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Users of Transport Modes and Multimodal Travel Behavior: Steps Toward Understanding Travelers’ Options and Choices
Abstract
Results are presented from analysis of individual mode choice behavior in the longitudinal section in Germany. The findings show that about half of German drivers also use public transport. Because they use different modes, they can be characterized as multimodals. This group will constitute an increasing share of the public transport clientele in coming decades because the decline of captive public transport riders is foreseeable. Therefore it is necessary to understand multimodal behavior because in an environment where travelers have increasing options, it is important to know how they make use of their options. It was found that multimodals employ public transport for specific purposes, whereas the car is universal. Less than 20% of multimodals use public transport on a regular basis, for example, to commute. Most multimodals use it occasionally. Multimodals opt for public transport in specific situations because it is the better option and not because there is no car available. Although for families the car is often the better choice, single persons tend to be more multimodal. Commuting by public transport was found to be a gateway to the use of public transport for other purposes.
... Comparison of findings about multimodality across studies is challenging given the inherently different transportation systems across geographies, target data sources, temporal frames, and definitions of multimodality. However, some relevant results are common among studies: the percentage of multimodal persons decreases with advancing age [14,33,46],car availability is negatively correlated with multimodal behaviour, and positively correlated with monomodal driving [19,33,46],and having a driver's license is negatively associated with multimodal users [33,46]. Multimodality is generally measured by considering the fraction of users that use a given number of travel modes. ...
... Comparison of findings about multimodality across studies is challenging given the inherently different transportation systems across geographies, target data sources, temporal frames, and definitions of multimodality. However, some relevant results are common among studies: the percentage of multimodal persons decreases with advancing age [14,33,46],car availability is negatively correlated with multimodal behaviour, and positively correlated with monomodal driving [19,33,46],and having a driver's license is negatively associated with multimodal users [33,46]. Multimodality is generally measured by considering the fraction of users that use a given number of travel modes. ...
... Comparison of findings about multimodality across studies is challenging given the inherently different transportation systems across geographies, target data sources, temporal frames, and definitions of multimodality. However, some relevant results are common among studies: the percentage of multimodal persons decreases with advancing age [14,33,46],car availability is negatively correlated with multimodal behaviour, and positively correlated with monomodal driving [19,33,46],and having a driver's license is negatively associated with multimodal users [33,46]. Multimodality is generally measured by considering the fraction of users that use a given number of travel modes. ...
Worldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.
... Investigating mode use requires observing individuals over a period of time. Kuhnimhof et al. (2006) investigated mode use in Germany using a 7-day travel diary. They found that during the first three to four days the number of modes used increased significantly and thereafter stabilised. ...
... Kuhnimhof (2009) found that for commuting most individuals repeatedly uses the same mode (72%) over the course of 7-days This is supported by Hensher and Ho (2016), who found that use of a mode increases the likelihood of using that mode again. However, the variation in modes between individuals is higher for commuting compared to other trip purposes (Kuhnimhof et al., 2006). Lavery et al. (2013) investigated commuting trips to McMaster University in Canada. ...
... Thus, individuals have a relatively small choice set for commuting trips, where most individuals only use one mode for their commute over a period of half a year. This was also found by Kuhnimhof (2009) and Kuhnimhof et al. (2006), however they explored the mode use behaviour over only seven days. Our findings suggest that this unimodality is still largely present over a period of half a year, providing a first indication that individuals are habitual in their mode use for commuting trips. ...
Due to increasing urbanisation rates worldwide combined with growing transportation demand, liveability of the urban environment is under pressure (UN, 2018). In response, many governments worldwide have set goals for increasing the share of trips made using sustainable modes of transport, such as walking and cycling. The use of active modes (i.e. walking and cycling) provides health benefits for individuals due to increased activity levels, and on a network level these modes (standalone or in combination with public transport) can potentially reduce traffic jams and the associated externalities (including air and noise pollution) when substituting the car. To achieve the desired increase in active mode shares, targeted policies need to be implemented. This requires a better understanding of who currently uses these modes, who could be persuaded to switch to active modes, and which determinants are driving active mode choice. This intended change towards active modes requires an adequate representation of walking and cycling in the transportation planning models in order to assess the effect of active mode policies on modal shares and distribution over the network. However, this is often not the case. Moreover, integration of active modes in these models occurs very slowly. Walking and cycling are often missing in transportation planning models, treated as a ‘rest’ category, or combined into slow/active modes, all of which result in incorrect estimates of the active mode shares, making it impossible to correctly identify the impact of potential policy measures on active mode shares. Examples of these policy measures are introduction of new infrastructure or changes to existing infrastructure, which impact route choice and distribution over the network, and reimbursement of using the bicycle to go to work, which impacts the mode choice of individuals.Investigating mode and route choice of active mode users increases the knowledge on active mode choice behaviour. By bridging this gap, the transportation planning models can potentially be improved. The objective of this thesis is ‘to understand and model mode and route choice behaviour of active mode users’. We identify six topics that are imperative to travel choices. First, we investigate the daily mobility patterns of individuals in relation to attitudes towards modes, because attitudes are considered to influence travel behaviour (Chapter 2). Afterwards, we zoom in on individual trips. We aim to understand which determinants drive the choice to walk or cycle (Chapter 3). In this topic we define the mode choice set as all feasible modes per individual and trip. However, not all feasible modes are used by individuals. Therefore, the third topic focuses on modes used over a long period of time, which we coin the experienced choice set. We investigate which determinants are relevant for including or excluding modes in this choice set (Chapter 4). Regarding cyclists’ route choice, we investigate the determinants influencing this choice (Chapter 5). This research is based on the experienced choice set. Accordingly, we compare this method to frequently used choice set generation methods to identify the added value of the experienced choice set (Chapter 6). Finally, we perform a literature review on how mode and route choice can be modelled simultaneously (Chapter 7).
... To explain the reasons for or against multimodal behaviours, most of the studies focus particularly on the linkage with sociodemographic factors, spatial structures or available mobility tools as independent variables (Heinen and Chatterjee, 2015;Krygsman and Dijst, 2001;Kuhnimhof et al., 2006;Kuhnimhof et al., 2011;Kuhnimhof et al., 2012c;Kuhnimhof et al., 2012a;Kuhnimhof et al., 2012b;Nobis, 2007;Scheiner et al., 2016). Besides, some studies observe the multimodal behaviours related to attitudinal dimensions (Diana, 2012;Diana and Mokhtarian, 2009;Molin et al., 2016;Olafsson et al., 2016). ...
... -'Monomodal car drivers' tend to be fully employed middle-aged men (Blumenberg and Pierce, 2014;Heinen and Chatterjee, 2015;Nobis, 2007;Scheiner et al., 2016;Vij et al., 2013). They have a high level availability of cars (Buehler and Hamre, 2015;Diana and Mokhtarian, 2009;Kuhnimhof et al., 2006;Molin et al., 2016;Nobis, 2007) and generally live with their families (Klinger, 2017) in private homes in suburban or rural areas (Diana, 2012;Heinen and Chatterjee, 2015;McLaren, 2016). -'Multimodal travellers' tend to be young adults (Kuhnimhof et al., 2011;Kuhnimhof et al., 2012a;Kuhnimhof et al., 2012b) or retirees (Nobis, 2007;Scheiner et al., 2016). ...
... -'Multimodal travellers' tend to be young adults (Kuhnimhof et al., 2011;Kuhnimhof et al., 2012a;Kuhnimhof et al., 2012b) or retirees (Nobis, 2007;Scheiner et al., 2016). They have a rather low income (Kuhnimhof et al., 2012b) and can be located in the densely populated and diversified residential areas of large cities (Heinen and Chatterjee, 2015;Kuhnimhof et al., 2010;Kuhnimhof et al., 2006;McLaren, 2016;Nobis, 2007). They have high availability of the bicycle and a season ticket for public transport (Heinen and Chatterjee, 2015;Vij et al., 2013); moreover, they benefit from easy access to public transport (Buehler and Hamre, 2015;Diana, 2012;Scheiner et al., 2016). ...
This paper makes a critical contribution to the discussion about the transition from an automobile society to a multimodal society in Western transport and mobility research, which is characterised by a flexible use of different transport options. This discussion is fuelled, in particular, by the emergence of smart mobility, in which information and communication technologies (ICTs) – e.g., the smartphone – are used to switch flexibly between new interconnected mobility services (such as carsharing, ridesharing, bikesharing, bus or train). The starting point for the scepticism towards this transition is the research on transport poverty, which problematizes social exclusions from participation in mobility due to the unequal distribution of mode options. This paper suggests a change of perspective from the real mode choice to potential/optional mode choice in order to account for this scepticism in the research on multimodal behaviours. Multioptionality is conceptualised as a necessary precondition for multimodal behaviours to achieve this change of perspective. Based on this conceptualisation, three theses result from a quantitative analysis of a data set from an area in the Rhine-Main region in Germany. The theses challenge the often-postulated potential ubiquity of multimodal behaviours: (i) Transport poverty – represented by a lack of mode options – inhibits the potential production of multimodal behaviours, particularly by socially marginalised people (low income, low education, precarious job situation, etc.). (ii) A multimodal divide describes the reproduction of transport poverty in the guise of modernisation, as the transport poor – with few mode options – also lack certain ICTs that provide central access media to smart mobility. (iii) Another (perfidious) form of social exclusion from participation in smart mobility concerns critical thinkers who avoid installing mobility applications to protect their privacy. This exclusion occurs because these apps do not have an alternative as access software to smart mobility.
