Behavioural interventions change individual transport choices but have a limited impact on transport mode split. Evidence from a systematic review

doi:10.21203/rs.3.rs-2084989/v1

Demand-side behavioural change interventions promote a reduction in car use, and shift to low carbon transport modes, thereby addressing economic, health and GHG emissions-related costs associated with car-dependent lifestyles. However, the relative effectiveness of such interventions in initiating transport behaviour change has not been evaluated systematically. Here, we conduct a systematic review and meta-analysis to evaluate the evidence on behavioural interventions in changing transport-related behavioural patterns. Relying on 37 literature reviews and 41 additional new studies, we identify more than 450 cases of behavioural interventions. Our analysis reveals that behavioural measures are effective in increasing the percentage growth of public transit and active transport mode use as well as decreasing car use at the personal level. However, their effectiveness in shifting commuters away from cars to more sustainable modes of transport at the aggregate level is limited. Our findings suggest that behavioural interventions have a role to play in reducing the health, economic and environmental costs of car-centric transportation systems. We find that, under the best-case scenario, behavioural interventions can contribute up to a 5% per cent reduction of global GHG emissions from urban transport, or about 150Mt CO₂/yr, a notable but limited potential compared to the reductions necessary to meet sectoral goals. Critical analysis of studies in our review indicates that interdisciplinary researchers interested in these issues should take a more strategic and rigorous approach—better study designs, more representative samples, and consideration of infrastructure effects—to determine which interventions are most effective in tackling multi-faceted urban transport issues.

Scientific community and society/Social sciences/Decision making

Earth and environmental sciences/Environmental social sciences/Environmental economics

Earth and environmental sciences/Environmental social sciences/Psychology and behaviour

Earth and environmental sciences/Environmental social sciences/Climate-change policy

Earth and environmental sciences/Environmental social sciences/Sustainability

Urban mobility and transportation are intrinsically linked to sustainability across several domains such as public health, environmental quality, urban biodiversity as well as climate change mitigation^1,2.

The transportation sector as a whole is a major contributor to GHG emissions, with some estimating that it is responsible for nearly 25% of direct CO₂ emissions from fuel combustion worldwide³. GHG emissions from the transportation sector have not shown any signs of decrease and are projected to rise steadily even if existing commitments to decarbonize transport are fully implemented⁴. In addition to GHG emissions, widespread private car use is associated with negative outcomes both in terms of personal and public health outcomes. The health costs of car-centric sedentary lifestyles even trump GHG emissions as negative outcomes^5–7. Meanwhile, alternatives to the car such as biking and walking are associated with large health benefits^5,8. Car use is not only a major contributor to high levels of air pollution but also a leading cause of personal exposure to the most dangerous pollutants. Car commuters are exposed to more air pollutants as compared to active commuters⁹. Furthermore, traffic noise is one of the biggest pollutants in European cities, with most car dominated cities being linked to unsustainably high noise levels, leading to an increased risk of early death, decreased productivity, and decreased mental wellbeing¹⁰. Economic costs of congestion and the misallocation of precious urban land is another byproduct of car-centric urban mobility. According to an assessment by European Commission, congestion costs in European cities are projected to increase by 50% from 2010 levels to nearly €200bn annually by 2050¹¹.

Given the acute social, health and environmental impact of car-centric lifestyles, addressing urban car use requires effective action. Although technical advances, such as electric cars, can help in this transition to a certain extent, timely successful action needs to combine reductions in the demand for motorized travel with increased adoption of sustainable, low-carbon transport modes². Behavioural or soft interventions are among the policy interventions that can initiate changes in avoiding car use as well as shifting it to other sustainable modes. Broadly defined, behavioural interventions address a wide range of different initiatives that share the common feature of trying to encourage more sustainable travel behaviour (Cairns et al., 2004) without initiating large-scale changes in infrastructure or rules and regulations governing urban transportation. Several different types of behavioural interventions have been put forward targeting behavioural change for reducing car use as well as promoting climate-friendly transport modes such as public transit, bicycling, and walking^12,13. There is a rich and growing literature available on solutions focusing on individual behavioural change. However, the lack of effort to systematically quantify the impact of interventions in urban travel behaviour has led to an over-reliance on supply-side solutions such as electric vehicle promotion that do not necessarily provide the ancillary benefits associated with other transport modes¹⁴.

