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Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP's constraints, the EVRP requires vehicles to visit a charging station before the battery level is insufficient to continue service. EV battery consumption is linearly correlated to their weight. These additional constraints make the EVRP more challenging than the conventional CVRP. This study proposes an improved Harmony Search Algorithm (HSA), with performance validated by testing 24 available benchmark instances in the EVRP. This study also proposes a novel update mechanism in the improvement stage and a strategy to improve the routes with charging stations. The results show that in small and large instances, the proposed HSA improved the number of trips to the charging stations by 24% and 4.5%, respectively. These results were also verified using the Wilcoxon signed-rank significant test. [ABSTRACT FROM AUTHOR]

Public transportation users increase as the population grows. In Taipei, Taiwan, this tendency is observed by analyzing historical data from the Mass Rapid Transit (MRT) and economy-shared bicycle (known as YouBike) riders. While this trend exists, the Taipei City government promotes green transportation by providing discounts to users who transfer from MRT or bus to YouBike within a particular period. Therefore, this study focuses on analyzing the patterns of users in order to identify possible clusters. Clusters of customers can be considered fundamental and competitive factors for the Ministry of Transportation to encourage the use of green transportation and promote a sustainable environment. Based on big data smart card information, this paper proposes using the RFM and K-means clustering algorithm to analyze and construct mode-switching traveller profiles on MRT and YouBike riders. As a result, three distinct clusters of MRT-YouBike riders have been identified: potential, vulnerable, and loyal. There are also suggestions regarding the most profitable groups, which customers to focus on, and to whom give special offers or promotions to foster loyalty among transit travellers., Author(s): Angela H. L. Chen (corresponding author) [1]; Yun-Chia Liang (corresponding author) [2]; Wan-Ju Chang [1]; Hsuan-Yuan Siauw [1]; Vanny Minanda [2] 1. Introduction Public transport, defined as high-capacity vehicle [...]