... Concerning the time frame of the study, the previous research had taken multi-week information (Axhausen et al. 2002) and selected a single week (Buehler and Hamre 2015) or a single day of information (Buehler and Hamre 2016) for consideration. Concerning the place of study, related research has been confined to Western European countries, such as Germany (Nobis 2007;Kuhnimhof et al. 2006Kuhnimhof et al. , 2012aChlond 2012;Vij et al. 2011;Kroesen and van Cranenburgh 2016;Klinger 2017), the Netherlands (Molin et al. 2016;Givoni and Rietveld 2007), France (Diana and Mokhtarian 2009), Great Britain (Scheiner et al. 2016;Heinen and Ogilvie 2016;Heinen and Chatterjee 2015) and North America (Diana and Mokhtarian 2009;Buehler and Hamre 2016;Buehler and Hamre 2015;Block-Schachter 2009). ...
... Researchers are unanimous that age is negatively correlated with multimodality car use (Chlond 2012;Kuhnimhof et al. 2006Kuhnimhof et al. , 2012aBuehler and Hamre 2015;Heinen and Chatterjee 2015). This correlation is justified by the fact that younger people might have less access to private cars or less money to maintain a vehicle, and they are more likely to choose monomodal walk, bike, or public transit modes. ...
... They further mentioned that individuals with higher incomes are more likely multimodal than monomodal car users in the U.S. In a study in Great Britain, Heinen and Chatterjee (2015) found with an increase in the level of income, the probability of modal variability increases. Previous research further pinpointed a positive correlation between the number of individuals in a household and monomodal car use (Buehler and Hamre 2015;Vij et al. 2011;Nobis 2007;Kuhnimhof et al. 2006). ...
This study investigates the behavior of multimodal and monomodal car users in school trips using 1 week of travel information of 735 Iranian students between 7 and 9 years old. We develop a hybrid choice model (HCM) and contribute to the literature of school trips by: (1) exploring the effects of latent psychological factors including parental attitudes, environmental norms, preferences, and concerns on multimodality behavior, (2) mediating the role of each psychological factor as a latent variable between socioeconomic variables and modal groups in an HCM-framework, and (3) contrasting the multimodality behavior in school trips with other trip purposes discussed in transportation literature. The results show (1) unfavorable attitudes toward safety and environment are positively associated with multimodal and monomodal car use among schoolchildren, (2) latent factors play a mediating role between socioeconomic variables and modal groups. For instance, boys are negatively related to a weaker priority of safety in transport, which indirectly influences multimodality or monomodality, and (3) unlike previous multimodality studies, the age of schoolchildren, car availability, and access to public transit are not found significant predictors of multimodal car use in school trips. We also indicate that a longer travel distance is negatively related to multimodality. The findings highlight that interventions including providing contextual preconditions for walking facilities, increasing parental personal norms about reducing car use, and increasing the safety and security of walking routes could increase monomodal sustainable transport use.
Keywords: Multimodal car use; Monomodal car use; Latent variable; Hybrid choice model; Mode choice; School trips
... Hence, shifting to multiple modes may reduce GHG emissions, depending on individual travel [14]. Even occasional exposure to non-vehicle transportation modes has been shown to increase non-vehicle mode use and decrease intent to use personal vehicles over time [2,7,[15][16][17][18]. Understanding how the local transportation environment, human behavior, and preferences are associated with multimodal behavior is essential for developing transportation policies to meet sustainability, resilience, and efficiency goals [19][20][21][22]. ...
... Commuting behavior was focused on, as commuting contributes to peak traffic congestion [61] and may be relatively consistent within respondents. A one-week timeframe was chosen to capture the typical variability of routine travel [1,2,17,31]. ...
This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.
... Many surveys are still based on the concept of the ''main transportation mode,'' each trip being represented by a single central mode (2). In addition, mobility behavior needs to be monitored over several days to support multimodality study at the individual level, that is, the use of different modes of transport over time (3). The work of the few authors who have benefited from such data is described below. ...
... Some works were based on German national surveys: the MOP, a seven-day panel survey, and the MiD, a single-day cross-sectional survey but containing retrospective questions on the frequency of use of several transport modes (3,6,7). In these papers, multimodality of people was defined as the use of more than two modes (including car, transit, and bicycle) over a week. ...
Despite the desired transition toward sustainable and multimodal mobility, few tools have been developed either to quantify mode use diversity or to assess the effects of transportation system enhancements on multimodal travel behaviors. This paper attempts to fill this gap by proposing a methodology to appraise the causal impact of transport supply improvement on the evolution of multimodality levels between 2013 and 2018 in Montreal (Quebec, Canada). First, the participants of two household travel surveys were clustered into types of people (PeTys) to overcome the cross-sectional nature of the data. This allowed changes in travel behavior per type over a five-year period to be evaluated. A variant of the Dalton index was then applied on a series of aggregated (weighted) intensities of use of several modes to measure multimodality. Various sensitivity analyses were carried out to determine the parameters of this indicator (sensitivity to the least used modes, intensity metric, and mode independency). Finally, a difference-in-differences causal inference approach was explored to model the influence of the improvement of three alternative transport services (transit, bikesharing, and station-based carsharing) on the evolution of modal variability by type of people. The results revealed that, after controlling for different socio-demographic and spatial attributes, increasing transport supply had a significant and positive impact on multimodality. This outcome is therefore good news for the mobility of the future as alternative modes of transport emerge.
... Thus, individuals have a relatively small choice set for commuting trips, where most individuals only use one mode for their commute over a period of half a year. This was also found by Kuhnimhof (2009); Kuhnimhof et al. (2006), however they explored the mode use behaviour over only seven days. Our findings suggest that this unimodality is still largely present over a period of half a year, providing a first indication that individuals are habitual in their mode use for commuting trips. ...
... This means that regardless of the characteristics of an individual, certain modes have a higher or lower probability to be used depending on the type of environment in which one lives. This finding is confirmed by research on mode use and mobility patterns (Kuhnimhof et al., 2006;Ton et al., 2019b). Furthermore, the work conditions prove to be important explanatory variables of the experienced mode choice set for commuting trips. ...
Active modes take up an increasingly important place on the global policy-making agenda. In the Netherlands, a country that is well-known for its high shares of walking and cycling, the government aims at achieving a modal shift among 200,000 commuting car drivers towards using the bicycle. To this end, policy measures need to be introduced. When the aim is to achieve a modal switch over an enduring period of time, it is more relevant to know the likelihood of including or excluding a mode in the mode choice set, compared to choosing a mode for a single trip. Therefore, we investigate the formation of the experienced choice set (set of modes used over a long period of time), where the aim is to identify determinants that influence the inclusion or exclusion of a mode in this set. We estimate discrete choice models, based on survey data from the Netherlands Mobility Panel (MPN) and a complementary survey, where individuals were asked to report the frequency of using certain modes of transport for commuting trips over the course of half a year. This study shows that the experienced choice set for commuting is unimodal for the majority of the individuals, and remains constant over time for most individuals. Reimbursement by the employer for using a certain mode is the most important determinant influencing the experienced choice set, followed by ownership characteristics and urban density. We show that the mode choice set formation depends on more determinants than previously assumed.
... One less studied aspect of millennials' travel behavior is their use of multiple travel modes in a given period, or multimodality (Kuhnimhof et al. 2006;Nobis 2007). By multimodality, scholars imply travel patterns that present some balance among various modes (e.g., half of trips made by driving and the other half by non-motorized modes), instead of relying on a single mode. ...
... Second, unlike deterministic classification schemes (Buehler and Hamre 2014;Diana and Mokhtarian 2009;Kuhnimhof et al. 2006;Nobis 2007), latent profile analysis estimates individuals' probabilities of belonging to various latent classes. Each of these classes shows its own profile consisting of average frequencies of use of various modes. ...
Millennials tend to use a variety of travel modes more often than older birth cohorts. Two potential explanations for this phenomenon prevail in the literature. According to the first explanation, millennials often choose travel multimodality at least in part because of the effects of the economic crisis, which affected young adults more severely than their older counterparts. Another explanation points to the fact that millennials may have fundamentally different preferences from those of older birth cohorts. This paper presents an examination of millennials’ travel behavior as compared to the preceding Generation X, based on a survey of 1069 California commuters. It shows that millennials adopt multimodality more often than Gen Xers, on average. However, the analysis also points to substantial heterogeneity among millennials and indicates that, perhaps contrary to expectations and the stereotype in the media, the majority of millennials are monomodal drivers in California. The paper contributes to the literature on millennials’ mobility in several ways. First, it rigorously classifies various forms of travel multimodality (on a monthly basis and distinctively taking trip purpose into account) through the analysis of a rich dataset that includes individual attitudes and preferences; second, it explores gradual changes of multimodality across age and generation; and third, it analyzes the effects of various demographic, built environment, and attitudinal attributes on the adoption of multimodality.