In this study, we address this gap by conducting an interdisciplinary systematic review of reviews and meta-analyses of interventions that tackle individual transport behaviour in urban settings. Using 37 literature reviews as our evidence base, we extract around 400 cases of behavioural interventions. We complement this database with an extra round of extensive literature search for original studies published from 2015 onwards, resulting in 41 additional studies. Compared to earlier literature, our review relies on more comprehensive data, almost doubling the number of estimates^13,15. We consider the evidence for different types of sustainable modes, as opposed to just reducing car use or focusing only on one or two particular modes¹⁶. Compared to some of the earlier reviews that are constrained by study areas, e.g. the UK or Japan,^17–19 we collect evidence from 37 countries. Furthermore, earlier reviews typically do not conduct a critical appraisal of underlying studies; whereas we present a detailed analysis of the quality and strength of evidence available.

Our analysis reveals that behavioural interventions can increase the usage of sustainable modes of transport like walking, Public transit (PT) and bikes by 10%-35% on average. Similarly, behavioural interventions can also be successful at reducing car use in percentage terms at the individual level. Their effectiveness is, nevertheless, limited in shifting commuters away from the car to sustainable transport modes at the aggregate level. On average, behavioural interventions can shift commuters from cars to bikes, PT or walking by 1% to 3%, a relatively low impact compared to estimates for percentage growth in a particular mode-use. In particular, studies that investigate area-wide transport interventions report smaller estimates than the more limited site-specific studies. Given the interdependence of transport behaviour, area-wide estimates are more relevant for GHG reduction outcomes, meaning that behavioural policy measures have limited potential in reducing urban transport-related GHG emissions.

Based on earlier literature we identified five types of components that are used in behavioural interventions to try to change travel behaviour. We identify the components (Information, Advice & goals, Promotional programs & activities, Incentives, Soft infrastructure improvements) used in each intervention in our database. In terms of specific components, we do not find any single component that is systematically effective at achieving desired transport changes. We find that incentives are better at reducing car use and promoting public transit use, whereas other components such as soft infrastructure are associated with higher biking and walking, respectively. Our analysis does not indicate large differences based on location or scope of behavioural intervention, study design or study quality.

Our work suggests that behavioural interventions, acting alone, are unable to initiate necessary behavioural change to satisfy ambitious targets for transformational change in urban mobility behaviour. Their potential is limited in successfully delivering the required reduction in transport-related GHG emissions. Behavioural policy measures however can facilitate transport-related GHG reduction strategies, which focus on growth in sustainable transport modes as a starting point. These strategies may rely on niche creation to build a base of users who demand improvements in conditions for alternative, more sustainable transport mode use. Behavioural interventions can also support infrastructure and economic policy instruments/measures, providing a bridge between pull and push policies. In addition, by promoting active travel, behavioural interventions have a high potential to address the health effects of sedentary lifestyles. Moreover, behavioural interventions can change specific travel behaviour e.g. trips to school or work that can reduce congestion.

The effectiveness of behavioural interventions: data collection, extraction and organization.

We performed an exhaustive review and meta-analysis of the literature (for details see Methods) on behavioural interventions in urban transport mode choice. Our strategy to collect a diverse body of work is twofold. First, we conducted a review of reviews looking at behavioural interventions. We identify and code 37 literature reviews. Using these review studies, we identify sources (studies, reports, etc.) for extracting information about the interventions and their effectiveness in changing transport behaviour. Based on these studies and reviews, we extract 400 cases of transport behaviour change interventions. Second, we conduct a systematic review of recent literature (see methods) targeting original studies published from 2015 onwards, to include the latest studies from the time of the last comprehensive review in our database. Based on this second review, we identify and code 41 original studies resulting published after 2015.

As explained in Fig. 1, our data extraction strategy is focused on collecting and arranging information in three different types, (i) evidence about the effectiveness of behavioural interventions on transport behaviour, which includes information about intervention types as well as information about travel choices, and (ii) contextual information, such as intervention details (where the intervention took place, for how long, etc.) and lastly, (iii) information for evidence evaluation, mostly information related to study characteristics. Following previous literature, we classify different aspects of the interventions based on what type of behavioural strategy or instrument was used (Information, Advice & goals, Promotional programs & activities, Incentives, Soft infrastructure improvements). Supplementary table A.3 provides a brief description of the different components of behavioural interventions that are reported in the literature.

Studies looking at behavioural interventions in transport report a multitude of travel behaviour indicators that can be used for evaluating the effectiveness of interventions. We restrict our evaluation to four types of transport modes (car use, public transport use, biking and active travel/walking) and three outcome indicators (percentage growth in mode use, change in mode split, change in individual´s transport behaviour[1]) (Supplementary table A.5a). While average growth in mode use is a useful indicator, it is by no means the most important way to evaluate change in transport behaviour. This is especially true due to the inter-connected nature of travel mode choice. Growth in usage of a certain mode does not necessarily mean that its relative use compared to other transport modes (mode shift) changes in the same way. For this reason, we collect separate estimates in mode shift. Here we make a distinction between three different classifications, taking into consideration the outcome variable, the sampling strategy, and the scope of the intervention (more details in Methods & supplementary files). These three classifications are individual-level estimates where estimates are only applicable to the individual samples, site-specific estimates where estimates are based on data collected from the site of intervention and only applicable to the transport behaviour at that site, and lastly area-wide estimates (as explained in Supplementary table A.5b). This distinction is necessary as calculating mode split relies on gathering information at the collective level. For instance, this may be employees or school children for the site where the intervention took place (site-specific estimate) or a more general populace of transport users in the area (area level).

