# Integrated People and Freight Transportation:
## 4 Integrated People and Freight Transportation (IPFT)
### Strategic Decision Level (SDL) for IPFT  
The strategic decision level (SDL), which includes long-term decisions and is difficult to change, includes high-level decisions and important investment decisions such as network design, plant locations, plant dimensioning, and capacity planning. Previous research has typically been examined at the operational planning level and neglected the strategic perspective. For this reason, studies conducted in this context are quite limited [90]. [PERSON] et al. [91] considers the citywide package delivery problem using CS transportation systems. The distribution scheme is modeled as an example of a multi-product flow problem and formulated as MILP. [PERSON] et al. [92], who consider a similar problem and solution approach, consider the hub location problems by considering the proportion of taxi drivers willing to carry packages. [PERSON] et al. [93], [PERSON] et al. [94], and [PERSON] et al. [95] aim to plan a network for a metro-based logistics system. [PERSON] et al. [93] study results show that the proposed system can significantly reduce metro construction costs and alleviate traffic congestion. In contrast, [PERSON] et al. [94] provides strategic planning for the location selection of Shanghai metro distribution centers. [PERSON] et al. [95] also measure system performance to understand strategic interactions and resulting system effects in the context of metro-integrated systems. [PERSON] et al. [96] evaluate location decisions and system performance using MILP and a heuristic method. The result shows up to 7.1 percent of savings are possible by using public transport capacity to promote urban transportation. [PERSON] et al. [97] study presents an approach to assess maturity in formulating and implementing sustainable IPFT strategies. It is stated that the study was conducted and that the developed model can be used by local governments operating in various environments. This study shows that cities that achieve the best safety and environmental degradation outcomes by implementing the most comprehensive passenger and freight transport measures have strong, ongoing cooperation with stakeholders and integrated transport strategies. [PERSON] et al. [23] address strategic decisions for last-mile deliveries in IPFT. In this study, where different formulations and solution approaches for the problem are proposed, managerial information about the performance and efficiency of the system is also presented. Uncertainty at SDL for IPFT  
This title presents studies addressing uncertainty (such as dynamic structures, stochasticity, and real-time activities) within the scope of SDL. [PERSON] et al. [98] is developed as a two-stage stochastic programming model, and the study includes stochastic delivery locations and stochastic travel times. The results show that stochastic information is valuable in real life and can significantly improve the performance of taxi-sharing systems. [PERSON] et al. [99] study investigates the potential of integrating common freights and passenger on-demand rapid transportation systems in urban areas. The study examines how a rapid public transport network can be shared and how a city's existing transport capacity can be used more efficiently and interconnectedly. [PERSON] et al. [100] propose an environmentally friendly urban freight transport alternative using the passenger rail network. The study provides decision makers with a decision support tool to assess technical feasibility, impact on passenger services, infrastructure needs, and investment. The study addresses issues that must be addressed at strategic, tactical, and operational levels by examining different models. The problem of storage space sizing at stations is strategically addressed. [PERSON] et al. [101] and [PERSON] et al. [90] address the warehouse location problem in the last mile context in two stages and present a stochastic approach. [PERSON] et al. [101] state that the stochastic model results provide between 3.35% and 6.08% better solutions compared to the deterministic model.