Determinants of low-carbon transport mode adoption: systematic review of reviews

The analysis of behavioural factors is guided by psychological theories, with 15 different frameworks identified (Chng et al 2018). The Theory of Planned Behaviour and/or the Norm Activation Model is employed in at least half of the cases (50% of the total studies). The remaining theories are only applied in a limited number of studies (most are only applied in one or two studies). The similarity in frameworks, reflecting also the uniformity of reported variables, facilitates comparison across studies and paves the way for in-depth meta-reviews.

In the domain of travel-mode choice, three meta-analyses on the determinants of individual travel mode intentions and behaviour have been published (Gardner and Abraham 2008, Hoffmann et al 2017, Lanzini and Khan 2017). Two meta-analyses (Gardner and Abraham 2008, Lanzini and Khan 2017) also study the determinants of intention to use particular travel mode in addition to travel mode choice in itself.

Intentions are significant and substantive predictors of the individual driving. But whereas Lanzini and Khan (2017) report an averaged pooled car use—intention correlation of r₊ = 0.83, the averaged pooled car use—intention correlation reported by the other two meta-analyses is r₊ = 0.53, and r₊ = 0.50 respectively (Gardner and Abraham 2008, Hoffmann et al 2017). Because the reported confidence intervals do not cover these huge differences, they cannot be attributed to chance influence. The results reported by Lanzini and Khan (2017) are based on a systematically different information set than the quite similar results reported by the two other meta-analyses.

Attitudes are one of the most frequently included item in studies on individual level factors. As with intentions, there is significant disagreement, in terms of magnitude of the association, between the three meta-analysis. Hoffmann et al (2017) report low correlation between car use and attitudes towards car use (r₊ = 0.22^***; k = 38; n = 4647) as well as non-car options (r₊ = −0.23^**; k = 3; n = 812). Whereas, Lanzini and Khan (2017) report medium correlation between car use and attitudes to driving (r₊ = 0.41^***; k = 15; n = 4290) and non-car options (r₊ = −0.36^***; k = 7; n = 3283). There is greater agreement on medium level association between attitude towards non-car options and non-car travel mode usage (r₊ = 0.31^***; k = 12; n = 13 282) (Lanzini and Khan 2017).

Beliefs are key to explain travel behaviour. The results of all three meta-analyses show that as proposed by the theory of planned behaviour (TPB), perceived behavioural control (PBC), a person's expectation that performance of the behaviour is within her control, is significant and substantive predictors of individual car use. According to best estimates, the relationship between car use and PBC related to car use is medium-to-strong (r₊ = 0.39^***; k = 9; n = 1605). This is similar for PBC related to non-car and its association with car and non-car use, although as expected the direction of effect is opposite to each other. For other types of beliefs, the meta-analysis focuses on beliefs about the ascription of responsibilities- attributing own action to different social/environmental outcomes. Both meta-analysis studying this find no significant effect of 'ascription of responsibilities' on car use (Hoffmann et al 2017, Lanzini and Khan 2017), although Lanzini and Khan (2017) report a significant but small effect of 'ascription of responsibilities' on non-car use (r₊ = 0.22^***; k = 4; n = 1746).

Awareness is less relevant in explaining the observed mode choice. Behavioural processes commonly studied include 'problem awareness' and 'awareness of consequences'. In both cases, there are differences between the two major meta-analyses. Lanzini and Khan (2017) find that the association between 'awareness of consequences' and car use as well as non-car use is small and not significant. However, this is based on a small number of studies (and observations). On the other hand, using a larger sample, Hoffmann et al (2017) find that the relationship between 'awareness of consequences' and car use, although not big, is negative and significant (r₊ = 0.22^***; k = 6; n = 2139). This is same for 'problem awareness' effect on car use (r₊ = −0.17^***; k = 6; n = 5545).

