scholarly journals Traffic safety analysis of inter-tunnel weaving section with conflict prediction models

2020 ◽  
pp. 1-25
Author(s):  
Pengying Ouyang ◽  
Jiaming Wu ◽  
Chengcheng Xu ◽  
Lu Bai ◽  
Xuefeng Li
Keyword(s):  
Traffic Safety ◽  
Prediction Models ◽  
Safety Analysis ◽  
Conflict Prediction

On-board monitoring system for road traffic safety analysis

2018 ◽  
Vol 98 ◽  
pp. 208-217 ◽  
Author(s):  
Sebastiano Battiato ◽  
Giovanni Maria Farinella ◽  
Giovanni Gallo ◽  
Oliver Giudice
Keyword(s):  
Monitoring System ◽  
Traffic Safety ◽  
Road Traffic ◽  
Safety Analysis ◽  
Road Traffic Safety ◽  
Board Monitoring

Traffic safety analysis of powered two-wheelers (PTWs) in Slovenia

2012 ◽  
Vol 49 ◽  
pp. 36-43 ◽  
Author(s):  
Matjaž Šraml ◽  
Tomaž Tollazzi ◽  
Marko Renčelj
Keyword(s):  
Traffic Safety ◽  
Safety Analysis

A community traffic safety analysis of pedestrian and bicyclist injuries based on the catchment area of a trauma center

2014 ◽  
Vol 76 (4) ◽  
pp. 1103-1110 ◽  
Author(s):  
Dekeya R. Slaughter ◽  
Nick Williams ◽  
Stephen P. Wall ◽  
Nina E. Glass ◽  
Ronald Simon ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Trauma Center ◽  
Catchment Area ◽  
Safety Analysis

scholarly journals Assessment of CHF and Post-CHF Heat Transfer Models for High-Pressure Condition

2021 ◽  
Vol 9 ◽  
Author(s):  
Meiqi Song ◽  
Xiaojing Liu
Keyword(s):  
Heat Transfer ◽  
High Pressure ◽  
Literature Review ◽  
Prediction Models ◽  
Safety Analysis ◽  
Pressure Condition ◽  
Predictive Capability ◽  
The World ◽  
Transfer Models

Supercritical heat transfer systems may undergo trans-critical procedures and work at subcritical conditions during startup, shutdown, or some accidents. However, well-validated heat transfer models for the high-pressure condition (P/Pc>0.7) are still missing. In the present work, with exhaustive literature review, extensive experimental databanks of CHF and post-dryout heat transfer under high-pressure condition are established, respectively. Existing prediction models for the high-pressure condition are also summarized from all over the world. Thereby, with the aid of the high-pressure experimental databank, prediction models get evaluated. It has been demonstrated that CHF correlation developed by Song et al. shows good predictive capability. Post-dryout heat transfer could get well predicted by the Song correlation. These recommended prediction models could be implemented to upgrade safety analysis codes for simulation of trans-critical transients.

scholarly journals Driver Lane-Changing Behavior Prediction Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Cheng Wei ◽  
Fei Hui ◽  
Asad J. Khattak
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Traffic Safety ◽  
Prediction Models ◽  
Time Window ◽  
Driving Safety ◽  
Physiological Data ◽  
Lane Changing ◽  
Proposed Model ◽  
Neural Network Prediction

A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

Traffic Safety Implications of Travel Demand Management Policies

2014 ◽  
pp. 115-140
Author(s):  
Ali Pirdavani ◽  
Tom Bellemans ◽  
Tom Brijs ◽  
Bruno Kochan ◽  
Geert Wets
Keyword(s):  
Spatial Correlation ◽  
Traffic Safety ◽  
Travel Demand ◽  
Prediction Models ◽  
Demand Management ◽  
Primary Objective ◽  
Travel Demand Management ◽  
Crash Prediction ◽  
Crash Prediction Models ◽  
Crash Types

Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system’s effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios: a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, Zonal Crash Prediction Models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the Number Of Crashes (NOCs) (including fatal, severe, and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network, and socio-demographic variables of 2200 traffic analysis zones in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this chapter, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

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