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Does the joint implementation of hard and soft transportation policies lead to travel behavior change? An experimental analysis
Abstract
Over the last few decades, there has been a growing interest in a variety of travel demand management strategies, both hard and soft, aimed at persuading people to reduce their car use. However, only few studies employed predictive models to assess the effectiveness of soft interventions and understand the impact of both objective and socio-psychological variables on changes in travel behavior. Additionally, though a combination of hard and soft measures is recognized as achieving the best results in reducing car use, few studies differentiate between the effects of the two types. The aim of this work is to quantify the effect of a combination of hard (introduction of a new light railway line) and soft measures (Personalized Travel Plan program) among a group of car drivers in the metropolitan area of Cagliari (Sardinia, Italy). We used data collected before and after the implementation of a Personalized Travel Plan program, where a control group was identified to disentangle the effect of the hard from the soft measure. We specified and estimated an Integrated Choice and Laten Variable (ICLV) model to assess the effect of both objective characteristics and some socio-psychological variables on the choice to use a new light railway service or not. Model results point out that people who lived along the light rail corridor and received and read their Personalized Travel Plan were more likely to switch from car to the light rail. Furthermore, we found that the parameters associated with the psycho-social variables Attachment to the car and Dislike of public transport have a negative influence on the probability to use the new travel alternative. At the same time, our findings on the effect of the soft measure need to be interpreted with some caution as its impact on choice probability was mitigated by travel distance and psycho-social variables.
... Hence, it is possible that individuals who already adopt a sustainable travel behaviour are more willing to use a new LR service, leading to a transfer from the bus system to the LR system (Termida et al., 2016;Piras et al., 2018). Regarding car drivers, their lack of trust in the quality of public transit services may make them less interested in switching from the private vehicle to the light rail (Piras et al., 2022), even though the new means of transport offers a higher level of comfort than the bus and is faster than using a car. ...
... Moreover, several studies indicate that, beyond travel time and costs, individual and household characteristics also influence the decision to use light rail. It has been shown that car ownership negatively influences the propensity to use light rail (Senior, 2009;Creemers et al., 2012;Sottile et al., 2019;Piras et al., 2022). Concerning age, some authors found that younger individuals are more likely to start using a new light rail service (Creemers et al., 2012;Termida et al., 2016), while others did not detect a significant importance for this variable (Senior, 2009;Sottile et al., 2019;Piras et al., 2022). ...
... It has been shown that car ownership negatively influences the propensity to use light rail (Senior, 2009;Creemers et al., 2012;Sottile et al., 2019;Piras et al., 2022). Concerning age, some authors found that younger individuals are more likely to start using a new light rail service (Creemers et al., 2012;Termida et al., 2016), while others did not detect a significant importance for this variable (Senior, 2009;Sottile et al., 2019;Piras et al., 2022). Similarly, it seems that there is no significant difference between males and females in terms of light rail usage (Senior, 2009;Creemers et al., 2012;Termida et al., 2016;Piras et al., 2022). ...
In the last decade, Italian municipalities implemented urban light rail lines, yet forecasting passenger numbers often overlooks heterogeneity in individuals' characteristics and travel habits. For this reason, in the current paper, we investigate which factors influence the frequency of using a new light rail service. The data employed for the study come from a survey conducted in the city of Cagliari (Italy) among a sample of light rail users who started to use this means of transport after its introduction. The analysis reveals a preference for light rail due to factors like travel time and traffic stress over cost considerations. The construction of an ordered probit model highlights the significance of performance characteristics, prior modal choices, and socio-demographic variables in determining light rail usage frequency. The paper concludes with actionable recommendations: acknowledging individual heterogeneity in promoting light rail use, envisioning scenarios beyond infrastructure, and emphasizing monitoring to assess the new line's impact and formulate effective behavioural measures.
... Strategies are designed to change people's attitudes and intentions towards sustainable transport choices, with the goal of decreasing reliance on cars and f light and bringing about environmental advantages. Travel Behaviour Change Programmes often incorporate 'soft' transport interventions, which involve providing information about alternative transport options, as well as offering appropriate support, motivation and disincentives for car use (Piras et al., 2022). Travel Behaviour Change Programmes that focus on soft transport measures such as Personal Travel Planning, travel feedback programmes and persuasion strategies have been found to contribute to substantial decreases in car usage (García-Garcés et al., 2015;Skarin et al., 2017;Sottile et al., 2021). ...
Carbon emissions from transport are increasing on a global scale with transport accounting for the largest portion. While there is greater awareness of climate change, few people are willing to change their travel habits. To gain a better understanding of this phenomenon, we were motivated to conduct a study to identify the obstacles encountered by travellers when opting for sustainable transport modes, assess their knowledge of carbon emissions from various transport modes and explore how this knowledge affects their decision-making process. The study employed mixed-method approaches, including the use of informational videos and gamified quizzes for intervention, pre-and post-intervention knowledge assessment tests, semi-structured interviews, observations, Self-Assessment Manikin for emotion measures and card sorting. The findings primarily derived from thematic analysis indicated that knowledge-based intervention can be a strong catalyst to encourage sustainable attitude and behaviour change. Insights so gained have important theoretical and practical implications for designing sustainable behaviour change in the future, especially the deployment of persuasive technology.
... In a slightly related work on a different modal switch, Piras et al. (2022) indicate that living along the light rail corridor and receiving and reading Personalized Travel Plans increased the likelihood of switching from car to light rail. Additionally, psycho-social variables such as "Attachment to the" car and "Dislike of public transport" negatively affected the probability of choosing the new travel option. ...
... Whether it is push 22 or pull, MM strategies can be implemented through hard measures or soft measures (Brög et al., 23 2009). Hard measures, also termed structural measures (Piras et al., 2022), involve altering the 24 physical and/or legislative context to influence decision-making. For instance, this could entail 25 constructing a new metro line or implementing park and ride facilities (referred to as 'hard-pull'), 26 or temporarily closing or modifying roads (known as 'hard-push') (Fujii et al., 2001). ...
Passenger transport is an important contributor to unsustainable urban systems. To achieve the necessary socio-ecological transition will require overcoming the entrenched system of automobility. Composed of several mutually reinforcing components, this system has conferred psychosocial dimensions to car ownership and use that leads to important institutional, political, and individual resistance to change both car-centric transportation infrastructure and individual travel behaviour. For this reason, a growing consensus suggests that transitioning to a sustainable mobility system requires a more holistic approach that applies a synergistic integration of “hard” supply-side measures and “soft” demand-side solutions. This means increasing non-automobile accessibility and supporting such change with soft travel behaviour change solutions that target social-psychological barriers to change. While such approaches have demonstrated their effectiveness around the world, this second category of interventions remains underutilized, particularly in North America. Drawing from social psychology and a North American case study, this chapter proposes a theory-to-practice guide for practitioners to designing effective voluntary travel behaviour change interventions based on the Stage Model of Self-Regulated Behaviour Change (SSBC). A four-level integration framework for intervention design based on the SSBC is proposed. The framework proposes intervention approaches from using the model as a simple diagnostic tool to a complete integration to deliver a fully individualized and stage-tailored intervention. Stage-specific messages and strategies are described to shift people away from car use towards active, collective, and shared mobility options. The chapter concludes on suggestions for collaborative efforts between researchers and practitioners to design, evaluate, and enhance the effectiveness of these interventions, thus moving beyond infrastructure-only solutions to foster a successful transition to sustainable mobility in Québec.