Moral and environmental norms are important change variables. Misunderstanding the TPB's reasoned action assumption (Fishbein and Ajzen 2011) to mean that in this framework people are assumed to be rational utility-maximizers (Bamberg and Schmidt 1998), investigators have criticized the theory for underestimating the impact that moral beliefs and norms may have on people's travel-mode choices. To remedy this situation, researchers have distinguished between subjective and moral norms (e.g. Parker et al 1996) and have assumed that, in the context of climate change, moral norms may be an additional predictor of a person's intention to use more environmentally friendly modes of transportation. For instance, Abrahamse et al (2009) find that the TPB constructs (such as intentions and PBC) were more powerful than moral norms in explaining current car use for commuting but that moral consideration were more powerful predictors than the TPB constructs for intentions to reduce car use in the future. In their meta-analysis, Hoffmann et al (2017) report a pooled correlation of r₊ = −0.35 between perceived personal obligation of not driving (moral norm) and self-reported driving. For the same association, the other two meta-reviews report a pooled correlation of r₊ = −0.41, and r₊ = −0.26, respectively (Gardner and Abraham 2008, Lanzini and Khan 2017). Based on these results there is a growing consensus within the research community to view moral norms as an additional travel-mode-choice predictor, especially of the decision to use more environmentally friendly travel modes.

Other than personal moral norms, several other personality-relevant factors are also identified in the reviews, these include; environmental values, altruistic value orientation (AVO), and valuation of different travel concerns. For instance, Altruistic value orientation (AVO) shows medium-sized negative association with (self-reported) car use (r₊ = −0.31^***; k = 3; n = 184). (Hoffmann et al 2017). Overall, while these factors show small effects on car use, the evidence base for them is limited as most studies rely on small convenience samples.

Habits are an especially important predictor of travel mode choice and go beyond models of rational choice. It has also been argued that the TBP does not adequately account for behaviours that may be regulated by automatic processes (Triandis 1977; but see Ajzen and Dasgupta 2015). For instance, when behaviours are practised in stable environments over time, they can be automatically initiated by environmental prompts with little or no conscious deliberation (Strack and Deutsch 2004). Thus, since daily travel tends to occur in stable contexts, the transport mode choice may over time become less of a conscious choice and more of a habitual response executed with little reflection (Gärling and Axhausen 2003).

However, the use of past behaviour frequency as an indicator of habit strength is problematic (Fishbein and Ajzen 2011). Thus, there is a growing consensus that a realistic model of travel-mode choice should also include an independent habit measure. For this reason, in their meta-analysis Hoffmann et al (2017) include a summary of six studies using an independent measure of habit strength (Verplanken and Orbell 2003) for predicting a person's travel-mode choice. They find a pooled correlation of r = 0.47 between the habit measure and travel mode used. Gardner and Abraham (2008) report a pooled correlation of r+ = 0.50, and Lanzini and Khan (2017) of r₊ = 0.42.

While independent habit measure addresses some of the issues related to habit as a psychological construct, other relatively new areas of research explore different ways. Two prominent approaches are covered in our review studies.

First, the studies looking at the impacts of involuntary workplace relocation on travel mode choice (Zarabi and Lord 2019). This approach can be useful in exploring the causal relationship between habit formation and travel mode choice. This research area is motivated by 'habit discontinuity hypothesis' (Verplanken et al 2008), which posits that big changes in context offer an opportunity to move away from habitual decision-making to more deliberately considered decision-making. As a result, alternate travel behaviours are more likely to be considered and adopted under the guise of these large changes in choice contexts.

Second, the studies conducted in the tradition of mobility biographies, which look at the impact of key life events on travel behaviour (Müggenburg et al 2015). They identify three key life events, where ample research exist: (i) household changes (formation, childbirth), (ii) labour market entry, and (iii) residential relocation. In the first two cases, the key life events are accompanied by greater reliance on the car. The third key event is often thought as an entry point to break old habits and establish new ones (often for instance by giving free public transport tickets for a limited time period).

Socio-demographic factors affect travel behaviour and travel mode choice, but these factors remain largely ignored in meta-analyses, and the overall evidence base is weak.

Car use differs between socio-demographic segments. Although women travel more frequently than men, the total distance of travelling is lower for women (Rosenbloom 2004) (Veterník and Gogola 2017). Besides, women are less likely to use cars (Best and Lanzendorf 2005). While this relationship is moderated by different factors such as employment status and household characteristics, it holds even after controlling for these factors (Rosenbloom 2004). Income and employment status are also often associated with car use. Typically, people with higher income are able to spend higher amounts on their transportation needs, translating into higher car ownership and use of car.