... One less studied aspect of millennials' travel behavior is their use of multiple travel modes in a given period, or multimodality (Kuhnimhof, Chlond, & von der Ruhren, 2006;Nobis, 2007). By multimodality, scholars imply travel patterns that present some balance among various modes (e.g., half of trips made by driving and the other half by non-motorized modes), instead of relying on a single mode. ...
... Second, unlike deterministic classification schemes (Buehler & Hamre, 2014;Diana & Mokhtarian, 2009;Kuhnimhof et al., 2006;Nobis, 2007), latent profile analysis estimates individuals' probabilities of belonging to various latent classes. Each of these classes shows its own profile consisting of average frequencies of use of various modes. ...
Millennials tend to use a variety of travel modes more often than older birth cohorts. Two potential explanations for this phenomenon prevail in the literature. According to the first explanation, millennials often choose travel multimodality at least in part because of the effects of the economic crisis, which affected young adults more severely than their older counterparts. Another explanation points to the fact that millennials may have fundamentally different preferences from those of older birth cohorts. This paper presents an examination of millennials' travel behavior as compared to the preceding Generation X, based on a survey of 1,069 California commuters. It shows that millennials adopt multimodality more often than Gen Xers, on average. However, the analysis also points to substantial heterogeneity among millennials and indicates that, perhaps contrary to expectations and the stereotype in the media, the majority of millennials are monomodal drivers in California. The paper contributes to the literature on millennials' mobility in several ways. First, it rigorously classifies various forms of travel multimodality (on a monthly basis and distinctively taking trip purpose into account) through the analysis of a rich dataset that includes individual attitudes and preferences; second, it explores gradual changes of multimodality across age and generation; and third, it analyzes the effects of various demographic, built environment, and attitudinal attributes on the adoption of multimodality.
... The reasons for this may also lie in the universal applicability of the car. Kuhnimhof et al. (9) found that many car drivers also use PT. People who use various transport modes opt for PT in specific situations because it is the better option and not because no car is available. ...
To achieve climate goals in the transport sector, many countries are trying to promote the use of public transport. However, to implement effective policies, one must understand the motivations of people who use or do not use public transport today. In this study, we examine the psychographic profiles using latent class analysis to identify the reasons why people use or do not use public transport and link these profiles to reported travel behavior. For the latent class analysis, we use selected psychological items of the German Mobility Panel (MOP), a national household travel survey, that capture attitudes toward public transport. The results highlight four classes that differ based on their psychological profiles: PT-Averse, Privacy Aware Environmentalists, Pragmatists, and PT-Lovers. The results further show that Privacy Aware Environmentalists and PT-Lovers, who have a strong personal norm, frequently use public transport and environmentally friendly transport modes. Thus, the personal norm is a driver of public transport use. The lack of privacy, which the Privacy Aware Environmentalists complain about in public transport, is not a barrier to public transport use.
... The estimation of transportation modes using traditional statistical methods and machine learning techniques is a well-established problem in transportation research. Traditionally, estimates of mode split have been derived from data collected through household travel or travel diary panel surveys, involving retrospective, self-reporting records of how often people use various modes of transport (Lee et al., 2020;Molin et al., 2016), or of individuals' trips for a selected number of days (Heinen and Chatterjee, 2015;Kuhnimhof et al., 2006). Data collection through these surveys is however costly and time-consuming posing significant limitations to their scalability to cover large geographical areas, population samples and complex multimodal systems. ...
Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorisation techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit (all public transportation), motorised (cars and similar vehicles), active (pedestrian and cycle trips), and taxi (traditional taxi and ride-hailing services). Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. Findings also point to a rise in the share of taxi usage, and a drop in active mobility, suggesting a modal shift towards less sustainable modes of travel. We validate our findings comparing our updated estimates with official smart card transaction data. The consistency of findings with expert domain knowledge from the literature and historical transport usage trends further support the robustness of our approach.
... The study depended on an extensive database of smart cards and found that the spatial and temporal aspects are the main features that affect the pattern, while it doesn't differ in the regularity of commuters. Kuhnimhof et al. (2006) examined the difference between regular and occasional users of PT commuting. The study found that less than 20% are regular commuters and that the key factors to this differentiating are age and gender. ...
This study examines the differences in travel behavior between regular and occasional demand-responsive transport users (public transport users), determines the level of service satisfaction, and identifies the key factors of commuters' preferences of using the demand-responsive transport regularly or occasionally for a small-sized urban area (<50 km 2). Data were supplemented through field surveys and by focus group discussions. Binary logistic regression and correlation models were used. It is found that probabilities of irregularity are higher for rural areas, male commuters, short trips, educational trips, low-income groups, and non-direct trips. All users are generally satisfied with the service. The most important factors for occasional users are waiting time, trip cost, and trip duration. On the other hand, regular users pay more attention to cleanliness, safety, and comfort. Scheduling of public transportation lines that serve educational zones and provide accessibility to rural areas are needed to improve the quality and attractiveness of the services.
... For this purpose, we classify the next ride occurrence of users into four categories: within 1 day, 2-7 days, ≥ 7 days, and no use of the service after that ride within the studied period. The reason for setting the maximum observation period as 7 days for each ride is motivated by prior surveys on commute mode choice that over 70% of people show a habitual behavior within 7 days [28]. On the other hand, as a commuter service operator, it is important to understand why some customers use the ride service daily, weekly or lower frequency. ...
The progress of microtransit services across the world has been slower than expected due to institutional, operational, and financial barriers. However, how users' ride experiences and system attributes affects their future ride decisions remain an important issue for successful deployment. A Bayesian network approach is proposed to infer users’ next ride decisions on a microtransit service based on historical ride data from Kussbus, a pilot microtransit system operating in the Belgium–Luxembourg cross-border areas in 2018. The results indicate that the proposed Bayesian network approach could reveal a plausible causal relationship between different dependent factors compared to the classical multinomial logit modeling approach. By examining public transport coverage in the study area, we find that Kussbus complements the existing public transport and provides an effective alternative to personal car use.
... The estimation of transportation modes using traditional statistical methods and machine learning techniques is a well-established problem in transportation research. Traditionally, estimates of mode split have been derived from data collected through household travel or travel diary panel surveys, involving retrospective, self-reporting records of how often people use various modes of transport (Lee et al., 2020;Molin et al., 2016), or of individuals' trips for a selected number of days (Heinen and Chatterjee, 2015;Kuhnimhof et al., 2006). Data collection through these surveys is however costly and time-consuming posing significant limitations to their scalability to cover large geographical areas, population samples and complex multimodal systems. ...
Cities often lack up-to-date data analytics to evaluate and implement transport planning interventions to achieve sustainability goals, as traditional data sources are expensive, infrequent, and suffer from data latency. Mobile phone data provide an inexpensive source of geospatial information to capture human mobility at unprecedented geographic and temporal granularity. This paper proposes a method to estimate updated mode of transportation usage in a city, with novel usage of mobile phone application traces to infer previously hard to detect modes, such as bikes and ride-hailing/taxi. By using data fusion and matrix factorisation, we integrate socioeconomic and demographic attributes of the local resident population into the model. We tested the method in a case study of Santiago (Chile), and found that changes from 2012 to 2020 in mode of transportation inferred by the method are coherent with expectations from domain knowledge and the literature, such as ride-hailing trips replacing mass transport.
... Against this backdrop, multimodality is increasingly gaining attention. Multimodality is most commonly defined as the use of at least two modes during a specific time period (Kuhnimhof et al., 2006;Buehler and Hamre, 2015;Nobis, 2010;Kroesen and Van Cranenburgh, 2016). By using clustering techniques to profile respondents based on their mode choices within a given time, researchers illustrated the variability of behaviour not only between, but also within individuals. ...
In this study, we test the extent to which people who reside in hypothetically car-independent neighbourhoods travel multimodally and perceive themselves as car dependent. We used the Flemish region as our study case, and defined a car-independent neighbourhood as an area with a high node and a high place value. A cluster analysis with four constituent variables – car use frequency, bicycle use frequency, vehicle kilometers travelled (VKT) and the need for a car to carry out daily activities - led to defining four heterogeneous groups of car owners. We labelled the groups as car-dependent motorists - long distance, car-dependent motorists - short distance, car-independent cyclists and car-dependent cyclists. We found all clusters to be to some extent multimodal. For our selected study area, car ownership does not necessarily induce perceived car-dependence among people who can easily get around by bicycle. Nevertheless, even in an urban setting and when exhibiting multimodal travel patterns, people can perceive their car as indispensable. Perceived car dependence is not necessarily correlated with high VKT or high frequency of car use, neither can we conclude that multimodal behaviour necessarily leads to less VKT.
... Concerning route planning and the choice of transportation, the term multimodality generally refers to the possibility of a person to use different modes of transport [11]. Consequently, multimodality can be seen as a variation or combination of different transport vehicles, and a destination can be reached by using different concatenations of transportation. ...
... Instead, the main transport modes are primarily included in the analyses (e.g. Diana, 2012;Klinger, 2017;Kuhnimhof et al., 2006;Nobis, 2007). Hence, possible comparability with these other studies could be established in this respect. ...
