# How to create a sustainable growth in bicycle traffic?
## The case of Paris

Alexandre Lanvin, Jean Charléty, Guillaume Ferreol, Anatole Homann, Elliot Massey, Alix Vermeulen, Alexandre Chasse

► To cite this version:

Alexandre Lanvin, Jean Charléty, Guillaume Ferreol, Anatole Homann, Elliot Massey, et al.. How to create a sustainable growth in bicycle traffic? The case of Paris. Journal of Cycling and Micromobility Research, 2025, 5, pp.100081. 10.1016/j.jcmr.2025.100081. hal-05454035

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# How to create a sustainable growth in bicycle traffic? The case of Paris

Alexandre Lanvin<sup>a,†</sup>, Jean Charléty<sup>a</sup>, Guillaume Ferreol<sup>b,c</sup>, Anatole Homann<sup>b,c</sup>, Elliot Massey<sup>b,c</sup>, Alix Vermeulen<sup>b,c</sup>, Alexandre Chasse<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Bicycle traffic

Public policies

Sensitivity analysis

Longitudinal study

## ABSTRACT

In recent years, cycling has received notable investments in response to the urge to decarbonize the transportation sector, leading to an increase in its modal share in many territories. The present article presents a longitudinal study that evaluates the impact of various public policies on bicycle growth in Paris over the period 2018–2023. Variables of interest can be classified according to five themes: targeted cycling policies, restrictions imposed on motorized vehicles, disruptions on other modes of transport, livability policies, and economic conditions. The methodology is based on dependence-aware sensitivity analysis techniques to assess the separate contributions of each of these variables on bicycle traffic, aggregated at the city scale, and extracted from ground counter data. In order to be exhaustive, variations due to weather and calendar dynamics are also controlled for. Results indicate that local authorities should primarily focus on the development of bicycle infrastructure, including bicycle lanes, parking slots, and effective bicycle-sharing systems. While these measures are instrumental, sustainable growth relies on the implementation of a diverse range of policies. Bicycle usage can be further expanded by making it more attractive, specifically through the creation of pedestrian zones and green spaces, or the promotion of e-bikes. Additionally, cycling tends to increase as car usage becomes more difficult, although this finding calls for equity considerations.

## 1. Introduction

The transportation sector is the second largest emitter of greenhouse gases globally<fnref n="1" /> and the top emitter in France.<fnref n="2" /> In consistence with the zero-net carbon objectives by 2050,<fnref n="3" /> this significant impact necessitates a coordinated response from various levels of government to transition the industry towards more sustainable practices. Efforts include the development of new transportation options (e.g., addition of new public transport lines, introduction of new bicycle lanes, etc.) and restrictions on certain practices (e.g., implementation of Low Emission Zones (LEZ), ban on sales of vehicles with internal combustion engines, etc.).

In this context, cycling stands out as a key solution for low-carbon transportation, as it produces no emissions once in use. As an alternative to motorized transport, it helps alleviate congestion (Bergström and Magnusson, 2003; Hamilton and Wichman, 2018), reduce air

pollution (Huang et al., 2022; Keall et al., 2018; McQueen et al., 2020), and minimize noise pollution (Monazzam et al., 2021). Similarly, when replacing public transportation, it can help ease overcrowding (Lin et al., 2023; Sun and Zacharias, 2017), potentially increasing the attractiveness of public transit and further decreasing motorized traffic. Additionally, the advantages of cycling extend beyond environmental concerns. It promotes better physical (Garrard et al., 2012; Huy et al., 2008; Oja et al., 2011) and mental health (Kaplan et al., 2019; Ma et al., 2021; Ryu et al., 2020), with benefits such as reduced cardiovascular risks and improved mood. Overall, cycling has been shown to reduce societal costs, with the example of approximately 1€ gain per kilometer cycled in the Benelux and North Rhine-Westphalia (Vanpée and Van Zeebroeck, 2022). To this end, countries are setting ambitious goals to raise their cycling modal share, as illustrated by France's target of 12% by 2030, a significant increase from the 2% measured in 2019.<fnref n="4" />

<footnote><sup>a</sup> Department of Control, Signal and System, IFP Energies Nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France</footnote>

<footnote><sup>b</sup> ESSEC Business School, 95000, Cergy, France</footnote>

<footnote><sup>c</sup> CentraleSupélec, Université Paris-Saclay, 91190, Gif-sur-Yvette, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: alexandre.lanvin@ifpen.fr (A. Lanvin), jean.charlety@ifpen.fr (J. Charléty), guillaume.ferreol@essec.edu (G. Ferreol), anatole.homann@essec.edu (A. Homann), elliott.massey@essec.edu (E. Massey), alix.vermeulen@gmail.com (A. Vermeulen), alexandre.chasse@ifpen.fr (A. Chasse).</footnote>

<footnote n="1">¹ https://www.statistiques.developpement-durable.gouv.fr/edition-numerique/chiffres-cles-du-climat/fr/5-panorama-mondial-des-emissions-de.</footnote>

<footnote n="2">² https://www.statistiques.developpement-durable.gouv.fr/edition-numerique/chiffres-cles-du-climat-2023/pdf/pages/partie4/partie4.pdf.</footnote>

<footnote n="3">³ https://climate.ec.europa.eu/eu-action/climate-strategies-targets/2050-long-term-strategy_fr.</footnote>

<footnote n="4">⁴ https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000039666574.</footnote>
To achieve these objectives, local authorities are multiplying initiatives. These measures have already shown positive results, as evidenced in Paris, where bicycle counters have recently registered a record number of crossings.<fnref n="5" /> This case provides a historical basis for evaluating the effectiveness of different interventions, enabling comparisons to determine the most efficient strategies for future implementation.

The present study introduces a longitudinal study of the effects of public policies on bicycle traffic using dependence-aware sensitivity techniques, with a focus on the whole city of Paris from 2018 to 2023.

Section 2 provides a literature review of the temporal variability of bicycle traffic due to factors such as weather, calendar dynamics, and public policies, introduces the research gaps, and presents an overview of sensitivity analysis. Section 3 outlines the sources of bicycle traffic and exogenous variables data, along with the introduction of the methodology implying kernel SHAP and StratIMPORT. Section 4 covers a detailed analysis and discussion of the results of the sensitivity analysis, followed by the conclusion in Section 5.

## 2. Literature review

Bicycle traffic fluctuates over time (Ashqar et al., 2019; Bédécarrats, 2021; Feng et al., 2025) and space (Harms et al., 2014; Nelson et al., 2023; Yang et al., 2023). By focusing on the entire city of Paris, this study will exclusively examine its temporal dimension.

