Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Wu, Peijie; Song, Li

doi:10.1016/j.jsr.2021.09.001

Cited by 17 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Road accidents is associated with several risk indicators of vehicle factors, including worn-out tyres, unexpected mechanical breakdowns, broken back seatbelts, no side airbags fitted, no fire extinguisher, no rear-view camera, and older automobiles (McGwin et al, 2003;Keall et al, 2011;Bose et al, 2013;Jansen et al, 2015;Cicchino, 2017;Hordofa et al, 2018;Chand et al, 2021). Additionally, the aspects of the road environment had an impact on both the performance of the car and the driver (Batrakova & Gredasova et al, 2016;Wu et al, 2021). Furthermore, this component may help to encourage and support the formation of safe driving style.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, this component may help to encourage and support the formation of safe driving style. Significant risk indicators for the aspects of the road environment include driving at night, malfunctioning streetlights, malfunctioning traffic lights, rainy weather, crosswinds, sun glare, foggy weather, poor road markings and traffic signs, road pavement failure, roadside objects, and road congestion (Tay et al, 2008;Bullough et al, 2013;Cai et al, 2013;Mannera & Wünsch-Zieglerb, 2013;Zhang et al, 2013;Mhirech & Alaoui-Ismaili, 2015;Chengula, 2018;Montella et al, 2019;Wu et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Internal Consistency Reliability and Construct Validity of the Safety Questionnaire for Ride-Hailing Car

Daud¹,

Osman²,

Hashim³

et al. 2023

IJARBSS

View full text Add to dashboard Cite

Road traffic accidents involving ride-hailing car (RHC) have become more prevalent in Malaysia in recent years. Therefore, the present study aims to develop a reliable and valid questionnaire for assessing the safety performance of RHC in Malaysia. Three risk domains were identified in the literature, comprising driving behaviour factors, vehicle safety factors, and road environment factors. In these risk domains, a total of 32 risk indicators were selected, and these items were subsequently added to the questionnaire. Afterwards, these questionnaires were randomly distributed to 350 RHC passengers. To have a reliable and valid questionnaire, the internal consistency reliability by Cronbach's alpha as well as construct validity by exploratory factor analysis were used. The finalized findings showed that all of the measurements have excellent reliability standards between 0.823 and 0.925. From the results of construct validity tests, there were three factors extracted from 21 risk indicators. Consequently, a number of questions were amended to better quantify the constructs. The finalized RHC safety questionnaire is a valid and an internally reliable tool for assessing the safety performance of RHC in Malaysia.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Internal Consistency Reliability and Construct Validity of the Safety Questionnaire for Ride-Hailing Car

Daud¹,

Osman²,

Hashim³

et al. 2023

IJARBSS

View full text Add to dashboard Cite

show abstract

“…In the above formula G represents the data exchange information; K|v| represents the data center; b t represents the data conversion factor; m represents the data storage parameter [27]. Based on the creation of the above information, the meaning, name, type, width and other contributions to all information of financial information management can be determined, and the following definitions can be made as in the equation:…”

Section: Financial Information Managementmentioning

confidence: 99%

Application of Association Rule Mining Algorithm based on 5G Technology in Information Management System

Juan¹,

Chen

2023

SCPE

View full text Add to dashboard Cite

In this paper, an application method of association rule mining algorithm based on 5G technology in information management system is proposed to solve the problems of long running time and low processing efficiency in traditional financial information processing system. The association rule mining algorithm's employment in information management systems is the main topic of this research study, which is based on 5G technology. The efficiency and efficacy of information management systems have a lot of room to grow with the introduction of 5G. Large datasets may be mined for patterns and associations using the potent approach known as association rule mining. We want to improve the performance of information management systems by fusing association rule mining with the capabilities of 5G technology. The experimental findings indicate that in the first group of trials, the traditional system’s time for information mining is identical to that of the developed system, which is around one minute. The typical system's time to mine financial information, however, steadily grows with the amount of experimental data. The difference between the two is most obvious in the sixth experiment. Because the design system can delve deeply into the financial information, the overall information mining time of the financial information management system based on the association rule mining algorithm of the design is shorter. It is confirmed that the system for automatically processing financial information described in this study has a high level of processing accuracy and a positive processing outcome.

show abstract

“…These studies often used a fusion of two or more databases such as crash data, traffic data, weather data, and road geometry data. Those studies mostly used ML techniques such as XGBoost and RF to predict crash frequency 15,16,18,[27][28][29][30][31][32][33] . In the current studies, some factors are less seen, such as traffic volume hourly and daily, time headway in traffic data, free-flow speed, road capacity in road geometric data, snow depth, sunlight during 24 h in weather data and public holidays, month and year in calendar data.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying high crash risk segments in rural roads using ensemble decision tree-based models

Iranmanesh

Seyedabrishami

Moridpour

2022

Sci Rep

View full text Add to dashboard Cite

Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial–temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, time headway), road characteristics (e.g. road type, number of lanes), weather data (e.g. daily rainfall, snow depth, temperature), and calendar variables (e.g. day of the week, public holidays, month, year) are used for model calibration. Three different decision tree algorithms, including, Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) have been employed to predict crash frequency. Results show that based on the traditional evaluation measures, the XGBosst is better for the explanation and interpretation of the factors affecting crash frequency, while the RF model is better for detecting trends and forecasting crash frequency. According to the results, the traffic flow rate, road type, year of the crash, and wind speed are the most influencing variables in predicting crash frequency on rural roads. Forecasting the high and medium risk segment-day in the rural network can be essential to the safety management plan. This risk will be sensitive to real traffic data, weather forecasts and road geometric characteristics. Seventy percent of high and medium risk segment-day are predicted for the case study.

show abstract

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Cited by 17 publications

References 45 publications

Internal Consistency Reliability and Construct Validity of the Safety Questionnaire for Ride-Hailing Car

Internal Consistency Reliability and Construct Validity of the Safety Questionnaire for Ride-Hailing Car

Application of Association Rule Mining Algorithm based on 5G Technology in Information Management System

Identifying high crash risk segments in rural roads using ensemble decision tree-based models

Contact Info

Product

Resources

About