This paper refers to the concept of social milieus – which classifies societies according to socio-economic and socio-cultural criteria – and applies it to the everyday mobilities of young Millennials. Starting from three contrasting young social milieus from different social classes – cosmopolitans, middle-class and precariat – differences in material mode options and psychological evaluations of transport modes were investigated, which can be understood as (individual and collective) preconditions for participation in certain hegemonic or alternative transport regimes. For this purpose, quantitative data from Germany from the early 2010s of 852 young people aged 17 to 24 were used. As a result, this study contrasts the often-proclaimed linear regime shift towards multimodality, which would treat Millennials as a supposed collective driving force. The data reveal milieu-specific deviations from the predominant transition narrative as follows: First, young cosmopolitans seem to be the only group to share the historically momentous radical emotional distance from private automobility, which is reflected in ‘green’ multimodal behaviours. In contrast, the young middle-class shows signs of (conservatively) reproducing car-oriented behaviours. Finally, the young precariat faces socio-economic restrictions and tends to be outside the dualistic categorisation of automobility/multimodality. In conclusion, we see the concept of social milieus as an important thought-provoking impetus for a necessary change of perspective in international transport and mobility research to make the problem of social division in transportation more visible. If the direction towards multimodality aligns with the normative compass for a socio-ecological transformation, (transport) policies must provide even stronger support to milieu-specific framework conditions.
... In the literature, several approaches have been applied to segment individuals based on their modal preferences. Some studies, such as Kuhnimhof et al. (45,46) and Nobis (47), have opted to classify users are either unimodal or multimodal travellers based on whether they use more than one mode over a specific period of time. Other studies, such as Lavery et al. (48) and Lin et al. (49) have classified individuals based on the number modes that they reported using, however, they do not consider the combinations of modes that are used. ...
The ongoing COVID-19 pandemic has drastically altered daily life in cities across the world. To slow the spread of COVID-19, many countries have introduced mobility restrictions, ordered the temporary closure of businesses, and encouraged social distancing. These policies have directly and indirectly influenced travel behaviour, particularly modal preferences. The purpose of this paper to explore modality profiles for non-mandatory trips and analyze how they have changed in response to the pandemic and pandemic-related public health policies. The data used for this study were collected from web-based surveys conducted in the Greater Toronto Area. Modality profiles were identified through the application of latent class cluster analysis, with six modality profiles being identified for both the pre-pandemic and pandemic periods. The results indicate that the importance of public transit has declined during the pandemic, while the role of private vehicles and active modes have become more prominent. However, individuals’ changes in modal preferences vary based on their pre-pandemic modality profile. In particular, it appears that pre-pandemic transit users with access to a private vehicle have substituted public transit for travel by private vehicle, while those without private vehicle access are continuing to use public transit for non-mandatory trips. Consequently, pandemic-related transportation policies should consider those who do not have access to a private vehicle and aim to help those making non-mandatory trips using transit or active modes comply with local public health guidelines while travelling. The results highlight how the changes in modal preferences that occurred due to the pandemic differ among different segments of the population.
... However, most existing studies are focused on one transport mode with few employing big data to investigate multimodal travel behaviour; a possible reflection of limits imposed by computational demands. To better inform transport planning and operation, it is still important to understand the different roles of different transport modes in accommodating daily mobility (Kuhnimhof et al., 2006) and data fusion from multiple sources (e.g., smart cards, mobile phones) may serve as a promising research direction. ...
Big data have become one of the most important information sources for urban transport research in Chinese cities, where rapid economic development and motorisation are drastically shaping people’s travel behaviour. Yet, a comprehensive summary and assessment of this body of research is still lacking. This chapter addresses this deficit by providing an overview of the big data applications in transport research in Chinese cities. Research is grouped into five thematic areas, namely, data enrichment and travel-behaviour mining, understanding variability of travel behaviour, transport system assessment and planning, evaluating urban structure and function, and environmental and social implications of transport and mobility. Overall, it is argued that the existing literature has substantially extended our understanding of the urban system and mobility in Chinese cities. Methodological advancement concerning big data manipulation and analysis has also been observed. However, certain important limitations, including data validation and sample representativeness, remain largely unresolved. Furthermore, theoretical contributions have been relatively limited. A series of avenues have been identified as a starting point for developing future big-data transport research in China and beyond, including more research in medium and small-sized Chinese cities, multimodal transport system and mobility, pedestrian travel behaviour, linkage between residential and daily travel behaviour, and finally, attitudinal dimensions of travel behaviour.
... This factor also restricts car use and gives rise to multimodal behavior. Kuhnimhof et al. 2006;2011;Molin et al., 2016;Nobis 2007;Scheiner et al. 2016;Vij et al. 2013 License holding (-); Car availability (-); Increasing number of cars per household (-) Second, multimodality studies discover a connection between multifunctional settlement structures and the flexible use of alternatives to the private car. However, since monofunctional and disperse settlement structures are a reality in many instances, the bicycle, for example, represents a typical component of a multimodal transport choice set. ...
... Assuming three persons in a family, it is expected that most participants (representing his/her family) have access to or could at least, afford to buy a car due to the highly-developed economy. In addition, scholars have argued that it is the perception of transport modes that affects people's travel behavior to a larger extent, rather than the mere existence of those modes (see Kuhnimhof et al. 2006;Pronello and Camusso 2011). ...
Successful and sustained interventions to obtain travel behavior change would be achieved based on a thorough understanding of individual’s decision-making process on travel. To narrow the gap between the theory of planned behavior (TPB) and green travel behavior, this study extends classical TPB by accommodating the moderating effect of affective-cognitive congruence of attitudes. Based on a cluster analysis utilizing affective and cognitive attitudes towards private driving, four groups are obtained, each characterized by different extent of feelings/emotions for private driving and of evaluations/beliefs about the consequences on environment due to car-use. A multi-group structural equation model analysis explicitly confirms the moderating role of affective-cognitive congruence of attitudes, given that the structural relations between overall attitude, subjective norm, active and passive PBC, green travel intention, as well as green travel behavior significantly differ depending on the extent and direction of the congruence between affective and cognitive attitudes. It is expected that the empirical findings might be useful for transport administrators to maximize the effects of their limited resources and funds.
... During the past decades, public transit service quality has been widely researched. Some works mainly focused on the impact factors of using public transit: Ye et al. proposed that trip characteristics, such as purposes of trip, time of trip and regularity of trip, and demographic characteristics, such as age, gender and income level, were shown to be significant factors [3]. Other factors which have been identified are lack of connection, SQ, access distance to and from stations, and distance to/from home or work [4]. ...
Public transport system needs to serve passengers continuously, accurately and effectively. Service quality in public transportation has been widely researched since it can evaluate public transportation systems within the passengers' aspect. At present, however, the study of public transport system service quality is usually based on survey, which requires a lot of time and economic cost. Besides, the result is insufficient in some cases since few passengers take the survey. Commuter traffic in peak hours is always a hot topic in public transport research. Based on field experiments, this paper proposes an evaluation method for public transportation service quality based on the energy cost of passengers. This method utilizes the heart rate, acceleration and speed data automatically collected by the experimenters when they are walking in subway transfer stations, fits these data to Physical Activity Intensity and uses it as the index of travel energy cost. Subsequently, the accuracy, theoretical and practical prospect of this method are verified by the transfer passenger data of Beijing Subway Line 1 and Line 2 in May, 2017. The results show that the service quality evaluation method can accurately perceive the change of system service efficiency and its recovery ability according to different travel demands of the passengers. At the same time, this method uses automatic data collection to analyze, improves its accuracy and analysis adaptability compared with the traditional methods.
... The ARIS is a new types of multimodal transport integration, which also has been given widely discussion on mainly three aspects: the spatial distribution of both freight and travellers of the multimodal transport (e.g., Kuhnimhof et al., 2006;Li et al., 2007;Munizaga and Palma, 2012;Arentze and Molin, 2013;), the design of multimodal transport network (e.g., Grotenhuis et al., 2007;Bock, 2010;Li et al., 2015;Saeedi et al., 2017), and the modelling of multimodal transport integration with the supernetwork approach (e.g., Nagurney, 2006;Ramadurai and Ukkusuri, 2010;Yamada et al., 2011;Liao et al., 2010Liao et al., , 2013Liao et al., , 2017Liao et al., , 2020. ...
The fast expansion of high speed train network acts as a double-edged sword for the development of air passenger transport all over the world. An air-rail integrated service(ARIS) has been regarded as a new trend for the air passenger transport. However, the launch of ARIS involves multiple stakeholders, mainly includes airport, regional railway bureau, airlines and passengers. Thus, the passenger demand forecasting of ARIS, directly impacts on the operations of both airports and airlines, further the development of both regional transport market and economics. This paper proposed a Bass+BL+Seasonality model, which combined Bass diffusion model, disaggregate choice model, and seasonal fluctuations to forecast the passenger demand and trend of ARIS. The ARIS of Shijiazhuang Airport in China was taken as an example to verify its performance. The results showed that compared with other typical methods, the proposed Bass+BL+Seasonality model could forecast the passenger demand trend of ARIS with higher accuracy.
... By a plurality of 31%, high-decision satisficers tended to cycle, whereas by a majority of 65.9% low-decision satisficers tended to use the car for their daily commute. Several studies (e.g., Kuhnimhof, Chlond, and von der Ruhren, 2006;Simma and Axhausen, 2001;Sivasubramaniyam et al., 2020) have found car users tend to use their mode most of the time instead of using a combination of several modes. So, the low-decision satisficers in our study tended to make more trips using their usual modes possibly because they are mostly car users as shown in our chi-square analyses. ...