### 2.1. Factors influencing cycling

Bicycle traffic fluctuates dynamically due to various influencing factors, which fall into two timescale categories: day-to-day variations and long-term variations. Public policies, which aim at gradual and sustainable behavioral changes, address the latter. Conversely, day-to-day variations are associated with events that have a fairly immediate impact, such as precipitation. Additional details are given below.

#### 2.1.1. Day-to-day variations

Weather is a primary determinant in cycling fluctuation (Bean et al., 2021; Heinen et al., 2010; Nankervis, 1999). Key weather elements include temperature, precipitation, humidity, wind speed, and snow fall, which impact cycling in nonlinear ways. Precipitation, wind speed, and snow fall tend to decrease bicycle traffic (Hong et al., 2022; Noland, 2021; Zhao et al., 2018), while temperature and humidity exhibit a U-shaped relationship. Bicycle traffic increases with rising temperatures up to a certain threshold, beyond which it becomes too hot to cycle (Nosal and Miranda-Moreno, 2014; Pazdan et al., 2021; Wessel, 2020). Similarly, conditions that are excessively dry or humid deter cyclists (Lanvin et al., 2024; Zhou et al., 2023). Other weather-related factors, such as daylight hours (Morton, 2020; Phung and Rose, 2007; Thomas et al., 2013), visibility (Kutela and Teng, 2019; VE and Cho, 2020; Yang et al., 2016), atmospheric pressure (Lanvin et al., 2024), and nebulosity (Wessel, 2020), are also occasionally considered.

Calendar dynamics also play a crucial role in determining bicycle traffic. People's cycling patterns are dependent on the day of the week and their specific routines (Galich et al., 2021; Hong et al., 2022; Noland, 2021). Weekday travel is typically centered around commuting for work or school, while weekends see a shift towards leisure-oriented trips. Even within weekdays, variations occur. For instance, remote work is more common on certain days than others (Motte-Baumvol et al., 2024), altering travel demand. Holidays contribute to changes as well (Chibwe et al., 2021; Morton, 2020; Phung and Rose, 2007) as leisure activities get more prominent, locals often leave, and certain areas become hubs for tourists.

The relationship between air quality and bicycle traffic remains under discussion. In Asian cities like Seoul and Beijing, significant associations have been found (Hong et al., 2022; Kim, 2020; Wang et al., 2022), while European cities such as London (Morton, 2020), Paris, Lyon, Nantes, and Tours (Lanvin et al., 2024) show contrasting results. This suggests that the effect may be region-dependent and could also vary based on a city's overall pollution levels.

#### 2.1.2. Long-term variations

While the previous section discusses events that influence bicycle traffic over short periods, public policies have a more lasting influence, shaping traffic patterns not just for a day but over the long term. Some policies are specifically designed to promote bicycle usage, such as the implementation of bicycle lanes and bicycle-sharing systems (BSS). However, the broader transportation system also plays a pivotal role. For example, issues related to public transportation can encourage a modal shift towards cycling. Overall, five categories of public policy variables affecting bicycle traffic can be identified: targeted cycling policies, restrictions imposed on motorized vehicles, disruptions on other transportation modes, livability policies and economic conditions. This section provides an overview of the corresponding literature.

Research consistently highlights the role of targeted cycling policies in fostering its development. Investments in dedicated bicycle lanes have been shown to increase ridership (Félix et al., 2020; Karpinski, 2021; Macedo Filho and Cunquero, 2024) by enhancing both safety (Feuerberg, 2004; McNeil et al., 2015; Vanparijs et al., 2015) and inclusivity (AitBihiOuali and Klingen, 2022; Aldred et al., 2017; Garrard et al., 2008), as well as reducing conflicts with motorized traffic. To be efficient, bicycle lanes must follow a series of rules, such as a clear separation for the pavement, a width adapted to traffic, and a continuity of the network, as suggested by the CEREMA guidelines in France.<fnref n="6" />

The expansion of BSS can further support cycling adoption (Chibwe et al., 2021; Fuller et al., 2013; Xu and Chow, 2020), particularly in urban areas where first- and last-mile connectivity is crucial (Collectif Mobilité, 2023; Fan et al., 2019). Yet, a well-thought implementation is fundamental for the BSS to succeed, as several cases have resulted in failure. In Poznań, Poland, the BSS was introduced in 2012 and initially grew until being ultimately shut down in 2022. Key factors leading to the closure include the competition from free-floating e-scooters, which have been available since 2018, and poor maintenance. Likewise, many BSS companies in China overproduced bicycles due to low manufacturing costs and inaccurate estimation of potential users, resulting in the infamous bicycle graveyards. Different elements should be considered for an efficient BSS, such as a good legal framework, an efficient mobile application, and prevention of theft and destruction.

The popularization of electric bicycles (e-bikes) has also been linked to increased ridership (Fyhri and Fearnley, 2015; Rérat, 2021; Tolzluğlu et al., 2024), especially among older adults (De Haas et al., 2022) and commuters covering longer distances (Fyhri and Fearnley, 2015; Melia and Bartle, 2021). E-bikes have extended the use of mechanical bicycles, as new technologies have increased its carrying capacity and introduced new practices, such as transporting children or delivering parcels. If the popularization does not constitute a policy per say, it usually results from political initiatives (typically, public funding).

In like manner, the provision of secure and accessible bicycle parking has been identified as a key factor in encouraging cycling (Buehler, 2012; Heinen and Buehler, 2019), as inadequate infrastructure and potential thefts often serve as a barrier to regular use (Collectif Mobilité, 2023). It also constitutes a key lever for intermodality when located near public transportation stations (Geurs et al., 2016; Pucher and

<footnote n="5">⁵ https://www.paris.fr/pages/quatre-pistes-cyclables-equipees-de-comteurs-velos-7489.</footnote>

<footnote n="6">⁶ https://www.cerema.fr/fr/actualites/8-recommandations-reussir-votre-piste-cyclable.</footnote>
Buehler, 2012), improving accessibility, and consequently increasing ridership in both transportation modes.

Other targeted cycling initiatives include company-funded schemes or promotional programs (Dubuy et al., 2013), improved coordination between cycling and public transportation (Jayarathne et al., 2024; Martens, 2007), and the development of dedicated digital platforms (Meireles and Ribeiro, 2020; Su et al., 2010).

