## 2 Study Area and Trip Chain Data
### Trip Chain Data  
In this study, a trip chain refers to the whole process of intercity travel [44], which includes travel within the origin city, intercity travel by using a high-speed railway, and travel within the destination city, as shown in Figure 2. Regarding the travel process from City A to Terminal A, travelers departing from Beijing have longer travel distances and are more likely to choose subway transportation [45], because Beijing has the largest urban area and the longest subway mileage among the three cities. Regarding the intercity travel process using a high-speed railway, it takes 30 min between Beijing and Tianjin, 80 min between Beijing and Shijiazhuang, and 130 min between Tianjin and Shijiazhuang. Regarding the travel process from Terminal B to City B, travelers arriving at Beijing experience the most developed public transportation system and congested roads. The travel process from City B to City A has the same pattern as the travel process from City A to City B. A trip chain contains a lot of information, such as travel time, space trajectory, mode of transportation, and so on, and is accompanied by the whole process of travel. The raw data used to generate trip chains is smartphone data, and it is collected from the platform of Data-as-a-Service (DAAS) which is operated by one of the Chinese communication operators with a 30% market share within the Beijing-Tianjin-Hebei urban agglomeration. Based on this smartphone data, the intercity trip chains between three cities were extracted through three steps: identifying intercity travelers, extracting travel features within the city, and generating intercity trip chains [46]. There are 19 variables included in the trip chains as shown in Table 1.  
Figure 1: Study area: Beijing–Tianjin–Hebei urban agglomeration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Variable** & **Description** & **Information Fields** \\ \hline
1 & Weekday & Type of the day & 1: Holiday 2: Weekend \\
2 & Gender & The sex of the traveler & 1: Male 2: Female 1: 0–18 \\  & & & 2: 19–24 \\
3 & Age & The age of the traveler & 3: 25–29 \\  & & & 4: 30–34 \\  & & & 5: 35–39 \\
4 & Type-O & Origin Type & 1: Residence \\
5 & Type-D & Destination Type & 2: Work \\
6 & Mode-O & Transportation mode of origin city & 3: Visit \\  & & Transportation mode of origin city & 1: Road transport \\
7 & Mode-D & Transportation mode of destination city & 2: Railway 3: Airplane 4: Subway 5: Others \\
8 & MOT-O & Departure time from origin city & 1: 0:00–1:00 \\
9 & MOT-D & Arrival time at destination city & 2: 1:00–2:00 \\
10 & THub-O & Arrival time at origin city hub & 3: 2:00–3:00 \\
11 & THub-D & Arrival time at destination city hub & 4:30–4:00 \\
12 & De-O & Departure time from origin city hub &... \\
13 & De-D & Departure time from destination city hub & 23: 23:00–24:00 \\
14 & Ds-O & Travel distance within origin city & 1: 0–5 km \\  & & & 2: 5–10 km \\
15 & Ds-D & Travel distance within destination city & 3: 10–25 km \\
16 & Tr-T-O & Travel time within origin city & 1: 0–5 min \\
17 & Tr-T-D & Travel time within destination city & 2: 5–10 min \\
18 & Wt-O & Waiting time at origin city hub & 3: 10–15 min \\
19 & Wt-D & Waiting time at the destination city hub & 4:15–20 min \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of trip chain data variables. Figure 2: Schematic diagram of urban agglomeration trip chain. This study analyzed intercity travel data for May 2019 and May 2021, which are before the outbreak of COVID-19 and the period of the post-COVID-19 phase, respectively. Unlike other countries, Chinese residents resumed travel, and the tourism industry was gradually recovering since April 2020 [47]. China had entered into a special and unique recovering period that was distinct from the other countries that were still experiencing serious impacts from COVID-19. China observes a five-day holiday in May for Labor Day, but it does not include longer breaks like winter or summer vacations that can cause changes in daily travel patterns.