Search Results (71)

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

Traffic congestion poses a significant challenge to modern urban environments, impacting both driver satisfaction and road safety. This paper investigates the effectiveness of a smart traffic light system (STL), a solution developed under the Intelligent Transportation System (ITS) initiative by the Ministry of Works Malaysia, to address these issues in Malaysia. The system integrates a network of sensors, AI-enabled cameras, and Automatic Number Plate Recognition (ANPR) technology to gather real-time data on traffic volume and vehicle classification at congested intersections. This data is utilized to dynamically adjust traffic light timings, prioritizing traffic flow on heavily congested roads while maintaining safety standards. To evaluate the system’s performance, a comprehensive study was conducted at a selected intersection. Traffic patterns were automatically analyzed using camera systems, and the performance of the STL was compared to that of traditional traffic signal systems. The average travel time from the start to the end intersection was measured and compared. Preliminary findings indicate that the STL significantly reduces travel times and improves overall traffic flow at the intersection, with average travel time reductions ranging from 7.1% to 28.6%, depending on site-specific factors. While further research is necessary to quantify the full extent of the system’s impact, these initial results demonstrate the promising potential of STL technology to enhance urban mobility and more efficient and safer roadways by moving beyond traditional traffic signal functionalities. Full article

The quality of campus environments plays an important role in the mental health of college students. However, the impact of nighttime lighting in campus settings has received limited attention. This study examines how different landscape lighting conditions affect emotions and the perceived restorative potential, providing a mixed-method research framework to assess nighttime landscapes. The study was conducted on a section of campus roadway under three scenarios: daytime (cloudy conditions) and two nighttime settings (landscape lights and streetlights, and streetlights only). We employed wearable biosensors, visitor-employed photography tasks, affective mapping, interviews, and self-reports to comprehensively assess the participants’ emotional responses and perceptions. Statistical analyses, including the Friedman test, Wilcoxon signed-rank test, one-way ANOVA, Getis–Ord Gi* statistic and kernel density analysis, were used to evaluate differences in emotional and restorative perceptions across lighting scenarios. The results showed that nighttime environments with well-designed landscape lighting enhance the restorative potential more compared to street lighting alone and, in some cases, even surpass daytime settings. Skin conductance data, integrated with spatial–temporal trajectories and affective mapping, revealed clear patterns of emotional responses, emphasizing the role of lighting in shaping environmental quality. These findings provide actionable insights for architects and lighting designers to create nighttime landscapes that promote emotional well-being and restoration. Full article

Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack the available illumination data. Therefore, the objective of this research is threefold, as follows: (i) to develop machine learning models that use readily available roadway characteristic data to predict the severity of nighttime crashes; (ii) determine the effect that illumination has on crash severity; and (iii) develop a tool to assist DOT decision makers in collecting illumination data. To accomplish this objective, we have extracted data from the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database, which was then further split into a training and a test dataset. Then, seven machine learning techniques, namely binary logistic regression, k-nearest neighbors, naïve Bayes, random forest, artificial neural network, Extreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM) model, were all applied to the unseen test data. The random forest model produced the most promising results by predicting severe crashes with 97.6% accuracy. In addition, we conducted a pilot study to test the collection of illumination data using a light meter. In the future, we aim to complete the development of a smartphone application, which can be used in conjunction with the random forest model presented in this paper, to collect crowdsourced illumination data and predict nighttime crash hotspots. This may assist DOT decision makers to prioritize funding for illumination at the hot spots. Full article

Urban multilane roundabouts and signalized intersections are two major roadway devices used for controlling and managing traffic flow. This paper presents a comparative analysis of the saturation flow rate between urban multilane roundabouts and multilane signalized intersections using field data from the Dammam Metropolitan Area (DMA) in Saudi Arabia. The data of this study were collected at four roundabouts and four signalized intersections in Dammam metropolitan area (DMA), Saudi Arabia. A total of 7028 saturation headways at the roundabouts and 2626 saturation headways at the signalized intersections were included. The results indicated that the signalized intersections had a higher saturation flow rate at the exit lane than the roundabouts at about 1046 vehicles per hour. These findings emphasize that signalized intersections outperform roundabouts in terms of the vehicular movement rate during green lights. Moreover, when the light is green, it takes 1.82 s for a car to move through the middle lane of a traffic light intersection. This study draws a unique connection between speed fluctuations in roundabouts with energy consumption, concluding how vehicles consume more energy this way. Thus, single-lane roundabouts are recommended for optimal traffic flow management in all directions. Full article

