# Integrated People and Freight Transportation:
## 4 Integrated People and Freight Transportation (IPFT)
### Operational Decision Level (ODL) for IPFT  
The operational decision level (ODL) includes short-term plans and results from SDL and TDL. Activities such as vehicle routing, vehicle loading and unloading, and scheduling are within the scope of this decision level. [PERSON] et al. [110] synthesize the basic issues regarding the first and last mile in IPFT and, based on this, investigate time-based sharing of resource use. The article concludes that IPFT in urban areas is a promising approach to the last-mile problem. In the first phase of the study, [PERSON] et al. [111] consider the transportation of goods from a distribution center to bus stops by urban buses, using the spare capacity of the buses. In the second stage, final customers consider deployment by a fleet of near-zero-emission urban freighters. [PERSON] et al. [112] serve some customers by picking up packages from the central or intermediate warehouse, considering the OD. The study aims to minimize the total cost, the conventional vehicle cost, and OD compensation. The computational results in the study show that the proposed heuristic is quite effective and can solve large-sized examples in short computational times. [PERSON] et al. [14] develop an operational strategy in which urban rail transport is used for freight transportation. An environmentally friendly urban freight transportation alternative is analyzed using optimization techniques to support IPFT. A combined optimization model is proposed to maximize the profit from the balance of revenues and costs the transportation service brings. The study is planned to determine the effective train schedules and freight allocation plans. A MILP model has been developed for this problem. [PERSON] et al. [113] investigate the potential of modular vehicle notions and consolidation to raise the efficiency of urban goods and people transportation. The study shows a 48% cost savings due to modularity and an additional 9% cost savings due to consolidation. While the reduction is mainly due to reduced operating costs and travel time, demand can be met in all cases. It is stated that empty vehicle mileage has decreased by more than 60% thanks to consolidation and modularity. [PERSON] et al. [114] consider an urban freight transportation strategy that uses the excess capacity resources of the airport rail line to solve the problem of intercity freight transportation by adding special freight trains. The model is developed to integrate and optimize passenger and freight train schedules to maximize the system's profits. Uncertainty at ODL  
This title presents studies addressing uncertainty (such as dynamic structures, stochasticity, and real-time activities) within the scope of ODL. [PERSON] et al. [115] consider the uncertainty of the ODs and the dynamic appearance of the vehicles as a stochastic VRP problem. [PERSON] et al. [100] operationally examine 2D/3D box packaging problems, train schedules, goods delivery, and shipping problems. [PERSON] and [PERSON] [21] consider the shared railway network between people and freight trains and present a decision support framework for the problem of urban freight movement and mathematical methods that address certain and uncertain situations for the optimal distribution of freights. [PERSON] et al. [116] include OD in VRP. In the study, each OD indicates the time window during which it is available. Penalties apply if time windows are violated. This approach allows dynamic requests to be met and adjusts the real-time distribution plan. [PERSON] et al. [117] address the problem of transferring freight demand using a fleet of autonomous pickup and delivery robots in the area where public transportation service is located. Passengers are given priority in capacity sharing, and transportation demands are handled stochastically. [PERSON] [35] addresses the problem of package routing in registered ODs and develops a CS model in which couriers record their trips in advance.