Teleworking in the Netherlands: an evaluation of changes in travel behaviour

This article describes the first experiment on teleworking in the Netherlands, and presents the results of an analysis of the impact of teleworking on the travel behaviour of the participants in the experiment and their household members. It was concluded that teleworking has resulted in a significant decrease in the total number of trips by teleworkers (–17%). Peak-hour traffic by car has been reduced even more (–26%). An unexpected result was that the household members of the teleworkers also appeared to travel less than before the experiment.     

E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands

Transportation - In recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to...     

Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges

In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.     

The bicycle-train travellers in the Netherlands: personal profiles and travel choices

Transportation - The Netherlands seems to exhibit the unique conditions that allow cycling on the country level instead of only the city level. Moreover, the national transit system seemingly...     

Explore the relationship between online shopping and shopping trips: An analysis with the 2009 NHTS data

The rapid growth of ecommerce brings great changes to the transportation system. However, most existing studies focus on the impact of ecommerce on freight system. Its impact on personal trips is relatively less studied. It is reasonable to argue that online shopping reduces the need of shopping trips by making goods accessible via door-to-door deliveries. On the other hand, online shopping may also create more shopping trips as online shoppers travel to stores to experience, compare or pick up the goods. Understanding the connections between online shopping and shopping trips is critical for transportation planners to prepare for changes that information technology will continue to bring to this nation in the future. Using the 2009 National Household Travel Survey (NHTS) data and a structural equation model (SEM), this paper disentangles the bidirectional connections between online shopping and shopping trips. Results show that online shopping encourages shopping trips while shopping trips tend to suppress the online shopping propensity. Besides, both online shopping and shopping trips are influenced by exogenous factors such as shoppers’ demographic features, regional specific factors and household attributes. A closer examination at the state level further confirms model validity while disclosing spatial variation in their relationship.     

Understanding neighbourhood design impact on travel behaviour: An application of structural equations model to a British metropolitan data

The objective of this study is to explore whether changes in neighbourhood characteristics bring about changes in travel choice. Residential self-selection is a concern in the connections between land-use and travel behaviour. The recent literature suggests that a longitudinal structural equations modelling (SEM) approach can be a powerful tool to assess the importance of neighbourhood characteristics on travel behaviour as opposed to the attitude-induced residential self-selection. However, the evidence to date is limited to particular geographical areas and evidence from one country might not be transferrable to another because of differences in land-use patterns and land-use policies. The paper is to address the gap by extending the evidence using British data. The case study is based on the metropolitan area of Tyne and Wear, North East of England, UK. A SEM is applied to 219 respondents who reported residential relocation. The results identify that neighbourhood characteristics do influence travel behaviour after controlling for self-selection. For instance, the more people are exposed to public transport access, the more likely they drive less. Neighbourhood characteristics also impact through their influence on car ownership. A social environment with vitality also reduces the amount of private car travel. These findings suggest that land-use policies at neighbourhood level can play an important role in reducing driving.     

Modelling the impact of weather conditions on active transportation travel behaviour

Three weather sensitive models are used to explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data are restricted to non-captive commuters who have the option of selecting among five basic modes of auto driver, auto passenger, transit, bike and walk. Daily trip rates in various weather conditions are assessed. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.     

Information impact on quality of multimodal travel choices: conceptualizations and empirical analyses

This paper investigates the impact of a variety of travel information types on the quality of travel choices. Choice quality is measured by comparing observed choices made under conditions of incomplete knowledge with predicted choice probabilities under complete knowledge. Furthermore, the potential impact of travel information is considered along multiple attribute-dimensions of alternatives, rather than in terms of travel time reductions only. Data is obtained from a choice experiment in a multimodal travel simulator in combination with a web-based mode-choice experiment. A Structural Equation Model is estimated to test a series of hypothesized direct and indirect relations between a traveler’s knowledge levels, information acquisition behavior and the resulting travel-choice quality. The estimation results support the hypothesized relations, which provides evidence of validity and applicability of the developed measure of travel-choice quality. Furthermore, found relations in general provide some careful support for the often expected impact of information on the quality of travel choices. The effects are largest for information services that generate previously unknown alternatives, and lowest for services that provide warnings in case of high travel times only.