Performing stability studies of waterproof coal pillars is one of the key measures for preventing mine water disasters. As some areas of the coal pillar were affected by goaf water in the Nanhu Second Mine, the coal pillar and surrounding roadway were somewhat deformed. To investigate whether the pillar can ensure safe production in the mine, the source of goaf water and the direction of water infiltration were analyzed using exploration holes, and it was concluded that the goaf water originated from the V₃ aquifer and was static. Thus, a theoretical analysis was carried out to determine the relationship between the mechanical parameters of the coal and rock structures affected by water. On this basis, a numerical simulation was employed to examine the key changes in the coal pillar and roadway affected by goaf water. The simulation results showed that the plastic area was 6–11 m and the elastic area in the middle was 6–8 m after excavating the working faces on both sides of the coal pillar, and the water flow vector of the aquifer could not pass through the pillar. Finally, in situ monitoring using ground-penetrating radar, deformation measurement, and loosening circle detection revealed that the development degree of internal cracks in the coal pillar was relatively light; thus, the pillar could effectively prevent water damage. These monitoring and analysis methods comprehensively evaluate the stability of the coal pillar and provide a guarantee for the safe mining of the working face. Full article

Traffic crashes contribute significantly to non-recurrent congestion, thereby increasing delays, congestion pollution, and other challenges. It is important to have tools that enable accurate prediction of incident duration to reduce delays. It is also necessary to understand factors that affect the duration of traffic crashes. This study developed three machine learning models, namely extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and a light gradient-boosting machine (LightGBM), to predict crash-related incident clearance time in Louisiana rural interstates and utilized Shapley additive explanations (SHAP) analysis to determine the influence of factors impacting it. Four ICT levels were defined based on 30 min intervals: short (0–30), medium (31–60), intermediate (61–90), and long (greater than 90). The results suggest that XGBoost outperforms CatBoost and LightGBM in the collective model’s predictive performance. It was found that different features significantly affect different ICT levels. The results indicate that crashes involving injuries, fatalities, heavy trucks, head-on collisions, roadway departure, and older drivers are the significant factors that influence ICT. The results of this study may be used to develop and implement strategies that lead to reduced incident duration and related challenges with long clearance times, providing actionable insights for traffic managers, transportation planners, and incident response agencies to enhance decision-making and mitigate the associated increases in congestion and secondary crashes. Full article

Drivers’ safety and overall road functionality are key triggers for deciding on road interventions. Because of the socioeconomical implications of traffic incidents, either fatal or no, continuous research has been dedicated over the previous decades on the assessment of factors contributing to crash potential. Apart from the behavioral aspects of driving, which are commonly studied through simulation and advanced modelling techniques, the road infrastructure status is of equal or even higher significance. In this study, an approach is presented to discuss the road redesign potentials based on the evaluation of network-level historical incident records from road crashes in Greece. Based on total and fatal crash records, the following infrastructure-related aspects were assessed as critical for the discussion of the road redesign potential needs: the status of road’s surface (i.e., dry, wet, etc.), the issue of improving driving conditions near at-grade intersections, the presence and suitability of signage and/or lighting, and the consideration of particular geometric design features. Overall, it is deemed that intervention actions for at least one of these pillars should aim at enhancing the safety and functionality of roadways. Full article

Current international optical science research focuses on the non-visual effects of lighting on human cognition, mood, and biological rhythms to enhance overall well-being. Nocturnal roadway lighting, in particular, has a substantial impact on drivers’ physiological and psychological states, influencing behavior and safety. This study investigates the non-visual effects of correlated color temperature (CCT: 3000K vs. 4000K vs. 5000K) and illuminance levels (20 lx vs. 30 lx) of urban motor vehicle road lighting on driver alertness during various driving tasks. Conducted between 19:00 and 20:30, the experiments utilized a human-vehicle-light simulation platform. EEG (β waves), reaction time, and subjective evaluations using the Karolinska Sleepiness Scale (KSS) were measured. The results indicated that the interaction between CCT and illuminance, as well as between CCT and task type, significantly influenced driver alertness. However, no significant effect of CCT and illuminance on reaction time was observed. The findings suggest that higher illuminance (30 lx) combined with medium CCT (4000K) effectively reduces reaction time. This investigation enriches related research, provides valuable reference for future studies, and enhances understanding of the mechanisms of lighting’s influence on driver alertness. Moreover, the findings have significant implications for optimizing the design of urban road lighting. Full article