Similarly, for biking, there is some evidence that being young and a man increases the likelihood of bicycling (Muñoz et al 2016). There is ambiguous evidence for the impact of higher income and education level on bicycle usage. In the literature review of bicycle use, 6 studies show a positive association with income, 5 studies show a negative association, while a majority of studies (k = 13) finds no association (Muñoz et al 2016). The impact of income level on the use of low-carbon travel modes, especially biking, may be mediated by car ownership (Muñoz et al 2016). A similar picture emerges for education level, where 5 studies show a positive association, 2 studies show a negative association, while 6 studies find no association (Muñoz et al 2016).

Our search query resulted in only a few literature reviews that deal with social influence effects directly. (Maness et al 2015) and (Kim et al 2018) are examples of more comprehensive literature reviews in this domain. However, as these are narrative reviews rather than systematic or meta-analysis, therefore our estimates of social influence effects are based on original studies cited in these literature reviews (supplementary appendix B for more details on these individual studies).

The local social environment matters for mode choice indicating the influence of spatial influence effect. For aggregate choices over pre-defined spatial configurations, the choice of transport mode tends to be influenced by the behaviour of others who live in the same residential area (Dugundji and Walker 2005). Controlling for endogeneity¹ in discrete choice models, the parameter on the spatial influence effect drops (c = 1.53, n = 2888) (Walker et al 2011). Students who live in neighbourhoods dominated by bicycle users, are less likely to drive (c_b = −2.14^**; n = 483) (Pike 2014). However, greater public transport or driving by others in the same neighbourhood has no significant impact on driving (c_pt = −0.79; c_d = −0.56; n = 483). In the case of public transport use, average spatial network effect for transit use on transport mode choice is positive and significant for transit users in New York (c_pt =1.813^***; n = 1652) (Goetzke 2008). Additionally, a significantly negative spatial network effect exists between frequency of bike users, and the individual public transit use (c_b = −1.99^***; n = 483) (Pike 2014).

Travel mode commonly used by people in one´s social networks is an influential predictor of own mode choice, this is often referred to as social influence effect. The probability of choosing a mode increases with the share of one's social group that selects that mode (Walker et al 2011). Here again, the issue of endogeneity looms large. The parameter on the social influence effect drops in the discrete choice models controlling for endogeneity (c = 1.75^***; n = 2888) (Walker et al 2011). Concerning specific travel modes and social network effect, the presence of bike-users among individual's social network is significantly correlated with lower car use (c_b = −2.40^**; n = 483) (Pike 2014). However, this not true for car (c_d = 1.70; n = 483) or bus users (c_pt = 0.38; n = 483) in one´s social network members and own car-use. The presence of bus-users in one´s social network is positively associated with higher bus usage by the individual herself (c_pt = 3.66^***; n = 483). Lastly, based on an instrumental variables approach, greater propensity to biking is related to the proportion of social network members that also bike (c_b = 0.978^***; n = 388) (Pike and Lubell 2016).

While these studies look at overall social network effect there is relatively little work on the specific mechanisms. Based on earlier work, we classify two different types of social influence effects. (i) Indirect effect, where a social network effect is primarily due to social or observational learning from one´s social group (community and social class), and (ii) direct effect (social network effect, peer pressure), where a social network effect is due to the explicit impact of others via., negative or positive feedback from one´s social group (e.g. relatives, friends, acquaintances, colleagues, etc.).

Both indirect and direct social network effects are relevant for mode choice, but the evidence base is sparse. Mimicking others in one's socio-economic group has a stronger behavioural influence than similar underlying preferences exhibited by those in the same socio-economic group (Walker et al 2011). Another study shows that those who have frequently observed others owning a bicycle tend to be more positively inclined to have a bicycle than those who have less frequently observed bike ownership (Maness and Cirillo 2016). For more direct forms of social influence, Sherwin et al (2014) document that a significant number of the new regular bicyclists received encouragement (positive feedback) from colleagues, while others mentioned social disapproval (negative feedback) for using car especially from their friends.