Satisficing is the tendency to make ‘good enough’ decisions. Consumers tend to satisfice when making routine decisions (e.g., grocery shopping). Commuters also make routine decisions about their daily commute. Our goals were to investigate whether, like consumers, commuters tend to satisfice when deciding to use the modes they typically use for commuting and to understand the psychological and travel characteristics that distinguish commuters with strong from those with weak tendencies to satisfice. A sample of New Zealand commuters (n = 313) completed an online questionnaire measuring their satisficing scores, psychological and travel characteristics. A factor analysis revealed two measures of satisficing such that commuters may satisfice when deciding to use (decision-satisficing) and when using (behaviour-satisficing) their usual modes for their daily commute. Commuters tend to satisfice when deciding to use modes that they use frequently (usual modes) compared to modes that they use infrequently (alternative modes). Commuters with high satisficing tendencies (decision and behaviour) tend to be more positive and more satisfied with their usual-mode commutes compared to commuters with low satisficing tendencies. Cyclists had the strongest decision-satisficing tendencies while solo drivers had the weakest decision-satisficing tendencies. We demonstrated that commuters do satisfice during their daily commutes and there are some differences between high- and low-satisficing commuters. Mode-shift interventions could target commuters' satisficing decision-making strategy to encourage the use of sustainable modes.
... Based on vehicle ownership and possession of transit pass among other individual and household socio-demographic attributes, the authors identified three modality styles of habitual drivers (who mostly consider walk and auto), time-sensitive multimodals (who are highly sensitive to travel time), and time-insensitive multimodals (who are not much sensitive to the travel time). Further empirical evidence on the habits of travel includes Kuhnimhof et al. (2006), where the authors distinguished between unimodal and multimodal travelers. The authors observed several trends based on a seven-day trip diary data; for instance, they found that families mostly utilize car while single persons are more likely to be multimodal. ...
... This is due to a combination of factors including lower marriage and fertility rates, an ageing population, rapid urbanisation, and rising wealth in emerging markets (Euromonitor, 2013). Car ownership is significantly lower for single households than for multiple-person households (Fornells & Arrue, 2014), and single parents, those in one-person households and people living apart together relationships are found to use the car less often and/or display more multimodal transport behaviour than is the case with members of more traditional forms of family (Chlond & Ottmann, 2007;Kuhnimhof et al., 2006;Kunert, 1994;Haustein, 2006). Owing to the smaller household sizes seen today, more complex mobility is needed to establish and maintain social contacts as well as to conduct other activities, and this in turn increases the demand for transport (Brög et al., 2005). ...
... Buehler and Hamre (2016) also reported that walking was the only non-car mode used by the majority of car users. Other researchers have described commitment as either mono-modal when only one travel mode is used for most trips or multi-modal when multiple travel modes are used (Heinen and Chaterjee, 2015;Kuhnimhof et al., 2006;Nobis, 2007). In these terms, Kuhnimhof et al. (20060 reported that car drivers can usually be characterised as mono-modal. ...
In New Zealand, like many other developed countries, a majority of trips (67%) involve the use of private cars, producing negative effects on the environment and public health. Interventions aimed to reduce car use can be successful if we not only understand the reasons car users drive but also why other commuters use more sustainable alternatives. Although a range of possible motivating factors have been previously identified in the literature, the significance of the present study was to address the question of whether these motivating factors interact with each other to influence commuters' intentions to choose a particular mode for their daily commute. A sample of commuters completed an online survey and a subset completed a 1-week travel diary later. Social norms were a significant predictor of drivers' and car passengers' intentions to use the car, whereas ease-of-use was a significant predictor of drivers' intentions to drive and active commuters' intentions to walk or cycle. All commuters had comparable ecological beliefs and mode-related status which were not related to their intentions to use their travel modes. Although all the commuters were committed to their mode choice for daily commute, drivers and pedestrians were more likely to use only their respective travel modes for daily commute, whereas passengers, bus users, and cyclists were more likely to use a combination of several modes. Future research might productively explore subtypes of car commuters and additional analysis techniques to identify ways of nudging car commuters to reduce their car use in favour of sustainable alternatives.
... Buehler and Hamre (2016) also reported that walking was the only non-car mode used by the majority of car users. Other researchers have described commitment as either mono-modal when only one travel mode is used for most trips or multi-modal when multiple travel modes are used (Heinen and Chaterjee, 2015;Kuhnimhof et al., 2006;Nobis, 2007). In these terms, Kuhnimhof et al. (20060 reported that car drivers can usually be characterised as mono-modal. ...
In New Zealand, like many other developed countries, a majority of trips (67%) involve the use of private cars, producing negative effects on the environment and public health. Interventions aimed to reduce car use can be successful if we not only understand the reasons car users drive but also why other commuters use more sustainable alternatives. Although a range of possible motivating factors have been previously identified in the literature, the significance of the present study was to address the question of whether these motivating factors interact with each other to influence commuters' intentions to choose a particular mode for their daily commute. A sample of commuters completed an online survey and a subset completed a 1-week travel diary later. Social norms were a significant predictor of drivers' and car passengers' intentions to use the car, whereas ease-of-use was a significant predictor of drivers' intentions to drive and active commuters' intentions to walk or cycle. All commuters had comparable ecological beliefs and mode-related status which were not related to their intentions to use their travel modes. Although all the commuters were committed to their mode choice for daily commute, drivers and pedestrians were more likely to use only their respective travel modes for daily commute, whereas passengers, bus users, and cyclists were more likely to use a combination of several modes. Future research might productively explore subtypes of car commuters and additional analysis techniques to identify ways of nudging car commuters to reduce their car use in favour of sustainable alternatives.
... The car is often the better choice for families. However, public transport was a better option for single people in multimodal specific situations [33]. Regarding bicycle travel mode in multimodal, the decisions of occasional cyclists to commute by bicycle are more affected by positive weather conditions; frequent cyclists are discouraged from cycling by more practical barriers, including wind speed and the need to be at multiple locations [34]. ...
Alleviating traffic congestion and developing sustainable transportation systems in a city can be assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transport. Strategies for promoting these desirable transportation modes can be identified based on a sound understanding of how commuters choose travel modes. In this study, multi-day commuting travel mode data was used to explore factors that influenced commute mode choice. A multinomial logit model and a binary logit model were proposed to study commuter travel behavior. The results showed the following. (1) Age, gender, and marriage indirectly influence the commute mode choice; (2) The cost of travel mode has little effect on commute mode choice; (3) The probability of commute mode change mainly influences the car mode choice; (4) The number of transfer times and the distance to the nearest public transport stations are main factors that restrict commuters from choosing public transport; (5) The number of bicycles in the family and commute distance are main factors that restrict commuters from choosing cycling for commuting. Based on these findings, several potential measures are demonstrated to policymakers and transportation planners to alleviate traffic congestion and develop sustainable transportation systems.
... Besides, the share of monomodal green use in summer among those who had more than 30 min walking time to the university was 4.91% lower than other students. This finding aligns with previous studies wherein distance has been reported as an important barrier to walking and cycling (Kuhnimhof et al., 2006;. Furthermore, the results revealed that the perceived walking time to the nearest public transit station (PWT_PT) on trips to the university and perceived cycling time to the university (PCT_U) play significant roles in modality use during wintertime than summertime. ...
... Some studies show that women who live alone have more mobility in the use of public transportation (Scheiner, 2006) (Páez et al., 2007). Some research suggests that women use public transport more than men for travel purposes other than commuting (Kuhnimhof et al., 2006). In most cases, men in the family have a driver role, which this gender difference shows that men in their lives use much more than their own car, and vice versa, women are much less likely to use the car (Hjorthol et al., 2010). ...
... These changes may be made voluntarily, for example through tourism experiences (Le-Klähn et al. 2014), changes or the introduction of new transit services (Chatterjee and Ma 2009;Nordlund and Westin 2013) or the provision of free transit passes (Bamberg and Schmidt 1999;Gould and Zhou 2010). They may also be undertaken wholly or partially involuntarily, through changes in the generalized cost of road transport such as rising petrol prices (Lane 2010), freeway closures (Fujii et al. 2001) or temporary loss of access to driving or a car (Kuhnimhof et al. 2006). ...
Travel behavior change has become an area of interest in many cities around the world, particularly to encourage people to change from car use to transit use. Previous research indicates that habits can act as a barrier to travel behavior change and that new use of transit can be associated with some negative characteristics such as increased anxiety and difficulty with wayfinding. However, thus far little research has focused on gaining an in-depth understanding of the process of travel behavior change from the perspective of the new transit user. The present research seeks to fill this gap through a rich qualitative exploration of the process of undertaking ‘new’ (to the participants) journeys on transit, how this experience differs from familiar or habitual travel, and the process of learning and habituation that is undertaken as unfamiliar travel is repeated and evolves into familiar travel. This is achieved by using Grounded Theory to collect and interpret data from 30 semi-structured interviews where participants described both familiar and unfamiliar trip experiences in their own words. The process of undertaking unfamiliar transit travel is characterized by a number of barriers and obstacles and it is characterized by uncertainty and anxiety. If unfamiliar travel is repeated, a change process occurs which includes a number of cognitive processes and adaptations to streamline the process of travel. From the findings, a number of recommendations to support long term travel behavior change are identified.
