## 4 Key Technologies of CPSS-UPTDN
### Implementation of CPSS-UPTDN Based on ACP Method  
The proposed CPSS-UPTDN is a kind of APTS framework, which can be implemented through the artificial system, computational experiments, parallel execution (ACP) method [28]. These modules for AI and big data processing are integrated according to the principles as Figure 3 to achieve the connection and interaction between the real and artificial systems.  
Figure 2: Data collection and transmission to CPSS-UPTDN Subplatform.  
Specifically, the complex transportation system is modeled and constructed as an artificial system which is the simulation of the real system. Then, numerous computational experiments are performed on the system in the artificial society, so that the artificial system can face scenes that are less frequent or rarely appear in the real world. Aiming at the difficulty in modeling and analyzing traditional social methods for complex social objects, the combination of the artificial environment and computational experiments has changed the solution from \"modeling-control\" to \"artificial scenario generation-control scenario response\".  
The CPSS-UPDDN can be executed when the artificial system is built. The overall pipeline is described in Algorithm 1. The hierarchy of a typical computational procedure in CPSS-UPDDN framework can adopt the service patterns of cloud computing, as Figure 4 shows. Firstly, the physical resource management layer (Infrastructure as a Service, IaaS) is constructed as a foundation, which is used to access and visualize the original data. Then, the logic resource processing layer (Platform as a Service, PaaS) is used to process and analyze data. Finally, the data center operation and maintenance layer (Software as a Service, SaaS) is carried out to optimize various tasks. There are also operation management and security interfaces. The whole platform is composed of several subsystems, among which each one provides specific services of information, management and surveillance.  
Figure 4: Hierarchy of CPSS-UPDDN computational procedure.  
Figure 3: The ACP based Artificial System construction.  
```
0: Multi-source big data generated by real urban public transportation
0: The optimal dispatching service and personalized recommendation
1: Data acquisition systems capture and store the big data.
2: The basic models of the urban public transportation system are established by the fusion and analysis of multi-source data.
3: The artificial public transport system is established by the basic model and artificial social method, then the CPSS-UPTDN platform is built.
4:while The real system generates real-time travel information do
5: Travel demand is fed back into the artificial system.
6: According to the computational experiments, the effective bus dispatching scheme is evaluated and verified in an artificial system.
7: yeild Output effective scheme and provide personalized service for travelers.
8: The real transportation system generates new multi-source traffic big data.
9:if Periodical time is up then
10: Data acquisition systems collect new traffic data.
11: Update the basic traffic models.
12: Update the artificial traffic system and platform.
13:endif
14:endwhile
```  
**Algorithm 1** The pipeline of CPSS-UPTDN