To be noted, the cultural and legislative context of a country plays a significant role in defining bicycle usage. On the one hand, countries like the Netherlands, Germany, Japan, and Denmark are associated with high cycling modal shares (Buehler and Pucher, 2012b; Goel et al., 2022), which derive from decades of political focus. On the other hand, countries like the United States of America (USA), known for its car-centric culture, or Brazil, report modal shares closer to 1% (Goel et al., 2022). The USA, in particular, presents a high cyclist fatality rate (Buehler and Pucher, 2017), which reflects the marginalization of cyclists within its broader mobility ecosystem, partially explaining the situation. Remarkably, progress in cycling policies contribute to greater cyclist diversity, thus to a virtuous circle of increased usage, reducing gender gaps in countries or cities with high cycling modal shares. In contrast, areas with low modal shares tend to display a notable male over-representation among cyclists (Pucher et al., 2011; Sá et al., 2018).

Alternatively, an increase in bicycle usage can result from **restrictions imposed on motorized vehicles**, encouraging a modal shift. Many cities have implemented LEZs to control greenhouse gas emissions. As a side effect, these measures make it more difficult for drivers to access certain areas, particularly city centers. This has led to a rise in bicycle usage in several cities (Diallo et al., 2025; Gonzalez et al., 2022; Tarriño-Ortiz et al., 2022). Furthermore, motorized travel is heavily influenced by fuel prices, as over 95% of cars in France, as of 2024, run on diesel or petrol.<fnref n="7" /> Consequently, a rise in fuel prices often prompts a shift towards cycling as an alternative mode of transportation (Findenegg and Wessel, 2024; Nguyen and Pojani, 2024; Rashad, 2009). Yet, certain groups, such as low income households, are very sensitive to cost variations (Mattioli et al., 2018) or more generally to restrictions on motorized vehicles (Liotta, 2023), with a fraction of them unable to shift to cycling. This highlights the socially divisive nature of such measures, as showcased by the Yellow Vest movement in France that started after a rise of fuel price.

Building on the idea that restrictions on motorized vehicles can drive a shift towards greater bicycle usage, **disruptions on other transportation modes** similarly encourage individuals to turn to cycling. Evidence suggests that public transportation disruptions, whether due to strikes, service malfunctions, or station closures, often lead to increased bicycle usage as commuters seek alternatives, reliable modes of transport (Fuller et al., 2012; Li et al., 2022; Saberi et al., 2018). It should be noted, however, that while cycling draws trips from other modes, its development may concurrently reduce walking, which can be viewed as a potential negative externality.

Beyond mobility-specific events, **livability policies**, such as the development of green spaces (Lu et al., 2019; Ullmann et al., 2022; Zhao et al., 2020) and the implementation of pedestrian zones (Santos et al., 2013), can indirectly promote increased bicycle usage. The more enjoyable and accessible cycling becomes, the more individuals are inclined to adopt it as a mode of transportation.

Finally, cycling levels are closely tied to **economic conditions**, as bicycles offer an affordable mode of transportation. During economic downturns, individuals may turn to cycling as a cost-effective alternative to cars or public transit. For instance, Chibwe et al. (2021) highlights a correlation between higher unemployment rates and increased bicycle traffic, which is consistent with the dependence of mobility patterns on purpose (Ile-de-France Mobilités, OMNIL, DRIEAT,

2020). Likewise, Zahran et al. (2008) underscores that bicycle traffic is subject to the wealth of the inhabitants of a territory. Conversely, in lower-income communities, the lack of safe cycling infrastructure and affordable bicycle-access programs can hinder cycling adoption (Braun et al., 2019).

### 2.1.3. Gap analysis

Most studies on the effects of public policies on bicycle traffic fall into one of three categories: (1) cross-sectional analyses (Buehler and Pucher, 2012a; Rietveld and Daniel, 2004; Santos et al., 2013), (2) longitudinal studies at a local scale (typically at the link level or within a surrounding buffer) (Chen et al., 2017; Le et al., 2019), or (3) evaluations focused on only one or a few policies rather than a comprehensive set (Chibwe et al., 2021; Findenegg and Wessel, 2024; Karpinski, 2021). Cross-sectional studies lack a dynamic perspective; localized longitudinal studies may misinterpret growth, where an increase in one area could stem from a decline in a neighboring region rather than overall growth; and policy-specific studies risk bias, as policies are often intertwined, making it difficult to isolate their individual effects.

This paper presents a longitudinal analysis that evaluates the impact of a comprehensive range of public policies on cycling. By considering multiple policies simultaneously, this study provides a more holistic understanding of their collective impact. As it focuses on an entire city, the approach mitigates the risks of misinterpreting localized growth patterns. The interconnected nature of policies are accounted based on the methodological points mentioned in the following section.

## 2.2. Sensitivity analysis

Sensitivity analysis evaluates the contribution of input variables to an output, helping to understand model behavior. Techniques can be broadly categorized into global and local explanations. The former provides an overall assessment of feature influence across the entire input space, with methods such as permutation importance (Breiman, 2001; Strobl et al., 2008), Gini importance (Breiman, 2001; Nembrini et al., 2018), and Shapley Additive Global importanceE (SAGE) (Covert et al., 2020), that quantify the impact of each variable on model performance. The latter, on the other hand, focuses on instance-specific explanations, offering insights into how individual predictions are made. Notable techniques in this category include SHapley Additive exPlanations (SHAP) (Lundberg and Lee, 2017), and Local Interpretable Model-agnostic Explanations (LIME) (Ribeiro et al., 2016) which help interpret model outputs at a sample level. While local methods provide case-by-case explanations, they can be aggregated to form a comprehensive global understanding of feature importance.

Although some methods, such as SHAP, do not explicitly assume input independence, they still require it in practice or, at least, a low level of collinearity. Similarly, the interpretation of linear regression coefficients, a commonly used technique for modeling relationships between variables, literally relies on the assumption of input independence. To address this limitation, Aas et al. (2021) and Sellereite and Jullum (2020) extend the kernel SHAP method, a model-agnostic version of SHAP, to accommodate to co-dependent variables. Similarly, Parr and Wilson (2021), Parr et al. (2024) introduce StratIMPORT, a novel stratification-based technique designed to support correlated features without relying on a fitted model. Both of these methods are considered in the study.

In summary, this paper contributes to the literature by conducting a longitudinal study of a comprehensive set of public policies on bicycle traffic, using dependence-robust sensitivity methods such as kernel SHAP and StratIMPORT, with a focus on the Paris case from 2018 to 2023.

