Recently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics’ ranges to reduce the expected noise levels. The results indicate that the rate of L_eq increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that L_eq has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R² = 95.87% and R² adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.

Recently, ridesharing has been noticed as an effective and sustainable mode of transport. In which, each passenger carries out a journey that benefits travelers and society greatly, such as reducing travel costs, reducing journey times, relieving road traffic congestions, preserving fuel and reducing air pollution. While the importance and efficiency of ridesharing, ridesharing among travelers has not been commonly utilized. This paper introduces an upgraded disaggregated utility model for a mode choice step in the four-step traffic demand model for transport of passengers in Tanta city - Gharbiya governorate. For the first time, the analysis in this paper includes ridesharing mode in addition to other three common modes; private vehicle, bus, and taxi . The aim of this research is to involve the ridesharing mode and new parameters in the mode choice analysis of the case study. This is done by including additional parameters such as travel cost and comfortable in addition to travel time. Based on surveys, the developed Multinomial logit utility models have been assessed using the McFadden pseudo R2 values. The values demonstrated high compatibility of results with real data as pseudo R2 values ranges between 0.2 and 0.4. In addition, all utility models developed for modes are found to have P-values less than 0.05 indicating the significance of the considered utility characteristics.

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.