Sejalan dengan pertumbuhan penduduk DKI Jakarta yang semakin meningkat maka semakin meningkat pula kebutuhan akan layanan transportasi. Hal ini mengakibatkan terdapat pertumbuhan jumlah kendaraan bermotor, tingginya jumlah kendaraan pribadi di DKI Jakarta menyebabkan banyaknya permasalahan transportasi sekaligus akan meningkatkan jumlah zat pencemar udara yang dapat mencemari udara dan berdampak negatif terhadap lingkungan dan kualitas udara, salah satu kebijakan pemerintah yang mulai diterapkan untuk mengurangi penggunaan kendaraan pribadi sekaligus mengurangi polusi udara adalah penerapan kebijakan Low Emission Zone (LEZ).di Kawasan Kotatua Jakarta, dengan adanya kebijakan ini diharapkan masyarakat akan mengurangi penggunaan kendaraan pribadi untuk melakukan perjalanan dan berpindah menggunakan transportasi umum. Penelitian ini bertujuan untuk mengetahui pengaruh penerapan kebijakan LEZ terhadap perubahan perilaku perjalanan dan untuk mengidentifikasi faktor – faktor yang mempengaruhi preferensi pemilihan moda menggunakan metode regresi logit biner, setelah pemberlakuan kebijakan LEZ terdapat perubahan perilaku perjalanan komuter di kawasan Kotatua antara lain persentase responden yang berasal dari Jakarta Barat meningkat sebesar 7%, terdapat perpindahan moda dominan yang digunakan responden dari kendaraan pribadi ke transportasi umum sebanyak 21,42% terlihat pula bahwa pemakaian kendaraan pribadi seperti sepeda motor dan mobil pribadi menurun dari 62,14% menjadi 42,85%, terdapat penurunan biaya perjalanan dan peningkatan waktu perjalanan sedangkan faktor – faktor yang mempengaruhi preferensi pemilihan moda antara lain tingkat pendidikan, pendapatan, pola perjalanan dan biaya perjalanan.
The dramatic experience due to COVID-19 spread has reshaped travel preferences of public transport (PT) users worldwide, especially in urban areas. As the PT is expected to recover its major role in such areas, it is important to understand the factors influencing PT users’ willingness to pay (WTP) for onboard safety measures, in the event of future pandemic scenarios. Furthermore, both individual latent traits (e.g. concern for the pandemic, trust/distrust in city services and national government actions) and perceived entity of the pandemic are expected to influence preferences for PT users under such a post-pandemic scenario. This paper analyses the preferences and attitudes of PT users in the Naples metropolitan area (Italy) through a hybrid choice model (HCM). First, WTPs for onboard service features are assessed in three hypothetical pandemic alert scenarios, which are explicitly introduced in the model as context variables. Second, the model allows for assessing the relative importance of onboard characteristics as the pandemic scenario evolves. Third, the model incorporates psycho-attitudinal variables and shows how they impact WTPs. Finally, several policy implications for policymakers and transport companies operating in the study area are derived. In particular: (a) WTPs for increased/reduced occupancy rate and green pass check at the entrance significantly depend upon the latent traits investigated; (b) relative importance of safety measures varies significantly between the pandemic alert scenarios; (c) possible ticketing strategies for PT users have been investigated based on the HCM findings, searching for the configuration of safety measures to ensure that users accept a 100% allowed capacity on board during moderate/high pandemic scenarios without varying the price, as well as the price variations needed to stay in an indifference range of the utility in restricted conditions of the service; (d) the acceptability of safety measures has been assessed through a simulation exercise, finding that non-vaccinated travellers are 2.6 and 2.1 times more willing to accept a full capacity of the buses/trains on board than vaccinated people if subscribers or not, respectively.
App-based persuasive technologies emerged as promising tools to promote sustainable travel behavior. However, the opt-in, self-selection framework characterizing their use in real-life conditions might actually lead to wrongly estimate their potential and actual impact in analyses that do not rely on strict randomized controlled trials. To investigate evidence of such biases, we analyze mobility data gathered from users of a persuasive app promoting public transport and active mobility launched in 2018 in Bellinzona (Switzerland). We consider the users' baseline mobility data: km per day (total and by car) traveled during the app validation period, when behavior change motivational features were not enabled. To estimate the possible self-selection bias, we compare these data with the reference population, using data from the Swiss Mobility and Transport Census; to study the possible attrition bias, we look at the relations between baseline mobility and the number of weeks of app's active use. We find evidence of neither self-selection nor critical attrition biases. This strengthens findings by earlier non RCT-based analyses and confirms the relevance of app-based persuasive technologies for mobility behavior change.
Private car dependence has adverse effects on both mental and physical health. Travel Demand Management (TDM) strategies are adopted by public and private organizations to encourage users to switch to sustainable transport for daily commutes. Originally, mobility management only focused on employees, but recently various universities worldwide have started to adopt policies specifically targeted at students. To improve their effectiveness, an innovative approach to TDM based on a GPS smartphone application is tested at University of RomaTre (Italy) on a sample of students. The strategy identifies the integration of all mobility management activities into one smartphone application called IPET (Individual Persuasive Eco-Travel Technology). The IPET enables activity-travel data collection and automatic processing, personalized information provision, use of persuasive technology and implementation of a gamification system. Two questionnaires were conducted before-and-after the measure, to assess its effects on psycho-attitudinal factors. The main objective of the investigation was to analyze individuals’ voluntary behavioral changes with regard to travel mode and everyday behavior following implementation of a personalized travel plan. Data analysis indicated a strong positive change in the level of awareness regarding CO2 emissions and calories burnt after the program implementation. Comparison of GPS data collected in the first and second weeks showed, for those who received a personalized travel plan, an 8.1% shift towards more sustainable transport modes.