<footnote n="7">⁷ https://www.statistiques.developpement-durable.gouv.fr/393-millions-de-voitures-en-circulation-en-france-au-1er-janvier-2024.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. Location of bicycle counters in Paris, represented in red.</figcaption></figure>

# 3. Method and data

## 3.1. Data description

This study focuses on Paris, the capital of France, which has a population of over 2 million inhabitants but draws more than 13 million people from its broader functional area. Following significant investments in cycling policies, the cycling modal share of the city has risen from 3% in 2018 (Ile-de-France Mobilités, OMNIL, DRIEAT, 2020) to 11% in 2023 (Institut Paris Région, 2023), with projections ranging between 20% and 30% by 2030 (Courbe, 2020). Overall, Paris ranks as the 12th most cycling-friendly big city in France.<fnref n="8" />

### 3.1.1. Bicycle traffic data

Bicycle traffic data is collected from bicycle counters, which are accessible via the open-data portal of Paris. These counters exclusively record bicycle activity, excluding other types of vehicles, with reliable levels of accuracy<fnref n="9" /> evaluated by Nordback and Janson (2010) and Proulx et al. (2016). The city has a total of 114 bicycle counters, strategically located, as shown in Fig. 1.

To ensure accuracy, outlier data are excluded. This includes instances where counters record no bicycle traffic and data points that significantly deviate from typical values. For a comprehensive city-wide analysis, the data is aggregated daily using the median, which is robust to potential remaining outliers. Let $\Omega_t$ be the set of the preprocessed counter data on day t and $DBT_{counter,t}$ the daily bicycle traffic measured at counter-level on that day. The overall daily bicycle traffic in Paris on t, denoted as $DBT_{Paris,t}$, is defined as follows:

$$DBT_{Paris,t} = \underset{counter \in \Omega_t}{\text{median}} DBT_{counter,t} \qquad (1)$$

$DBT_{Paris,t}$ thus represents the median traffic recorded by counters on day t. The resulting values are displayed on Fig. 2. For clarity reasons, the monthly and yearly means of $DBT_{Paris}$ are presented to avoid overloading the figure with daily data. A drastic decrease in bicycle traffic is observed in 2020 due to the COVID-19 pandemic from March to May 2020, followed by a surge in cycling popularity and then another

decrease during the second lockdown from November to December 2020, after which the growth speed resumes to its pre-pandemic levels. Overall, between 2018 and 2023, there was a rise of more than 240% in yearly mean of $DBT_{Paris}$. A recurring M-shaped temporal pattern can also be noted annually, with bicycle traffic increasing steadily from January to July, dropping in August due to summer holidays, rising again to high levels in September, and then gradually declining until next January.

### 3.1.2. Weather and calendar data

Weather data is retrieved from the closest World Meteorological Organization (WMO) station to Paris (Orly), which stands as a world reference in terms of meteorological observation. The data is scraped and aggregated, considering key meteorological variables identified in the literature: temperature, precipitation, humidity, wind speed, and snowfall (Lanvin et al., 2024; Wessel, 2020; Zhao et al., 2018). On the one hand, daily mean temperature, humidity and wind speed are considered, while on the other hand, precipitation and snow fall are aggregated using maximum daily value to account for short-term extreme weather conditions.

Holidays are derived from the official French public holiday calendar. Lockdowns are taken into account according to their official start and end dates. To address the increase in bicycle traffic between the two first lockdowns, this period is treated as a separate category. Additionally, the strike against the pension reform, which significantly disrupted public transportation from December 5, 2019, to February 20, 2020, is also considered.

### 3.1.3. Public policies data

Data on public policies are gathered from a variety of sources, summarized in Table 1. The total length of bicycle lanes and the number of bicycle parking slots are derived from historical data available on OpenStreetMap (OSM), which serves as the most suitable proxy. This includes the short-term bicycle lanes created during the COVID-19 pandemic, named coronistes, which have since been progressively made permanent. To prevent any duplication, the bi-directionality of bicycle lanes is not counted twice, therefore measuring the total length of road segments that include a bicycle lane. Data on bicycle-sharing stations are sourced from Vélib' Nocle, which maintains a historical record of Vélib' data. As data can be missing for some days, null values

<footnote n="8">⁸ https://barometre.parlons-velo.fr/2021/.</footnote>

<footnote n="9">⁹ https://www.eco-counter.com/solutions/counting-solutions/urban-zelt.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Evolution of DBT.</figcaption></figure>

are replaced by the value of the day before. BSS data are available only from 07/01/2018 onward; therefore, all variables in the study are considered starting from that date. The popularity of e-bikes is estimated by the proportion of sold bicycles that are electric.

Information on the LEZ is taken from its official schedule, which restricts the circulation of diesel vehicles registered before 2000 (CRIT'Air 5 and unclassified) starting from July 2019, and diesel vehicles registered before 2005 (CRIT'Air 4) starting from June 2021. Diesel price is considered to account for fuel price as it is the most used vehicle fuel in France.<fnref n="10" /> It is derived from carbu.com at national level.

Île-de-France Mobilités (IDFM), the regional transportation authority, provides data on the punctuality of suburban trains (RER and Transilien) within a five-minute tolerance. To account for the cost of public transportation, the monthly price of the all-zone Navigo pass, which increased in January 2023, is taken into consideration. Meanwhile, in April 2023, Parisians voted as part of a public consultation to ban self-service e-scooters. The measure is effective since September 1, 2023 and modeled as a Boolean variable.

Concerning information on livability policies, the open data platform of the City of Paris provides rich data, including details about pedestrian zones and the creation of additional green spaces. Of note, pedestrian zones include school streets, which have been gradually prohibited to motorized vehicles since 2020 for both safety and environmental considerations. For simplicity reasons, extensions to preexisting green spaces are not included.

Economic data are obtained from INSEE, the French National Institute of Statistics and Economic Studies. Unemployment rate statistics are provided at the city level while purchasing power statistics are derived from the national level as a percentage of the value of the last quarter of 2017.

Due to differences in sampling frequency, monthly or less frequent data are interpolated to daily data by assuming that they remain constant over their respective intervals. Correlation between public policy data is presented in Table 2.

As can be observed, most public policies are correlated, as they collectively contribute to a broader effort towards greater sustainability. Indeed, all targeted cycling policies see high correlations between themselves (ranging from 0.63 to 0.95), but also to the additional surface of green spaces or the number of pedestrian zones (up to 0.97). Consequently, the assumption of uncorrelated features does not hold, which justifies the use of dependence-aware sensitivity techniques, discussed further in the next section.

## 3.2. Methodology

This study relies on two sensitivity analysis techniques, StratIMPORT and kernel SHAP, to mitigate over-reliance on the assumptions of any single method and to ensure more robust results.

