate three terminal node. First terminal node, there are 12 people were classified as heavy traffic accident characteritics with single accident, and second terminal nodes, there are 68 people were classified as minor traffic accident characteristics by type of traffic accident front-rear, front-front, front-side, pedestrians, side-side and location of traffic accident in district road and sub-district road. For third terminal node, there are 291 people were classified as medium traffic accident characteristics by type of traffic accident front-rear, front-front, front-side, pedestrians, side-side and location of traffic accident in municipality road and explanatory variables into the main splitter to make of CART is type of traffic accident with maximum homogeneity measure of 0.03252.

Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

Abstract The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches

Simulation and Analysis of Highway Traffic Accident Based on VISSIM

When the highway traffic accident happened, the capacity will decrease, and if the upstream traffic volume was more than the remaining capacity, the traffic jam will happen. The simulation software VISSIM was used to simulate and analyze the highway traffic accident under different number of blocked lane, upstream traffic volume and traffic accident duration. Simulation result shows that, the capacity has something to do with the number of blocked lane, but has nothing to do with the location of traffic accident. The time of biggest delay has something to do with the number of blocked lane and traffic accident duration, but has nothing to do with the upstream traffic volume. The capacity has nothing to do with traffic accident duration. The longer traffic accident duration, the more delay. And the queue length increases when the number of blocked lane, upstream traffic volume and traffic accident duration increase.

Black Spot Determination of Traffic Accident Locations and Its Spatial Association Characteristic Analysis Based on GIS

In general, the location of traffic accidents is described as an address with text, so they are difficult to display on the map. The paper discusses how to utilize the geocoding technology and VRS-GPS positioning technology to record the traffic accidents with Geo-spatial information. Based on the spatial relationship between traffic accidents and road network elements, two-way association relationship is defined by spatial relationship computation. Then the paper presents the method which takes the potential of reducing accidents as an index to extract the black spots. Finally, in the discussion, the association relationship between black spots and traffic attributes is used to analyze the factors that resulting in traffic accidents.

Predict model for hotspot identification in Vietnam

Road traffic accidents have become a leading cause of accidental death in Vietnam today, especially in Hochiminh city which is the biggest city. It is revealing that number of accident...

Driving performance assessment: Effects of traffic accident location and alarm content

According to accident statistics for Taiwan, the two most common traffic accident locations in urban areas are roadway segments and intersections. On roadway segments, most collisions are due to drivers not noticing the status of leading vehicle. At intersections, most collisions are due to the other driver failing to obey traffic signs. Using a driving simulator equipped with a collision warning system, this study investigated driving performance at different accident locations and between different alarm contents, and identified the relationship between crash occurrences and driving performance. Thirty participants, aged 20–29 years, were recruited in this study. Driving performance measures were perception-reaction time, movement-reaction time, speed and a crash. Experimental results indicated that due to different demands for processing information under different traffic conditions, driving performance differed at the two traffic accident locations. On a roadway segment, perception-reaction time for a beep was shorter than the time for a speech message. Nevertheless, at an intersection, a speech message was a great help to drivers and, thus, perception-reaction time was effectively reduced. In addition, logistic regression analysis indicates that perception-movement time had the greatest influence on crash occurrence.

Grappling with Distributed Usability: A Cultural-Historical Examination of Documentation Genres over Four Decades

Traditional models of usability assume that usability is a quality that can be designed into a particular artifact. Yet constructivist theory implies that usability cannot be located in a single artifact; rather, it must be conceived as a quality of the entire activity in which the artifact is used. This article describes a distributed approach to usability, based on activity theory and genre theory. It then illustrates the approach with a four-decade examination of a traffic accident location and analysis system (ALAS). Using the theoretical framework of genre ecologies, the article demonstrates how usability is distributed across the many official and unofficial (ad hoc) genres employed by ALAS users.

Ranking and selecting motor vehicle accident sites by using a hierarchical Bayesian model

Identification, ranking and selecting hazardous traffic accident locations from a group under consideration is a funda- mental goal for traffic safety researchers. Few methods exist that can quantitatively, accurately and easily discriminate between sites that commonly have small and variable observation count periods. One method that embodies all these advantages is the hierarchical Bayesian model, the method proposed in this paper. The particular hierarchical Bayesian approach that we use incorporates expert knowledge about accident sites as a group believed a priori to be exchangeable, the Poisson assumption and a conjugate gamma prior. We then propose three natural strategies for ranking and selecting the most hazardous subgroup of accident locations. Also presented is an especially useful procedure that gives the probability that each particular site is worst and by how much it is worst. All proposed strategies are illustrated using previously published fatality accident data from 35 sites in Auckland, New Zealand