After 30 years, the German Mobility Panel ceased data collection in the summer of 2023, despite being considered a successful and benchmark-worthy survey about everyday travel. This article gives an overview of the survey’s central ideas, purpose, and design, thoughts about the usefulness and applicability of the data, and explains why the survey has been terminated. It ends up with the experiences made with the MOP. Lastly, a synthesis based on the lessons learned is used to derive recommendations that can help implement future longitudinal surveys.
Modern studies of approaches to urban development in general and to urban transport in particular pay great attention to the interests and needs of various population groups. In particular, the factor of gender identity becomes important in the formation of urban transport policy, namely, understanding the need to take into account the different requirements of men and women in the formation and development of urban infrastructure. In this paper, based on the results of empirical data collection, we show that the transportation behavior and factors of transportation mode choice are different for men and women. This allows us to conclude that developing urban transport infrastructure, city authorities have the opportunity not just to meet the needs of representatives of different gender, but also effectively influence the peculiarities of transportation behavior of citizens.
Despite municipal investments in multimodal mobility infrastructure, monomodal automotive travel patterns still dominate work-related mobility. As policymakers aim to reduce associated externalities like traffic congestion, noise, and air pollution, encouraging multimodality can be a promising route toward diversified, more sustainable mobility. However, studies on modal choice and modal shift have mainly focused on investigating the consumer decision-making process concerning specific monomodal travel modes and external factors but are characterized by a lack of dedicated applications in the commuting context. Therefore, insights into consumers’ motivational patterns determining intentions to engage in multimodal commuting and factors influencing their willingness to alter the modal mix remain scarce. With a qualitative means-end chain (MEC) analysis, we explore consumers’ overarching motivational structures to choose multimodal commuting behavior through laddering interviews with forty employees from two large German employers. We contribute to existing research by revealing five motivational patterns that promote consumers' decision to become multimodal commuters: autonomy, physical health, sustainability, quality of life, and interpersonal connections, which we juxtapose with previous findings. Interestingly, we find that economic interest, security, and fun are only motives of secondary importance. Consequently, we propose implications for academics, policymakers, and practitioners to foster commuters choosing more sustainable, multimodal mobility.
In this paper, we aim to answer two main questions in the context of multimodal travel and new modes of transportation, such as autonomous vehicles: firstly, as travelers gain the ability to readily compare modes side by side for each trip, will they become more willing to select the option that best meets their needs in the moment, or will they continue to prefer using a single mode for a whole tour? Secondly, we compare two approaches to estimating mode choice models in the context of a typical workday tour: one in which we enumerate each possible sequence of modes, and one in which we calculate the expected utility given all modes available for each trip separately, and sum over all trips in the tour. We find that the latter approach returns similar estimation results to the former, but is much faster and easier to compute, an advantage that would only grow with more mode alternatives or more trips in the tour. In addition, we discovered a substantial “mode inertia” in our sample: the utility of the mode used for the previous trip is significantly higher for the present trip. This finding indicates that respondents in our sample are more likely to stick with unimodal tours than multimodal ones.
Recent methods to measure multimodality only consider the diversity and evenness of mode use, while ignoring that the classification of transport modes also matters. This study proposes a multigroup multimodality index to measure the extent of being multimodal at both single mode and mode group levels in a nested manner. The index is compared with the two most commonly used indices, the Herfindahl-Hirschman index and the Shannon Entropy index, to assess its reliability and improvement over existing approaches. Results show that the multigroup multimodality index can simultaneously distinguish the degree of being multimodal at both mode level and group level, which addresses the classification issue in measuring multimodality.
Existing research has revealed that there is a growing dominance of multimodality in urban areas, which is frequently considered to be an important part of the solution to the burdens associated with heavy car use, such as traffic congestion, noise, and air pollution, as well as associated risks to human health. Yet, while the role of private motorized transport is decreasing in densely populated urban areas, monomodal travel patterns, as exemplified by single occupancy car use, are still dominating work-related mobility. To date, studies on modal shift have investigated the decision-making processes with regard to particular travel modes. However, the intention to switch to multimodal mobility in a commuting context, which may represent a first step towards car use reduction, remains relatively unexplored. Therefore, this paper aims to understand and identify the predecessors of car commuters’ intention to switch to multimodal commuting via an online questionnaire. Our theoretical framework is based on an extension of Ajzen’s Theory of Planned Behaviour (TPB) with Lindenberg and Steg’s Goal-framing Theory (GFT), including a novel fourth goal frame, habit, and person-organisation fit (POF) as additional constructs. Contrary to our expectations, gain and hedonic goals, as well as POF and habit, were not found to affect switching intention. Instead, our findings suggest that a switch to multimodal mobility is strongly dependent on people's normative goals, underlining the need for employers to encourage and reward sustainable multimodal commuting. These findings can assist policy makers, corporate mobility, and HR managers to promote more sustainable, multimodal mobility behaviour.
An important aspect of post-secondary student travel behaviour is their commute mode usage frequencies. How frequently students use different transportation modes for their daily travel directly characterizes their habits, routines, and predispositions, which can ultimately affect long-term social welfare of the region, congestion of the transportation network, and fuel consumption. Thus, obtaining an in-depth and unbiased understanding of the various factors influencing this travel behavior is key to the sustainable transportation development of a region. Existing studies on this topic are not comprehensive enough in terms of the types of commute modes analyzed and most of them relied on relatively small samples for their investigation. This paper attempts to address the gap by modelling the influence of personal and household socio-demographic attributes, the built environment, commute characteristics, and personal attitudes of the students on the monthly usage frequency of five different types of commute modes (auto, transit, ride-hailing, bicycle, and walk). It uses data from a large-scale student travel survey conducted among 10 post-secondary institutions in the Greater Toronto and Hamilton Area. The study makes use of a sophisticated modelling approach, consisting of a multiple indicators multiple causes model and a zero-inflated ordered probit model to analyze the factors affecting the decision to use and the usage frequency of the exhaustive set of commute modes. The findings emphasize the importance of commute distance, available resources (in terms of mobility tools, living situation, household vehicles, and income), and built environment attributes in the usage frequency of different commute modes. Marginal effects are used to inform actionable policy recommendations for both the institutions and the regional municipalities. The recommendations include offering discounted and promotional transit passes to encourage students to use public transit frequently, increasing the capacity of student housing and enhancing the sidewalk and bicycle infrastructure within 3–5 km of the campus locations, and increasing the transit accessibility of the institutions by establishing subway stops in proximity to the campuses. These recommendations, when implemented, will help to adequately meet the travel needs of the students while also improving their overall campus life experience.
The goal of this study was to discuss possibilities to use Covid-19 as a context situation to change citizens’ transportation behavior. The concept of switching costs is applied to construct algorithm for search of suitable post-pandemic urban transportation policy tools based on analysis of citizens’ transportation behavior and transportation mode choice factors before and during Covid-19 pandemic. To accomplish the goal this work provides analysis of St.Petersburg citizens’ survey results. The study examines what changes in the transport preferences of citizens have occurred. The reasons that contributed to changes in transportation preferences were analyzed. One of the conclusion states that context (objective) changes influence transportation behavior of just some groups of citizens. For others the greater role in post-pandemic transportation behavior is played by subjective factors of transportation mode choice. So, we conclude that those subjective factors should be used to construct switching costs to preserve positive transportation behavior, and switching benefits to avoid negative transportation behavior, which occurred during Covid-19 pandemic.
Sustainable transport research and policy making currently identify multimodality as an important way to reduce carbon emissions and other negative transport externalities. This emphasis is consistent with the ‘behaviour change agenda’ for sustainable mobility, which places responsibility for changing behaviour on ‘citizen-consumers’, while policy makers help them make ‘better’ modal choices, rather than introducing regulatory or pricing measures. In this paper, we present findings based on the British National Travel Survey, which lead us to qualify the emphasis currently placed on multimodality. We first focus on the relationship between multimodality and CO2 emissions, at the individual and trip level. While multimodal trips produce less CO2 than unimodal trips over comparable distances, they are typically longer and therefore have higher average emissions. At the individual level, there is an association between greater multimodality and lower emissions, although of weak magnitude. Second, we investigate trends in multimodality between 1995 and 2015. Contrary to expectations, we find that individual-level multimodality has decreased over time, notably among younger adults, and this during a period of declining car travel distances per capita. We conclude that there is merit in encouraging greater multimodality, but this can hardly be the only or primary goal of sustainable transport policies. More policy attention needs to be directed to the pivotal role of high levels of travel activity, and the reduction of these.