StratIMPORT is a model-free method that quantifies the effects of variables, even when correlated, on a target variable y (in this case, DBT<sub>Paris</sub>). It is based on the estimation of the ideal partial dependence PD<sub>i</sub> (definition in Eq. (2)) of y on a variable x<sub>i</sub> by stratifying the data.

$$PD_i : z \mapsto \int_{\min(x_i)}^{z} \frac{dy}{dx_i} dx_i \qquad (2)$$

Following the definitions given in Parr et al. (2024), we focus on importance, which reflects empirical effects based on observed data, rather than impact, which represents theoretical effects. This choice aligns with the retrospective nature of our study, which assesses the importance of variables influencing bicycle traffic in Paris from 2018 to 2023. To enhance the reliability of StratIMPORT, we apply bootstrapping with 2000 trials. Let p<sub>i</sub> be the density function of x<sub>i</sub> and n the number of variables. Normalized importance is defined as follows.

$$NI_{StartIMPORT,t} = \frac{\int_{\min(x_i)}^{\max(x_i)} p_i(z) |PD_i(z)| dz}{\sum_{j \in \mathcal{N}_n} \int_{\min(x_j)}^{\max(x_j)} p_j(z) |PD_j(z)| dz} \qquad (3)$$

Kernel SHAP is a local sensitivity technique that provides post-hoc explanations of the results of a pretrained model (denoted hereafter as f) based on Shapley values. In this study, we rely on XGBoost (Chen and Guestrin, 2016), a tree-boosting model that accounts for nonlinear dependencies and inter-dependencies. The formal definition of the kernel SHAP estimation φ<sub>i,t</sub> of variable i on day t can be expressed as follows.

$$\phi_{i,t} = \sum_{S \subseteq \mathcal{N}_n \setminus \{t\}} \frac{|S|! (n - |S| - 1)!}{n!} [\mathbb{E}[f(x_t) | x_{S \cup \{t\}}] - \mathbb{E}[f(x_t) | x_{S,t}]] \quad (4)$$

In practice, kernel SHAP requires as parameters a number of coalitions to consider, set to 10 000, and a target reference. The target reference is defined as the mean of DBT<sub>Paris</sub> in 2018, meaning that we aim to quantify how variables influenced bicycle traffic relative to this baseline. As kernel SHAP is a local sensitivity analysis technique, we aggregate the results similarly to Eq. (3) in the discrete case.

Intuitively, both methods intend to distribute the responsibility for the variation in the outcome between the set of determinants. In the present context, it means explaining how much a change in bicycle traffic can be attributed to each policy and day-to-day variable. The higher the score, the greater the influence. A normalized importance value of 0 showcases an absence of connection to cycling, while a value of 1 means that the variable is the unique determinant of DBT<sub>Paris</sub>.

<footnote n="10">¹⁰ https://www.statistiques.developpement-durable.gouv.fr/393-millions-de-voitures-en-circulation-en-france-au-1er-janvier-2024.</footnote>
<table><caption>**Table 1**

Descriptive table of the data.</caption><thead><tr><th></th><th>Policy category</th><th>Unit</th><th>Frequency</th><th>Source</th></tr></thead><tbody><tr><td>Total length of bicycle lanes</td><td>Targeted cycling policies</td><td>km</td><td>Daily</td><td>OSM</td></tr><tr><td>Number of bicycle-sharing stations</td><td>Targeted cycling policies</td><td>Number</td><td>Daily</td><td>Vélib' Nocle</td></tr><tr><td>Number of bicycle parking slots</td><td>Targeted cycling policies</td><td>Number</td><td>Daily</td><td>OSM</td></tr><tr><td>E-bike popularity</td><td>Targeted cycling policies</td><td>%</td><td>Yearly</td><td>Observatoire du cycle</td></tr><tr><td>LEZ</td><td>Restr. on motor. vehicles</td><td>categories</td><td>Daily</td><td>Métropole du Grand Paris</td></tr><tr><td>Fuel price</td><td>Restr. on motor. vehicles</td><td>€/L</td><td>Daily</td><td>carbu.com</td></tr><tr><td>Additional green space surface</td><td>Livability policies</td><td>m²</td><td>Yearly</td><td>Paris</td></tr><tr><td>Pedestrian zones</td><td>Livability policies</td><td>Number</td><td>Daily</td><td>Paris</td></tr><tr><td>Regularity of public transportation</td><td>Disr. on other modes</td><td>%</td><td>Monthly</td><td>Île-de-France</td></tr><tr><td>Public transportation price</td><td>Disr. on other modes</td><td>€</td><td>Daily</td><td>IDFM</td></tr><tr><td>Self-service e-scooter ban</td><td>Disr. on other modes</td><td>Boolean</td><td>Daily</td><td>Paris</td></tr><tr><td>Unemployment rate</td><td>Economic conditions</td><td>%</td><td>Quarterly</td><td>INSEE</td></tr><tr><td>Purchasing power</td><td>Economic conditions</td><td>%</td><td>Quarterly</td><td>INSEE</td></tr><tr><td>Temperature</td><td>Control variables</td><td>*C</td><td>Daily</td><td>WMO</td></tr><tr><td>Precipitation</td><td>Control variables</td><td>mm</td><td>Daily</td><td>WMO</td></tr><tr><td>Humidity</td><td>Control variables</td><td>%</td><td>Daily</td><td>WMO</td></tr><tr><td>Wind speed</td><td>Control variables</td><td>m/s</td><td>Daily</td><td>WMO</td></tr><tr><td>Snow fall</td><td>Control variables</td><td>mm</td><td>Daily</td><td>WMO</td></tr><tr><td>Day of the week</td><td>Control variables</td><td>categories</td><td>Daily</td><td>-</td></tr><tr><td>Holidays</td><td>Control variables</td><td>categories</td><td>Daily</td><td>Official calendar</td></tr><tr><td>Lockdowns and in-between surge</td><td>Control variables</td><td>categories</td><td>Daily</td><td>-</td></tr><tr><td>Strike</td><td>Control variables</td><td>Boolean</td><td>Daily</td><td>-</td></tr><tr><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total length of bicycle lanes</td><td>320</td><td>343</td><td>385</td><td>416</td><td>411</td><td>417</td></tr><tr><td>Number of bicycle-sharing stations</td><td>798</td><td>1339</td><td>1397</td><td>1414</td><td>1437</td><td>1464</td></tr><tr><td>Number of bicycle parking slots</td><td>45 587</td><td>56 232</td><td>61 695</td><td>75 417</td><td>83 573</td><td>99 614</td></tr><tr><td>E-bike popularity</td><td>13</td><td>15</td><td>19</td><td>24</td><td>28</td><td>30</td></tr><tr><td>LEZ</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Fuel price</td><td>1.44</td><td>1.45</td><td>1.28</td><td>1.45</td><td>1.86</td><td>1.82</td></tr><tr><td>Additional green space surface</td><td>44 307</td><td>186 853</td><td>273 420</td><td>284 623</td><td>290 951</td><td>305 041</td></tr><tr><td>Pedestrian zones</td><td>153</td><td>166</td><td>185</td><td>251</td><td>314</td><td>342</td></tr><tr><td>Regularity of public transportation</td><td>88</td><td>90</td><td>90</td><td>92</td><td>91</td><td>91</td></tr><tr><td>Public transportation price</td><td>75.2</td><td>75.2</td><td>75.2</td><td>75.2</td><td>75.2</td><td>84.1</td></tr><tr><td>Self-service e-scooter ban</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Unemployment rate</td><td>7.02</td><td>6.55</td><td>6.48</td><td>6.47</td><td>5.75</td><td>5.68</td></tr><tr><td>Purchasing power</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>Temperature</td><td>13.02</td><td>12.82</td><td>13.54</td><td>12.00</td><td>13.52</td><td>13.50</td></tr><tr><td>Precipitation</td><td>1.17</td><td>0.99</td><td>1.02</td><td>1.16</td><td>1.05</td><td>1.03</td></tr><tr><td>Humidity</td><td>73.46</td><td>72.29</td><td>71.48</td><td>75.56</td><td>72.06</td><td>72.84</td></tr><tr><td>Wind speed</td><td>3.57</td><td>3.78</td><td>3.94</td><td>3.62</td><td>3.46</td><td>3.95</td></tr><tr><td>Snow fall</td><td>1.59</td><td>0.41</td><td>0.00</td><td>0.16</td><td>0.00</td><td>0.16</td></tr><tr><td>Day of the week</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Holidays</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Lockdowns and in-between surge</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Strike</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

