Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.

     

The bicycle as a feedering mode: experiences from three European countries

Bike-and-ride, or the combined use of bicycle and public transport for one trip, is a multimodal alternative for the car. This paper discusses the use of bike-and-ride in three countries with widely differing bicycle cultures and infrastructures: the Netherlands, Germany and the UK. The share of the bicycle in access trips is comparable to general levels of bicycle ridership in each country, but only for train services and other fast modes of public transport. Strong similarities are found in the characteristics of bike-and-ride trips and users, in terms of travel distances, travel motives, and the impact of car availability. The majority of bike-and-ride users travels between 2 and 5 km to a public transport stop, with longer access distances reported for faster modes of public transport. Work and education are the main travel motives, with the first dominating the faster modes and the second the slower modes of public transport. Car availability hardly influences the choice for a combined use of bicycle and train, but strongly affects the levels of bike-and-ride for slower modes of transport.     

“I can do perfectly well without a car!”

This article presents the results of a study exploring travellers’ preferences for middle-distance travel using Q-methodology. Respondents rank-ordered 42 opinion statements regarding travel choice and motivations for travel in general and for car and public transport as alternative travel modes. By-person factor analysis revealed four distinct preference segments for middle-distance travel: (1) choice travellers with a preference for public transport, (2) deliberate-choice travellers, (3) choice travellers with car as dominant alternative, and (4) car-dependent travellers. These preference segments differ in terms of the levels of involvement and cognitive effort in travel decision making, the travel consideration-set and underlying motivations. The study showed that for most people there is more to travel than getting from point A to point B, and that there is considerable heterogeneity in middle-distance travel preferences. Policy implications for reducing the need for travel and promoting a modal shift from car to other travel modes are discussed.     

Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria,Australia

Autonomous vehicles (AVs) potentially increase vehicle travel by reducing travel and parking costs and by providing improved mobility to those who are too young to drive or older people. The increase in vehicle travel could be generated by both trip diversion from other modes and entirely new trips. Existing studies however tend to overlook AVs’ impacts on entirely new trips. There is a need to develop a methodology for estimating possible impacts of AVs on entirely new trips across all age groups. This paper explores the impacts of AVs on car trips using a case study of Victoria, Australia. A new methodology for estimating entirely new trips associated with AVs is proposed by measuring gaps in travel need at different life stages. Results show that AVs would increase daily trips by 4.14% on average. The 76+ age group would have the largest increase of 18.5%, followed by the 18–24 age group and the 12–17 age group with 14.6 and 11.1% respectively. If car occupancy remains constant in AV scenarios, entirely new trips and trip diversions from public transport and active modes would lead to a 7.31% increase in car trips. However increases in car travel are substantially magnified by reduced car occupancy rates, a trend evidenced throughout the world. Car occupancy would need to increase by at least 5.3–7.3% to keep car trips unchanged in AV scenarios.     

Identifying travel mode with GPS data

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.     

Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers

For developing sustainable travel policies, it may be helpful to identify multimodal travelers, that is, travelers who make use of more than one mode of transport within a given period of time. Of special interest is identifying car drivers who also use public transport and/or bicycle, as this group is more likely to respond to policies that stimulate the use of those modes. It is suggested in the literature that this group may have less biased perceptions and different attitudes towards those modes. This supposition is examined in this paper by conducting a latent class cluster analysis, which identifies (multi)modal travel groups based on the self-reported frequency of mode use. Simultaneously, a membership function is estimated to predict the probability of belonging to each of the five identified (multi)modal travel groups, as a function of attitudinal variables in addition to structural variables. The results indicate that the (near) solo car drivers indeed have more negative attitudes towards public transport and bicycle, while frequent car drivers who also use public transport have less negative public transport attitudes. Although the results suggest that in four of the five identified travel groups, attitudes are congruent with travel mode use, this is not the case for the group who uses public transport most often. This group has relatively favorable car attitudes, and given that many young, low-income travelers belong to this group, it may be expected that at least part of this group will start using car more often once they can afford it. Based on the results, challenges for sustainable policies are formulated for each of the identified (multi)modal travel groups.     

