# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 2 Related Work
This research problem consists of an inference section that predicts the appearance and intention of customers and a planning section that handles the taxi fleet either under a centralized or distributed authority. Several methods, such as reinforcement learning [50], matrix factorization [51], random-based strategy [52], or neural-network-based [53], are proposed for the constrained route planning. Despite the emphasis on profit and cost, some researchers propose a multicriteria routing problem that optimizes the profit from taxi fleet and the trust from customers [44]. In this case, convolutional long short-term memory network (convLSTM) and spatial-temporal dynamic network (STDN) are adopted for inferring the real-time pick-up location as well as the destinations (drop-off locations) of customers, and a greedy-based heuristic algorithm named J* is proposed for the cruising route for the taxi fleet. The method successfully puts the constraints of booking information from future customers into consideration by suggesting proper customers to pick up. Although the targeted research problem is different from this paper, the concept of utilizing inferred results in heuristic function is adopted and modified in our algorithm. Similarly, some research also focuses on stationary resource searching but with their application in parking slots [54]. For the parking slot cruising problem, methods such as the greedy-based strategy [55], gravitational approach for incomplete information [56], and stable-matching-based technique for a centralized scenario [57] have been proposed. Among them, some target the distributed competitive searching for a parking slot that optimizes the drivers' searching experience with various preference towards time consumption and the resource utilization for social welfare based on limited accessibility to data [45]. Here, a spatiotemporal availability inference model is first adopted to infer the occupied probability of each parking slot under a certain time slot, and an algorithm named Contrans is proposed to solve the multiple-criteria competitive searching. The concept is that multiple factors, including but not limited to estimated availability, cost for reaching their destination, competition with others, and the impact of abnormal events are jointly considered. Although it targets a different research problem, the technique of integrating uncertainties of a nonmonotonic nature in urban space for algorithm design is adopted and modified for creating our proposed method. On the other hand, ref. [46] investigates multidepot route planning for violation fine collection that maximizes profit based on the estimated patterns of infringing cars while minimizing the working hours of the officers. A probability estimation model is adopted to infer both probability and period of violation for each parking area, and ant colony optimization (ACO) is applied to generate the tour for officers. However, this work focused on the collaboration among multiple agents, and the adaptability of the proposed method is severely limited due to the need for pre-clustering, which makes it difficult to adopt for solving mass-transit planning. On the other hand, others have proposed to solve the multicriteria route planning by focusing on waste collection [47]. This technique aims to minimize both the visited waste bins and the traveling distance. A knowledge-based decision-making algorithm is developed to select the waste bins to be serviced, and [PERSON]'s algorithm is modified to identify the optimum route among selected bins. As a greedy-based approach, it seems to achieve near-optimal performance in solving the bin-collection problem; however, nonmonotonic characteristic in urban space is not considered, and geographical features as well as competitive relationship are not addressed. Some target the emergency vehicles planning with real-time traffic conditions considered [48; 49]. In these works, the objective is to minimize the traveling time while maximizing the reliability of the recommended route in the road network.