The optimization of energy consumption during asphalt mixture production and compaction is a challenge in producing durable, sustainable, and environmentally friendly asphalt products. This study investigated the effects of crude palm oil (CPO) and/or tire pyrolysis oil (TPO) on shear viscosity and mixing and compaction temperatures of asphalt. Moreover, the possibility of using response surface methodology (RSM) and machine learning (ML) to develop predictive models for the shear viscosity and mixing and compaction temperatures of CPO- and/or TPO-modified asphalt was studied and compared. The results showed that the mixing and compaction temperatures significantly decreased with increasing CPO and TPO, and the shear viscosity consequently declined because of the light components, resulting in softer binders. However, at 5% of both materials, a balance between the required temperatures and a similar or better viscosity compared to the base asphalt were demonstrated. RSM analysis showed that CPO had a significant effect on the viscosity and production temperatures of the base and modified asphalts compared with TPO, which had no significant effects. The developed predictive models based on RSM exhibited a correlation coefficient (R²) of more than 0.82 for all responses. In addition, it was found that extreme gradient boosting (XGB) regression was the best among all evaluated algorithms for predicting shear viscosity, whereas random forest regression (RFR) was the best for mixing and compaction temperatures, with R² values greater than 0.93. The performance evaluations of XGB and RFR showed extremely small differences between the predicted and experimental data. ML outperformed RSM in terms of prediction accuracy.

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Road safety of vulnerable road users in low- and middle-income countries (LMICs) is a major concern. Footbridges or overpasses are pieces of infrastructure designed to allow pedestrians to cross safely. Although there is tension over whether footbridges are the most inclusive and sustainable way to support safe road crossing for vulnerable road users, footbridges are still a predominant piece of infrastructure in LMICs. Therefore, research aimed at optimising the use of footbridges could have an impact on the safety of vulnerable road users. The objective of the present study is to understand the motivational factors influencing pedestrian crossing decisions to use a footbridge in Malaysia. Additionally, the present study investigates the effectiveness of some proposed interventions to change the crossing behaviour of footbridge non-users. An intercept study was conducted in five major cities in Malaysia. Pedestrians answered questions regarding their perceptions about footbridges and previous walking experiences. The analysis was divided into two parts: Firstly, a decision tree analysis was conducted to study the relationship between the observed decision to use or not a footbridge and pedestrians’ perceptions about footbridges and experiences. Secondly, a decision tree analysis was used to identify the effectiveness of proposed countermeasures in increasing the footbridge usage among non-users. The findings showed that the footbridge height and the frequency of using it were associated with a decrease in the likelihood of utilising this structure. Being in a hurry was highly associated with crossing at the street level. In addition, footbridge non-users might change their behaviour if escalators or fences are provided as interventions. The results indicated that safety messages delivered through posters to alert footbridge non-users have the potential to sustain safe mobility. A user-centred approach is necessary to increase the usability of footbridges in LMICs.@en