Our search yielded three meta-analyses where social norms are the focal point. In all three of these studies, social norms are studied in the context of TPB.

Descriptive norms (perception of others' mode choice) show a low-medium size relationship with travel mode choice. First, with respect to car use and perceptions about other people's car use behaviour (car use descriptive norm); there is substantial disagreement between the three studies. Hoffmann et al (2017) find small negative relationship, however this effect is not significant across studies (r₊ = −0.07, k = 3, n = 532). Gardner and Abraham (2008) report medium-sized positive relationship (r₊ = 0.36^***, k = 2, n = 993), similar result is obtained by Lanzini and Khan (2017) (r₊ = 0.255^*, k = 6, n = 2199). Furthermore, Lanzini and Khan (2017) find medium-sized positive relationship between self-reported intention of car use and perceptions about other people's car use behaviour (r₊ = 0.27^**, k = 7; n = 2706). Relatedly, Lanzini and Khan (2017) also find similar effect in the case of non-car use descriptive norm and non-car use (r₊ = 0.214^**, k = 4) as well self-reported intention of non-car use.

For subjective norms, the evidence base does not yield unambiguous answers. However, best estimates suggest a small to medium effect size between subjective norms and travel mode choice. More specifically, (Hoffmann et al 2017) report a small to medium-sized positive association between car use and car-use subjective norms (k = 6; n = 1455; r₊ = 0.20^**). Effect sizes varied considerably across studies (range from r = 0.03 to r = 0.52) with half of the studies observing non-significant associations. Similarly, (Gardner and Abraham 2008) report a small highly unreliable negative effect for car use subjective norm (r₊ = −0.07, k = 2, n = 555). However, based on a larger number of studies, (Lanzini and Khan 2017), observe a medium-sized positive relationship between self-reported car use and subjective norm about car use behaviour (r₊ = 0.23^***, k = 10, n = 2199). Furthermore, they also find stronger positive relationship between self-reported intention of car use and subjective norm about car use behaviour (r₊ = 0.42^***, k = 7, n = 2906).

The research on specific low-carbon transport modes and subjective norms is limited. Rather, the meta-analysis in our database provides results on the general category of non-car alternatives. For instance, (Hoffmann et al 2017) report that non-car-use is positively associated with stronger subjective norms towards not driving, though the effect is small to medium and heterogeneous (r₊ = 0.28^***, k = 6). Stronger effects of subjective norm on non-car use are reported in Lanzini and Khan (2017), both for self-reported non-car use (r₊ = 0.23^***, k = 12, n = 12 737) as well as self-reported intention of non-car use (r₊ = 0.41^***, k = 20, n = 16 770).

Social signalling theory implies that a significant portion of individual mode choices can be explained by their value in terms of signalling certain values and/or social status (in addition to the utility gained from the choice). The evidence for social signalling and status is scattered across different domains and often requires interpretation. For instance, there is some qualitative evidence that the perception of who else is bicycling is more important than the perception that bicycling is common or frequent among community members (Handy et al 2010). If biking is perceived to be associated with higher-status individuals, then the likelihood of others being influenced is greater. This point towards the importance of perceived social status and the signal it sends the person engaging in the activity. Meanwhile, the social comparison variable shows a small positive effect (r₊ = 0.16^**, k = 6, n = 1247) meaning that people who compare their actions with others' and try to exceed others are also more likely to use the car (Hoffmann et al 2017). Concerning social identity effects, most of the studies rely on Theory of Planned behaviour as their theoretical basis, and find a very small, negative association between car use and anti-car identity measures (r_nc = −0.08^**, k = 11, n = 1609), while a very small positive association is observed between car use and pro-car identities (r_c = 0.05^**, k = 11, n = 4229) (Hoffmann et al 2017).

So far, social level factors considered in our analysis rely on psychological theories and therefore tend to focus on individuals as a unit of analysis. Sociological literature and especially social practice theory, on the other hand, emphasize the primacy of 'social practices' as the key unit of analysis.

