The recovery of ridership varied by type of public transport system and the types of routes served. In Bogota, the trunk line public transport services had recovered much less so than the zonal line services, which by October 2021 were already above the pre-pandemic level (Figure 11). In AMBA, by far the least recovered public transport mode was the subway, which prior to the pandemic had experienced the most consistent growth in demand. Even as of May 2022, subway ridership was still 30-35 percent below the pre-pandemic level, while bus ridership had nearly recovered.

# **6. Policy implications**

Two years after the start of the pandemic, trip generation and temporal distribution in both metropolitan areas were still far from 2019 values. Trip distances had returned to 2019 patterns in AMBA, but not in Bogotá. While in Bogota the number of NMT trips continued to increase in 2021, in AMBA it fell back to even below the 2019 level. In neither metropolitan area had public transport trips recovered. The persistence of these changes in the coming years will determine the key challenges of transport planning and the priorities for mobility studies in the two metropolitan areas.

**_Implement demand management policies combined with investment in NMT_** _._ The modal shift from public transport in Bogotá towards non-motorized modes, in contrast to the shift primarily to private vehicles in AMBA, can be at least partly attributed to the underlying regulatory context, in which only the former urban area had in place policies that strongly limit private vehicle use. For AMBA and other global cities, the example of Bogotá illustrates that appropriately targeted and calibrated transport demand management policies can help achieve a more sustainable modal shift, especially when combined with investments in infrastructure that supports alternative modes such as cycling. However, as shown by the example of CABA, significant gains in non-motorized mobility can also be achieved just through consistent, long-term NMT policies and investment in safe NMT infrastructure.

**_Improve the quality of the public transport system and adjust it to the changing mobility needs._** The decline in the use of public transport occurred to a greater extent among high-income groups. Therefore, comfort, travel time, and safety in public transport will need to be improved to recover this demand segment. At the same time, planners and decision makers should consider systemic inequalities and unequal levels of risk and exposure, and support the mobility needs of the most vulnerable residents who during the pandemic were less able to switch to modes they considered safe due to public transport “captivity”. The spatial dispersion of mobility is a relevant factor for the competitiveness of public transport systems, since they are better suited to efficiently absorb concentrated flows than dispersed flows. The spatial dispersion of mobility in Bogota increased significantly in 2020 and even more so in 2021; in other words, the density of trips per OD pair decreased. In AMBA, the initial decline in trip density per OD pair was partially reversed in 2021. At the same time, in both metropolitan areas, the hourly profile of trips changed significantly, especially so in AMBA, with a decreased peak during the usual morning and evening peak hours. With the expectation of remote and hybrid work and study arrangements persisting and possibly increasing in the future, the transport planning agencies may need to adjust the supply of public transport services during the traditional “commute” hours while possibly deploying additional services elsewhere.

**_Leveraging of state-of-the-art data tools._** The dynamics affecting transport systems require to frequently review whether the tools and procedures used to analyze mobility are still valid for capturing the transport demand information that is necessary to plan and manage transport services (ITF 2021). The pandemic posed a major disruption to people’s lives and daily activities, and, therefore, to transport demand in cities. Thanks to _big data_ , urban policy makers now have more effective and agile tools at their disposal to test, evaluate, and scale new measures in a more responsive manner.

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