Integration of gamification in a traffic education platform for children

[EN] Children are highly represented in injuries and fatalities caused by road accidents. The major reasons are children’s lack of ability to scan the environment, inconsistent behaviour, distraction in traffic situations, ability to estimate speed and distance, and less developed hazard perception skills. Therefore, traffic education for children is very important. This study will look at a platform about traffic education for children including gamification elements. Gamification is a relatively new concept which has gathered a lot of attention over the last few years with its application in many diverse fields. Gamification is defined as the application of game mechanics to non-game activities in order to change behaviour. The education community has discovered the power it has to increase students’ performance and engagement. The current study focuses on educating school going children on traffic safety in Flanders (Belgium). We expect the platform to be effective in increasing traffic knowledge, situation awareness, risk detection and risk management among children and a positive change in (predictors of) behaviours of children who will be using the platform. To investigate the effect of the platform, a pretest-posttest design with an intervention group and a control group will be used. Data will be collected and analyzed in the spring of 2018 and results, limitations and policy recommendations will be provided during the conference in June 2018.http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Riaz, MS.; Cuenen, A.; Janssens, D.; Brijs, K.; Wets, G. (2018). Integration of gamification in a traffic education platform for children. Editorial Universitat Politècnica de València. 1189-1197. https://doi.org/10.4995/HEAD18.2018.8174OCS1189119

Serviceability Analysis of Pedestrian Overhead Bridges and Underpasses

A grade-separated crossing allows a bicycle/pedestrian to continue over or under a barrier without conflict with a vehicle. However, the serviceability of these facilities is compromised in underdeveloped countries, including Pakistan. This research examines the effectiveness of pedestrian bridges and underpasses in terms of their usage by pedestrians. A total of 80,017 pedestrian crossings were observed at four sites (3 overhead bridges and one underpass) for four weeks (one week per site) using manual and video photography. The data about age, gender, and serviceability of each pedestrian was collected and analyzed using the chi-square test, t-test, and descriptive analysis. The study site selection was based on different characteristics, i.e., the number of lanes, type of median barriers, and type of facility (bridge/underpass). The analysis shows that most of the pedestrians (71.83%) did not use the crossing facilities, resulting in the poor serviceability of these structures. A comparison between bridges and underpasses also reveals that underpass usage (62.5%) is statistically more significant than bridge usage (11.62%). There is an effect of age (p<0.001) and gender (p<0.001) on the serviceability of these facilities as well, with pedestrians aged more than 25 years old and females using the facilities more than their counterparts. The study also provides implications for the effect of barriers and the height of facilities on the serviceability of these facilities. The number of lanes and the presence of a median barrier, as well as the height of the facility (number of steps), are the primary factors influencing the serviceability of grade-separated pedestrian crossings. Doi: 10.28991/CEJ-2023-09-04-09 Full Text: PD

Evaluation of a Road Safety Education Program Based on Driving Under Influence and Traffic Risks for Higher Secondary School Students in Belgium

Road safety education has been recognized as an instrument for reducing road accidents. This study aims to evaluate the road safety education program &ldquo;Traffic Weeks&rdquo; among higher secondary school students (age 16&ndash;19) in Belgium. The program focuses on driving under influence (DUI) and traffic risks. This study investigates whether the program has an effect on socio-cognitive variables using a questionnaire based on the theory of planned behavior. During the pre-test, 445 students filled in the questionnaire, while 253 students filled in the questionnaire during the post-test. Of these, 175 questionnaires could be matched. The results indicate that the students already had quite a supportive view of road safety at pre-test, with female students showing a more supportive view of road safety than male students. The DUI workshop had a positive effect on most socio-cognitive variables (attitude, subjective norm-friends, and intention) of female students in general education, while the traffic risks workshop only affected perceived behavioral control of female students. In terms of appreciation, students had a significantly higher appreciation of the DUI workshop compared to the traffic risks workshop. During the focus groups, students gave recommendations to improve the program

Child Pedestrian Safety: Study of Street-Crossing Behaviour of Primary School Children with Adult Supervision

Road traffic accidents are the primary cause of injuries and fatalities among children. The current study focuses on children’s (un)safe crossing behaviour in a real traffic situation accompanied by an adult at a crosswalk in front of their school. The study aims to investigate if there are differences in crossing behaviour related to road infrastructure (i.e., one-way and two-way street, elevated and non-elevated street crossing), the gender of the child, and the effect of the accompanying adult’s behaviour on the child’s crossing behaviour. Primary school children from two urban schools in Flanders (Belgium) were observed for three days while crossing the street in front of their school in the morning and afternoon. A total of 241 child–adult pairs were observed. Descriptive analysis, Pearson chi-square tests, and binary logistic regression models were used to find differences between groups. More than half of the crossings exhibited two or more unsafe behaviours. Not stopping at the curb before crossing was the most unsafe behaviour, exhibited by 47.7% of children; not looking for oncoming traffic before and during the crossing was the second most unsafe behaviour, exhibited by 39.4% of the children. The only difference between boys’ and girls’ crossing behaviour was in stopping at the curb with girls 1.901 times more likely to stop before crossing as compared to boys. Adults holding hands of the child resulted in safer behaviours by children. The children not holding hands displayed significantly riskier behaviour in running or hopping while crossing the street and being distracted. The study reinforces the need to improve the transportation system through infrastructural interventions (elevated crosswalks), as well as educating and training children and the parents on safe crossing behaviour in traffic