Cairns et al (2014) review the growing body of social practices' literature on travel behaviour. Although empirical estimates are hard to come by, broadly speaking, three main insights arise from this literature. First, over time car has become associated with a unique set of values and meanings (such as concepts of freedom, moral commitment to family and care for others) (Sheller 2004), and other travel modes will need to tap into those values and meanings to achieve modal shift. Secondly, the meanings and symbols associated with cars are a result of complex interplay between individual and social values (Wollen and Kerr 2002). Car is construed as a cultural object, embedded in self-created social identity. Other travel modes which do not offer such possibilities are less likely to have the same allure as the car. Lastly, the 'diversity of meanings and values attached to cars results from temporal and cultural specificity' (Gartman 2004). Therefore, different groups form different norms around these meanings and values.

The search query yielded 16 reviews that investigate the role of the built environment in modal choice and travel demand.

Land use modifies travel prices both by distance and travel speed (Boarnet 2011). Reduced form estimates that regress travel behaviour to land-use determinants is the dominant methodology employed in this literature (Ewing and Cervero 2010); however such models are unable to reflect the endogenous role of land use planning, even though it is well understood that the interaction between travel and land use is bidirectional (Wegener and Fuerst 2004). Residential self-selection denotes that people chose their residence according to modal choice availability. The causal role of land use on travel behaviour may be attenuated but not negated by residential self-selection (Boarnet 2011; Cao et al 2009). However, elasticities are higher with self-selection controlled for; this suggests that there is latent demand for transit/bike oriented communities and that elasticities that do not control for self-selection underestimate the role of the built environment (Chatman 2009, Ewing and Cervero 2010). A review of residential self-selection confirmed the relevance of self-selection, highlighting that perceived characteristics rather than objectively measured characteristics were predictive; attitudes matter (Bohte et al 2009). The role of habits was not investigated in empirical studies on the built environment, begging for further investigation.

Neighborhood characteristics, such as walkability scores, parks, safety, aesthetics, and social environment are commonly related to active transport, especially walking. Yet, most studies are purely observational and cross-sectional, thus remaining ignorant on the causal role of the built environment; instead social characteristics of inhabitants might explain the difference in active transport. A systematic review screened 3324 studies, finding 23 studies that investigated the influence of the built environment on active mobility longitudinally, specifically by observed behaviour before and after relocation between neighbourhoods with different characteristics (Ding et al 2018). Retrospective studies show unambiguously that built environment characteristics shape active mobility patterns and intensity, while prospective studies show a weaker relationship. A key study performed a meta-regression relating land-use and socio-demographic variables on travel behaviour. It found a strong impact of accessibility, as the single most important land use metric, on travel behaviour (Gim 2013).

There is less clear evidence of how the preferences of inhabitants shape land-use planning. It is possible that cities planned low-density developments despite people's preference for more compact cities (Levine 2010). It may simply be that cities plan similar to what is already there and that citizens accept this rationale, as they are used to it, and as others see this as a sensible choice to (Boarnet 2011). The question of the malleability of preferences in mode choice looms large but has hardly been studied. Meta-analytic results reveal that vehicles miles travelled (VMT) only marginally reduces with population density (e = −0.04, k = 9) and land-use diversity (e = −0.09, k = 10), more with street design (street connectivity: e = −0.12, k = 6), and most with destination accessibility (e = −0.20, k = 5) (Ewing and Cervero 2010). Together, these urban form parameters may change VMT by about−0.25 (Boarnet 2011). Walkability improves most with intersection density (e = 0.39, k = 7) and job-housing balance (e = 0.19, k = 4); transit with street connectivity/4-way-intersections (e = 0.29, k = 5) and distance to transit stop (e = 0.29, k = 3) (Ewing and Cervero 2010).

Modelling studies suggest that increases in population density only produce a small reduction in VMT (about 0.5–1.7%). However, combining densification with improved transit access and road charging can increase this value to 25% over the time horizon of 40 years (Rodier 2009), in accordance with other modelling studies that investigate the confluence of push (pricing), pull (transit improvements), and land use (Creutzig and He 2009, Creutzig, Mühlhoff, and Römer 2012).