Restr. on motor. vehicles stands for restrictions on motorized vehicles and Disr. on other modes for disruptions on other transportation modes. The annual values displayed in the lower section represent the average value of each variable for the year.

# 4. Results and discussion

## 4.1. Results

Fig. 3 presents the results of the sensitivity analysis, with the abscissa sorted according to the mean value of the two methods. Normalized importance can be interpreted as the respective influence of each variable on DBT<sub>Paris</sub> throughout the study period (2018–2023). Concretely, a normalized importance of 0.05 indicates that 5% of the total average absolute contribution to the estimated DBT<sub>Paris</sub> can be attributed to the corresponding policy.

The general structure of the rankings aligns between StratIMPORT and kernel SHAP, with the exception of the number of bicycle parking slots and pedestrian zones. This illustrates the disagreement problem (Krishna et al., 2022), a common problem in sensitivity analysis where different techniques might provide different explanations given their respective assumptions. Nonetheless, the results are overall consistent and, despite disagreement in ranking and importance strength, both policies are shown to be significant. It is worth noting that, as pedestrian zones affect positively bicycle usage, walking and cycling are not necessarily in competition, but rather complementary. Evidence from Collectif Mobilité (2023) indeed attests that only a fraction of

bicycle trips have replaced walking trips in the region, demonstrating that it is not one or the other.

The increase in total length of bicycle lanes is the most important factor in increasing bicycle traffic. Surveys consistently identify it as the top priority in the region (Collectif Mobilité, 2023), and additional findings confirm its dominant influence (Pearson et al., 2023). This is largely because the presence of extensive bicycle lanes enhances both objective and subjective safety (Feuerberg, 2004; McNeil et al., 2015; Vanparijs et al., 2015), a critical factor reported to shift towards cycling (Collectif Mobilité, 2023; Pearson et al., 2023). For example, this explains why Canada, despite its colder climate, achieves higher cycling levels than the USA, as its bicycle network is more developed (Pucher and Buehler, 2006). This finding is in line with the common practice of many cities to prioritize bicycle lanes within their mobility strategies (Pucher et al., 2012, 2021), and supported by studies highlighting their benefits in a diversity of cities such as New Orleans, New York, Lisbon, São Paulo, and Seoul (Chahine et al., 2025; Félix et al., 2020; Kim and Lee, 2023; Macedo Filho and Cunquero, 2024; Parker et al., 2013). However, it contrasts with the situation in Tokyo, which presents a remarkably high cycling modal share, higher than in Paris, New York or London, despite having fewer bicycle lanes. Instead, Tokyo compensates with a significantly higher number of
<table><caption>**Table 2**
Correlation between public policies.</caption><thead><tr><th></th><th>Total length of bicycle lanes</th><th>Number of bicycle-sharing stations</th><th>Number of bicycle parking slots</th><th>E-bike popularity</th><th>LEZ</th><th>Fuel price</th><th>Additional green space surface</th><th>Number of pedestrian zones</th><th>Regularity of public transportation</th><th>Public transportation price</th><th>Self-service e-scooter ban</th><th>Unemployment rate</th><th>Purchasing power</th></tr></thead><tbody><tr><td>Total length of bicycle lanes</td><td>1</td><td>0.74</td><td><strong>0.86</strong></td><td><strong>0.90</strong></td><td><strong>0.91</strong></td><td>0.45</td><td><strong>0.91</strong></td><td>0.84</td><td>0.41</td><td>0.40</td><td>0.25</td><td>-0.58</td><td>0.06</td></tr><tr><td>Number of bicycle-sharing stations</td><td></td><td>1</td><td>0.71</td><td>0.63</td><td>0.68</td><td>0.30</td><td><strong>0.85</strong></td><td>0.56</td><td>0.39</td><td>0.26</td><td>0.15</td><td>-0.60</td><td>0.05</td></tr><tr><td>Number of bicycle parking slots</td><td></td><td></td><td>1</td><td><strong>0.95</strong></td><td><strong>0.88</strong></td><td>0.71</td><td>0.79</td><td><strong>0.95</strong></td><td>0.30</td><td>0.70</td><td>0.45</td><td>-0.75</td><td>-0.01</td></tr><tr><td>E-bike popularity</td><td></td><td></td><td></td><td>1</td><td><strong>0.92</strong></td><td>0.72</td><td>0.84</td><td><strong>0.97</strong></td><td>0.34</td><td>0.60</td><td>0.33</td><td>-0.76</td><td>-0.14</td></tr><tr><td>LEZ</td><td></td><td></td><td></td><td></td><td>1</td><td>0.63</td><td>0.84</td><td><strong>0.90</strong></td><td>0.30</td><td>0.45</td><td>0.25</td><td>-0.77</td><td>-0.06</td></tr><tr><td>Fuel price</td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.36</td><td>0.80</td><td>0.06</td><td>0.52</td><td>0.32</td><td>-0.68</td><td>-0.19</td></tr><tr><td>Additional green space surface</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.73</td><td>0.41</td><td>0.36</td><td>0.20</td><td>-0.65</td><td>-0.06</td></tr><tr><td>Number of pedestrian zones</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.27</td><td>0.64</td><td>0.38</td><td>-0.76</td><td>-0.04</td></tr><tr><td>Regularity of public transportation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.11</td><td>-0.15</td><td>-0.14</td><td>-0.02</td></tr><tr><td>Public transportation price</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.54</td><td>-0.50</td><td>-0.12</td></tr><tr><td>Self-service e-scooter ban</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>-0.20</td><td>0.09</td></tr><tr><td>Unemployment rate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.38</td></tr><tr><td>Purchasing power</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Sensitivity analysis results.</figcaption></figure>

