Due to their innovative treatment, Unconventional Arterial Intersection Designs (UAIDs) have been developed to alleviate congestion at conventional signalized intersections, in an effort towards the sustainable development of crowded capitals. A methodological framework for economic assessment, however, has not been investigated properly for such designs, particularly under mixed traffic environments. This article aims to outline a methodological framework that can be followed for the socio-economic assessment of such designs. A cost–benefit analysis approach was developed to figure out the different determinants of costs and benefits of an overpass interchange (as a widespread treatment) and two selected UAIDs (as alternative measures). The two studied UAID schemes in this article are Continuous Flow Intersection (CFI) and Restricted Crossing U-Turn (RCUT). Seeking credible results, a set of three signalized intersections in downtown Cairo, Egypt was selected as a proof-of-concept for the developed method. PTV-VISSIM, a simulation-based platform, was utilized to estimate the benefits gained by road users. Our research objectives were to identify, evaluate, and compare the economic feasibility of the different alternatives. Compared to the overpass intersection, we found that the CFI and RCUT designs ensure higher economic efficiency, while mitigating congestion at conventional signalized intersections under heterogeneous traffic conditions.

For sustainable transportation systems, modeling road traffic accidents is essential in order to formulate measures to reduce their harmful impacts on society. This study investigated the outcomes of using different datasets in traffic accident models with a low number of variables that can be easily manipulated by practitioners. Long-horizon aggregated and disaggregated road traffic accident datasets on Egyptian roads (for five years) were used to compare the model’s fit for different data groups. This study analyzed the results of k-means data clustering and classified the data into groups to compare the fit of the base model (Smeed’s model and different types of regression models). The results emphasized that the aggregated data used had less efficiency compared with the disaggregated data. It was found that the classification of the disaggregated dataset into reasonable groups improved the model’s fit. These findings may help in the better utilization of the available road traffic accident data for determining the best-fitting model that can assist decision-makers to choose suitable road traffic accident prevention measures.

Over recent decades, planners in the U.S. have increasingly adopted mixed-use projects to reduce automobile dependency and strengthen local community identity, although results remain inconsistent across cities. Urban health and fitness outcomes are shaped by complex interactions between the built environment, socioeconomic factors, and demographic characteristics. This study introduces a Health and Fitness Index (HFI) for 28,758 U.S. ZIP codes, derived from normalized measures of walkability, healthcare facility density, and carbon emissions, to assess spatial disparities in health-supportive environments. Using four modeling approaches—lasso regression, multiple linear regression, decision trees, and k-nearest neighbor classifiers—we evaluated the predictive importance of 15 urban and socioeconomic variables. Multiple linear regression produced the strongest generalization performance (R² = 0.60, RMSE = 0.04). Key positive predictors included occupied housing units, business density, land-use mix, household income, and racial diversity, while income inequality and population density were negatively associated with health outcomes. This study evaluates five statistical formulations (Metropolis Hybrid Models) that incorporate different combinations of walkability, land-use mix, environmental variables, and socioeconomic indicators to test whether relationships between urban form and socioeconomic conditions remain consistent under different variable combinations. In cross-sectional multivariate regression, although mixed-use development in high-density areas is strongly associated with healthcare facilities, these areas tend to serve younger and more racially diverse populations. Decision tree feature importance rankings and clustering profiles highlight structural inequalities across regions, suggesting that enhancing business diversity, land-use integration, and income equity could significantly improve health-supportive urban design. This research provides a data-driven framework for urban planners to identify underserved neighborhoods and develop targeted interventions that promote walkability, accessibility to health infrastructure, and sustainability. It contributes to the growing literature on urban health analytics, integrating machine learning, spatial clustering, and multidimensional urban indicators to advance equitable and resilient city planning.

In seeking sustainable, safe, and efficient school commuting tours as non-recurring sources of congestion, it is essential to investigate the dynamic interaction between school students’ pick-up and drop-off (P&D) movements and the traffic operational performance of the surrounding area. This study investigates the traffic operational performance in the vicinity of schools at various P&D time intervals. The Travel and Planning Time indices (TTI, PTI), along with the Level of Service (LOS) are utilized as traffic operational performance indices in this article. A Python script was developed to employ the Google API for estimating the travel times from a real traffic dataset comprising 40 schools distributed across six cities in the Kingdom of Saudi Arabia (KSA). The results indicate that LOS varies from C to D for all cities except Riyadh, which exhibits the poorest traffic performance during P&D time intervals. This paper serves as a guideline for city planners and policymakers seeking to provide valuable insights to enhance traffic operational performance in Saudi Arabia.

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that the total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings.

The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have not yet been deployed. This study investigates the willingness to adopt AVs in a developing city where AVs have not been deployed yet. A comprehensive travel behavior questionnaire was conducted among local commuters in Alexandria, Egypt, to identify the influential variables affecting AV choice. The well-known machine learning classifier, Extreme Gradient Boosting (XGB), was employed to develop a forecasting model, which indicated a notable accuracy. The results indicated that trip cost was the most influential feature. On the other hand, there is a considerable level of mode captivity, since most travelers prefer to remain with their current mode, regardless of the effects of other variables. A significant share of travelers expressed concerns about shifting to AVs due to safety worries associated with the travel behavior of other transportation modes’ commuters. The analysis provides nuanced perspectives on the variables promoting modal shift toward the AVs, supporting future policies for smart urban mobility.