Across different categories, we find 15–20 major studies where substantial portions are devoted to the transport-system factors. As explained earlier, we divide the transport system factors into three main sub-categories.

First, concerning transport infrastructure factors, especially the provision of hard infrastructure, added capacity in the form of new highways increases VMT (Kitamura 2009). However, the impact on induced traffic, residential choice and car ownership remain less clear. There are several caveats which come with these findings including the shortcoming in methodology and data. More recent studies have come to the opposite conclusion (for instance, Duranton and Turner 2011).

In terms of low-carbon transport modes, provision of travel-mode specific and dedicated hard infrastructure is vitally important. For public transport, there is a clear indication that the provision of new PT infrastructure can lead to a shift away from cars to public transport. An international review of public transport report that new infrastructure such as BRT, LRT and rail transit systems can achieve up to 40% mode shift away from car traffic to public transport systems (Ingvardson and Nielsen 2018a). However, given the diversity of contexts and the infrastructure (such as type of infrastructure, length, etc) there is relatively high variance whether new infrastructure attracts previous car users or not (average mode shift estimates range from 5%–40% for new PT infrastructure in different cities). Overall, to achieve notable impacts on car traffic, attractive high-capacity systems (such as LRT) seem more effective (Ingvardson and Nielsen 2018a). There is a consensus in the literature that separated bicycle infrastructure, especially bike lanes and bike paths, increase bicycle usage. Indeed, a supportive network of different types of infrastructure appears necessary to attract new people to bicycling (Buehler and Dill 2016). According to Nuworsoo and Cooper (2013) cyclists associate 1 min of stress in mixed traffic with roughly 4 min of stress in a bike lane. Lastly, the literature shows a positive impact of infrastructure such as pavements on walking among adults (Bauman and Bull 2007). Some studies argue that while infrastructure is necessary for the promotion of walking, it is not sufficient and the proximity of activities to areas linked by such infrastructure can lead to a greater number of walking trips (Nuworsoo and Cooper 2013).

Factors related to transport system characteristics are consistently associated with a medium-sized effect size on travel mode choice. For public transport, system characteristics can be divided into two main aspects, coverage and connectivity of public transport systems. Based on a meta-analysis of 48 European metropolitan areas, Ingvardson and Nielsen (2018b) find that transport system coverage (rail, metro length in km/km²) shows high correlation with the total number of public transport trips per capita per year (c_metro = 79.4***; c_sub = 85.7***; k = 48). This is especially true for metro coverage. However, at the same time, most indicators of transport system coverage are linked with higher density, so it is difficult to parse out the role of PT coverage separately from the urban density. In terms of connectivity, they find that PT system connectivity as measured by two separate indicators, the cyclomatic number² and the number of transfer stations, is linked with higher ridership (c_network = 56.64**; k = 48). Focusing on bicycle infrastructure characteristics, the literature is not as extensive on bicycle infrastructure characteristics as compared to the literature on Public transit. However, there are some indications that bike infrastructure characteristics are important in much the same. For instance, bike lanes tend to produce a more positive feeling among users as compared to bike paths (Nuworsoo and Cooper 2013). However, this may be due to the limited availability of bike paths and the fact that they tend to be disconnected from each other. Furthermore, in the context of a public bike system in Ireland, connectivity to other public transport modes is a crucial factor in increased use of these bike systems (Mcbain and Caulfield 2018).

Last major sub-category of the transport system, quality of transport system, tends to produce only a marginal impact on travel mode choice. The literature on these factors focuses more on public transit, although walking and biking infrastructure qualities also appear in some restricted cases. Concerning public transit services, De Gruyter et al (2019) conduct a meta-analysis of customer amenity values in six countries (Australia, India, NewZealand, Norway, Sweden, and United Kingdom). They identify 556 separate customer amenity values relating to 97 separate amenity types and collapse them in 5 major categories (facilities, information, security, environment, condition). Overall, the meta-analysis indicates that these amenities have a relatively low impact on transit ridership. In travel time comparative terms, improvements in these aspects are very rarely valued higher than the equivalent of 1 mins reduction in in-vehicle travel time. In the context of bus networks, improvements associated with 'soft' variables are not likely to increase patronage by more than a few percent (Currie and Wallis 2008) (m ranges from 2%–5% for the most effective measures). More concrete service attributes (such as speed, reliability, frequency) can increase ridership numbers substantially (Redman et al 2013) (m ranges between ∼ 25% for speed, 10%–40% for reliability, and 20%–80% for frequency improvements).

