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Transport policy is constrained by the dilemma of the positive impacts of lowering the generalised costs of travel and their today non-ignorable negative externalities. This chapter details this dilemma and discusses current policy ideas to manage and overcome it against the impacts of the COVID-19 pandemic on travel habits and work behaviour. The impacts are presented for Switzerland for which a large-scale GPS tracking survey spanning the autumn 2019 to winter 2021 period is available. The chapter concludes by highlighting the dilemma of transport policy by discussing a number of potential solutions for the future.

Purpose — The paper reports on a research project exploring new approaches for analysing travel demand induced by changes in generalised costs of travel and activity participation. The description of the survey approach, which to our knowledge is novel in its application, reports descriptive analyses of the respondents' reactions to the changes implied in the household interviews.

Methodology — A sample of respondents were administered a 5 day travel diary, from which 1 day was selected for further analysis. Travel times for trips conducted that day were changed using predefined heuristics based on the household characteristics to attain significant changes in the generalised costs of the reported trips. Respondents were then presented with these hypothetical scenarios in face-to-face interviews. All household members were asked to state how the implied changes would have affected their activity scheduling on the specified day, i.e. to adapt their reported schedule to the new conditions.

Findings — The postulated induced travel effect could be observed, in that the modifications to the generalised costs of travel affect the respondents' travel patterns in general, and the number and durations of conducted out-of-home activities in particular. However, the predominant reaction to changing travel times is the adaptation of departure time, which does not directly interfere with trip generation. Indicators of the effects have been shown, and are quite weak as far as activity generation effects are concerned. The activities most likely to be re-planned are leisure activities and sojourns at the home location, as is consistent with expectations.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

In the recent past, mobile technologies that track the movement of people, freight and vehicles have evolved rapidly. The major categories of such technologies are reviewed and a number of attributes for classification are proposed. The willingness of people to engage in such technologically based surveys and the reported biases in the make-up of the sample obtained are reviewed. Lessons are drawn about the nature of the samples that can be achieved and the representativeness of such samples is discussed. Data processing is addressed, particularly in terms of the processing requirements for logged data, where additional travel characteristics required for travel analysis may need to be imputed. Another issue explored is the reliability of data entered by respondents in interactive devices and concerns that may arise in processing data collected in real time for prompting or interrogating respondents. Differences, in relation to the data user, between data from mobile devices and data from conventional self-report surveys are discussed. Potentials that may exist for changes in modelling from using such data are explored. Conclusions are drawn about the usefulness and limitations of mobile technologies to collect and process data. The extent to which such mobile technologies may be used in future, either to supplement or replace conventional methods of data collection, is discussed along with the readiness of the technology for today and the advances that may be expected in the short and medium term from this form of technology.