The value of time and external benefits in bicycle appraisal

We estimate the value of time savings, different cycling environments and additional benefits in cost-benefit analysis of cycling investments. Cyclists’ value of travel time savings turns out to be high, considerably higher than the value of time savings on alternative modes. Cyclists also value other improvements highly, such as separated bicycle lanes. As to additional benefits of cycling improvements in the form of health and reduced car traffic, our results do not support the notion that these will be a significant part in a cost-benefit analysis. Bicyclists seem to take health largely into account when making their travel choices, implying that it would be double-counting to add total health benefits to the analysis once the consumer surplus has been correctly calculated. As to reductions in car traffic, our results indicate that the cross-elasticity between car and cycle is low, and hence benefits from traffic reductions will be small. However, the valuations of improved cycling speeds and comfort are so high that it seems likely that improvements for cyclists are cost-effective compared to many other types of investments, without having to invoke second-order, indirect effects. In other words, our results suggest that bicycle should be viewed as a competitive mode of travel and not primarily as a means to achieve improved health or reduced car traffic.     

Latent lifestyle and mode choice decisions when travelling short distances

In the quest for sustainable travel, short distances appear the most amenable to curbing the use of the automobile. Existing studies about short trips evaluate the potential of shifting from the automobile to sustainable travel options while considering the population as homogeneous in its preferences and its tendency to accept these alternative travel options as realistic. However, this assumption appears quite unrealistic and the current study offers a different perspective: the mode choices when travelling short distances are likely related to lifestyle decisions. Short trip chains of a representative sample of the Danish population in the Copenhagen Region were analysed, and more specifically a latent class choice model was estimated to uncover latent lifestyle groups and choice specific travel behaviour. Results show that four lifestyle groups are identified in the population: car oriented, bicycle oriented, public transport oriented and public transport averse. Each lifestyle group has specific perceptions of travel time (with extremely different rates of substitution between alternative travel modes), transfer penalties in public transport trip chains, weather influence (especially on active travel modes), and trip purpose effect on mode selection. Consequently, when thinking about measures to increase the appeal of sustainable travel options, decision-makers should look at specific individuals within the population and more sensitive individuals to comfort and level-of-service improvements across the lifestyle groups.     

Bicycle commuting market analysis using attitudinal market segmentation approach

The market segmentation analysis for bicycle commuting can help identify distinct bicycle market segments and develop specific policies or strategies for increasing the bicycle usage in each segment. This study aims to use the approach of attitudinal market segmentation for identifying the potential markets of bicycle commuting. To achieve the research objective, the household survey is conducted to obtain the travelers’ attitudes towards their commuting travels. The factor analysis is used to explore the latent attitudes. The structural equation modeling (SEM) simultaneously estimates the correlations between the attitudinal factors. The K-means clustering is conducted to segment the bicycle commuting market into several submarkets. Finally, six segments of bicycle commuting market with distinct attitudes are identified by four dividing factors including the willingness to use bicycle, need for fixed schedule, desire for comfort, and environmental awareness. The attitudinal characteristics, socioeconomic features, and actual bicycle choices in each market segment are analyzed and compared. The policy implications that best serve the needs of each submarket are discussed to promote the bicycle commuting.     

Gauging interventions for sustainable travel: A comparative study of travel attitudes in Berlin and London

So-called ‘soft’ policy instruments that respond to the psychological aspects of travel are regularly acknowledged as necessary complements to ‘hard’ infrastructure investments to effectively promote sustainable travel in cities. While studies investigating subjective orientations among travellers have proliferated, open questions remain including the role of recent technological advances, the expansion of alternative mobility services, locally specific mobility cultures and residential selection. This paper presents the methods, results and policy implications of a comparative study aiming to understand mobility attitudes and behaviours in the wider metropolitan regions of Berlin and London. We specifically considered information and communication technology (ICT), new types of mobility services such as car sharing, electric cars and residential preferences. In each region, we identified six comparable segments with distinct attitudinal profiles, socio-demographic properties and behavioural patterns. Geocoding of the home address of respondents further revealed varying contextual opportunities and constraints that are likely to influence travel attitudes. We find that there is significant potential for uptake of sustainable travel practices in both metropolitan regions, if policy interventions are designed and targeted in accordance with group-specific needs and preferences and respond to local conditions of mobility culture. We identify such interventions for each segment and region and conclude that comparative assessment of attitudinal, alongside geographical, characteristics of metropolitan travellers can provide better strategic input for realistic scenario-building and ex-ante assessment of sustainable transport policy.     

Do modes of transportation and GPS affect cognitive maps of Londoners?