Overall, we collect more than 1000 estimates related to different aspects of travel behaviour change (578 estimates for percentage growth in different modes, 660 mode shift estimates). We can trace back the original source, i.e., the paper or report that gives details on the research design, the specifics of treatments being applied and the findings, in 44% of the cases. A vast majority of estimates are from academic studies. A further 10-20% of the estimates are from studies where some details are missing, mostly in terms of research design, rendering a proper critical analysis difficult or impossible. Most of these estimates originate from public or privately conducted reports. In many cases, the intervention is described in greater detail in these reports. Around 30% of the estimates come from meta-analysis, systematic reviews or reports where significant details on the key elements are missing and there is no access to the original source to verify the reported results. In terms of study design, more than half of the estimates (55%) come from studies with some form of before & after design. Out of these at least 20% indicate that they include some form of the control group, while the remaining 30% either do not include a control group or don´t mention it. Additionally, 10% employ a quasi-experimental design. Nearly, 15% of the estimates employ experimental design. For 10% of the estimates, the study design is not mentioned or clarified.

Our final sample represents research conducted in more than 100 different cities across 37 different countries. However, most of our estimates come from the UK, Germany, Netherlands and the US. Only 8% of estimated from our sample are from Asia (or countries outside the EU, North America & Australia) and even then, they originate mainly from studies in two countries (Japan and China).

Behavioural interventions are quite effective at promoting the growth of sustainable transport modes. However, evidence from area-wide estimates shows that modal shift is harder to achieve.

Fig. 2 (a) shows that the average effect size for percentage growth in car-use is estimated to be around -10% (mean M_car= -10.6 ± 13.1; n = 175). Similarly, for alternate travel modes, the reported effect size is quite large for average percentage growth in mode use, ranging from a 35% increase in bike mode use (mean M_bike= 35.6 ± 84.4; n = 112) to a 10% increase in walking mode (mean M_active= 11.6 ± 19.8; n = 147). The effect size for walking is especially sensitive to the model used for estimation. Restricting our sample to estimates extracted from original studies and reports does not have any substantial impact on effect size calculations as a whole.

A major complication in interpreting these results is the general lack of baseline travel mode use in many of the studies. This makes it hard to assess the actual change in behaviour relevant to climate change mitigation potential. In order to get a better assessment, we restrict our sample to only estimates where the baseline travel mode share is available (Supplementary section B.1). This restriction reduces the sample size for growth estimates substantially. The estimates for car-use and walk mode from this reduced sample are similar to the full sample, whereas the average growth estimates for public transport mode and bike mode are reduced. Furthermore, this analysis indicates that for bicycle use, higher growth estimates are from studies where the baseline bicycle use is low (less than 10% bike mode share, with the highest estimates coming from studies with baseline bike share lower than 5%), while the few estimates from studies with high baseline bike share do not indicate high growth potential. This has two implications, first, even small changes in travel behaviour are magnified in the case of low baseline mode share, as the growth estimates are large. Secondly, it can also mean that behavioural interventions are more important in an environment where there is low bicycle uptake as they can capture the low-hanging fruit of relatively easily persuadable, motivated or inclined to change individuals.

Fig. 2 (b) & (c) reveal that the average effect sizes for mode-shift are substantially lower than average percentage growth estimates across different transport modes. Furthermore, for sustainable transport modes, except bikes, the average effect size for mode shift is lower for area-wide studies as compared to site-specific studies. Studies that report area-wide mode shift in car-use (-4.7; 95% confidence interval (CI) = (2.022, 8.22)), find that the average effect size is lower than those that report mode split at the site-specific level (-7.08; 95% confidence interval (CI) = (-7.11, -5.30)). However, even though the car-shift estimates are lower than car growth estimates, they are still substantial, especially when we consider area-level estimates. On the other hand, mode shift estimates for public transport are much lower, both for area-level (1.02; 95% confidence interval (CI) = (-0.31, 2.2)) as well as the site-specific estimates (3.07; 95% confidence interval (CI) = (2.3, 5.1)). For biking, the average change in mode split at the area level is higher than the site-specific estimates. Typically, mode split estimates range between 1-3%. Our main results are robust to influential study analysis (see supplementary appendix). However, the results are likely to be subject to publication bias, which has been reported earlier in this literature, and incomplete reporting of all different transport behavioural changes (Supplementary file section B.3).

