Environmental impact becomes an emerging problem since global warming has caused climate change issues, especially natural disasters in recent years. Based on International Energy Agency (IEA), the concentration of CO₂ in 2015 was 399 parts per million by volume and was about 40% higher than in the mid-1800s. Since the transportation sector accounts for great responsibility for emissions, how to reduce CO2 emissions and keep the efficiency of transportation has become a more important issue. Dial-a-ride Problem (DARP) is a new form of mobility-on-demand public transportation, and the route and schedule of DARP are flexible to accommodate customer needs. This study aims at integrating the concept of eco-efficiency into DARP, and a bi-objective dial-a-ride problem with time-dependent costs is formulated. Two objectives, CO₂ emissions and travel time, are explicitly considered. The formulation considers the perspective of eco-efficiency and fluctuation of travel time for the dial-a-ride problem. A revised branch-and-price solution algorithm with a large neighbor search (LNS) is adopted to solve the problem. In order to solve the two objectives simultaneously, this study applies the weighted sum with the normalization approach. Due to the difficulties of estimating emissions, a traffic simulation model is incorporated with the solution algorithm to provide emissions values. Several experiments based on a city network are conducted to evaluate objectives based on different factors, including traffic condition, time window, and maximum ride time. The results show that (1) weights for objectives need to be designed appropriately to reflect the preference; (2) the travel times and CO₂ emissions reduce with respect to the increase of time window length; (3) The total travel time and CO₂ emissions decrease with respect to the length of maximum ride time.

由于全球变暖已经引起气候变化问题，特别是近年来的自然灾害，环境影响成为一个新问题。根据国际能源署 (IEA)，CO _2的浓度2015 年的体积为百万分之 399，比 1800 年代中期高出约 40%。由于交通运输部门对排放的责任很大，如何减少二氧化碳排放并保持交通运输效率成为一个更重要的问题。Dial-a-ride Problem (DARP) 是一种新型的按需出行公共交通方式，DARP 的路线和时间安排灵活，可满足客户需求。本研究旨在将生态效率的概念整合到 DARP 中，并制定了具有时间依赖性成本的双目标拨号问题。两个目标，CO ₂排放和旅行时间，被明确考虑。该公式考虑了拨号问题的生态效率和旅行时间波动的角度。采用改进的具有大邻居搜索（LNS）的分支和价格求解算法来解决该问题。为了同时解决这两个目标，本研究将加权和与归一化方法结合使用。由于估算排放的困难，交通仿真模型与求解算法相结合以提供排放值。进行了几个基于城市网络的实验，以评估基于不同因素的目标，包括交通状况、时间窗口和最长乘车时间。结果表明（1）需要适当设计目标的权重以反映偏好；₂排放量随着时间窗长度的增加而减少；(3)总行驶时间和CO ₂排放量相对于最长行驶时间的长度减少。