This study investigates the potential market demand of shared-ride taxi and shuttle services designed to serve members of organizations in dense urbanized areas. It develops and compares two different multivariate count data modeling approaches, the multinomial distribution and the full enumeration of count alternatives, under an integrated choice and latent variable framework. The study accounts for day-to-day variability in commuting behavior, also known as multimodality, by modeling the weekly frequency of commuting by different travel modes instead of modeling choices for a single trip/day. Using stated preference data collected in the Spring of Academic Year 2016–2017, the models are applied to a case study of students who are highly dependent on private cars at the American University of Beirut (AUB), Lebanon. Policy analysis is conducted to investigate the impact of different price levels and modal attributes on the students’ mode choice behavior. Under practical scenarios, results show that more than 55% of students would adopt a multimodal travel behavior in a given week and that 9–20% of trips are expected to be made by shared-taxi and 12–25% by shuttle. Thus, modeling single trip/day choices instead of weekly decisions would lead to limitations in model forecasts related to the full impact of the proposed policies over longer periods. Results also show that the full enumeration model guarantees higher prediction accuracy and results in an estimate of value of time that is closer to other local estimates for the study area.
Zusammenfassung
Anlässlich der aktuellen Debatte um den Übergang von der automobilen zur multimodalen Gesellschaft, in der es um eine Abkehr von der weitgehend exklusiven Nutzung des privaten Automobils hin zur flexiblen Nutzung mehrerer Verkehrsmittel geht, wird mit dem vorliegenden Beitrag der sonst vor allem in der Soziologie verwendete Multioptionalitätsbegriff in die Verkehrs- und Mobilitätsforschung übersetzt. Ziel ist es, die im Subtext der Übergangsdebatte mitgeführte potenzielle Ubiquität multimodaler Verhaltensweisen kritisch zu betrachten. Dafür wird Multioptionalität als Voraussetzung für multimodales Verhalten konzeptualisiert, womit innerhalb des Forschungsfeldes eine Perspektivverschiebung von der tatsächlichen hin zur potenziellen Verkehrsmittelwahl angestoßen wird. Die Qualität dieser Perspektivverschiebung wird im Beitrag mithilfe eines Dreischritts aus zwei Narrativen und einem Konzeptvorschlag herausgearbeitet: i) Ein Reflexivitätsnarrativ, wonach die hierarchisch organisierte Ordnung von Verkehrsmitteln innerhalb moderner Alltagsmobilität – das heißt dem privaten Automobil als dominante Option und allen anderen Verkehrsmitteln als rezessive Optionen – zunächst enthierarchisiert zu werden scheint. ii) Ein Regressionsnarrativ, wonach großen Bevölkerungsteilen zunehmend die optionalen Grundlagen zur gesellschaftlichen Teilhabe auf der Basis von Mobilität entzogen werden. Hier scheint eine Nonoptionalität Multioptionalität entgegenzulaufen und die Möglichkeiten zu multimodalen Verhaltensweisen zu unterbinden. iii) Die Ausformulierung eines konzeptionellen Vorschlags zur Integration des Multioptionalitätsbegriffs in die empirisch angelegten Studien zu multimodalem Verhalten, um auf diese Weise ein kritisches Korrektiv in der Übergangsdebatte zur multimodalen Gesellschaft zu verstetigen.
Globally, transport authorities are investing to improve the quality of public transport (PT) services by developing integrated networks. The success of these networks relies on multimodal transfers. However, making a transfer means more time will be spent outside the vehicle compared with a direct route, and for women, this means being exposed to the urban environment of stations for longer. The purpose of this study is to investigate the role gender plays in people’s decisions to make transfers, in order to develop integrated systems that are equitable. A survey was undertaken in three major cities in New Zealand. A total of 2,173 people, car drivers, and PT users participated. Results show that women drivers are 30% more likely than men to make a transfer, given time savings. Waiting time is the most influential factor and has a greater effect on women’s decisions to make transfers. The factor “perceived safety at stations” was only significant for female riders. With the presence of security guards, female car drivers were three times more likely to ride a route with transfer, compared with males who were two times more likely. Women, car drivers, and PT users were slightly more likely to make a transfer given good quality information and covered walkways. It is expected that these results will provide practitioners with some guidance when designing transfers for an integrated system. The limitations women face during their travels can be alleviated by implementing a PT system that is designed to meet their needs more closely.
Bu çalışmanın amacı farklı gelişmişlik düzeyine sahip illerde yaşayan bireylerin toplu taşıma kullanımınaetki eden faktörlerin belirlenmesidir. Çalışma kapsamında Türkiye’de gelişmişlik indeksine göre gelişmiş, gelişmekteolan ve gelişmemiş il gruplarından seçilen sırasıyla Ankara, Erzurum ve Bayburt illeri çalışma alanı olarakbelirlenmiştir. Bu üç ilin merkez ilçe nüfuslarına göre örneklem büyüklükleri hesaplanmıştır. Çalışma alanlarındaŞubat- Nisan 2015 tarihleri arasında saha çalışması yaptırılarak hanehalkı ulaşım anketleri uygulanmıştır. Hanehalkıulaşım anketleri sonucu elde edilen veri seti çalışmanın amacına uygun olarak hazırlanmış ve oluşturulacak istatistikselmodelin değişkenleri belirlenmiştir. Bağımlı değişken olarak toplu taşıma kullanımı seçilmiştir. Bağımlıdeğişken nitel ve ikili cevap (evet-hayır, var-yok) içerdiği için ikili lojistik regresyon analizi uygulanmıştır. Modeldebağımsız değişken olarak cinsiyet, yaş, eğitim durumu, sürücü belgesi sahipliği, yolculuk amacı, yolculuk süresigibi değişkenler kullanılmıştır. Sonuç olarak, üç farklı gelişmişlik düzeyindeki iller için ayrı ayrı oluşturulan toplutaşıma kullanım ikili lojistik regresyon modeli sonuçları karşılaştırılmıştır. Toplu taşıma kullanımında Ankara ilindeehliyet sahipliği, yolculuk amacı, yolculuk süresi değişkenleri, Erzurum ilinde eğitim durumu, ehliyet sahipliği,yolculuk amacı, yolculuk süresi değişkenleri, Bayburt ilinde ise cinsiyet ve yolculuk süresi değişkenleri istatistikselolarak anlamlı değişkenlerdir. Hanehalkı ulaşım anketlerinden elde edilen veriler sonucunda toplu taşıma kullanımoranı en yüksek olan il gelişmekte olan il grubundan seçilen Erzurum ili, toplu taşıma kullanım oranının en düşükolduğu il gelişmemiş il grubundan seçilen Bayburt ilidir.
Travellers commit themselves to particular behaviours through the ownership of cars and sea-son-tickets. They trade a large one-off payment for low or zero marginal cost at the point of use. It can be assumed that these commitments influence travel behaviour and future com-mitment situations. To the knowledge of the authors there is no literature which addresses the choice between the commitment to the one or the other mode and its impacts on travel behav-iour as well as the temporal dimension. The paper presents models using structural equation modelling to test a-priori hypotheses on the paths linking car-availability, season-ticket-ownership and modal usage at three different time periods. Modal usage is operationalised as the number of trips by car respectively public transport. The models are based on two different panel surveys (Germany and the Nether-lands).
The recent shift in transport policy towards travel demand management has directed the attention of transport research towards the dynamic processes in travel behaviour; learning and change on the one hand and rhythms and routines on the other. Progress in the understanding of these processes requires from observation or self-reports data over long duration. The survey reported here provides for the first time in 30 years a data source which allows these issues to be addressed. The project Mobidrive, funded by the German ministry of Research and Education, conducted a six-week continuous travel diary with the aim of analysing the rhythms in the behaviour of the respondents. The paper describes the implementation of this travel diary survey, which was conducted in Karlsruhe and Halle, two German cities of about 270,000 inhabitants each, in the autumn of 1999. A total of 317 persons over 6 years of age in 139 households participated in the main study. The description covers the development of the forms, the design of the survey protocol, the screening experiences (including participation rates) and an assessment of the data quality in terms of item/unit non-response and reporting fatigue. The paper closes with an outlook for the analyses possible with the data set.
The project ILUMASS (Integrated Land-Use Modelling and Transportation System Simulation) aims at embedding a microscopic dynamic simulation model of urban traffic flows into a comprehensive model system incorporating changes of land use, the resulting changes in transport demand, and the impacts of transport on the environment. Microsimulation modules include models of demographic development, household formation, firm liefecycles, residential and non-residental construction, labour mobility on the regional labour market and household mobility on the regional housing market. These modules will be closely linked with the models of daily activity patterns and travel and goods movements modelled in the transport parts of ILUMASS. The acquisition of data of daily activity patterns are described in a new approach. The goal was to utilize a computerized hand-held survey instrument that allows the gathering of information from subjects at regular intervals as close in time to real decision points. The computerized platform, a handheld personal digital assistant (PDA), also enables the instrument to automatically trace and/or prompt for certain attributes of the decisions process. The ILUMASS project aims at intergrating modules to a complete modelling system. In the ILUMASS model system there are both uni-dimensional cause-effect realtionships as well as strongly linked connections between the activity reports of the surveyed individuals, the daily modules. Consistency between the data set thus necessitates consideration of the interactions between the modules. The solution of this problem therefore requires an iterative approach.
Using 6-week travel diary data from Karlsruhe and Halle, Germany, this study examines the characteristics of individuals' action space. The extension of action space is represented by the second moment of the activity locations that it contains. Day-to-day variation in the second moment is examined. The results show that out-of-home activity orientation and commitment (e.g., obligatory activities on weekdays and discretionary activities on weekend days) influence the extension of action space. For workers and students on weekdays, the spread of activity locations and the distance from home to the centroid of activity locations are relatively stable from day to day. A substantial portion of the variations in their action spaces is due to unexplained differences across individuals that remain stable over time for each individual (unobserved heterogeneity). In contrast, random factors have dominant influences on nonworkers' weekday action spaces and on all individuals' weekend action spaces.