In longwall coal mining, significant deformation of small-pillar roadways presents challenges for the safe and efficient retreat of mining panels. Non-penetrating directional pre-splitting alters the roof structure of these roadways and effectively manages their stability under high stress during mining operations. In this study, a three-dimensional experimental model for the non-penetrating pre-splitting of small-coal-pillar roadway roofs was established, the apparent resistivity change in the rock layer during mining of the working face was determined, the propagation law of high-frequency electromagnetic waves in the overlying rock was studied, and the stress distribution law of the surrounding rock was investigated. After non-penetrating pre-splitting in the roof, the apparent resistivity change rate of the overlying rock increased and the electromagnetic waveform exhibited scattering and diffraction, forming a short cantilever beam. After mining, the stress in the adjacent mining panel gateway reduced, resulting in a pressure relief effect on the surrounding rock. These findings were further validated through field application, where the overall deformation of the roadway was reduced by 57%. The research results shed light on the management of roof control in small-coal-pillar roadways and the rational determination of non-penetrating pre-splitting parameters. Full article

Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time state perception of vehicles on expressways, we propose an algorithm to estimate the arrival time of vehicles in the next segment using Electronic Toll Collection (ETC) data. Firstly, the characteristics of ETC data and GPS data are meticulously described. We devise algorithms for data cleaning and fusion, subsequently segmenting the vehicle journey into multiple sub-segments. In the following step, feature vectors are constructed from the fused data to detect service areas and analyze the expressway segment characteristics, vehicle traits, and the influence of service areas. Finally, an algorithm utilizing LightGBM is introduced for estimating the arrival time of vehicles at various segments, corroborated by empirical tests using authentic traffic data. The $MAE$ of the algorithm is recorded as 20.1 s, with an $RMSE$ of 32.6 s, affirming its efficacy. The method proposed in this paper can help optimize transportation systems for improving efficiency, alleviating congestion, reducing emissions, and enhancing safety. Full article

Expansive clays present serious issues in a variety of engineering applications, including roadways, light buildings, and infrastructure, because of their notable volume changes with varying moisture content. Tough weather conditions can lead to drying and shrinking, which alters expansive clays’ hydro-mechanical properties and results in cracking. The hydro-mechanical behavior of Al-Ghatt expansive clay and the impact of wetting and drying cycles on the formation of surface cracks are addressed in this investigation. For four cycles of wetting and drying and three vertical stress levels, i.e., 50 kPa, 100 kPa, and 200 kPa, were investigated. The sizes and patterns of cracks were observed and classified. A simplified classification based on main track and secondary branch tracks is introduced. The vertical strain measure at each cycle, which showed swell and shrinkage, was plotted. The hydromechanical behavior of the clay, which corresponds to three levels of overburden stress as indicated by its swell potential and hydraulic conductivity was observed. It was found that at low overburden stresses of 50 kPa, the shrinkage is high and drops with increasing the number of cycles. Al-Ghatt clay’s tendency to crack is significantly reduced or eliminated by the 200 kPa overburden pressure. The results of this work can be used to calculate the depth of a foundation and the amount of partial soil replacement that is needed. Full article

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. Full article

Traditional roadway lighting is intended to provide safe guidance for drivers and pedestrians, but the large-scale application of roadway lighting has resulted in significant energy consumption and light pollution. However, road markings prepared by luminous coating are a kind of multi-functional road marking that can meet the needs of highway lighting at night and save energy. Here, CaAl₂O₄:Eu²⁺,Nd³⁺,Gd³⁺ blue long-afterglow phosphor is obtained by the high-temperature solid-state method, and the blue luminescent coating is synthesized by the blending method. The phase composition, microscopic morphology, luminescence properties and water resistance of the phosphor and luminescent coatings are characterized. The best components and processes of the luminescent coating are explored to meet the application of an expressway. Considering the afterglow’s performance, the optimal calcination temperature of the phosphor is determined to be 1300 °C. The afterglow of the phosphor can be over 8 h after 2 h of daylight excitation. The addition of 1.25% SiO₂ to the luminescent coating improves the uniformity of the components, and the incorporation of 3.5% CaCO₃ improves the denseness of the coating. When the coating thickness is 0.8mm, the luminescent coating can achieve the best luminous effect. After 120 h of immersion in water, the afterglow intensity of the luminescent coating reduced to 70% of the original, which has excellent water resistance. The blue luminescent coating with the addition of appropriate amounts of CaCO₃ and SiO₂ improves the dispersion as well as the densification of the components in the coating to achieve the best luminescent effect. In the Shenyang area, different weather conditions (cloudy, sunny, rainy) have no significant effect on the afterglow performance of the luminescent coatings, all of which can achieve over 5 h of afterglow and are suitable for expressways. Full article

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques. Full article