The global COVID-19 outbreak has demanded drastic actions and policies from the governments and local authorities to stem the spread of the virus. Most of the measures involve behavioural changes from citizens to reduce their social contact to a minimum. Thus, these actions influence individual activity patterns and transport systems in different ways. This paper studies the short-term impacts on the transport system caused by the different policies adopted by the Colombian government and local authorities to contain the COVID-19 spread. Using official and secondary data concerning the seven most populated cities in Colombia, we analyse the impacts on three components of the transport system: air transport, freight transport, and urban transport. Results show that national policies and local decisions have decreased the demand for motorised trips across the cities, diminishing congestion levels, reducing transit ridership, and creating a reduction in transport externalities. The country banned air transport for passengers and only allowed air cargo for medical and necessary supplies, which will have negative consequences for the economics of the airline industry. During the first three months of the COVID-19, freight was the most resilient transport component. However, freight trips diminished around 38%, affecting mainly the supply chain of nonessential products. During the pandemic, governments need to provide subsidies to maintain the system supply to avoid crowdedness and promote active transport by allocating less-used street space to cyclists and pedestrians. In the short term, transportation service providers will face a financial crisis, deepened by the pandemic, which will require government assistance for their recovery.

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T he concept of habit or inertia in the context of (reluctance to) change in travel behavior has an important bearing on transport policy (e.g., how to break car use habits) and has remained an unresolved issue in demand modeling. Another major problem in modeling the response to policy measures is the potential correlation or dependence between the choices made by a given individual over time (i.e., serial correlation). The two phenomena are closely related. This paper discusses the effects of considering inertia and serial correlation on travel choices. We formulate a fairly general discrete choice model that incorporates randomly distributed inertia thresholds and allow for serial correlation. The inertia thresholds may also be a function of an individual's socioeconomic characteristics and choice conditions. The model can be applied with panel data as well as with mixed revealed and stated preference data. We applied it to real and simulated data, confirming that if these phenomena exist in the population but are not considered, serious errors in model estimation and prediction may arise, especially in the case of large policy impacts.

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This paper details research to design an estimation process for Deprivation Cost Functions (DCF) using Contingent Valuation, and to apply it econometrically to obtain a DCF for drinkable water. The paper describes both the process and results obtained. The results indicate that deprivation costs for drinkable water have a non-linear relation with deprivation times. The estimated DCFs provide a consistent metric that could be incorporated into humanitarian logistic mathematical models, eliminating the need to use proxy metrics, and providing a better way to assess the impacts of delivery options and actions. The research reported in this paper is the first attempt in the literature to produce estimates of the economic value of human suffering created by the deprivation of a critical supply or service.