Evaluation of a Road Safety Education Program Based on Driving Under Influence and Traffic Risks for Higher Secondary School Students in Belgium

Road safety education has been recognized as an instrument for reducing road accidents. This study aims to evaluate the road safety education program &#8220;Traffic Weeks&#8221; among higher secondary school students (age 16&#8722;19) in Belgium. The program focuses on driving under influence (DUI) and traffic risks. This study investigates whether the program has an effect on socio-cognitive variables using a questionnaire based on the theory of planned behavior. During the pre-test, 445 students filled in the questionnaire, while 253 students filled in the questionnaire during the post-test. Of these, 175 questionnaires could be matched. The results indicate that the students already had quite a supportive view of road safety at pre-test, with female students showing a more supportive view of road safety than male students. The DUI workshop had a positive effect on most socio-cognitive variables (attitude, subjective norm-friends, and intention) of female students in general education, while the traffic risks workshop only affected perceived behavioral control of female students. In terms of appreciation, students had a significantly higher appreciation of the DUI workshop compared to the traffic risks workshop. During the focus groups, students gave recommendations to improve the program

Conservation Environments&rsquo; Effect on the Compressive Strength Behaviour of Wood&ndash;Concrete Composites

This paper addresses the issues in making wood&ndash;concrete composites more resilient to environmental conditions and to improve their compressive strength. Tests were carried out on cubic specimens of 10 &times; 10 &times; 10 cm3 composed of ordinary concrete with a 2% redwood- and hardwood-chip dosage. Superficial treatments of cement and lime were applied to the wood chips. All specimens were kept for 28 days in the open air and for 12 months in: the open air, drinking water, seawater, and an oven. Consequently, the compressive strength of ordinary concrete is approximately 37.1 MPa. After 365 days of exposure to the open air, drinking water, seawater, and the oven, a resistance loss of 35.84, 36.06, 42.85, and 52.30% were observed, respectively. In all environments investigated, the untreated wood composite concrete&rsquo;s resistance decreased significantly, while the cement/lime treatment of the wood enhanced them. However, only 15.5 MPa and 14.6 MPa were attained after the first 28 days in the cases of the redwood and the hardwood treated with lime. These findings indicate that the resistance of wood&ndash;concrete composites depends on the type of wood used. Treating wood chips with cement is a potential method for making these materials resistant in conservation situations determined by the cement&rsquo;s chemical composition. The current study has implications for researchers and practitioners for further understanding the impact of these eco-friendly concretes in the construction industry

Sub-Surface Geotechnical Data Visualization of Inaccessible Sites Using GIS

Geotechnical investigation, in hilly areas, for high-rise projects, becomes a problematic issue and costly process due to difficulties in mobilization and assembling the drilling equipment on mountainous terrains. The objective of this study is to map soil properties of study areas, especially at inaccessible sites, for reconnaissance. Digital soil maps for Tehsil Murree, Pakistan, have been developed using the emerging Geographical Information System (GIS). The research work involved the creation of an exhaustive database, by collecting and rectifying geotechnical data, followed by the digitization of the acquired data through integration with GIS, in an attempt to visualize, analyze and interpret the collected geotechnical information spatially. The soil data of 205 explanatory holes were collected from the available geotechnical investigation (GI) reports. The collection depth of soil samples, which were initially used for the design of deep and shallow foundations by different soil consultancies in the Murree area, was approximately 50 ft. below ground level. Appropriate spatial interpolation methods (i.e., the Kriging) were applied for the preparation of smooth surface maps of soil standard penetration tester number values, soil type and plasticity index. The accuracy of developed SPT N value and plasticity maps were then evaluated using the linear regression method, in which the predicted values of soil characteristics from developed maps and actual values were compared. SPT N value maps were developed up to a depth of 9.14 m below ground level and at every 1.52 m interval. The depth of refusal was considered in the developed maps. Soil type and plasticity maps were generated up to 15.24 m depth, again at every 1.52 m intervals, using color contours, considering the maximum predicted foundation depth for high-rise projects. The study has implications for academics and practitioners to map the soil properties for inaccessible sites using GIS, as the resulting maps have high accuracy

Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature

Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature