Mobility solutions usually focus on time savings, proposing to users solutions that include the shortest or fastest paths. Nevertheless, users might perceive travel time as valuable (worthwhile) when it can be associated with other activities.

In an ICWSM 2021 paper, with Cristian Consonni, Silvia Basile, Matteo Manca, André Freitas, Tatiana Kovacikova, Ghadir Pourhashem, and Yannick Cornet, we present a new dataset, named MoTiV (Mobility and Time Value), which contains data about travelers and their journeys, collected from a dedicated mobile application. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive and negative experience factors.

Data collection

Worthwhile time is a central concept for our data collection. While the traditional view is to consider travel time as something to minimize, we consider travel time as an opportunity, i.e., time that can be characterized by other activities. We have collected data through a dedicated mobile app, called Woorti, to ground the definition of worthwhile time into multiple dimensions.

Thanks to Woorti, users were allowed to record their trips. When validating a trip, the user is asked about which activities they have performed during the trip, which factors in there have influenced the trip positively or negatively, and which was the trip’s purpose.

Dataset Description

The dataset can be downloaded at: https://zenodo.org/record/4027465. The code used for pre-processing the raw data and performing the case study is available at: https://github.com/MoTiV-project/data-analysis.

Our dataset collects information about user mobility, capturing both raw information about trips and their legs (e.g., coordinates, time, and weather), plus information about the worthwhileness of a trip leg. Readers can refer to the full paper for the details of the 13 csv files constituting the dataset.

Case study

We analyze our dataset to find users that have traveled a given route multiple times using both cars and alternative modes of transport. Specifically, we look at which are the factors that have impacted negatively the travel experience when using bikes or public transport. With this use case, we want to answer the question: “What are the negative experience factors of cyclists and users of public transport for the same trip legs performed by car?”.

The paper contains, in detail, the factors negatively influencing the experience of the users. Here, we summarize some main outcomes:

• When cycling, we find two main areas of concern: safety (availability of bicycle paths, safety from other cars, visibility, and traffic signals) and quality (noise level, air quality).
• Road path directness has a somewhat more important role when cycling is used as an alternative to traveling by car w.r.t. the general negative experience factors.
• For short-distance public transport, the main obstacles are lack of privacy and crowdedness in many forms (including noise level and air quality).

Research Opportunities

Our dataset can support numerous research initiatives, with applications that can provide benefits both to the end users and to transport stakeholders, such as transport operators. Possible areas of interest include cost-benefit analyses, user profiling and clustering, recommender systems, and ad targeting.