# Integrated People and Freight Transportation:
## 4 Integrated People and Freight Transportation (IPFT)
### Tactical Decision Level (TDL) for IPFT  
The tactical decision level (TDL) includes medium-term decisions and can be defined as the reflection of SDL in the medium term. It covers production planning, distribution planning, procurement planning, purchasing, sales planning, inventory management, pricing, and forecasting. [PERSON] et al. [102] address the problem of pickup and delivery with time windows and scheduled lines, aiming to route a given set of vehicles to transport freight demands from their origins to their respective destinations. In the study, an arc-based mixed integer model is developed. [PERSON] and [PERSON] [10] propose and develop a mathematical model for urban logistics distribution planning for a passenger transport network. The study concludes that the IPFT flow will support higher efficiency rates for the passenger transport network and improve living conditions in large urban centers. [PERSON] et al. [34] develop a load and flow formulation for the pickup and delivery problem associated with ODs. Additionally, this study focuses on the design of compensation plans for ODs. [PERSON] et al. [103] examine the demand and supply of green crowd-shipping potential. The study includes estimating and investigating the mobility of crowd shippers and the population using the subway for travel. [PERSON] et al. [104] examine the optimization problem of train unit scheduling, space-time-state network construction and lower bound estimation structures, and addressing IPFT. The study results considered freight and passenger flows, as well as train unit inventories, to minimize the sum of operational and travel costs and provide appropriate and effective information for the IPFT system in underground logistics systems. Uncertainty at TDL  
This section presents studies addressing uncertainty (such as dynamic structures, stochasticity, and real-time activities) within the scope of TDL. [PERSON] et al. [39] deal with the static and dynamic model for human-freight taxi sharing by developing the MILP formulation. The obtained numerical results provide valuable information for the successful implementation of taxi-sharing with multi-commodity structure. [PERSON] et al. [105] propose a crowdsourcing-enabled system for urban package pick-up and delivery. Initially, delivery information is published online, and offers are collected from crowd sources such as cyclists and pedestrians. Then, the offer is selected, and coordination is ensured. [PERSON] et al. [100] tactically address the problem of frequency and sizing of trains. [PERSON] and [PERSON] [106] present a mathematical model for workforce planning. The study covers workforce scheduling problems at the tactical level and dynamic routing problems at the operational level. [PERSON] et al. [107] propose a dynamic and domain-based pricing strategy for crowdsourced delivery. The results show that the average delivery time has improved, and the economic benefits and stable assignment rate have increased. [PERSON] et al. [108] consider the network flow problem for IPFT within the scope of stochastic demand. [PERSON] and [PERSON] [109] plan flexible pricing for last-mile delivery, where the company provides part of delivering products to its customers through ODs. The problem is based on a stochastic and dynamic environment within time windows. [PERSON] and [PERSON] [5] present the MIP model that optimizes the train schedule and allocates cargo to investigate the effects of integrated planning under different operational modes. The study analyzes the interaction of operational modes, planning solutions, and demand settings. It presents a model that integrates the tactical planning of cargo and passenger trains and cargo allocation to vehicles. These studies collectively showcase a diverse range of solutions--from crowdsourcing and dynamic pricing to integrated passenger-freight systems--aimed at addressing the complex logistics challenges in urban environments.