Multitasking on the go: an observation study on the Brussels public transport

1. Background and research questions

Previous researches consider the work commute as one of the least appreciated daily activities regarding emotional well-being and travel time spent is wasted. Yet, this assumption is challenged by recent researchers who believe travel time can also be positively productive, especially with the support of information and communications technologies (ICTs). Besides, Brussels has speedily placed a significant need on the public transport (PT) mobility. Such potential condition, together with the shortage of studies conducted with regard to in-vehicle time use in this region, attracts the author’s attention and encourages him to carry out this research.

The research is to answer these questions: (1) Is there any relationship between demographic, distance or crowdedness and PT passengers’ decision to multitask? If yes, to what extent do those factors influence the multitasking behaviors of PT users and how they experience those activities? (2) Which are the major multitasking activities? What are the popular ICT devices passengers carry along and how interactions take place in relation to those devices? (3) Is there a linkage between passengers’ behaviors and different modes of transport? (4) Will the findings help enhance transport experience by offering the transport operators chances to improve their utilities or policies?

2. Research Design

2.1. Technique

Structured observation is used and logistic regression is the main technique to analyze data. In addition, ordinary least squares (OLS) and moderation analysis are adopted in certain cases.

2.2. Observation design

Where to observe? Brussels Capital Region.

When to observe? May 3rd to May 8th 2016, from the late morning to late evening.

Who to observe and what to observe? All passengers, excluding children.

2.3. Population and sampling

In total, 1216 actual objects are observed.

3. Results, discussion and recommendations

3.1 Results and discussion

Four separate logistic regressions are run in correspondence to four most popular dependent multitasking variables: “doing nothing and/or gazing out of window”, “talking with other passengers”, “messaging” and “listening to music/radio”. In which, “talking with other passengers” is positively influenced by “travel in group”. Furthermore, the possibility that people travelling in group have conversation with others is higher than passengers who travel alone. Despite demographic factors do not have much impact on this activity, males less often talk to others than females.

Several variables have a significant impact on the passengers’ state of “doing nothing/gazing out of the window”. The more stops passengers travel, the more likely they would engage in activities other than just “doing nothing”. Age range also has some impacts; within the middle to old age group, the older the passengers are, the more likely they would involve in “doing nothing”.

The variables “number of stops”, “travel in group”, “age range” and “social group” impose a significant influence on “messaging”. In which, the longer the distance is, the higher probability that people will message. Moreover, people travel in group are less likely to message compared to whom travel alone. Finally, it can be statistically seen that women are more likely to message than men.

Another finding is that only “travel in group” and “age range” have an influential impact on “listening to music/radio”. In details, passengers do not often listen to music/radio when they travel in group. Also, it is clearly defined that the youngest group possesses the highest probability to listen to music/radio. Next to that, it is seen that males are more likely to enjoy this activity than females.

3.2 Other findings

Statistically, the demographic patterns significantly influence the frequency that the passengers interact with their ICT devices. Specifically, the passengers who are male, at the younger age range, belongs to the  social groups “working” or “studying” tend to use ICTs more frequently compared to those who are female, at the older age range and belongs to the social group “unidentified” respectively.

A cross tabulation between the frequency of using ICT devices and type of transport  reveals that passengers travelling by metro most often multitask continuously on ICTs than tram and bus. Following model 1 proposed by Hayes (2013), it is shown that the transport type influences the frequency of interacting with ICTs; yet the level of usage depends on the level of crowdedness on the transport vehicles.

It is also learnt that the people on trams and metros are most likely to have communication with others. Besides, they prefer engaging in eating or drinking to passengers on buses do. Interestingly, more bus users involved in messaging activities than metro and tram users. Regarding gender, women tend to have verbal face-to-face conversation more than men regardless of transport mode. This tendency might be explained by the natural difference of social psychology between the two genders.

A detailed comparison with a previous study in Brussels (Patriarche & Huynen, 2014) which adopts a different methodology, BELDAM (Belgium daily mobility) survey, but in the same context is conducted both to confirm the reliability and validity of this paper and to give an insight about Brussels public transport and its users’ multitasking tendency. Moreover, clashing points between the two researches raise a need for further studies of the same interest. A combination of both field work to observe the manifest behaviors and questionnaire could be a promising methodology for future researchers.

3.3 Recommendation

Demographic, distance and crowdedness should be paid sufficient attention as they turn out to significantly influence the propensity of performing certain multitask. Transport policy maker may refer to this research’s findings to implement better transport appraisal strategies to improve the infrastructure, services and clients’ satisfactions. ICT integration in vehicles can also enhance social participation of PT users and bring knowledge or educational training values to the society. Although this research presents the findings on the PT only, its conclusion is a good reference to travel agencies for how to improve their services, reinforce customers’ satisfaction and retention by tracking the historical database which gives demographic traits of targeted customers.