Exploring the role of social capital influence variables on travel behaviour

This paper explores the potential role of individual trip characteristics and social capital network variables in the choice of transport mode. A sample of around 100 individuals living or working in one suburb of Madrid (i.e. Las Rosas district of Madrid) participated in a smartphone short panel survey, entering travel data for an entire working week. A Mixed Logit model was estimated with this data to analyze shifts to metro as a consequence of the opening of two new stations in the area. Apart from classical explanatory variables, such as travel time and cost, gender, license and car ownership, the model incorporated two “social capital network” variables: participation in voluntary activities and receiving help for various tasks (i.e. child care, housekeeping, etc.). Both variables improved the capacity of the model to explain transport mode shifts. Further, our results confirm that the shift towards metro was higher in the case of people “helped” and lower for those participating in some voluntary activities.     

Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands

In the past few decades, travel patterns have become more complex and policy makers demand more detailed information. As a result, conventional data collection methods seem no longer adequate to satisfy all data needs. Travel researchers around the world are currently experimenting with different Global Positioning System (GPS)-based data collection methods. An overview of the literature shows the potential of these methods, especially when algorithms that include spatial data are used to derive trip characteristics from the GPS logs. This article presents an innovative method that combines GPS logs, Geographic Information System (GIS) technology and an interactive web-based validation application. In particular, this approach concentrates on the issue of deriving and validating trip purposes and travel modes, as well as allowing for reliable multi-day data collection. In 2007, this method was used in practice in a large-scale study conducted in the Netherlands. In total, 1104 respondents successfully participated in the one-week survey. The project demonstrated that GPS-based methods now provide reliable multi-day data. In comparison with data from the Dutch Travel Survey, travel mode and trip purpose shares were almost equal while more trips per tour were recorded, which indicates the ability of collecting trips that are missed by paper diary methods.     

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

In this paper, we introduce a new trip distribution model for destinations that are not homogeneously distributed. The model is a gravity model in which the spatial configuration of destinations is incorporated in the modeling process. The performance was tested on a survey with reported grocery shopping trips in the Dutch city of Almelo. The results show that the new model outperforms the traditional gravity model. It is also superior to the intervening opportunities model, because the distribution can be described as a function of travel costs, without increasing the computational time. In this study, the distribution was described by a simple function of Euclidean distance, which provides a good fit to the survey data. The slope of the distribution is quite steep. This shows that most trips are made to nearby supermarkets. However, a significant fraction of trips, mainly made by car, still goes to supermarkets further away. We argue that modeling of these trips by the new method will improve traffic flow predictions.     

Between-mode-differences in the value of travel time: Self-selection or strategic behaviour?

Using stated preference survey data, we measure the value of travel time for several transport modes. We find, like many before us, that the value of travel time varies across modes in the opposite direction of what would be the consequence of differences in comfort. We examine three candidate causes for the observed differences: Comfort effects, self-selection and strategic behaviour of respondents. Using experiments with both the current and an alternative mode we find that the differences in the value of travel time are consistent with self-selection and comfort effects. Moreover, respondents having bus as the current or the alternative mode seem not to value comfort differently across modes. Strategic behaviour seems to play no role.     

Interactive stated choice surveys: a study of air travel behaviour

Stated preference (SP) experiments are becoming an increasingly popular survey methodology for investigating travel behaviour. Nevertheless, some evidence suggests that SP experiments do not mirror decisions in real markets. With an increasing number of real world decisions made using the internet, an opportunity exists to improve the realism of the SP counterparts of such choices by aligning the choice environment with such online portals. In this paper, we illustrate the benefits of such an approach in the context of air travel surveys. Our survey is modelled on the interface and functionality of an online travel agent (OTA). As with a real OTA, many ticket options are presented. Sort tools allow the options to be reordered, search tools allow options to be removed from consideration, and a further tool allows attributes to be hidden and shown. Extensive use of these tools is made by the 462 respondents. A traditional SP component was also completed by the respondents. Our exploratory analysis as well as random utility model estimation results confirm not only that respondents seem to engage more actively with the interactive survey, but also that the resulting data allows for better performance in model estimation compared to a more conventional SP experiment. These results have implications for the study of other complex travel choices where interactive surveys may similarly be preferable to standard approaches.     