Legibility has long been recognized as an important factor in creating a good image of a city in individuals’ minds. This image is perceived to assist people in understanding the city, finding their way, and recalling the city. The quality of the image affects individuals’ abilities in way-finding. This is especially important for cosmopolitan and global cities such as London in order to preserve resources and time, manage travel costs, limit pollution (air or noise) and enhance these cities as places to live, work and visit. This research examines the cognitive maps of London drawn by a sample of its residents to discover how different modes of transportation and GPS usage could affect individuals’ urban images. Such research is useful for town planners, local government departments, and urban and transport planners because of the way it considers the legibility of London as and provides a tool to study individuals’ urban images. 101 participants were recruited with at least a two-year residency from both genders (38.6% females and 61.4% males) with the average age of 33.88 and S.D. = 10.63. The results suggest car use has a positive correlation with seeing London in city scale and GPS usage has a negative correlation. Whilst recent studies have shown that there are differences between active travel modes (e.g., walking, bicycle riding or driving a car) and passive modes (e.g., as a passenger taking a bus, train or taxi), this study indicates that GPS usage also influences cognitive maps, with a negative correlation found between GPS usage and drawing maps on a city scale. Other significant associations were found for the car drivers with a positive relation with the number of roads mentioned on the maps, seeing London in city scale and having a two-dimensional façade image of the city in mind.     

The effects of activity-travel context and individual attitudes on car-sharing decisions under travel time uncertainty: A hybrid choice modeling approach

People’s daily decision to use car-sharing rather than other transport modes for conducting a specific activity has been investigated recently in assessing the market potential of car-sharing systems. Most studies have estimated transport mode choice models with an extended choice set using attributes such as average travel time and costs. However, car-sharing systems have some distinctive features: users have to reserve a car in advance and pay time-based costs for using the car. Therefore, the effects of activity-travel context and travel time uncertainty require further consideration in models that predict car-sharing demand. Moreover, the relationships between individual latent attitudes and the intention to use car-sharing have not yet been investigated in much detail. In contributing to the research on car-sharing, the present study is designed to examine the effects of activity-travel context and individual latent attitudes on short-term car-sharing decisions under travel time uncertainty. The effects of all these factors were simultaneously estimated using a hybrid choice modeling framework. The data used in this study was collected in the Netherlands, 2015 using a stated choice experiment. Hypothetical choice situations were designed to collect respondents’ intention to use a shared-car for their travel to work. A total of 791 respondents completed the experiment. The estimation results suggest that time constraints, lack of spontaneity and a larger variation in travel times have significant negative effects on people’s intention to use a shared-car. Furthermore, this intention is significantly associated with latent attitudes about pro-environmental preferences, the symbolic value of cars, and privacy-seeking.     

The relationship between the built environment and nonwork travel: A case study of Northern California

Many studies have found that residents living in suburban neighborhoods drive more and walk less than their counterparts in traditional neighborhoods. This evidence supports the advocacy of smart growth strategies to alter individuals’ travel behavior. However, the observed differences in travel behavior may be more of a residential choice than a travel choice. Applying the seemingly unrelated regression approach to a sample from Northern California, we explored the relationship between the residential environment and nonwork travel frequencies by auto, transit, and walk/bicycle modes, controlling for residential self-selection. We found that residential preferences and travel attitudes (self-selection) significantly influenced tripmaking by all three modes, and also that neighborhood characteristics (the built environment and its perception) retained a separate influence on behavior after controlling for self-selection. Both preferences/attitudes and the built environment itself played a more prominent role in explaining the variation in non-motorized travel than for auto and transit travel. Taken together, our results suggest that if cities use land use policies to offer options to drive less and use transit and non-motorized modes more, many residents will tend to do so.     

Speed,travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data

Very little is known about cyclist speeds and delays at the disaggregate level of each road segment and intersection in an entire city network. Speeds and delays serve as vital information for planning, navigation and routing purposes including how they differ for different times of the day and across road and bicycle facility types, after controlling for other factors. In this work, we explore the use of recent GPS cyclist trip data, from the Mon RésoVélo Smartphone application, to identify different performance measures such as travel time, speed and delay at the level of the entire network of roads and intersections on the island of Montreal. Also, a linear regression model is formulated to identify the geometric design and built environment characteristics affecting cyclist speeds on road segments. Among other results, on average, segment speeds are greater along arterials than on local streets and greater along segments with bicycle infrastructure than those without. Incorporating different measures of cyclist personality in the models revealed that the following characteristics all affect cyclist speeds along segments, each cyclist’s average speed on uphill, downhill and level segments as well as geometric design and built environment characteristics. The model results also identify that the factors that increase cyclist speeds along segments include, segments which have cyclists biking for work or school related purposes, segments used during morning peak and segments which do not have signalized intersections at either end.     

