## 2 Literature Review
[22] extended the share-a-ride problem to consider the use of autonomous vehicles equipped with parcel lockers and developed a mixed-integer linear program to solve the optimal vehicle routing strategies. [34] considered passenger-parcel pooling in a way that parcels can be transported to their destination in a multi-hop manner, i.e., parcels can be trans-shipped between different service vehicles before they arrive at their destinations. They proposed a distributed model-free deep reinforcement learning algorithm to determine the optimal vehicle dispatching policy. [23] developed a mixed-integer linear program to optimize the routing of a fleet of shared autonomous vehicles among a set of randomly generated trip requests of both passengers and parcels. [24] investigated passenger-parcel pooling in an autonomous vehicle intelligent system. The authors attempted to optimize the routing and loading strategies of the autonomous vehicles so that the total system operation cost was minimized. A mixed-integer linear program was proposed and solved with the Lagrangian dual decomposition method. [35] optimized the service vehicle routing and passenger-parcel matching in a ride-pooling market. The authors explicitly considered two types of service vehicle, i.e., conventional ride-pooling service vehicles to serve both passengers and parcels, and electric motorcycles tailored to transport parcels only. Moreover, the parcels were classified according to their sizes and urgency. A two-level lexicographic multi-objective optimization model was developed, and then divided into a set of small-scale subproblems for solving. Apart from the above studies that primarily focused on service vehicle routing, we also notice another stream of simulation-based studies aiming to evaluate the implementational effects of passenger-parcel pooling services. For example, [36] developed a static optimization model to evaluate the effects of two types of passenger-parcel pooling strategies using real-world vehicle scheduling and logistics data from Munich, Germany. The two investigated scenarios differ in service vehicles' pick-up and drop-off sequences, while serving passengers and parcels. The results obtained in this study were further validated by [37], in which a simulation framework was proposed to evaluate the performances of the two service scenarios with temporal and spatial dynamic demands. [25] conducted a simulation-based study to investigate the effects of passenger-parcel pooling with a fleet of autonomous and modular vehicles. The simulation results demonstrated that the additional demand for parcel transport barely affects the waiting time of passengers if the flee size is large. [38] conducted simulation-based pricing analysis on the integrated passenger-parcel transportation by e-hailing taxis. This literature review reveals the existence of the following research gaps. Firstly, none of the existing studies on the parcel locker location problem in transit co-modal systems considered ride-pooling services for last-mile delivery, in which the travel costs of ride-pooling passengers should be taken into consideration to ensure the practicability of integrating ride-pooling passengers with parcel delivery. Secondly, it remains unclear what the key factors are that affect the optimal parcel locker locations.