Soft travel interventions are generally regarded as effective measures for reducing personal car use. However, doubts about the validity of such claims are raised, primarily fueled by the low methodological quality of evaluation studies upon which such conclusions are based and by the fact that the literature is, for the most part, narratively synthesized. The present systematic review addresses these critiques by investigating the effect of soft interventions on car use through a meta-analysis, which includes only experimental and well-controlled quasi-experimental studies. Results revealed that interventions (k = 41) lead to a significant reduction of 7% in car modal split share (Hedges' g = 0.163). Moderators of interventions' effectiveness were investigated in a meta-regression. Effectiveness was moderated by the type of intervention and by the main psychological variable targeted by the interventions, whereas the other studied moderators (i.e. residential relocation of participants, study design, percentage of females in the study, the presence of incentives, passed time to follow-up, interventions' measurement instrument, city size in which interventions were applied and the setting where they were conducted) were non-significant. Limitations of the present findings, together with implications for policy and practice are discussed.
To date, although young adults seem to be less interested in obtaining a driving licence and a car, in several countries private vehicles are still the most widely used means of transport. The growing interest in promoting sustainable modes has motivated Mobility Managers (MM) to start awareness campaigns among workers who commute by private vehicle. University students, who regularly commute to their campuses, represent a major segment of the working population. Nevertheless, few studies have investigated university students' commuting habits.
The aim of this work is to examine in depth the tour-based university students’ travel behaviour, using panel data from a sample of students from Roma Tre University.
The contribution of this paper consists in understanding the processes underlying daily travel behaviour choices, to help MMs identify the right steps to promote a correct culture and to improve public transport (PT) services.
The data used were collected during a panel survey, split into two 2-days phases. Between the two phases, a personalised travel plan was provided in order to promote more sustainable means of transport. Daily travel behaviour was monitored by collecting data through a smartphone app which registered trips/activities in real-time. The data were then exhaustively analysed by classifying the tours into many layers (main purpose, time of day, mode used and user attributes).
Analysis of the results indicate how the preferred mode of transport is the private mode and that 25% of tours were related to non-study purposes, which could be a barrier to PT mode choice.
Across five field experiments with employees of a large organization (n = 68,915), we examined whether standard behavioural interventions (‘nudges’) successfully reduced single-occupancy vehicle commutes. In Studies 1 and 2, we sent letters and emails with nudges designed to increase carpooling. These interventions failed to increase carpool sign-up or usage. In Studies 3a and 4, we examined the efficacy of other well-established behavioural interventions: non-cash incentives and personalized travel plans. Again, we found no positive effect of these interventions. Across studies, effect sizes ranged from Cohen’s d = −0.01 to d = 0.05. Equivalence testing, using study-specific smallest effect sizes of interest, revealed that the treatment effects observed in four out of five of our experiments were statistically equivalent to zero (P < 0.04). The failure of these well-powered experiments designed to nudge commuting behaviour highlights both the difficulty of changing commuter behaviour and the importance of publishing null results to build cumulative knowledge about how to encourage sustainable travel. Kristal and Whillans conducted five field experiments (n = 68,915) designed to increase sustainable commuting using standard behavioural science tools. The interventions’ failures highlight the difficulty of changing commuter behaviour using this approach.
This paper presents a trial aimed at reducing parking demand at a large urban employer through an informational campaign and monetary incentives. A 6-week randomized controlled trial was conducted with (N = 2000) employee commuters at the Massachusetts Institute of Technology, all of whom frequently drove to campus. Split into four arms of five hundred each, one group received weekly informational emails highlighting MIT’s various new transportation benefits; a second group received monetary rewards for reducing their frequency of parking; a third group received both interventions, while a control group was monitored with no intervention. The paper aims to examine how behavioral incentives, namely targeted information provision and monetary rewards, can be used independently or in combination to encourage alternatives to drive-alone commuting. Success was measured as the extent to which drivers decreased their frequency of parking and increased their use of alternative modes during and after the campaign. While the combined treatment group contained the highest number of top-performing participants, no statistically significant differences-in-differences were observed amongst the treatment arms compared to the control. A post-experiment survey indicated a widespread increase in awareness of employer transportation benefits, and a much larger stated shift from driving towards transit than was supported by passively-collected data. Survey results suggested that while intent to reduce car use existed, complaints of insufficient quality of transit service and relative convenience of driving suppressed modal shifts. Most importantly, the discrepancy between self-reported and actual behavior change highlights important limitations and biases of survey-based travel behavior research.
The present field study investigates the reduction of car use through a voluntary travel behavior intervention program that provides participants with temporary free public transportation. Three factors – self-efficacy, social support and satisfaction – have previously been shown to be important for behavior change during physical activity intervention programs. In travel behavior interventions, however, these factors have often been studied individually and less is known about their combined effects on travel behavior change. Furthermore, while motives for participating in travel behavior interventions have been frequently studied within travel behavior interventions research, there is a lack of studies investigating the influence of motives on travel behavior change. To better understand the importance of different motives as well as the importance of self-efficacy, social support, and satisfaction with travel on behavior change, a series of surveys were administered to 181 participants before, during, and after their participation in a voluntary travel behavior intervention. The results show that greater self-efficacy and social support during the intervention led to greater travel behavior change. These results indicate that in order to gain better results from travel behavior interventions, individuals should be helped to increase their travel-related self-efficacy, and significant others should be involved to provide social support. We discuss possible ways of accomplishing this.
Travel behavior change programs (TBCP) are measures or actions specifically designed to motivate people to reduce their car use. This study contributes to the literature on rigorous evaluation of the effects of participating in TBCP. In particular, we analyze the effect of TBCP in relation to a potential for a reduction in car use. In contrast to previous studies, the effect of TBCP is analyzed considering two characteristics of the trip: travel companion and time of trip. The TBCP, consisting of three persuasion actions customized to the needs of the participants in the research, was designed and implemented in Valencia (Spain). To evaluate the effects of participating in the TBCP, a two-wave panel survey was carried out, which collected activity-travel scheduling process data from participants before and after the implementation of the TBCP. To properly identify the effects of taking part in the TBCP, participants were divided into two groups: those who were involved in the TBCP, and those who did not participate in any persuasion action (control group, CG). Descriptive and confirmatory analysis, which included the use of sample selection panel data models with treatment effects and random parameters, have been developed. The results suggest that the individuals most affected by participating in TBCP are those carrying out solo and afternoon trips. If traveling with companions, those who do so with household members are more influenced by participating in TBCP. Important research and transportation policy implications are derived from these results.
Urban sprawl is pervasive in Australian cities arising from the low density development of dwellings with the consequence that private vehicle use dominates daily travel in Australia. This paper examines a community based social marketing program, TravelSmart, which targeted reducing vehicle kilometres travelled as part of a transport demand management strategy. This paper uses 3-year panel data collected by GPS tracking and a conventional survey methodology in northern Adelaide, South Australia, to examine whether TravelSmart had a sustained impact and whether this was impacted by socio-economic and built-environment factors. A latent growth model is employed and demonstrates TravelSmart led to a declining trend in private car driving over the 3 years at both individual and household levels with effects being sustained beyond 1 year and up to 2 years. There is some evidence of compensatory behaviour between household members. Socio-demographic factors are significant with males decreasing their driving times faster than females. Built environment impacts were also significant with different levels of walkability showing different trajectories in the reduction of car trips after the implementation of TravelSmart, suggesting social marketing interventions work better when supported by hard policies such as a supportive built environment.