The dynamics of household travel time expenditures and car ownership decisions

A dynamic (panel data) structural equations model is developed that links four dependent travel behavior variables at two points in time, one year apart. The four dependent variables are: car ownership, travel time per week by car, travel time by public transit, and travel time by nonmotorized modes. Exogenous variables include 13 household characteristics and variables accounting for period effects over the 1985 to 1987 time frame in the Netherlands. The model treats car ownership as ordered-response probit variables and all travel times as censored (tobit) continuous variables. The model accounts for serially-correlated errors and panel conditioning biases. Results are interpreted in terms of recommendations for forecasting procedures.     

Travel mode attitudes,urban context,and demographics: do they interact differently for bicycle commuting and cycling for other purposes?

This study examined whether interactions between travel mode attitudes, urbanization level, and socio-demographics were different for bicycle commuting and cycling for other purposes. Data were obtained from the 2014 wave of the Netherlands mobility panel (MPN). In total, 2673 respondents (18?+?years) who had recorded at least one trip on the days covered by the survey were included in the sample. Four outcomes were constructed, two of which concerned commuting-related cycling: any commuting-related bicycle usage (yes vs. no) and average cycling duration (in hours per weekday). Likewise, two similar outcome variables concerning cycling for other proposes were constructed. These outcomes were analyzed by means of Tobit regression models (cycling duration) and binary logistic models (any bicycle usage). Attitudinal factors concerning different travel modes, namely bus, car, cycling, and train, were constructed by means of factor analysis. The results showed that a positive attitude toward cycling was positively related to bicycle commuting duration, but this association was less strong among those with a positive attitude toward bus use. Having a positive cycling attitude had a weaker association with both bicycle commuting usage and duration in those who do not always have a car available. Regarding cycling for other purposes, cycling attitude had a stronger positive association with cycling duration among residents of very highly urbanized area, compared to residents of less urbanized areas. The available evidence, though limited, suggests that targeting attitudes can have a measurable impact on bicycling, but not to the same extend among all people.

     

A structural model of temporal change in multi-modal travel demand

A simultaneous equation model is developed to describe temporal trends and shifts in demand among five modes of passenger transportation in the Netherlands. The modes are car driver, car passenger, train, bicycle, and public transit (bus, tram, and subway). The time period is one year (1984–1985). The data are from the week-long travel diaries at six-month intervals of a national panel of households in the Netherlands. The model explains the weekly trip rates for each mode in terms of three types of relationships: links from demand for the same mode at previous points in time (temporal stability or inertia); links to and from demand for other modes at the same point in time (complementarity and competition on a synchronous basis); and links from demand for other modes at previous points in time (substitution effects). a significant model is found with 15 inertial links, 21 synchronous links, and 16 cross-lag links among the variables. It is proposed in interpretations of the link coefficients and overall effects of one variable on another that relationships among the modes are evolving over time. In particular, the model captures the effect of a public transit fare increase that occurred during the time frame of the panel data.     

Vehicle value of travel time savings: Evidence from a group-based modelling approach

The value of travel time savings (VTTS) accounts for a majority of the total user benefits in economic appraisal of transport investments. This means that having an accurate estimate of VTTS for different segments of travel continues to retain currency, despite there being a rich literature on estimates of VTTS for different travel modes, travel purposes, income groups, life cycles, and distance bands. In contrast, there is a dearth of research and evidence on vehicle VTTS, although joint travel by car is an important segment of travel. This paper fills this gap by developing a group-based modelling approach to quantify the vehicle VTTS and compares this with the VTTS for a driver with and without a passenger. An online survey was conducted in Sydney in 2014 and the data used to obtain a number of new empirical estimates of vehicle and driver VTTS. The new evidence questions the validity of various assumptions adopted in current practice for valuing the time savings of car passengers and multiple occupant cars.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Do supra-modal traveller types exist? A travel behaviour market segmentation using Goal framing theory

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.