bicycle parking slots (Pucher et al., 2012). This observation does not diminish the importance of bicycle lanes. The strength of our study lies in its ability to quantify the effects of policies rather than relying solely on descriptive statistics. Results suggest either that Tokyo could potentially achieve an even higher cycling modal share with more bicycle lanes, or that its cyclists display a regional-specific behavior.

The number of bicycle-sharing stations also comes as a dominating factor. According to Collectif Mobilité (2023), a regional survey, 62% of bicycle-sharing users report relying on the service primarily because it eliminates the risk of theft, which ranks as the fourth most significant barrier to cycling. Moreover, a significant number of cyclists report exclusively using bicycles supplied by the BSS, indicating that part

of the shift to cycling relies on the system. The survey further highlights that the BSS serves as a complementary mode of transportation, integrating with other transportation modes and encouraging broader usage. Nonetheless, this empirical importance might also be attributed to the change of Vélib' operator in 2018, which began with a reduced number of stations, eventually returning to previous levels by early 2019, and later expanding the overall capacity (see Fig. 4). In this way, a smaller and more parametric increase in the BSS would likely indicate a lower importance and ranking compared to the disruption caused by the change, which likely led to a temporary decline in overall ridership. Another way to interpret this result is that installing a BSS in a city that has none is an efficient lever to increase DBT, as suggested by the success of the initial introduction of Vélib' in 2007 (Pucher
<figure><img src="image_6.png" /><figcaption>Fig. 4. Evolution of the number of Vélib' stations.</figcaption></figure>

et al., 2010; Shaheen et al., 2010). This remark should be contrasted with the examples of Poznań or China mentioned in the literature review (Dzięcielski et al., 2025; Lin, 2024), which suggest that the success of BSS rests on key ingredients such as good maintenance and integration within the wider mobility ecosystem.

The impact of the LEZ is notable, even though only the initial phases of restrictions have been implemented. This indicates that subsequent measures could have a substantial effect as additional vehicles become subject to restrictions. The finding is consistent with the observation that, by 2023, bicycle trips outnumbered car trips (Institut Paris Région, 2023), shifting from the situation in 2018 (Ile-de-France Mobilités, OMNIL, DRIEAT, 2020), following the introduction of LEZ measures. The effectiveness of the policy should not, however, be considered separately from the financial aids created to support it. In France, during the study period, programs such as the Bonus Vélo or vehicle conversion bonus existed to support individuals seeking to purchase a bicycle, with supplementary aid for people living in an LEZ. Such complementary policies are widespread, as illustrated by the Bruxell'Air bonus in Brussels. Therefore, the result should be interpreted as the outcome of an LEZ implementation paired with financial support.

E-bikes are seen as a good option for new cyclists and a mean to make cycling accessible to a broader audience in the region (Collectif Mobilité, 2023). A reduction in their cost is identified as the third most significant factor encouraging a switch to cycling (Collectif Mobilité, 2023). Despite accounting for 30% of bicycle sales in France in 2023, e-bikes are the most considered option for purchase in the survey. Furthermore, Heinen et al. (2010) reviews factors influencing bicycle commuting and highlights that topography, distance, and time constraints often act as barriers to traditional cycling. E-bikes have the potential to overcome each of these challenges. Inspired by these advantages, certain local authorities have made e-bikes a central component of their BSS. Notable examples include MEVO in the metropolitan area of Gdańsk, Gdynia, and Sopot that is composed of a majority of e-bikes, and BiciMAD in Madrid, which is entirely electric. However, the variable does not rank as high in Paris as the regional survey suggests. The fact that it displays similar importance, for both sensitivity methods, to green space surface indicates that it might be of secondary importance, introducing the activity to new cyclists by making it more enjoyable.

Despite the rise in public transportation price, its impact on bicycle traffic appears to be relatively modest. Numerous studies highlight the price of public transportation as a critical factor influencing rider-ship (Taylor and Fink, 2013). For instance, Erhardt et al. (2022) found that higher prices have contributed to the decline in public transportation usage in the USA. Conversely, Collectif Mobilité (2024) underlines that economic considerations are the primary reason individuals report increased use of public transportation. One possible interpretation of the limited effect on DBT<sub>Paris</sub> could be that, in the context of high fuel costs, public transportation remains a financially viable option,

particularly when employers partially cover the expense, thus limiting the switch to cycling.

The ban on self-service e-scooters has a negligible impact. Prior to the ban in 2023, approximately 35 000 trips<fnref n="11" /> were made daily using these scooters, a small fraction compared to the estimated 316 000 bicycle trips recorded in 2018 (Ile-de-France Mobilités, OMNIL, DRIEAT, 2020), which have since increased significantly. Additionally, only 9% of e-scooter users would have opted for BSS and just 3% for private bicycles instead (6-t bureau de recherche, 2019). Therefore, it is unsurprising that this ban has no significant effect on DBT. Nevertheless, as the analysis is city-wide, it remains possible that specific neighborhoods may experience localized impacts.

Overall, the results reveal a hierarchy of priorities. Local authorities should first and foremost focus on bicycle infrastructure, such as bicycle lanes, parking slots, and (well implemented) BSS. Restrictions on motorized vehicles also trigger a shift towards cycling, although they may have social consequences which should not be discarded, as evidenced during the Yellow Vests movement in France. Once the basic needs are set up, additional efforts can be made to improve the comfort and attractiveness of cycling by promoting e-bikes, adding green spaces, and creating pedestrian zones. Other policies can be interpreted as more parametric.

