The utilisation of reclaimed asphalt pavement (RAP) as a suitable substitute for natural aggregate and binder offers an energy-saving and cost-effective approach to enhance the performance of asphalt mix. Realising the potential use of RAP as a promising recycling technique, many countries are seeking to recycle RAP as part of the global effort to address the rising challenge of climate change and contribute to a sustainable environment. This study aimed to develop an integrated approach to determine the amount of RAP to be used in an asphaltic concrete wearing course with 14 mm nominal maximum aggregate size (ACW14). The RAP was incorporated with two waste materials comprising waste engine oil (WEO) as a rejuvenator and Crumb Rubber (CRM) as a binder modifier. A total of five different mixes, which include R0 (control mix), R30, R50, R70, and R100 (replacement of 30%, 50%, 70%, and 100% of RAP aggregates in the mix, respectively) were evaluated. The Marshall parameters, resilient modulus (M_R), indirect tensile fatigue, moisture susceptibility, and mass loss (ML) tests were conducted to investigate the performance of each mix. Finally, an arbitrary scale was developed to optimise the RAP content. The results showed that the Marshall parameters, moisture susceptibility, and ML values of the RAP mixes met the criteria outlined in the standard. According to the M_R performance, the R50, R70, and R100 mixes were more resilient than the R0. In terms of fatigue resistance, the R30, R50, and R70 mixes showed better performance than the R0. Overall, the collective performance of all RAP mixes was above the R0 and it increased with the increment of RAP content. Therefore, it was possible to design ACW14 mixes with up to 100% RAP in combination with WEO and CRM.

Malaysia has a high percentage of motorcycles. Due to lane-splitting, travel times of motorcycles are less than passenger cars at congestion. Because of this, collecting travel times using the media access control (MAC) address is not straightforward. Many outlier filtering algorithms for travel time datasets have not been evaluated for their capability to filter lane-splitting observations. This study aims to identify the best travel time filtering algorithms for the data containing lane-splitting observations and how to use the best algorithm. Two stages were adopted to achieve the objective of the study. The first stage validates the performance of the previous algorithms, and the second stage checks the sensitivity of the algorithm parameters for different days. The analysis uses the travel time data for three routes in Kuala Lumpur collected by Wi-Fi detectors in May 2018. The results show that the Jang algorithm has the best performance for two of the three routes, and the TransGuide algorithm is the best algorithm for one route. However, the parameters of Jang and TransGuide algorithms are sensitive for different days, and the parameters require daily calibration to obtain acceptable results. Using proper calibration of the algorithm parameters, the Jang and TransGuide algorithms produced the most accurate filtered travel time datasets compared to other algorithms