This paper uses a before-after, experimental-control group method to evaluate the impacts of the newly opened Expo light rail transit line in Los Angeles on personal vehicle greenhouse gas (GHG) emissions. We applied the California Air Resources Board's EMFAC 2011 emission model to estimate the amount of daily average CO2 emissions from personal vehicle travel for 160 households across two waves, before and after the light rail opened. The 160 households were part of an experimental-control group research design. Approximately half of the households live within a half-mile of new Expo light rail stations (the experimental group) and the balance of the sampled households live beyond a half-mile from Expo light rail stations (the control group). Households tracked odometer mileage for all household vehicles for seven days in two sample waves, before the Expo Line opened (fall, 2011) and after the Expo Line opened (fall, 2012). Our analysis indicates that opening the Expo Line had a statistically significant impact on average daily CO2 emissions from motor vehicles. We found that the CO2 emission of households who reside within a half-mile of an Expo Line station was 27.17 percent smaller than those living more than a half-mile from a station after the opening of the light rail, while no significant difference exists before the opening. A difference-in-difference model suggests that the opening of the Expo Line is associated with 3,145 g less of household vehicle CO2 emissions per day as a treatment effect. A sensitivity analysis indicates that the emission reduction effect is also present when the experimental group of households is redefined to be those living within a kilometer from the new light rail stations.
Integrated Choice and Latent Variable (ICLV) models are an increasingly popular extension to discrete choice models that attempt explicitly to model the cognitive process underlying the formation of any choice. This study was born from the discovery that an ICLV model can in many cases be reduced to a choice model without latent variables that fits the choice data at least as well as the original ICLV model from which it was obtained. The failure of past studies to recognize this fact raised concerns about other benefits that have been claimed with regards to the framework. With the objective of addressing these concerns, this study undertakes a systematic comparison between the ICLV model and an appropriately specified reduced form choice model. We derive analytical proofs regarding the benefits of the framework and use synthetic datasets to corroborate any conclusions drawn from the analytical proofs. We find that the ICLV model can under certain conditions lead to an improvement in the analyst's ability to predict outcomes to the choice data, allow for the identification of structural relationships between observable and latent variables, correct for bias arising from omitted variables and measurement error, reduce the variance of parameter estimates, and abet practice and policy, all in ways that would not be possible using the reduced form choice model. We synthesize these findings into a general process of evaluation that can be used to assess what gains, if any, might be had from developing an ICLV model in a particular empirical context.
Social marketing and the built environment are two important ‘tools’ to manage travel demand which have had significant attention in the literature separately. Most previous studies evaluating the effects of social marketing programs have relied on pre- and post- surveys, using self-reported measures without any objective measures of travel behaviour change. Further, there is a lack of evidence on whether the effects of the built environment are synergistic when combined with other intervention programs, such as social marketing programs. This study contributes by quantitatively evaluating the relative and combined effects of the TravelSmart and the built environment on travel behaviour using objective GPS measurements. Between 2012 and 2014, daily travel data were collected using GPS equipment in suburbs of inner northern Adelaide, South Australia. Individuals in the households aged over 14 carried a portable GPS device everywhere for a period of 15 days during March–May in each year from 2012 to 2014, providing a total of three waves of panel data. The empirical analysis suggests that the TravelSmart program as a ‘treatment’ significantly reduced the car trips soon after implementation with longer term effects on reducing car trips in high-walkable neighbourhoods. For walking and bus trips, the TravelSmart program increased these 1 year after the ‘treatment’ with stronger effects on travel behaviour change for the participants living in high-walkable neighbourhoods than for those living in low-walkable neighbourhoods.
This paper introduces the R package lavaan.survey, a user-friendly interface to design-based complex survey analysis of structural equation models (SEMs). By leveraging existing code in the lavaan and survey packages, the lavaan.survey package allows for SEM analyses of stratified, clustered, and weighted data, as well as multiply imputed complex survey data. lavaan.survey provides several features such as SEMs with replicate weights, a variety of resampling techniques for complex samples, and finite population corrections, features that should prove useful for SEM practitioners faced with the common situation of a sample that is not iid.
New transport infrastructure may promote a shift towards active travel, thereby improving population health. The purpose of this study was to determine the effect of a major transport infrastructure project on commuters' mode of travel, trip frequency and distance travelled to work.
Quasi-experimental analysis nested within a cohort study of 470 adults working in Cambridge, UK. The intervention consisted of the opening of a guided busway with a path for walking and cycling in 2011. Exposure to the intervention was defined as the negative of the square root of the shortest distance from home to busway. The outcome measures were changes in commute mode share and number of commute trips - both based on a seven-day travel-to-work record collected before (2009) and after (2012) the intervention - and change in objective commute distance. The mode share outcomes were changes in the proportions of trips (i) involving any active travel, (ii) involving any public transport, and (iii) made entirely by car. Separate multinomial regression models were estimated adjusting for commute and sociodemographic characteristics, residential settlement size and life events.
Proximity to the busway predicted an increased likelihood of a large (>30 %) increase in the share of commute trips involving any active travel (relative risk ratio [RRR] 1.80, 95 % CI 1.27, 2.55) and a large decrease in the share of trips made entirely by car (RRR 2.09, 95 % CI 1.35, 3.21), as well as a lower likelihood of a small (<30 %) reduction in the share of trips involving any active travel (RRR 0.47, 95 % CI 0.28, 0.81). It was not associated with changes in the share of commute trips involving any public transport, the number of commute trips, or commute distance.
The new infrastructure promoted an increase in the share of commuting trips involving active travel and a decrease in the share made entirely by car. Further analysis will show the extent to which the changes in commute mode share were translated into an increase in time spent in active commuting and consequent health gain.
The introduction of new public transport systems can influence society in a multitude of ways, ranging from modal choices and the environment to economic growth. This paper examines the determinants of light rail mode choice for medium/long distance trips (10-40km) for a new light rail system in Flanders, Belgium. To investigate these choices, the effects of various transport system specific factors (i.e. travel cost, in-vehicle travel time, transit punctuality, waiting time, access/egress time, transfers, and the availability of empty seats) as well as the travelers‟ personal traits, are analyzed using an alternating logistic regression model, which explicitly takes into account the correlated responses for binary data. The data used for the analysis stem from a stated preference survey which was conducted in Flanders, Belgium. The modeling results yield findings that are in line with literature: most transport system specific factors as well as socioeconomic variables, attitudinal factors, perceptions and the frequency of using public transport contribute significantly to the preference of light rail transit. In particular, it is shown that the use of light rail is strongly influenced by travel cost and in-vehicle travel time and to a lesser extent by waiting and access/egress time. It also appeared that seat availability plays a more important role than transfers in the decision process to choose light rail transit. The findings of this paper can be used by policy makers as a frame of reference to make light rail transit more successful.