Meta-regression analysis reveals that baseline transport mode share is a key variable in explaining the heterogeneity in intervention effectiveness.

Mode shift estimates for all travel modes are affected by the baseline mode share. Table 1 reports the meta-regression model used to explain the heterogeneity in estimates across studies. Higher baseline usage of a transport mode is associated with a higher effect of behavioural interventions for all transport modes except biking. Baseline car mode (car_base) has a statistically significant positive effect on car use modal shift, i.e., studies which start with a higher baseline car use, also report a higher reduction in car use as indicated by car use modal share. Baseline active travel mode use has a similar positive effect on modal shift estimates reported for active travel mode. The same is true for baseline public transport mode use (PT_base) and modal shift in public transport mode use, although the effect size is much smaller than car mode and active travel mode. On the contrary, the coefficient for base mode use (Bike_base) is negative (ß = -1.53 ± 0.01; p-value < 0.05), indicating that higher initial bike mode leads to lower estimates for bike mode shift after the intervention.

Our analysis indicates that, compared to other intervention settings, interventions delivered at a community scale are associated with a higher impact on mode share across different transport modes. Similarly, interventions conducted in the workplace setting also tend to higher impact on mode share, except for active travel mode. In both cases, the coefficients are not statistically significant. Meanwhile, in terms of geographical location, we find that typically estimates from the UK tend to be higher for all four transport modes as compared to estimates from the EU. Similarly, estimates originating from the US tend to be higher. However, the coefficients, especially for car-use mode shift is very small. Here, again most of the coefficients are not statistically significant at conventional levels. In summary, we do not find any systematic differences in the effectiveness of interventions based on intervention settings or locations across all transport modes.

Concerning study characteristics, there are a few noteworthy differences. First, we do not find any systematic differences between different sources of estimates, meaning that estimates, where we can find the original source, do not differ significantly from estimates that are based only on information from other review studies. There is great variance in study design in our sample, and in some cases, it helps explain the variation in estimates. In general, we find that, as expected, more robust study designs (purely in terms of empirical strategy), especially those that have a control group (experiments or controlled before/after design), are associated with lower estimates as compared to estimates from studies relying purely on before/after design. For instance, the coefficient for CBA and RCT is negative and statistically significant for PT and car travel modes. While the coefficients for RCT for other modes are not statistically significant they are negative as expected.

	Car	Public Transport	Bike	Active
Intercept	325.66	3.54	869.00	-197.66
	(308.19)	(3.64)	(677.73)	(313.76)
Region (base = EU)
Region.Asia	6.65	-6.63	15.28
	(12.27)	(7.68)	(19.14)
Region.Australia	-1.60	3.69	-1.14	0.22
	(3.17)	(3.16)	(7.92)	(2.72)
Region.UK	-5.90^**	11.24	-3.97	5.92
	(2.85)	(10.77)	(12.35)	(3.79)
Region.US	-0.06	-1.67	-27.33^***	2.27
	(3.63)	(2.34)	(5.58)	(2.48)
scope_sum (base = ind)
scope_sum.school-uni	3.97	-2.34	2.45	1.39
	(3.14)	(3.84)	(8.41)	(4.21)
scope_sum.Workplace	-0.46	0.68	6.46	-0.05
	(2.68)	(4.31)	(6.07)	(3.85)
scope_sum.res-community	-1.94	5.70	24.69	2.96
	(6.36)	(5.51)	(15.14)	(5.53)
scope_sum.area-wide	2.22	-2.45	-1.44	2.29
	(2.96)	(4.46)	(6.45)	(3.72)
scope_sum.NI	3.47
	(3.28)
Study_design_code (base = BA)[2]
Study_design_code_2.CBA	3.01^*	3.57	0.24	-2.93
	(1.83)	(3.98)	(7.43)	(3.48)
Study_design_code_2.CS	-8.58	2.57	8.30	7.82
	(5.60)	(2.58)	(12.83)	(8.25)
Study_design_code_2.CCS	-4.42	-6.04	-22.01	-6.07
	(9.20)	(7.53)	(23.61)	(5.93)
Study_design_code_2.Quasi-experiment	6.32	-13.09	-4.96	2.08
Study_design_code_2.Quasi-experiment	(6.30)	(12.43)	(15.92)	(5.90)
Study_design_code_2.RCT	0.45	-7.56^*	-28.62	-3.95
	(4.51)	(4.42)	(17.52)	(3.44)
Study_design_code_2.Experiment	-5.05
	(17.10)
Study_design_code_2.post intervention	6.46	-4.11		-2.77
Study_design_code_2.post intervention	(5.53)	(3.81)		(3.71)
Study_design_code_2.unclear	-1.91	-0.26		-10.43^***
	(2.15)	(4.95)		(3.58)
Study_design_code_2.natural experiment			-5.50
Study_design_code_2.natural experiment			(16.82)
Study_design_code_2.other			-5.54	-8.04
			(15.15)	(6.61)
Year	-0.16		-0.43	0.10
	(0.15)		(0.34)	(0.16)
factor(info.type).2	1.80	-13.79	-10.74	-2.17
	(3.77)	(11.02)	(12.85)	(4.11)
factor(info.type).2.5				-6.89
				(7.15)
factor(info.type).3	2.04	-5.47	-8.31	3.62
	(3.72)	(4.94)	(10.29)	(4.64)
car_base	-0.14^***
	(0.00)
Bike_base			-1.53^***
			(0.01)
PT_base		0.01^**
		(0.00)
active_mode_base				0.09^**
				(0.04)
No. of Effects	274	78	78	93