The mass transit industry has long focused on regular riders, large numbers of trips, and daily commuting. Captive rider and transit dependency concepts are also common in the transit industry's view of its markets. However, recent research challenges these views, especially the frequency of use. Results from three sources are discussed: onboard surveys, fare structure changes, and the transit voucher (commuter check) fare subsidy plan. The findings suggest that infrequent riders are a critical transit market and perhaps the key to building transit ridership and revenues.
All cities in Europe struggle with the problems of urban sprawl and traffic congestion, yet mostly with little success. There is a growing awareness that market forces will continue to lead to ever more dispersed, energy-wasteful urban settlement patterns and that only a combination of land use policies, such as the promotion of higherdensity, mixed-use urban forms, and of transport policies to promote public transport and contain the automobile can free metropolitan areas from their increasing autodependency. It is therefore necessary to develop modelling approaches in which the two-way interaction between transport and land use is modelled (Alvanides et al., 2001).
Using 6-week travel diary data from Karlsruhe and Halle, Germany, this study examines the characteristics of individuals’ action space. The extension of action space is represented by the second moment of the activity locations that it contains. Day-to-day variation in the second moment is examined. The results show that out-of-home activity orientation and commitment (e.g., obligatory activities on weekdays and discretionary activities on weekend days) influence the extension of action space. For workers and students on weekdays, the spread of activity locations and the distance from home to the centroid of activity locations are relatively stable from day to day. A substantial portion of the variations in their action spaces is due to unexplained differences across individuals that remain stable over time for each individual (unobserved heterogeneity). In contrast, random factors have dominant influences on nonworkers’ weekday action spaces and on all individuals’ weekend action spaces.
Travelers commit themselves to particular behaviors through the ownership of cars and season tickets. They trade a large one-time payment for a low or zero marginal cost at the point of use. It can be assumed that these commitments influence travel behavior and future commitment situations. Apparently, none of the literature addresses the choice between the commitment to one or the other mode and its impacts on travel behavior as well as the temporal dimension. Models that use structural equation modeling to test a priori hypotheses on the paths linking car availability, ownership of a season ticket for public transportation, and modal usage during three different time periods are presented. Modal usage is operationalized as the number of trips by car and public transport. The models are based on two different panel surveys (in Germany and the Netherlands). The results show that there is a high degree of stability in car ownership and a relatively high degree of stability in season ticket ownership (for Germany only). The commitments influence modal usage, whereby the influence on one mode is higher than the influence on the other mode. The relationship between the two modes is a substitutive one.
Thema der Arbeit ist die kostenseitig beleuchtete Entscheidung des Verkehrsteilnehmers zwi-schen einem Privat-Pkw und der Einrichtung eines multimodalen Sets von Verkehrsmitteln. Dafür werden die Begriffe Intermodalität, Multimodalität, Selbstbeweglichkeit, Routinisierbarkeit in Verbindung mit Transaktionskosten und routinisierbaren Verkehrsmittelsets (Mobilutions) teilweise neu definiert bzw. eingeführt. Verkehrsmittelwahl vor jeder Fahrt ist eine ökonomistische Fiktion. Vielmehr richten sich Ver-kehrsteilnehmer mittelfristig Sets von Verkehrsmitteln ein (Mobilutions). Dabei spielt - neben den anderen bekannten Einflussgrößen - auch die Höhe der Gesamtkosten, zusammengesetzt aus Transaktions- und monetären Kosten eine Rolle. Aber nicht nur die Höhe ist ein Entschei-dungsparameter sondern auch der Grad ihrer Variabilität. Mobilutions mit hohen variablen Kos-tenanteilen scheiden auch dann aus, wenn sie insgesamt deutlich günstiger sind als solche mit hohen fixen Anteilen. Dies führt zu den beschriebenen sehr geringen Umsätzen pro Carsharing-Kunden und erklärt so unter anderem die unbefriedigende Größe des deutschen Carsharing-Marktes. Quantitative Durchbrüche bedürfen nicht nur einer nationalen Oberfläche, wie sie die Deutsche Bahn zur Zeit entwickelt, sondern auch einer multimodalen Kostenfunktion, die alle Modes einer multimodalen Mobilution integriert. Multimodale Mobilutions müssen den öffentlichen Verkehr inhaltlich und preislich integrieren, sie müssen höhere Autoanteile erheblich stärker rabattieren und damit erst ermöglichen, sie müs-sen für alle Nutzungsintensitäten preisgünstiger sein als der Privat-Pkw und dennoch für die Betreiber akzeptabel bleiben. Sie müssen wie der Privat-Pkw in allen Modes rückwirkend best priced sein und die spezifischen Vorteile von Multimodalität stärker betonen. Das Papier stellt eine multimodale Kostenfunktion vor, die diese Forderungen annähernd erfüllt. -- Examined from a cost perspective, this paper is about a consumer's choice between private automobiles and the installation of a multi-modal set of transportation means. Therefore, the terms inter-modality, multi-modality, self-locomotion, quotidian routine, in com-bination with the costs of transaction and routinized means of transportation (mobilutions), are partially introduced or defined in a new way. It is an economical fiction that before every journey there is a choice of means of transporta-tion; rather, users of transportation systems install for themselves, in a median time span, sets of transportation means, mobilutions. Among the other known items of influence, the combi-nation of total transaction and monetary costs plays a role. Not only is the total costs a parameter of choice, but also the degree to which costs vary. Mobilutions with a high propor-tion of variable costs are not considered even if, as a whole, they are noticeably cheaper than those which are fixed at a higher proportion. Eventually this leads to lower inevitable returns per carsharing customer and also explains, among other things, the dissatisfactory extent of the German carsharing market. Quantitative breakthroughs require not only a national interface, like that currently being developed by Deutsche Bahn, but also a multi-modal cost function integrating all modes of a multi-modal mobilution. Multi-modal mobilutions must integrate public transportation systems in content and in price. They must vigorously discount higher proportions of automobiles thereby making these higher proportions possible. Independent of consumers intensity of use, mobilutions should be better in price than a private automobile, yet remaining affordable for the operating company. Like the private automobile, mobilutions must be best priced in all modes and emphasize the specific benefits of multi-modalitiy more strenuously. This paper presents a multi-modal cost function that approximates the fulfillment of these demands.
The mass transit industry has long focused on regular riders, large numbers of trips, and daily commuting. Captive rider and transit dependency concepts are also common in the transit industry's view of its markets. However, recent research challenges these views, especially the frequency of use. Results from three sources are discussed: on-board surveys, fare structure changes, and the transit voucher (commuter check) fare subsidy plan. The findings suggest that infrequent riders are a critical transit market and perhaps the key to building transit ridership and revenues.
Findings from various sources of information, such as mobility panels, permanent road traffic recording devices, and model calculation methods, indicate that for the past few years passenger traffic demand in Germany has not been increasing but has shown notable evidence of stagnation. An analysis of various relevant factors clarifies that this has in fact been an emerging tendency. This is true especially with respect to the demographic changes in Germany and the increased probability of more scarce and definitely more expensive oil resources; further stagnation of traffic demand appears imminent That leads to the conclusion that infrastructure development must not be based on the idea of everlasting growth with expansions in areas in which the symptoms of traffic growth have been most obvious. It will rather be necessary to identify accurately specific areas of growth or stagnation and to find a suitable scale for further development. Future planning should concentrate on modification rather than on expansion of infrastructure facilities.
The focus of this paper is the degree to which day-to-day variability in the individual's travel pattern has a systematic, or nonrandom, component. We first review the different sources of variability in travel, emphasizing the difference between between-individual and within-individual variation and the implications of this difference for travel analysis. After discussing the impact of measurement (i.e. the way in which travel behavior is measured) on the study of repetition and variability, we use the Uppsala data to examine the level of systematic variability in an individual's longitudinal travel record. The analysis focuses on two questions:
- How well does observation over one week capture longer-term (five-week) travel behavior; in other words, is behavior highly repetitive from week to week?
- How systematic is within-individual variability; in other words, are certain stops distributed over the five-week record in a nonrandom, that is either regular or clustered, fashion?
Using measures of travel that include more than one stop attribute (e.g. activity, mode, time of day, and location), we found that:
- A seven-day record of travel does not capture most of the separate behaviors exhibited by the individual over a five-week period, but it does capture, for most people, a good sampling of the person's different typical daily travel patterns.
- Whereas a considerable portion of intraindividual variability is systematic (nonrandom), clustering is a more important source of nonrandom variation than is regularity.
The results suggest that behavior does not follow a weekly cycle closely enough for a one-week travel record to measure the longer-term frequency with which the individual makes certain stops or to assess the level of day-to-day variation present in the individual's record. Because these results are likely to reflect the particular measures of behavior we used, one conclusion of this study is the need for other studies that replicate the aims of this one but use a variety of other travel measures. Only through such additional work can we truly assess the sensitivity of our findings to measurement techniques.
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Die "neuen Multimodalen
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