Transfer learning for spatio-temporal transferability of real-time crash prediction models

2022 ◽  
Vol 165 ◽  
pp. 106511
Author(s):  
Cheuk Ki Man ◽  
Mohammed Quddus ◽  
Athanasios Theofilatos
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Prediction Models ◽  
Crash Prediction ◽  
Spatio Temporal ◽  
Crash Prediction Models

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.

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.

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

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.

New Intersection Crash Prediction Models for the Second Edition of the Highway Safety Manual

2021 ◽  
pp. 036119812110398
Author(s):  
Darren J. Torbic ◽  
Daniel Cook ◽  
Joseph Grotheer ◽  
Richard Porter ◽  
Jeffrey Gooch ◽  
...  
Keyword(s):  
Traffic Control ◽  
Prediction Models ◽  
Highway Safety ◽  
Crash Severity ◽  
Crash Prediction ◽  
Part C ◽  
Highway Safety Manual ◽  
Rural And Urban ◽  
Diamond Interchanges ◽  
Crash Prediction Models

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

scholarly journals Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

2021 ◽  
Vol 13 (16) ◽  
pp. 9011
Author(s):  
Nopadon Kronprasert ◽  
Katesirint Boontan ◽  
Patipat Kanha
Keyword(s):  
Prediction Models ◽  
Safety Performance ◽  
Horizontal Curve ◽  
Crash Frequency ◽  
Rural Roads ◽  
Crash Prediction ◽  
Road Crashes ◽  
Highway Safety Manual ◽  
Safety Performance Functions ◽  
Crash Prediction Models

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

Effectiveness of Consistency Measures in Crash Prediction Models for Two-Lane Highways in Palestine

2018 ◽  
Vol 43 (10) ◽  
pp. 5645-5656 ◽  
Author(s):  
Khaled Al-Sahili ◽  
Mohammed Dwaikat ◽  
Sameer Abu-Eisheh ◽  
Wael Alhajyaseen
Keyword(s):  
Prediction Models ◽  
Crash Prediction ◽  
Crash Prediction Models

Calibration of Predictive Models for Estimating Safety of Ramp Design Configurations

2005 ◽  
Vol 1908 (1) ◽  
pp. 88-95 ◽  
Author(s):  
Dominique Lord ◽  
James A. Bonneson
Keyword(s):  
Predictive Models ◽  
Prediction Models ◽  
Free Flow ◽  
Flow Loop ◽  
Local Conditions ◽  
Crash Prediction ◽  
Calibration Process ◽  
Low Costs ◽  
Crash Prediction Models ◽  
Transportation Agencies

The goal for the calibration process is to use predictive models developed with data collected from other jurisdictions and apply them to the jurisdiction of interest by adapting the models for local conditions and characteristics. Given the large costs associated with data collection, this process is often the only method available to transportation agencies for estimating the safety of different transportation facilities. Thus, recalibrating models produced from other jurisdictions allows agencies to produce their own models at relatively low costs. The objective for the research was to recalibrate a set of crash prediction models for different ramp design configurations. The ramp design configurations addressed included diagonal ramps, non-free-flow loop ramps, free-flow loop ramps, and outer connection ramps. A total of 44 ramps located in and around Austin, Texas, were used in the calibration process. The results of the study showed that more crashes occur on exit ramps than entrance ramps by a ratio of about 6 to 4. The results also showed that the non-free-flow ramp experiences twice as many crashes as other types of ramp. Similarly, more crashes occur on rural than urban ramps.

Sign in / Sign up

Export Citation Format

Share Document