Table 1 | Results from the Meta-regression model. The dependent variable is different for each column. (i) Car-use mode shift, (ii) Public transport mode shift, (iii) Bike mode shift, and (iv) Active travel mode shift.

Interventions with an incentive component are effective at the increasing use of public transport and reducing car use

Our analysis shows that most of the interventions are a combination of different behavioural interventions and/or incentives (identified and defined by us based on theory; complementary files). Out of 515 cases of mode shift estimates where details of the intervention are available, we find that information provision is the most commonly reported intervention component, followed by (peer, expert) advice (or goal-setting) and soft-infrastructure improvements. Incentives and promotional activities are employed the least. In terms of combinations, information provision is most often accompanied by advice and soft infrastructure improvements (Fig. 3b). However, at the same time, it´s the leading intervention component which is implemented alone (in 20-25% of all the cases where information provision intervention is reported) (Fig. 3a). All other intervention components are typically accompanied by at least one or two more intervention components. The most extreme example of this tendency is the soft infrastructure component, which is accompanied by other intervention components in 95% of the cases.

Given the tendency to combine different intervention components, it is challenging to extract the individual impact of each intervention component. Fig. 3 (c) shows the average effect size for interventions when a component is present. Analyzing variation in average effect sizes for individual intervention components and starting with car use reduction, we find that interventions with incentives have the highest impact on car-use reduction (12.4; 95% confidence interval (CI) = (9.8, 15.02)). This is followed by interventions with (peer or expert) advice and a goal-setting component to them. Soft-infrastructure improvements and promotional activities both tend to have a similar impact. Lastly, out of all the intervention components considered, interventions with different types of information provision tend to have the lowest reported impact on car use mode shift. For public transport mode shift, there is also greater evidence for the effectiveness of incentives interventions. The effect size for intervention with incentive component is (6.1; 95% confidence interval (CI) = (4.1, 8.1)) significantly higher than other intervention components. Other than incentives, interventions with soft infrastructure and information provision seem to have high effectiveness as well.

The results for biking are inconclusive, we do not find any major differences in effectiveness between different interventions. For active travel mode, we find that the intervention with soft infrastructure improvements increases active travel compared to other intervention components. On the other hand, interventions with incentives show a negative impact on active travel modes. Since most of the incentive treatments in our sample are directed at increasing public transport use, the negative impact on active travel mode may be a side-effect of their effectiveness in increasing public transport.

Critical Appraisal

Some limitations should be considered when interpreting the data and designing future studies. First, most of the studies do not provide the full information needed for properly assessing the evidence quality or the intervention details needed. This extends to the method of evaluation, especially when it comes to estimates from reports. Even narrowing down to studies where the original source is available, we can only find all relevant information needed for critical appraisal of results of behavioural intervention on transport behaviour in only 30-40% of the cases. Often information is missing on how and when the intervention was implemented, what exactly the intervention entailed (different components incorporated) and the selection of control groups. A key detail missing in most studies is the parallel developments in transport infrastructure. The supplementary file (section B.3) provides a detailed summary of the information available for all the studies considered and the results based on the information availability.

Additionally, researchers take substantially different approaches to how measuring transport behaviour. As indicated earlier many different outcome variables can be chosen to assess the effectiveness of behavioural interventions. However, this is not the only issue. Even for the same outcome variable, there are many different ways in which estimates from one study may not be comparable to another. For instance, the growth in mode use can be based on the number of trips, the number of users or the total distance travelled. Furthermore, the duration, methodology and precision of how these indicators are measured may differ. Some studies may use a question, others may use multiple-day diaries or even objective measures of travel distance. This makes comparing outcomes over studies a very challenging task. There needs to be a concentrated effort among travel behaviour researchers to push toward coming up with standards for outcome reporting in travel behaviour studies.