The need to reduce private vehicle use has led to the development of soft measures aimed at re-educating car users through information processes that raise their awareness regarding the benefits of environmentally friendly modes, encouraging them to voluntarily change their mode choice behaviour (level-of-service characteristics being equal). It has been observed, though not scientifically demonstrated, that these measures can produce changes, being the result of mindful decisions. However, in some cases, as demonstrated by numerous environmental psychology studies such measures are not sufficient to overcome the effect of cognitive dissonance, one of the main factors hindering change. In fact it is not unusual to find discrepancies between attitudes and behaviour in travel behaviour research.
In this paper we analyze individual propensity to voluntary travel behavior change combining concepts from theory of change with the methodologies deriving from behavioral models. In particular, following the theory of voluntary changes, we set up a two-week panel survey including soft measure implementation, which consisted of providing car users with a personalized travel plan after the first week of observation (before) and using the second week to monitoring the post-behavior (after). These data have then been used to estimate a Mixed Logit for the choice to use a personal vehicle or a light metro; and a Multinomial Logit for the decision to change behavior. Results from both models show the relevance of providing information about available alternatives to individuals while promoting voluntary travel behavioral change.
A review of implementations of soft transport policy measures (Richter et al. 201041.
Richter J ,
Friman M ,
Gärling T . 2010 . Review of Implementations of Soft Transport Policy Measures . Transportation: Theory & Application 2 : 5 – 18 . View all references) demonstrates their effectiveness in motivating car users to voluntarily switch travel mode. This paper review research that addresses questions of when and why these measures are effective. Long-term effects, synergies between soft and hard policy measures, assessments of individual differences, differences in determinants of participation and of changes in travel, generalizability, and nonquantified benefits are gaps in knowledge. Knowledge is also lacking about the cost-effectiveness of techniques such as providing motivational support to set change goals, requesting plans for how to change, and providing customized information.
Policy-makers are interested in cost-effective and socially acceptable ways of encouraging the public to adopt more environmentally-friendly lifestyles. One area which UK policy-makers are focussing on is ‘catalyst behaviour’, the notion that taking-up a new behaviour (such as recycling) may cause people to adopt other pro-environmental behaviours. Yet, evidence for such ‘spill-over’ effects is so far limited, and it is unclear when and how cross-situational motivations (e.g., pro-environmental identity) may predict behaviour and when contextual factors are more important. We report on a postal survey (N = 551) of pro-environmental behaviours amongst the UK public. We assess the influence of pro-environmental self-identify on consistency across a range of behaviours. Pro-environmental values, perceived behavioural control, subjective norm, attitudes, and demographic factors were also measured. Findings show self-identity to be a significant behavioural determinant over and above theory of planned behaviour variables for carbon offsetting behaviour. However, pro-environmental self-identity was only a significant predictor for certain other pro-environmental behaviours; background variables were also important predictors. Limitations of the study, and implications for theory and policy, are discussed.
In recent years, there has been a growing interest in a range of transport policy initiatives which are designed to influence people’s travel behaviour away from single‐occupancy car use and towards more benign and efficient options, through a combination of marketing, information, incentives and tailored new services. In transport policy discussions, these are now widely described as ‘soft’ factor interventions or ‘smarter choice’ measures or ‘mobility management’ tools. In 2004, the UK Department for Transport commissioned a major study to examine whether large‐scale programmes of these measures could potentially deliver substantial cuts in car use. The purpose of this article is to clarify the approach taken in the study, the types of evidence reviewed and the overall conclusions reached. In summary, the results suggested that, within approximately ten years, smarter choice measures have the potential to reduce national traffic levels by about 11%, with reductions of up to 21% of peak period urban traffic. Moreover, they represent relatively good value for money, with schemes potentially generating benefit:cost ratios which are in excess of 10:1. The central conclusion of the study was that such measures could play a very significant role in addressing traffic, given the right support and policy context.
Relying on the theory of planned behavior (Ajzen, 1991), a longitudinal study investigated the effects of an intervention-introduction of a prepaid bus ticket-on increased bus use among college students. In this context, the logic of the proposition that past behavior is the best predictor of later behavior was also examined. The intervention was found to influence attitudes toward bus use, subjective norms, and perceptions of behavioral control and, consistent with the theory, to affect intentions and behavior in the desired direction. Furthermore, the theory afforded accurate prediction of intention and behavior both before and after the intervention. In contrast, a measure of past behavior improved prediction of travel mode prior to the intervention, but lost its predictive utility for behavior following the intervention. In a test of the proposition that the effect of past on later behavior is due to habit formation, an independent measure of habit failed to mediate the effects of past on later behavior. It is concluded that choice of travel mode is largely a reasoned decision; that this decision can be affected by interventions that produce change in attitudes, subjective norms, and perceptions of behavioral control; and that past travel choice contributes to the prediction of later behavior only if circumstances remain relatively stable.
It has been claimed that travel feedback programs (TFPs) are effective in reducing levels of car-use related congestion, noise, and air pollution. This article examines this claim, noting that many evaluation studies have employed less than optimal research designs. Demonstrating the use of meta-analysis, the results of 15 Japanese TFPs show that the effect sizes estimated for the frequently used research design lacking adequate control groups differ from the effect sizes estimated for research designs including adequate control groups. In addition, estimates of the homogeneity of treatment effects appear to differ, thus suggesting that inferences of causes of the effectiveness of a TFP vary with research design.
This paper reviews the voluntary travel behaviour change programs adopted across Australia in recent times. These programs facilitate individuals and households in changing their travel behaviour through personal choice and individual action. The paper examines the issues relating to the various programs and discusses the techniques used and the results and evaluations. The behaviour change programs reported to date show consistent evidence that participating households make substantial reductions in their usage of private motor vehicles. Further, a range of non-transport benefits have been found, albeit at the local level. These benefits include changes in land use, social interaction, economic development, and health indicators. One consequence is that other government agencies, responsible for areas such as community development, health, environment, energy, public safety, planning and even education, have begun to form interests in the programs. Two key challenges have emerged: (1) the actual measurement tools are hard to implement or the changes are on a scale smaller than that at which measurement is usually made, and (2) the clients for travel behaviour change programs have to date been transport organisations for whom the only relevant outcome is travel change. Given that these projects may be of value to other interested organisations, it may be that ultimately travel behaviour change may be intimately linked with all aspects of community life, which could lead to greater change, and certainly to greater sustainability.