Redman et al (2013) argue that hard infrastructure factors encourage car users to switch to PT, whereas the quality attributes determine the duration of this effect. Loyalty to PT use may be determined by soft transport factors. There is some evidence to suggests that regular transport users value these soft factors more that (non-regular) users (De Gruyter et al 2019). However, in many cases, it is difficult to measure whether the effect is produced by a change in quality attributes or by the provision of the infrastructure itself.

While, this is not the focus, there are several studies, which mention the impact of natural environment and weather on travel mode choice. This is especially true for biking and walking. In particular, the slope of the land and rainfall are mentioned as major deterrents to walking and biking. Böcker et al (2013) conduct a meta-review of studies related to weather conditions on active travel. They find that warm and dry weather conditions influence the use of active transport modes positively. Rain, snow, windy, overly cold or hot weather can have the opposite effect. Furthermore, subjective weather interpretations (speed, effort, safety or aesthetics during trips) tend to have higher explanatory value for behavioural responses than objectively measured weather conditions.

Most of the reviews on cost (price, time) elasticity of different transport modes are based on studies from the UK. For price elasticities, (Hensher 2008) conduct a meta-review of studies from a wide set of countries, and find that the mean elasticity estimate for fares is close to −0.395 (±0.274) which is similar to the estimates reported in earlier reviews. In a similar vein, meta-analysis for the UK finds that the long-run car kilometre elasticity ranges between −0.20 to −0.35 depending on the length of the trip (Wardman 2014). Similarly, rail price elasticity ranges from −0.18 to −0.31 in short term and −0.70 to −1.19 in the long-run, whereas bus elasticities can be taken to be −0.4 in the short term, and in the range of −0.7 to −0.9 in the long term. Importantly, both Wardman (2014) and Hensher (2008) report that the commuting and peak elasticities tend to be similar and, lower than the leisure and off-peak elasticities.

Secondly, for time-based elasticity values, in general, the meta-reviews find that in-vehicle time elasticities are on average greater than the headway elasticities (transit service frequency). Travel time elasticity related to car use varies considerably between the frequency of trips and vehicles miles travelled (VMT) in the long and short term evaluations. The representative value for long term (trip frequency) time elasticities is between −0.26 for EU data (De Jong and Gunn 2001) and −0.29 for UK data (Wardman 2012). Interestingly, long-term VMT time elasticity is around 2–3 times larger than the trip frequency-time elasticity, indicating that the long-term response to time variations is concentrated in the distance travelled by car (De Jong and Gunn 2001, Wardman 2012). Representative in-vehicle time elasticity for local bus (rail) range between −0.16 to −0.26 in the short-run and −0.4 to −0.6 in the long-run. Headway elasticities range from −0.07 to −0.12 for bus and train trips in the short-run, and −0.24 to −0.42 in the long-run (Wardman 2012). It´s important to note that these elasticity values vary between regular and non-regular users. Commuters tend to be less sensitive to fares and in-vehicle time, but more sensitive to headways, than non-commuters (Hensher 2008).

Lastly, in terms of inter-modal cross elasticities, elasticity values differ between price and time-based attributes. Representative fuel-price cross-elasticity for the shift away from the car to alternative transport modes show rather low values, ranging between 0.03 to 0.1 in the long-term (De Jong and Gunn 2001, Wardman et al 2018). The cross-elasticities of both bus and rail for the car are relatively large, long-term cross-elasticity values range from 0.2 to 0.33 for personal travel (Wardman et al 2018). Cross-elasticities to (in-vehicle) time-based attributes tend to exceed those for price-based attributes. This is especially true for public transport options, but also car elasticities. Overall, the findings from these reviews suggest that the role of improving public transit costs plays only a relatively minimal role in terms of reducing car demand. However, increasing in-vehicle time for cars can deliver relatively higher gains.