Our critical appraisal suggests that there are only a handful of well-conducted studies with appropriately chosen research designs necessary to establish the causal relationships between variables of interest. Only about 30% of the studies employ an experimental or quasi-experimental design with control groups. Moreover, the number of studies that conduct a proper follow-up to establish the stability of behavioural changes is very low. Even when studies do conduct a follow-up, there is not enough information on what other changes may have happened (such as infrastructure or price changes) in both intervention and control groups.

There is an active debate between transportation researchers on the right application of behavioural interventions. For instance, a sizeable number of interventions in our database are based on the idea of using behavioural interventions as a wedge to change travel behaviour during periods of interruption, such as new transport infrastructure provision²⁰ or moving to new housing²¹. However, given the small number of these studies and the lack of detailed information, it is difficult to say whether interventions with such a background are more effective than interventions that do not make these explicit considerations.

Lastly, we find a vast majority of the studies in our review of review are related to high-income countries and cities. Most studies were conducted in the UK (28%) followed by Europe (22%), Australia (17%) and North America (17%), meaning very little representation from Asia (8%) or even worse none from Latin America or Africa. The lack of a greater focus on transport issues in low and middle-sized cities coupled with limited information on conditional effects means that the generalizability and transferability of review findings are undermined. This lack of research in low & middle-income countries with rapidly urbanizing populations and growing car use in cities that are highly vulnerable to climate change is extremely concerning.

[1] We exclude Change in individual´s transport behavior from our anaylsis here.

[2] BA = Before /After design, CBA = Before /After with control group design, Experiment = Randomized control trials, Post-intervention = only post intervention design, Other = other forms of study design, Unclear = study design is not clear.

The importance of reducing private car use and promoting more sustainable transport modes has been amplified by the increasing realization of the need to reduce transport emissions as well as health and economic costs associated with high car use. Given this realization, there has been a push to formulate policy options that are quick and cost-effective in pursuing these objectives. Behavioural transport interventions are one such option. Using a large-scale systematic review of the literature (review of reviews and an original review), we identify more than 1000 estimates from 450 cases that are relevant to our study objectives.

Based on the data we collected, behavioural interventions have a significant immediate impact on the growth of sustainable transport modes, such as public transport use, biking and walking. However, this does not necessarily translate to a change in modal split for different transport modes. The average effect sizes for this crucial indicator are relatively smaller, especially when looking at the area-level changes rather than site-specific effects only.

These findings have several implications for sustainable urban transportation goals. Reducing GHG emissions in urban transportation requires either reducing travel mileage, or shifting to low-carbon fuels or modes. Urban transportation infrastructures are inter-connected and the change in transport behaviour of one individual has an impact on others through various means, so it is important to consider either modal split or total miles travelled as the relevant measure for the reduction in GHG emissions. Based on the average effect size for area-wide car-use mode shift (-4.7%; 95% confidence interval (CI) = (2.022, 8.22)) and assuming that area-wide estimates are long-lasting and apply globally, we calculate that behavioural interventions can, at best lead to a reduction of 150 Mt CO2/yr (ranging between 90 and 210 Mt CO2/yr) from the baseline global passenger transport GHG emissions from urban areas (3 Gt CO2) ²². Such a reduction constitutes a small part of the intended goal of reducing emissions by 30–50% by 2030. Given the relatively small changes in mode split, we can conclude that behavioural interventions have limited potential in reducing urban transport-related GHG emissions on their own.

In terms of public health objectives, there is a wide variety of goals that require different actions. For instance, both air and noise pollution reduction to a very large extent requires a mode shift away from private cars to more sustainable modes in the aggregate. Meanwhile, addressing sedentary lifestyles is more reliant on growth in active travel options. This means that the potential of behavioural interventions is greater for achieving some of the health goals as suggested by higher values for growth estimates in biking and walking modes. Similarly, economic costs are most directly associated with traffic congestion, especially during rush hours and along main arterial roads. Behavioural interventions have greater potential to reduce bottleneck congestion as site-specific mode-shift estimates are generally higher in reducing car modal share. Earlier research shows that behavioural measures and infrastructure measures are similar in effectiveness when it comes to health benefits, but that structural measures such as transport infrastructure are much more relevant for reducing GHG emissions.⁷ Our results confirm this insight.