This paper outlines a new approach to reducing car use in order to address environmental concerns. The individual action program, known as Travel Blending®, involves participating households being sent a series of four kits, containing information booklets and travel diaries, over a nine-week period. The travel diaries are analysed and a summary of the household’s travel patterns, and the emissions produced by their vehicles, is sent back in a subsequent kit along with suggestions explaining how they could reduce vehicle use. Households complete another set of travel diaries after four weeks and these are analysed so that a comparative summary can be returned to the household with the final kit. The paper describes results from two Australian studies. The first, a pilot study, involving about 50 individuals, was undertaken in Sydney, Australia. The second study involved about 100 households from Adelaide, Australia. Quantitative results from the Adelaide study indicate about a 10% reduction in car driver kilometres with a slightly higher percentage reductions in car driver trips and total hours spent in the car. These results, while very encouraging, must be interpreted cautiously. Further research will be required to explore the generalisability and magnitude of the effect of the Travel Blending® Program on travel behaviour.
Reducing car traffic to improve transport sustainability has become a major goal of transport policy. This is also the case in Hong Kong where car ownership and use is, by international standards, very low. The objectives here are first to determine why people own cars in Hong Kong, second to explore how dependent car owners are on their cars and third to identify the policy implications. Based on a survey of 401 car owners, the finding is that despite the existence of excellent public transport, once a car has been acquired, people become dependent on it for virtually all journey purposes. To achieve greater sustainability, therefore, car ownership and use must be controlled.
This paper reviews the literature on travel feedback programs (TFPs). These constitute soft measures designed to change travel behaviour, mainly from automobile to non-automobile travel, in mobility management. We classified TFPs according to place, technique, procedure, and communication media, and reviewed the effectiveness of 10 TFPs in Japan. We found that TFPs in Japan reduced CO2 emissions by about 19% and car use by about 18%, while increasing the use of public transport by about 50%. In addition, we found that TFP effectiveness increased when participants were asked to make behavioural plans to change their travel behaviour.
The development of effective behavioral change strategies that will convince individuals to voluntarily switch to pro-environmental travel behavior is a core research area for transportation and environmental experts. Personalized travel planning (PTP) is considered as an effective approach that encourages individuals to adopt environmental-friendly travel choices. This paper describes the design and implementation of a PTP intervention that was developed based on the persuasive techniques and the stage model of self-regulated behavior change (SSBC). Based on the recorded travel diary of the individuals, web-based customized pro-environmental travel plans were suggested along with pro-environmental and pro-healthy impacts. The effectiveness was assessed by comparing the travel behavior along with consequences before and after the implemented intervention. Significant differences were observed in an individual travel behavior regarding car dependency and active mobility with an effect size of 0.28 and 0.45 (Cohen’s d) respectively. On an average, 4.25 percentage points decrease in CO2 emission and 6.10 percentage point increase in physical activity level per individual was found due to their change in travel behavior. Stage analysis of the individual travel behavior revealed that the implemented PTP intervention triggers an individual’s transition towards more action-oriented stages in this behavior change process. Based on the results, it is concluded that intervention is effective to promote pro-environmental and pro-healthy travel choices and can bring higher benefits when implemented on a broader level.
In the framework of “smart city” strategies, cities worldwide are exploiting information and communication technologies to support their efforts in reducing car use. Recent understandings of smart cities suggest to put people at the core, by actively engaging citizens in co-creating innovative urban services. However, notwithstanding
the benefits of participatory processes in urban governance are well-known, low receptiveness towards them is still widespread, which can largely hinder the effectiveness of smart city interventions.
In this paper we present the experience of a Swiss city, where, against a past background of resistance to participatory processes, a “living” lab experiment was set for the co-creation of a behavior change app targeting a reduction in car-based traffic. Motivated by the opportunity to overcome limitations of such apps, the lab turned out to deliver wider benefits than initially expected. In fact, the concrete experience of new mobility behaviors stimulated by the app activated a collective learning process among lab participants. Discussions in the lab thus spontaneously scaled-up to far-reaching and potentially conflicting topics about future mobility scenarios. Furthermore, the protected space of the lab favored strengthening of reciprocal trust between citizens and policy-makers, opening-up possibilities for adoption of participatory practices also in future decision-making.
In recent years, persuasive interventions delivered through mobility-management tools have received attention as a means to motivate change for sustainable urban mobility. This paper aims to pinpoint and understand the drivers that influence individuals’ travel decisions when using travel apps including both travel information and persuasive features. The analytical framework relies on Lindenberg’s goal-framing theory in which individuals’ motives for using travel apps are grouped into three overarching goals, namely (1) gain, (2) hedonic and (3) normative goal-frames. Furthermore, technophile attitude, community trust and place attachment are incorporated into the framework in order to better explain user-sided heterogeneity. The case-study focuses on the travel information system under development in Copenhagen (Denmark). It questions 822 Danish travelers by distributing a technology-use preference survey. Structural equation models revealed that the choice motivators are specific to individual users and depend on wide ranging factors that go beyond traditional economic and socio-demographic methods. The study reveals: (i) trip efficiency improvement, enjoyment, social interaction and environmentally-friendly travel promotion are important motives for using the new app; (ii) individuals have different intentions according to the perceived value of the new information system as well as travel purposes; (iii) technophile attitude exerts a positive influence on both user motives and use intention; (iv) social and institutional trust in addition to an affective bond to the city influence the use intention of the travel app.
Although many studies explore built environment (BE) effects on commuting behavior, most overlook BE characteristics at workplace locations and their non-linear impacts. More importantly , limited effort is placed on the integrative effects of the BE and transportation policies. Using the data in Washington, D.C., this study applies a gradient boosting logit model to examine the influences of BE characteristics at both residential and workplace locations and commuting programs (transit/vanpooling subsidies and parking provision) on commute mode choice. We found that BE variables collectively contribute to 65% of the predicting power for mode choice. Although workplace BE variables are more important than residential BE elements, the difference is mainly due to distance to CBD (central business district). Furthermore, most variables show non-linear effects on car mode choice. There are also synergistic effects between BE variables and parking policy and between BE variables and transit/vanpooling subsidies. Therefore, land use policies will be more effective where supportive transportation policies exist.
The demand for urban mobility has seen a marked increase over the last few decades. The expansion of road networks, the increase in distance traveled and in the number of trips made has created car-dependent cities. One solution for achieving the switch from private car to public transport is the introduction of a new public transport line that improves the accessibility and frequency of service. However, an important behavioral process underpins travel choices and often a change in choice context is not sufficient to evoke behavior change. Voluntary travel behavior change (VTBC) programs were developed for heightening individuals’ awareness of the consequences of their travel choices and of the benefits to be reaped from using sustainable alternatives, through information provision and communication strategies. The objective of the present paper is to analyze the effect on travel mode choice of introducing a new light railway line into the choice set (hard measure) when implementing a VTBC program on a large scale, but with a high degree of personalization (soft measure). Although numerous studies have demonstrated the need to implement a (hard) measure that acts on the choice context, in combination with a (soft) measure that acts in a personalized way on demand, as far as the authors are aware, few have put this into practice. The first results have shown that a combination of hard and soft measures is more effective in evoking travel behavior change and personalized travel plans produce better results than generalized soft measures.