Understanding travel behaviour change during mega-events: Lessons from the London 2012 Games

This paper presents results from a longitudinal study of the travel behaviour change associated with the London 2012 Olympic and Paralympic Games (the ‘Games’). The research examines commuter travel behaviour through a panel approach enabling an understanding of individual behaviour across three waves (before, during and after), with the study utilising unique access to a Transport for London panel study (n = 1132). The findings indicate that a substantial amount of change occurred during the Games (54% made at least one change), with reducing or re-timing journeys being the most likely adaptations made. A key objective of this work was to advance the discussion about the theoretical constructs that are most applicable in the study of behaviour change associated with disruptive events, which was done through the application and critical evaluation of the Transtheoretical Model. The insights from the stages of change element of the model were relatively limited but the analysis shows significant differences in the underlying factors explaining change according to the type of change made (reduce, re-time, re-mode and re-route). Whilst the long-term behavioural impacts of events like the Games appear small, the study has uncovered a need to consider these behavioural choices as distinct rather than under the collective term of “travel behaviour change”, as is current practice.     

The impact of extreme weather conditions on long distance travel behaviour

This paper examines traveller attitudes and responses towards disruption from weather and natural events. An internet-based travel behaviour survey was conducted with more than 2000 respondents in London and Glasgow. Of these respondents, 740 reported information on over 1000 long distance trips affected by extreme weather and natural events over the previous three years. Results show respondents are generally cautious towards travelling during extreme weather events. For a slight majority in the case of air and public transport, and a greater one in the case of car, travellers did not considerably alter their travel plan following the disruption. This was explained not only by less disruptive weather conditions (with heavy snow and volcanic ash being the most disruptive) and impact, but also by the relative importance of their trips. Differences between transport modes were not substantial. Business trips sometimes appeared to give travellers more flexibility, some other times not. Origin and destination did have an impact on reaction, as well as the presence of children whilst travelling. Mixed results were obtained about socio-economic and attitudinal variables. Age in particular did not appear to have a significant effect. Whilst most respondents did acknowledge no external influence in their decision, results showed an important contribution of transport organisation staff, as well as home and mobile internet technology. A limited but still considerable number of respondents indicated their closest friends/relatives as the main influence of their decisions. The results will help planners deploy strategies to mitigate the negative effects of weather related disruptions.     

Modelling discretionary travel in the presence of imperfect data

The use of mathematical models in transportation and regional planning is limited by the need to obtain reasonably accurate, complete data sets. In particular complete spatial coverage is required for the usual discrete origin-destination models. Because of the time and cost constraints of obtaining such data, those charged with decision making responsibilities may choose to do without information that could be provided by quantitative models. This paper presents a procedure for estimating origin-constrained flows in situations where complete data collection is difficult or impossible. To this end an abstract model of origin-constrained travel is formulated. The required urban fields are constructed using interpolation and/or approximation techniques applied to available data. The tractability of the general model is demonstrated in the case of estimating the energy consumed in travel to existing or proposed facilities. The ability of the model to function with incomplete data was tested by using it to predict travel to the major retail centers located in the Albany-Schenectady-Troy Metropolitan Area.     

Impact of family in-home quality time on person travel demand

This paper introduces the concept of Primary Family Priority Time (PFPT), which represents a high priority household decision to spend time together for in-home activities. PFPT is incorporated into a fully specified and operational activity based discrete choice model system for Copenhagen, called COMPAS, using the DaySim software platform. Structural tests and estimation results identify two important findings. First, PFPT has a place high in the model hierarchy, and second, strong interactions exist between PFPT and the other day level activity components of the model system. Forecasts are generated for a road pricing and congestion scenario by COMPAS and a comparison version of the model system that excludes PFPT. COMPAS with PFPT exhibits less mode changing and time-of-day shifting in response to pricing and congestion than the comparison version.     

A dynamic analysis of person and household activity and travel patterns using data from the first two waves in the Puget Sound Transportation Panel

Daily activity participation and travel patterns are examined using data from the Puget Sound Transportation Panel (PSTP), which contains two-pairs of daily travel diary information (wave 1 in 1989 and wave 2 in 1990). Summary data of the travel diaries at the person and household levels are obtained using cluster analysis. At the person level, four clusters are found reasonable for both activity and travel. The four-cluster solutions indicate substantial day-to-day variation in activity participation and similarity in travel behavior over time. Temporal changes are analyzed using contingency table methods and log-linear models. The analysis reveals that activity participation and mobility present many regularities over time. Transitions within each wave show strong dependence between two days for both activity and travel, with higher dependency for the travel patterns than for the activity patterns. These are consistent when the analysis focuses on a longer period such as a year. Temporal dependencies appear to be stronger in the household-based than the person-based analysis. A hierarchical structure is also found in the relationship between activity and travel clusters. The link between activity and travel is much stronger within a day, weaker from one day to another, and the least strong from one year to the next. Important irregularities, however, are found and may be due to scheduling time-frames adopted by the respondents that are only partially captured in the data used.