Our findings guide the role of behavioural transport intervention as related to other forms of transport interventions, especially infrastructure provision and economic policy instruments. This finding is in line with recent advances in sustainable urban mobility transformation that emphasize the need for an integrative mix of strong policies, likely a combination of infrastructure provision, pricing mechanisms, rules & regulations and travel demand management measures such as behavioural interventions^23,24. Our review hints at the greater potential for using behavioural interventions strategically. A slew of studies in our database describe interventions that are combined with infrastructure provision (such as the opening of a major transit line or closing of a road, new housing provision) or use opportune moments in an individual´s lifecycle (family formation, moving to a new house, job change). While these interventions are motivated and grounded in particular theories of change, and thus more likely to succeed, the evidence base for the comparative advantage of this kind of intervention is still thin. More research is needed to establish their superior effectiveness.

Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information and on GitHub. All the information collected in this project is publicly available in line with the systematic reviews reporting protocol.

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Meta-analytical strategy

We conduct two searches for articles: (1) a string-based search of bibliographic databases for the relevant existing literature reviews and the studies referenced in them, and (2) a follow-up string-based search of bibliographic databases to collect more recent publications (original studies from 2015 to 2020). Both our searches used the following social scientific article databases: Scopus and Web of Knowledge. The reviews were conducted in accordance with the ROSES guideline (for more information about the ROSES guideline see ²⁵. The literature search was broad and inclusive.

For the first search, we focus only on review articles. We use a broad categorization of literature reviews ²⁵. We conduct the search in two waves. In the first and primary wave, we design our search query (search string) for the WoS database. Afterwards, we do a step-by-step screening process with multiple checks along the way to ensure consistency and agreement of exclusion/inclusion criteria among authors. In the secondary step, we complement the WoS search, with a query designed for Scopus (search queries (Supplementary Table 1). Lastly, rolling review from references in the already identified articles. In the first step, we include papers that review the literature on the adoption of low-carbon transport modes. This means we do not include original studies but only papers which collect/synthesize/review available evidence base. More specifically, we are interested in studies which look at the impact of behavioural interventions on urban transport behaviour. We do not consider studies which are primarily related to the benefits of using these low-carbon transport modes.

The first search yielded 3954 studies related to transport mode choice. After title and abstract screening, we can narrow down the potential list of articles to 276. A team of 3 authors did a full-text review of these articles. After full-text screening and reference tracing, we finally get 37 literature reviews that are focused on behavioural/soft interventions to change transport behaviour. After identifying these 37 review studies, we begin extracting information about the relevant underlying case studies referenced in these reviews. This process resulted in identifying around 400 different relevant cases. We define relevant case studies if they report some mobility (transport) behavioural change estimate (either modal shift, growth in one of the transport modes or individual behavioural change) for (at least) one of the relevant transport modes.

For the second search, we focus on original articles from the period 2015-onwards. The time-period was selected to target articles published after the last comprehensive review from our first search. The search string was developed for WoS and then adapted to Scopus. The search string for this second search differed from the first one on two accounts, first since we are interested in original studies so we remove strings related to review (and review types), secondly for this search we include search strings related to behavioural, soft interventions (keywords/strings developed from a list of relevant words from the review studies) to narrow down the potential studies to only those related to these interventions. The search string was developed iteratively to capture the highest proportion of a set of already identified relevant studies. This second search yielded 9946 articles. Here again, we apply hierarchical screening of results starting from titles to abstracts/ keywords to the full text. After full screening and reference tracing, we are left with 41 relevant cases that we use to extract information about the efficacy of behavioural interventions on transport mode choice. The full details of the search process including the search strings, list of review articles and the list of cases (from the first as well as second search are available in the complimentary files). We report the adapted ROSES (RepOrting standards for Systematic Evidence Syntheses) flowchart for screening and coding is available in Supplementary Fig. 1.

Data extraction & Standardization

In terms of data extraction, we first try to build a database of unique case studies from both the first search review papers as well as original studies from the second search. As a first step, we trace back the original papers or reports (wherever possible) that provide detailed information about the case study reported in our review papers obtained from the first search. In case, original studies are not available or the review paper/report is the most detailed description available we use all available information about the case study from the review article. Lastly, we repeat the same process for the studies obtained from the second search. To make sure that we keep the distinction between these different sources we classify each case based on the article source type.

For each case study, we extracted data on evidence about the effectiveness of behavioural interventions on transport behaviour, this includes information about intervention types as well as information about travel choices, contextual information, such as intervention details (where the intervention took place, for how long, etc.), information for evidence evaluation, such as study details (study design, randomization, control group type, data collection methods, etc.). Concerning information about intervention, we start by classifying whether or not the intervention contains any of the five different categories of behavioural interventions described in Table A.3 Supplementary files: Methods). Afterwards, we extract contextual information about the intervention, including where the intervention took place, its scope, and the duration of the intervention etc., more details about this are available in Supplementary files: Methods. Secondly, for information about the effectiveness of the intervention, our strategy is to keep broad criteria for extracting information about transport mode choice behaviour. While extracting estimates we define the following aspects (full discussion in supplementary files):