Personalized travel plans have been regarded as potentially effective soft measures in mobility management. This research conducted a randomized social experiment aiming at citizen car-use reduction, and examined the effect of implementing two personalized travel plans: action plans and coping plans. The two types of plans were designed respectively for enhancing action planning and coping planning as the volitional factors of behavior change. The results supported the effectiveness of the combined action-plus-coping plan intervention in reducing car use, but not of the action plan alone intervention. In addition, the influence of intervention on behavioral intention, action planning, and coping planning, were also presented.
This paper proposes a modeling approach for evaluating the effect of a personalized travel plan on a sustainable mode choice. A panel binary probit was estimated by using the approach of composite marginal-likelihood estimation. The formulation modeled the choice of using a light rail service (versus that of not using it) by means of daily individual panel observations collected in the context of a program of voluntary travel behavior change (VTBC) before and after the provision of a personalized travel plan. In this regard, a VTBC program was a policy measure that used communication and information to encourage individuals to use more sustainable travel modes. In this study, the VTBC program was implemented by providing car users with personalized information about how to introduce the light rail service into their travel patterns.
The introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of light rail mode choice for medium- and long-distance trips (10 to 40 km) for a new light rail system in Flanders, Belgium. To investigate these choices, the effects of various transport system-specific factors (i.e., travel cost, in-vehicle travel time, transit punctuality, waiting time, access and egress time, transfers, and availability of seats) as well as the travelers' personal traits were analyzed by using an alternating logistic regression model, which explicitly takes into account the correlated responses for binary data. The data used for the analysis stem from a stated preference survey conducted in Flanders. The modeling results are in line with literature: most transport system-specific factors as well as socioeconomic variables, attitudinal factors, perceptions, and the frequency of using public transport contribute significantly to the preference for light rail transit. In particular, the results indicate that the use of light rail is strongly influenced by travel cost and in-vehicle travel time and to a lesser extent by waiting and access-egress time. Seat availability appeared to play a more important role than did transfers in deciding to choose light rail transit. The findings of this paper can be used by policy makers as a frame of reference to make light rail transit more successful.
This paper offers an econometric model system that simultaneously considers six dimensions of activity-travel choices in a unifying framework. The six dimensions include residential location choice, work location choice, automobile ownership, commuting distance, commute mode, and number of stops on commute tours. The paper presents the modeling methodology in detail as well as estimation results for a joint model system estimated on a data set extracted from the 2009 National Household Travel Survey.
We describe the design and evaluation of a system named Quantified Traveler (QT). QT is a computational travel feedback system. Travel feedback is an established programmatic method whereby travelers record travel in diaries, and meet with a counselor who guides the user to alternate mode or trip decisions that are more sustainable or otherwise beneficial to society, while still meeting the subject's mobility needs. QT is a computation surrogate for the counselor. Since counselor costs can limit the size of travel feedback programs, a system such as QT at the low costs of cloud computing could dramatically increase scale, and thereby sustainable travel. QT uses an application (app) on the phone to collect travel data, a server in the cloud to process it into travel diaries, and then a personalized carbon, exercise, time, and cost footprint. The subject is able to see all of this information on the Web. We evaluate the system with 135 subjects to learn whether subjects will let us use their personal phones and data plans to build travel diaries, whether they actually use the website to look at their travel information, whether the design creates pro-environmental shifts in psychological variables measured by entry and exit surveys, and finally whether the revealed travel behavior records reduced driving. Before-and-after statistical analysis and the results from a structural equation model suggest that the results are a qualified success.
Traveler attitudes and preferences as well as demographic variables are important components of travel behavior. By using travel attitudes, factor and cluster analyses were conducted to segment the sample. Six distinct groups were extracted: transit enthusiasts, anxious status seekers, carless riders, green cruisers, frugal travelers, and obstinate drivers. The segments showed unique combinations of attitudes with distinct travel behaviors and various degrees of intention to use public transportation. Gender differences were then investigated, and the results suggest that women and men exhibit differences in attitudes, preferences, and behaviors. The design of strategies to promote alternatives to car use should target the market segments that are most motivated to change, focusing on the attitudes that can induce a change, such as pro transit or sensibility to the environment.
This paper presents an experimental Voluntary Travel Behavior Change (VTBC) program implemented in Cagliari (Italy) for promoting a light rail service. More specifically (1) Personalized Travel Planning (PTP) and (2) Public Transport Information and Marketing (PTIM) are analyzed both as forming an integral part of a single VTBC program, as well as separately. Further, they are compared in order to evaluate their impact on travel behavior change.
In particular, PTP offers personalized and customized travel solutions devised on the basis of individuals’ observed travel behavior, to encourage them to travel more sustainably while the PTIM uses more general information to promote public transit use through advertising campaigns. The results, in both cases, seem to have important policy implications. Specifically, the study confirms the importance of using motivational campaigns, combining PTP and PTIM approaches. In addition, the results indicate that providing car users with tailored travel solutions (PTP) could have a greater positive effect on behavioral change, than the mass communication approach (PTIM). PTIM was found to be useful for behavior change, but it would be more effective if used mostly as a recruitment tool for personalized travel plans (PTP).
Implementing economically efficient transport policies, in particular implementing price based instruments, is a politically challenging issue. Efficient and politically feasible policy alternatives could therefore make a very valuable contribution to solving transport challenges. Mobility management might be one such policy. We argue that a major weakness of earlier studies is that they only test bundles of different policy elements, and do not attempt to analyse how the elements work in isolation or how they interact to produce the large effects reported. Furthermore, there is often a lack of an appropriate control group against which to compare the treatment effects. We conduct a natural field experiment to test the effectiveness of tailored information, both in isolation, and in combination with free public transit passes, in encouraging commuters to shift from private cars to public transport. In our controlled experiment we find no significant treatment effects.
Evaluations carried out in many countries show that soft policy measures in the form of personalized travel planning reduce private car use and increase travel by public transport. Sweden is a sparsely populated country that poorly supports public transport, a country with long distances, a cold climate, and a high concentration of private cars, which is why soft policy measures implemented in Sweden may be less cost-effective than has been found in other countries. Thirty-two programs using personalized travel planning were analysed with regard to stewardship, geographic area of application, choice of techniques of exerting an influence, and effects on car use and choice of alternative travel modes. None of the evaluations of the documented programs met the method requirements for such evaluations as regards design and effect measurement. Additionally, reporting was substandard as well as non-standard in the way that is desirable in order to enable comparative analyses. With reservations for these shortcomings, it is inferred that positive effects on a par with the results in other countries have been obtained in some of the implemented programs. It is however necessary to conduct evaluations which are of higher quality. The requirements which will then have to be applied are defined.
