Frequent lane changes cause serious traffic safety concerns, which involve fatalities and serious injuries. This phenomenon is affected by several significant factors related to road safety. The detection and classification of significant factors affecting lane changing could help reduce frequent lane changing risk. The principal objective of this research is to estimate and prioritize the nominated crucial criteria and sub-criteria based on participants’ answers on a designated questionnaire survey. In doing so, this paper constructs a hierarchical lane-change model based on the concept of the analytic hierarchy process (AHP) with two levels of the most concerning attributes. Accordingly, the fuzzy analytic hierarchy process (FAHP) procedure was applied utilizing fuzzy scale to evaluate precisely the most influential factors affecting lane changing, which will decrease uncertainty in the evaluation process. Based on the final measured weights for level 1, FAHP model estimation results revealed that the most influential variable affecting lane-changing is ‘traffic characteristics’. In contrast, compared to other specified factors, ‘light conditions’ was found to be the least critical factor related to driver lane-change maneuvers. For level 2, the FAHP model results showed ‘traffic volume’ as the most critical factor influencing the lane changes operations, followed by ‘speed’. The objectivity of the model was supported by sensitivity analyses that examined a range for weights’ values and those corresponding to alternative values. Based on the evaluated results, stakeholders can determine strategic policy by considering and placing more emphasis on the highlighted risk factors associated with lane changing to improve road safety. In conclusion, the finding provides the usefulness of the fuzzy analytic hierarchy process to review lane-changing risks for road safety.

Frequent lane changes cause serious traffic safety concerns for road users. The detection and categorization of significant factors affecting frequent lane changing could help to reduce frequent lane-changing risk. The main objective of this research study is to assess and prioritize the significant factors and sub-factors affecting frequent lane changing designed in a three-level hierarchical structure. As a multi-criteria decision-making methodology (MCDM), this study utilizes the analytic hierarchy process (AHP) combined with the best–worst method (BWM) to compare and quantify the specified factors. To illustrate the applicability of the proposed model, a real-life decision-making problem is considered, prioritizing the most significant factors affecting lane changing based on the driver’s responses on a designated questionnaire survey. The proposed model observed fewer pairwise comparisons (PCs) with more consistent and reliable results than the conventional AHP. For level 1 of the three-level hierarchical structure, the AHP–BWM model results show “traffic characteristics” (0.5148) as the most significant factor affecting frequent lane changing, followed by “human” (0.2134), as second-ranked factor. For level 2, “traffic volume” (0.1771) was observed as the most significant factor, followed by “speed” (0.1521). For level 3, the model results show “average speed” (0.0783) as first-rank factor, followed by the factor “rural” (0.0764), as compared to other specified factors. The proposed integrated approach could help decision-makers to focus on highlighted significant factors affecting frequent lane-changing to improve road safety.

Background: Despite governmental interventions, the Gulf Cooperation Council (GCC) region continues to experience higher road traffic crash and fatality rates relative to Western nations. This trend suggests a potential disconnect between Road Traffic Injuries (RTI) research and the mitigation measures put in place. Method: Here, we present an in-depth bibliometric analysis to obtain a comprehensive understanding of RTI research in the GCC region. The Web of Science database was used to search and retrieve the relevant articles during the period of 1981-2019. Results: The volume of RTI research increased from 2015–2019, suggesting an increased focus on traffic safety in the GCC region. Saudi Arabia had the highest RTI research productivity level (126 publications); Bahrain had the lowest (7 publications). Inconsistent with its low publication volume, Hammad Medical Corps of Qatar had the highest citation impact score of 16.33. Global collaboration for RTI research was highest between Saudi Arabia and the United States. The most prevalent publication journal for the region was Accident Analysis and Prevention. The most common keywords were “road traffic accidents” and “road traffic injuries”; terms such as “mobile phones”, “pedestrian safety”, “pedestrians”, and “distracted driving” were least common. In the five most productive GCC nations with respect to RTI research (Saudi Arabia, United Arab Emirates, Qatar, Kuwait, and Oman), researchers tended to publish works related to road traffic safety in traffic safety-oriented journals. Conclusions: The quantity and quality of RTI publications in GCC is insufficient to meet the increasing related public health and economic burden in the region. The trends among publication volumes, citations, and impact were inconsistent. There is a lack of research collaboration among the institutions. Most of the research related to RTI is being conducted by researchers with a medical background. Research focusing on pedestrians, cyclists and road user behavior is also inadequate.