Estimate type: Mode shift, Growth in mode use, Change in transport mode use behaviour
Estimate scope: Area-wide, Site-specific, Individual-level

Typically, these two aspects go together, for instance, mode shift estimates can only be area-wide or site-specific estimates, in terms of estimate scope and can not be individual-level estimates. The scope of estimates depends on the sampling three aspects, treatment scope, sampling strategy, and estimate type. Because most of the estimates from original studies (especially estimates for mode shift & growth in mode use) are reported in terms of percentage changes and the variance is often missing, so we keep this formulation and do not convert these estimates to cohen´s d or fisher´s Z value as is often the case in this literature. We used effect sizes as reported in the original study or, where possible, used information presented to calculate the outcome variables. It was common for studies to not report variance or p-values, therefore we rely mostly on changes in transport behaviour (in percentage terms).

Lastly, we also collect information about the study details which are essential to understanding the reliability of the estimates obtained from the case study. The information extracted in this context forms the backbone of our critical analysis that is discussed here, however full details of the information collected about the study and its relevance for critical analysis are given in Supplementary file: Methods.

Critical appraisal

We conduct two levels of critical appraisal. First, we conduct a critical appraisal of literature reviews that form the basis of our first search. We categorize the reviews in one of the 7 categories based on categories by Haddaway and Macura (2018) and summarized in Table A.8.1 Supplementary files: Methods. In terms of study criteria for meta-analysis (i.e. literature reviews with evidence aggregation), we rely on ²⁶ and ¹⁹ to come up with the following main criteria: appropriate scope and design of search query (comprehensiveness rating), whether underlying studies are identified clearly (study description), whether interventions are identified and described consistently and appropriately (intervention information rating), whether there is a selection process (selection) and if other steps are described transparently (protocol & transparency), whether results are presented in a consistent and appropriate manner (outcome reporting), whether results are analysed and standardized across studies using meta-analysis techniques (meta-analysis) and lastly whether any critical appraisal of the studies is done or not (critical appraisal).

Secondly, we also conduct a critical appraisal of original literature to assess the quality of the underlying studies and to assess the risk of bias in each included study. To do so, we consider the following aspects; Pre- post- data, Control group, Comparability between control and treatment groups, Sampling Biases, Outcome measurement, Randomization, Response rate, Attrition rate, Statistics, Follow-up, incomplete data, and ethical review. Based on the ratings we give an overall score to studies where the majority of this information is available. Outcomes of the critical analysis and the results filtered by critical analysis are available in supplementary files.

For each case study, we gave ratings about the availability of information with regards to three critical aspects; (i) evidence about the effectiveness of behavioural interventions on transport behaviour, this includes information about intervention types as well as information about travel choices, (ii) contextual information, such as intervention details (where the intervention took place, for how long, etc.) and lastly, (iii) information for evidence evaluation, mostly information related to study characteristics. Results based on critical analysis and information availability rating are available in supplementary files.

Investigating small-study effects and publication bias

We examined the asymmetry of the published evidence by generating funnel plots of effect size against the inverse of study size (Supplementary file Fig. B.7) and calculated the summary of Egger’s regression coefficient and P value. The coefficient from the Egger regression tests whether the y-intercept is zero. The expectation is that the y-intercept is zero if there is an even spatial spread of studies within the funnel. The coefficient is the effect size normalized. Small P values on the Egger regression coefficient suggest the presence of a small study bias that may produce larger effects. Both funnel plot and Egger’s test suggest that the estimates for Public transit and active transport mode show small-study effects/publication bias.

Moderator variables for effect size heterogeneity

The average treatment effects were estimated using a random effects model and a multilevel model²⁷. The multilevel analysis explicitly models that several of the effect sizes (level 1) come from the same study (level 2). Moderator variables in a meta-regression are factors that influence the conditional expectation of the effect size. The meta-regression models used to investigate the causes of heterogeneity in effect sizes were estimated using the random effects and multilevel models and by introducing moderator variables into the estimation equation. We consider the following three types of variables; (i) where the intervention took place for example region (US, UK, EU, etc.), treatment scope (individualized, res-community, school-uni, workplace, etc.); (ii) information about the study, e.g. study design (before-after, CBA, Quasi-experiment, RCT, etc.), Information type (original study with full details, reports with some details missing, report summaries, review mentions), and lastly, the baseline mode share against which the outcomes can be compared (baseline car mode share, baseline PT mode share, etc.).

There is NO Competing Interest.

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review data
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Supplementary material
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Review of Review

Behavioural interventions change individual transport choices but have a limited impact on transport mode split. Evidence from a systematic review

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Abstract

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Introduction

Main Text

Critical Appraisal

Discussion

Declarations

References

Methods