## 3 Scenario
### The Simulation and Replanning Process  
Using these input data, CCTSim can simulate the travel of person agents during rush hour. More importantly, in the following, we present detailed strategies for optimizing the departure time of each person agent in every iteration. 1. The determination of the initial departure time  
The attribute of empirical travel time is listed in the second column of Table 2. This quantity represents the time spent in travel and is returned together with the corresponding travel route. Using the empirical travel time, the initial departure time of each person agent is set to the difference between the obligatory arrival time and the empirical travel time when going to work. This procedure is more practical than a stochastic departure time and helps to determine the specific direction of replanning. The initial departure time for getting off work is set stochastically to a time between 17:30 and 18:30. 2. The risk of being late  
When going to work, each person agent employing a particular initial departure time confronts the risk of being late. Two factors drive lateness; the real travel time may be longer than the empirical travel time, and each person agent has the possibility of missing metro trips due to limited capacity and being forced to wait for the next metro train. This phenomenon can be described as congestion. When a person agent is late for work, the performance of the travel plan is bad because the plan receives a severe penalty in scoring. Congestion also extends travel time, which also decreases the utility of the corresponding travel plan. Thus, person agents tend to set out earlier to avoid the risk of missing their planned trip. However, an early departure time typically results in an increased travel time and premature arrival, which leads to the travel plan receiving a low score. This system resembles a game in which each person agent attempts to optimize his/her departure time. 3. The strategies used to optimize the departure time of each person agent  
The objectives of adjusting travel plans are mentioned in Section 2.2.4. The specific procedures followed in going to work and getting off work are shown in Figure 4a,b. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Empirical Travel Time** & **Trip Purpose** & **Travel Route** \\ \hline
1 & 24 min 8 s & Going to work & 1. Walk 604 m \\  & & Going to work & 2. At the Nonglin station, take Line 7 to the Shangsha station \\  & & & 3. Walk 545 m \\ \hline
2 & 26 min 5 s & Going to work & 1. Walk 183 m \\  & & Going to work & 2. At the Xiangmei North station, take Line 2 to the Jingtian station \\  & & Going to work & 3. Walk 51 m and transfer to Line 2 \\  & & & 4. At the Jingtian station, take Line 9 to the Chegongmiao station \\  & & & 5. Walk 727 m \\ \hline
3 & 17 min 8 s & Getting off work & 1. Walk 335 m \\  & & Getting off work & 2. At the Huaxin station, take Line 3 to the Children’s Palace station \\  & & & 3. Walk 380 m \\ \hline
4 & 1 h 25 min 28 s & Getting off work & 1. Walk 598 m \\  & & & 2. At the Airport East station, take Line 1 to the Convention and \\  & & & 2. Exhibition Centre station \\
4 & 1 h 25 min 28 s & Getting off work & 3. Transfer to Line 4 \\  & & & 4. At the Convention and Exhibition Centre station, take Line 4 to \\  & & & the Futian Port station \\  & & & 5. Walk 598 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on the metro travel routes of person agents. 1. Going to work 1. In the first iteration, in order to avoid congestion, the metro capacity is set to an infinite number. Thus, all of the person agents can execute their initial travel plan without congestion. The simulation tool counts person agents as being late if their real travel time is longer than the corresponding empirical time and notes how late they arrive. Subsequently, the initial departure times of these person agents are adjusted forward so that they will not be late again using the same travel routes.