Background: Despite governmental interventions, the Gulf Cooperation Council (GCC) region continues to experience higher road traffic crash and fatality rates relative to Western nations. This trend suggests a potential disconnect between Road Traffic Injuries (RTI) research and the mitigation measures put in place. Method: Here, we present an in-depth bibliometric analysis to obtain a comprehensive understanding of RTI research in the GCC region. The Web of Science database was used to search and retrieve the relevant articles during the period of 1981-2019. Results: The volume of RTI research increased from 2015–2019, suggesting an increased focus on traffic safety in the GCC region. Saudi Arabia had the highest RTI research productivity level (126 publications); Bahrain had the lowest (7 publications). Inconsistent with its low publication volume, Hammad Medical Corps of Qatar had the highest citation impact score of 16.33. Global collaboration for RTI research was highest between Saudi Arabia and the United States. The most prevalent publication journal for the region was Accident Analysis and Prevention. The most common keywords were “road traffic accidents” and “road traffic injuries”; terms such as “mobile phones”, “pedestrian safety”, “pedestrians”, and “distracted driving” were least common. In the five most productive GCC nations with respect to RTI research (Saudi Arabia, United Arab Emirates, Qatar, Kuwait, and Oman), researchers tended to publish works related to road traffic safety in traffic safety-oriented journals. Conclusions: The quantity and quality of RTI publications in GCC is insufficient to meet the increasing related public health and economic burden in the region. The trends among publication volumes, citations, and impact were inconsistent. There is a lack of research collaboration among the institutions. Most of the research related to RTI is being conducted by researchers with a medical background. Research focusing on pedestrians, cyclists and road user behavior is also inadequate.

This study evaluated the effectiveness of red-light cameras in Dammam, the Kingdom of Saudi Arabia. Two methods were used to assess changes in crash risk at the intersections at which the red-light camera programme had been operating. Geospatial analysis was used to visualize trends in road crash density over the geographical region of Dammam and a pre--post-camera crash analysis was undertaken. The distribution of injury crashes was greater than that of crashes which included property damage, the latter of which was concentrated around central Dammam. The five red-light cameras installed in 2012 were located outside areas of high-crash and injury density, and the total number of crashes reported in the three-month periods after installation was double that before the cameras were installed. This increase in the number of crashes also occurred at the five comparison sites (without red-light cameras), indicating a null effect. The findings from this study are contrary to previous evaluations of speed management programmes associated with red-light cameras. The study highlights the challenges in obtaining data for such research and illustrates that a reliance on overt, fixed camera's to manage speed is unlikely to deliver significant reductions in road trauma.

Municipalities within a metropolitan region do not grow in isolation; their development is shaped by pressures diffusing from neighboring jurisdictions through transportation, labor, and housing-market linkages, which conventional city-by-city forecasts cannot exploit. This paper develops a general, network-aware framework for forecasting municipal urban growth in interconnected metropolitan systems. The region is represented as a heterogeneous graph in which each municipality is a node carrying lagged housing, transportation, demographic, and income indicators, and edges encode geographic proximity and socioeconomic similarity. Within this framework we compare a hierarchy of architectures of increasing structure, city-isolated temporal models, spatial graph models, and a spatiotemporal graph model, so that successive comparisons isolate the contributions of temporal structure, spatial structure, and their combination. The prediction target is the year-over-year change in impervious-surface coverage, the planning-relevant increment of newly developed land. We demonstrate the approach on the Dallas–Fort Worth metropolitan area (216 municipalities, 2011–2022). Permutation-based spatial statistics confirm that growth is heterogeneous, temporally autocorrelated, and significantly spatially clustered. Under rolling-origin cross-validation with paired significance tests, graph-based and spatiotemporal models significantly outperform city-isolated baselines, the gain coming from a learned, identity-preserving aggregator rather than the graph alone. Growth exhibits its strongest statistical dependence through short-range geographic proximity rather than socioeconomic similarity. In a validated multi-year forecast, the inter-municipal signal improves prospective accuracy and tempers over-extrapolation of fast-growing fringe municipalities, yielding more accurate, generalizable, and decision-relevant growth forecasts.