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 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.

Predicting Imminent Crash Risk with Simulated Traffic from Distant Sensors

2018 ◽  
Vol 2672 (38) ◽  
pp. 12-21
Author(s):  
Zhi Chen ◽  
Xiao Qin ◽  
Renxin Zhong ◽  
Pan Liu ◽  
Yang Cheng
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Detection System ◽  
Data Retrieval ◽  
Transmission Model ◽  
Traffic Data ◽  
Crash Prediction ◽  
Station Data ◽  
Virtual Station ◽  
Crash Prediction Models

The aim of this research was to investigate the performance of simulated traffic data for real-time crash prediction when loop detector stations are distant from the actual crash location. Nearly all contemporary real-time crash prediction models use traffic data from physical detector stations; however, the distance between a crash location and its nearest detector station can vary considerably from site to site, creating inconsistency in detector data retrieval and subsequent crash prediction. Moreover, large distances between crash locations and detector stations imply that traffic data from these stations may not truly reflect crash-prone conditions. Crash and noncrash events were identified for a freeway section on I-94 EB in Wisconsin. The cell transmission model (CTM), a macroscopic simulation model, was applied in this study to instrument segments with virtual detector stations when physical stations were not available near the crash location. Traffic data produced from the virtual stations were used to develop crash prediction models. A comparison revealed that the predictive accuracy of models developed with virtual station data was comparable to those developed with physical station data. The finding demonstrates that simulated traffic data are a viable option for real-time crash prediction given distant detector stations. The proposed approach can be used in the real-time crash detection system or in a connected vehicle environment with different settings.

Enhancing the Prediction Performance of Real-Time Crash Prediction Models: A Cell Transmission-Dynamic Bayesian Network Approach

2018 ◽  
Vol 2672 (38) ◽  
pp. 58-68 ◽  
Author(s):  
Ananya Roy ◽  
Moinul Hossain ◽  
Yasunori Muromachi
Keyword(s):  
Bayesian Networks ◽  
Real Time ◽  
Traffic Flow ◽  
Prediction Models ◽  
Dynamic Bayesian Networks ◽  
Crash Prediction ◽  
Loop Detector ◽  
Cell Transmission ◽  
A Cell ◽  
Crash Prediction Models

Predicting crash probability in real time is a concept that has inspired studies into complex modeling methods, exploring more sophisticated data collection methods, reducing specification errors by introducing new traffic variables, etc. Most existing real-time crash prediction models (RTCPMs) are based on loop detector data and the model architecture, and their prediction performances are sensitive to the location and layout of detectors with respect to crashes, that is, the spacing of detectors on the road section. Different expressways/freeways have different detector layouts and these vary substantially even within the same expressway/freeway. This limitation is a major obstacle in developing a universal RTCPM. To address this, this paper takes one-minute aggregated loop detector data along with detector layouts as input and employs a cell transmission model (CTM) to interpolate states of traffic flow variables for a pre-defined hypothetical detector layout. Next, several RTCPMs are constructed using Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) for existing and CTM-based detector layouts. It is observed that the CTM can generate traffic flow parameters with a mean percentage error of 12.96%. The results further suggest that the model constructed with the CTM-based uniformly spaced simulated detector data coupled with the DBN method performed better than the model based on the BN and existing non-uniform detector layout.

scholarly journals Traffic Flow Prediction with Relevance Vector Machine

2021 ◽  
Author(s):  
W.-Z. Xiong ◽  
X.-M. Shen ◽  
H.-J. Li ◽  
Z. Shen
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Sequence Data ◽  
Machine Method ◽  
Traffic Flow Prediction ◽  
Correlation Vector ◽  
Model Based ◽  
Flow Prediction ◽  
Short Period

Abstract Real-time prediction of traffic flow values in a short period of time is an importantelement in building a traffic management system. The uncertainty, complexity andnonlinearity of traffic flow data make it difficult to predict traffic flow in real time,and the accurate traffic flow prediction has been an urgent problem in the industry.Based on the research of scholars, a traffic flow prediction model based on thecorrelation vector machine method is constructed. The prediction accuracy of thecorrelation vector machine is better than that of the logistic regression and supportvector machine methods, and the correlation vector machine method has the functionof generating prediction error range for the actual traffic sequence data. Theprediction results are very satisfactory, and the prediction speed is significantlyfaster than the other two models, which meets the requirement of real-time trafficflow prediction and is suitable for real-time online prediction, and the predictionaccuracy of the used method is relatively high. The three-way comparison analysisshows that the traffic flow prediction by the correlation vector machine methodcan describe the nonlinear characteristics of traffic flow change more accurately,and the model performance and real-time performance are better. The case studyshows that the traffic flow prediction model based on the correlation vector machinecan improve the speed and accuracy of prediction, which is very suitablefor traffic flow prediction estimation with real-time requirements, and provides ascientific method for real-time traffic flow measurement.

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 models: State-of-the-art, design pathways and ubiquitous requirements

2019 ◽  
Vol 124 ◽  
pp. 66-84 ◽  
Author(s):  
Moinul Hossain ◽  
Mohamed Abdel-Aty ◽  
Mohammed A. Quddus ◽  
Yasunori Muromachi ◽  
Soumik Nafis Sadeek
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
State Of The Art ◽  
Crash Prediction ◽  
Art Design ◽  
Crash Prediction Models
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