The paper examines, using English Census data, the impacts of four light rail schemes opened between 1991 and 2001 on car ownership and travel mode along the rail corridors. The effects of these schemes are isolated by comparing the changes in the new light rail corridors with those in ‘control’ areas. Control areas represent what would have occurred in the light rail corridors if the schemes had not been built. Despite two schemes achieving and even exceeding the forecast ridership, the proportion of households owning multiple cars increased in the light rail corridors and typically by more than in the control areas. Growing rail shares in the light rail corridors have mainly come from buses and the evidence for light rail reducing car use is less clear. This latter finding is of particular significance, given that a major justification for investment in light rail rather than bus schemes is their presumed ability to bring about major modal shift by attracting substantial numbers of car users.
Modal choice is determined by a whole range of factors that are interrelated to a larger or smaller extent. It is often the result of a very compound choice process that can take place consciously or unconsciously and that includes objective as well as subjective determinants. Despite its significance in our daily life, there is no uniform way to define and analyze the concept of modal choice. The aim of this review is to fill this gap by elaborating a common modal choice definition and by providing a comprehensive review on the concept of modal choice through linking it to Kaufmann’s motility concept. By doing so, this review will not only contribute to an improved knowledge on different modal choice determinants and their interdependencies, but can also assist to the understanding and modeling of modal choice decisions. The review can therefore help increasing the effectiveness of policy measures taken by environmental, urban and transport policy makers.
The private car is fast, comfortable, and convenient. However, worldwide massive car use causes serious environmental problems. Although breakthroughs in clean automobile technology may be under way, reducing car use seems necessary in order to achieve a sustainable transportation system. Several travel demand management (TDM) measures have therefore been proposed and some have been implemented with this aim. The article reviews research addressing the question of how effective, acceptable to the public, and politically feasible such measures are. The conclusion is that noncoercive TDM measures alone are unlikely to be effective in reducing car use. Therefore, coercive TDM measures such as increasing cost for or prohibiting car use may be necessary but are difficult to implement because of public opposition and political infeasibility. If combined with noncoercive TDM measures providing attractive travel alternatives and communicating the benefits of car-use reduction to the public, coercive TDM measures are likely to become more effective, acceptable, and politically feasible.
Newspapers, book clubs, telephone services and many other subscription services are often marketed to new customers by means of a free or substantially discounted trial period. This article evaluates this method as a means to promote commuting by public transport in a field experiment and based on a solid behavioural–theoretical framework. By measuring important antecedents and mediators, the applied approach offers important insights not only on what behavioural outcomes were produced by the intervention, but why they were produced. Copenhagen car owners received a free month travel card, either alone or together with a customised travel plan or a planning intervention. A control group receiving no intervention was also included. Attitudinal variables, car habits and travel behaviour were measured before and immediately after the intervention and again six months later. The only intervention that had an effect was the free month travel card, which led to a significant increase in commuting by public transport. As expected, the effect was mediated through a change in behavioural intentions rather than a change in perceived constraints. As expected, the effect became weaker when the promotion offer had expired, but an effect was still evident five months later. Possible reasons and implications of this are discussed.
In this study, the acceptability of different transport policy measures was examined. Three measures were assessed individually and as packages combining one push measure (a raised tax on fossil fuel) and one pull measure (in Package 1 improved public transport and in Package 2 a subsidy of renewable fuel). To analyze factors important for the acceptability, we proposed a model where the value-belief-norm theory combined with policy specific beliefs (perceived fairness and perceived effectiveness) predicted acceptability. Furthermore, we examined whether problem awareness or personal norm was more important for acceptability. In a questionnaire study conducted in Sweden, a sample of car users (N = 616) assessed the transport policy measures. Results showed that while the pull measures were perceived to be effective, fair, and acceptable, the push measure and the packages were perceived to be rather ineffective, unfair, and unacceptable. The proposed model was supported for the measures and problem awareness was found to have a direct effect on acceptability for the pull measures while personal norm was found to have a direct effect on acceptability for the push measure and the two policy packages. In addition, perceived fairness and effectiveness were found to be particularly important for acceptability.
The past 20 years has seen a rapid growth across the world in the number, range and scale of voluntary travel behaviour change (VTBC) initiatives. These so-called ‘soft’ measures have challenged the assumption that modal shift is only possible through ‘hard’ system-based measures, or through regulation. Among the most high-profile VTBC initiatives is a household-based behaviour change technique known as Individualised Travel Marketing. This dialogue marketing approach was developed by Socialdata (under the brand name IndiMark®) in response to its own research suggesting that a lack of information and motivation, and incorrect perceptions of the alternatives to the car, were significant barriers to modal shift. IndiMark has been applied in more than 100 pilot and nearly 150 large-scale projects, targeting a total of more than three million people on three continents. A key factor in this widespread take up has been the consistent use of a detailed evaluation design, employing travel behaviour surveys before and after the IndiMark intervention, using Socialdata's KONTIV® survey method. This well-established design uses a self-administered, mail-back questionnaire, coupled with motivation by post and telephone to encourage high response rates (typically between 60% and 80%) helping to provide reliable data on mobility behaviour. This paper reviews the development of the IndiMark technique and the key features of its evaluation using the KONTIV® survey method. It draws on this experience to address key challenges in the evaluation of VTBC initiatives, and to identify the common threads of an integrated approach which might strengthen the case for all soft measures.
Using an expanded version of a psychological theory of attitude-behaviour relations, namely the theory of planned behaviour (TPB), scores on factor analysed multi-dimensional attitude statements were used to segment a population of day trip travellers into potential ‘mode switchers’ using cluster analysis. Six distinct psychographic groups were extracted, each with varying degrees of mode switching potential. Each group represents a unique combination of preferences, worldviews and attitudes, indicating that different groups need to be serviced in different ways to optimise the chance of influencing mode choice behaviour. Socio-demographic factors had little bearing on the travel profiles of the segments, suggesting that attitudes largely cut across personal characteristics. The evidence clearly shows that the same behaviour can take place for different reasons and that the same attitudes can lead to different behaviours. The paper asserts that commonly used a priori classifications used to segment populations based on demographic variables or simple behavioural measures may oversimplify the structure of the market. Cluster analysis is rarely used in studies of travel behaviour but this study demonstrates its utility in providing a way of extracting naturally occurring, relatively homogenous and meaningful groups to be used in designing targeted hard and ‘soft’ transport policies.