scholarly journals INVESTIGATION OF ROADWAY GEOMETRIC AND TRAFFIC FLOW FACTORS FOR VEHICLE CRASHES USING SPATIOTEMPORAL INTERACTION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1163-1166 ◽  
Author(s):  
G. Gill ◽  
T. Sakrani ◽  
W. Cheng ◽  
J. Zhou
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Traffic Safety ◽  
Safety Management ◽  
Influential Factors ◽  
Vehicle Crashes ◽  
Crash Prediction ◽  
Crash Data ◽  
Crash Prediction Model ◽  
Vehicular Crashes

Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of various severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management processes are commonly implemented which include network screening, problem diagnosis, countermeasure identification, and project prioritization. The selection of countermeasures for potential mitigation of crashes is governed by the influential factors which impact roadway crashes. Crash prediction model is the tool widely adopted by safety practitioners or researchers to link various influential factors to crash occurrences. Many different approaches have been used in the past studies to develop better fitting models which also exhibit prediction accuracy. In this study, a crash prediction model is developed to investigate the vehicular crashes occurring at roadway segments. The spatial and temporal nature of crash data is exploited to form a spatiotemporal model which accounts for the different types of heterogeneities among crash data and geometric or traffic flow variables. This study utilizes the Poisson lognormal model with random effects, which can accommodate the yearly variations in explanatory variables and the spatial correlations among segments. The dependency of different factors linked with roadway geometric, traffic flow, and road surface type on vehicular crashes occurring at segments was established as the width of lanes, posted speed limit, nature of pavement, and AADT were found to be correlated with vehicle crashes.

scholarly journals Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic

2003 ◽  
Vol 1840 (1) ◽  
pp. 67-77 ◽  
Author(s):  
Chris Lee ◽  
Bruce Hellinga ◽  
Frank Saccomanno
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Previous Model ◽  
Traffic Condition ◽  
Freeway Traffic ◽  
Crash Prediction ◽  
Crash Prediction Model ◽  
Time Basis

The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives. Therefore, the objectives of the present study were to ( a) suggest the rational methods by which the crash precursors included in the model can be determined on the basis of experimental results and ( b) test the performance of the modified crash prediction model. The study found that crash precursors can be determined in an objective manner, eliminating a characteristic of the previous model, in which the model results were dependent on analysts’ subjective categorization of crash precursors.

scholarly journals Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tian Lei ◽  
Jia Peng ◽  
Xingliang Liu ◽  
Qin Luo
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Prediction Models ◽  
Traffic Data ◽  
Crash Prediction ◽  
Model Based ◽  
Crash Prediction Model ◽  
Variable Space ◽  
Crash Prediction Models

Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

scholarly journals Development of district-wise crash prediction model in Bangladesh

2020 ◽  
Vol 7 (1) ◽  
pp. 1762525 ◽  
Author(s):  
Soumik Nafis Sadeek ◽  
Shakil Mohammad Rifaat ◽  
Marinella Giunta
Keyword(s):  
Prediction Model ◽  
Crash Prediction ◽  
Crash Prediction Model

A Zero-Inflated Negative Binomial Crash Prediction Model for Freeway Bridge Sections

2021 ◽  
Author(s):  
Jianjun Song ◽  
Bingshi Huang ◽  
Yong Wang ◽  
Chao Wu ◽  
Xiaofang Zou ◽  
...  
Keyword(s):  
Prediction Model ◽  
Negative Binomial ◽  
Crash Prediction ◽  
Crash Prediction Model

A crash-prediction model for multilane roads

2007 ◽  
Vol 39 (4) ◽  
pp. 657-670 ◽  
Author(s):  
Ciro Caliendo ◽  
Maurizio Guida ◽  
Alessandra Parisi
Keyword(s):  
Prediction Model ◽  
Crash Prediction ◽  
Crash Prediction Model

A GIS Based Crash Assignment Model for Signalized T Intersections

2014 ◽  
Vol 543-547 ◽  
pp. 4472-4475
Author(s):  
Bipin Karki ◽  
Xiao Bo Qu ◽  
Kriengsak Panuwatwanich ◽  
Sherif Mohamed ◽  
Partha Parajuli
Keyword(s):  
Information System ◽  
Prediction Model ◽  
Geographical Information System ◽  
Assignment Problem ◽  
Geographical Information ◽  
Crash Prediction ◽  
Assignment Model ◽  
Gis Database ◽  
Crash Prediction Model

The crash assignment problem has long been considered as one of the most important components in an approach-level crash prediction model for intersections. A few pioneering studies have been carried out to properly assign the crashes in or nearby intersections to various approaches. However, the implementation of these models is very time consuming as it can only be done one by one manually. In this paper, a geographical information system (GIS) database is developed to complete the crash assignment. This tool has been applied in Queensland, Australia in the development of crash prediction model for signalized T-intersections.

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