## 2 Literature Review  
In terms of integrated passenger-parcel transportation, the most recent reviews were conducted by the authors of [9; 26; 27]. We refer the readers to the above works for a comprehensive overview of the studies on integrated passenger-parcel transportation. For the freight hub location problem in co-modal transit systems, seeking a well-planned freight hub location remains a critical technical challenge in the field, and the solution is expected to minimize potential conflicts between passengers and freight [26]. Notable studies on this problem include the following. [28] proposed an underground logistics network planning method using the metro system in Nanjing, China, as an example. The optimal locations of underground depots and ground terminals were determined through a mixed integer programming model. While this study considered the freight transportation from underground metro lines to ground delivery, last-mile delivery was not explicitly modeled, and the type of delivery vehicle was not specified. [29] formulated a location model to determine the distribution hubs for a metro-integrated logistics system that incorporates couriers as first/last-mile service providers. The couriers were assumed to deliver goods to clients using motorcycles or small trucks. [30] developed a hub-and-spoke structure for a multimodal passenger-and-package sharing service, in which the packages were transported between hubs via the metro, and the first/last-mile delivery between the hubs and the customers was performed by trucks and taxis. A \(p\)-hub median model and a \(p\)-hub assignment model were formulated to optimize the hub locations. [31] studied the optimal location of a freight transfer hub that minimizes the expected delivery distance between the hub and all customers, in which the authors considered the location of a single transfer hub in the system and the conventional (small) vehicles for last-mile delivery. The problem was formulated as a mixed linear program and solved by a heuristic algorithm. [32] envisioned a public transport-based crowd-shipping service with backup transfers, in which express delivery demands were fulfilled by crow-shippers and public transports, with zero-emission vehicles (e.g., electric vans) providing the transfer services between public transport connection points if necessary. The authors adopted a scenario-based two-stage stochastic programming approach to formulate the envisioned service and to optimize the location where a connection was provided between public transit lines. None of the studies mentioned above explicitly considered ride-pooling for first/last-mile delivery. Only [30] considered taxis as spoke transport that connects freight hubs with parcel senders and receivers and investigated how the proportion of taxi drivers willing to carry packages may impact the system's performance. Nonetheless, the authors did not consider pooling taxi passengers with packages, where passengers may need to detour to cater to package delivery. As a result, their hub location model did not count for the taxi passengers' travel costs, which is plausible in the context of separately transporting packages with idle vehicles, but may be flawed if we allow for the two parties to share their trips. Pooling passengers and parcels with on-demand ride service vehicles has gained attention from researchers recently. A large body of the related literature focuses on the route planning of service vehicles and mainly deals with developing mathematical models and solution algorithms for models (see a more detailed and comprehensive review in [33]). As a pioneer study, [21] investigated the so-called _share-a-ride problem_ in a taxi market with pooled passengers and parcels. They developed a mixed-integer linear program for the problem and considered a freight insertion problem to reduce computational complexity.