Fig. 5 illustrates the aggregated normalized importance categorized by policy type. As expected, targeted cycling policies are the most effective in boosting bicycle traffic, particularly according to StratIMPORT, as it attributes greater importance to the number of bicycle parking slots. Restrictions on motorized vehicles rank second in effectiveness for both methods, while disruptions to other transportation modes have a comparatively minor impact. The significance of restrictions on motorized vehicles aligns with the policies implemented in Tokyo, one of the megacities with the highest cycling modal share. In Tokyo, residents are required to show proof of parking availability before owning a car, which is also substantially taxed, reflecting a car-restrictive stance (Pucher et al., 2012). Nonetheless, it is important to acknowledge that restrictions on motorized vehicles can lead to significant social consequences (De Vrij and Vanoutrive, 2022; Liotta, 2023), such as reduced professional opportunities or social exclusion, particularly affecting low-income car owners. Interestingly, the majority of individuals who transition to cycling do so from public transportation, instead of motorized vehicles (Collectif Mobilité, 2023). This suggests, together with our results on relative importance, that improved cycling conditions encourage the shift from public transportation to cycling, while increased difficulties associated with car usage drive the (less prominent) transition from cars to bicycles. These dynamics may also

<footnote n="11">¹¹ https://www.paris.fr/pages/le-bilan-des-deplacements-a-paris-en-2023-27604#::text=Avec%208%20544%20582%20locations,mois%20de%20l'ann%C3%A9%202022.</footnote>
<figure><img src="image_7.png" /><figcaption>Fig. 5. Aggregated normalized importance per policy category.</figcaption></figure>

encourage an other shift from motorized vehicles to public trans-
portation, as the latter becomes less congested and therefore more
attractive. Additionally, livability policies play a notable role. The more
pleasant and accommodating the city is, the more likely people are to
adopt cycling. In contrast, economic conditions appear to have a less
pronounced effect.

## 4.2. *Strengths and limitations*

The present work showcases several strengths. First, it constitutes a longitudinal study accounting for 6 years of data, from 2018 to 2023 included, thus evaluating the long-term effects of public policies on cycling growth. The inclusion of 13 policies, distributed across 5 categories (targeted cycling policies, restrictions on motorized vehicles, disruptions to other transportation modes, livability policies, and economic conditions) ensures a comprehensive framework for the analysis of the structural determinants of bicycle usage. Methodologically, the robustness of the approach lies in its capacity to quantify the relative importance of each public policy while accounting for correlations among variables. The integration of control variables, such as weather and calendar effects, further strengthens the validity of the results. As a quantitative method, the study complements existing qualitative and survey-based research. Notably, the article provides a concrete case where public policies have successfully led to a significant increase in bicycle traffic. It delivers actionable indications for policymakers and stakeholders looking to promote cycling.

Nonetheless, the study is not without limitations. Biases can arise with the collection of data from various sources. For instance, certain variables, such as public transportation regularity or green space surface, are only available on a monthly to yearly basis, and do not capture the nuances of finer daily data. In addition, as some policies lack gold-standard or exact data, they require to be approximated using proxies. This is the case of the total length of bicycle lanes which is induced from OSM, potentially introducing uncertainties (see Section 3.1.3. Public policies data). Similarly, the target data of the study, i.e. DBT<sub>Paris</sub>, relies on counter data rather than capturing all bicycle trips (which are not measurable in practice). While they remain the most reliable dynamic source available on bicycle traffic, individual counters may present missing data (although the use of the median in DBT<sub>Paris</sub> helps mitigate these potential discrepancies). Certain aspects, such as cultural acceptance or corporate funding for bicycle initiatives, are intrinsically difficult to measure, especially in a longitudinal context, and are therefore not considered. Additionally, caution should be taken when generalizing the results. As Paris is a major European metropolis, the findings may not apply to territories of different nature (e.g., rural areas). Extending the analysis to finer resolutions, such as neighborhood or street levels, would likewise require further investigation. Lastly, the study does not address the equity implications of public policies, such as the impacts on various demographic groups, which are a key pillar when considering urban planning interventions.

5. Conclusion

To make cycling a more attractive and accessible mode of transport, local authorities implement a variety of initiatives, some designed for direct impact, while others work indirectly to support long-term cycling growth. Public policies affecting cycling can be categorized in five main groups: targeted cycling policies, restrictions imposed on motorized vehicles, disruptions on other transportation modes, livability policies, and economic conditions.

The present article introduces a longitudinal study assessing the impact of public policies on bicycle traffic, with a specific focus on Paris from 2018 to 2023, a period of drastic cycling modal share increase. The analysis incorporates control variables such as weather and calendar events.

The methodology is based on dependence-aware sensitivity methods
kernel SHAP and StratIMPORT. Results indicate that the development
of cycling, and more specifically the total length of bicycle lanes and
the growth of the local bicycle-sharing system, Vélib', are the most
effective measures in increasing cycling share. However, other variables
play an important role, including restrictions on motorized vehicles
such as fuel price and LEZ and livability policies such as pedestrian
zones and green spaces. In contrast, issues associated with public
transportation play a minor role. This suggests that people shift from
driving to cycling mainly due to rising difficulties in car usage and from
public transportation due to improved cycling conditions to cycling.
Furthermore, the ban of self-service e-scooters has had a negligible
impact on bicycle usage.

The analysis points out that local authorities should prioritize in-
vesting in bicycle infrastructure, such as bicycle lanes or parking slots,
and then extend bicycle usage by setting a more pleasant environment
(with green spaces and pedestrian zones) or promoting innovations
such as e-bikes, even though the findings indicate that a consistent
cycling growth relies on the implementation of a diversity of measures.

Overall, this work provides a deeper understanding of strategies
to effectively promote cycling. It is pivotal for future public policy
planning, especially in order to meet set targets. Future investigations
could explore how the quality of infrastructure affects cycling, the
influence of the initiatives of neighboring cities on the cycling growth
of a given city, and the potential maximum increase in cycling modal
share. Additionally, sociological aspects could be considered to assess
how different populations are affected, thereby ensuring fairness in
policy implementation.

CRediT authorship contribution statement

**Alexandre Lanvin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investiga-
tion, Formal analysis, Data curation, Conceptualization. **Jean Charléty:**
Writing – review & editing, Methodology. **Guillaume Ferreol:** Investigation, Data curation. **Anatole Homann:** Investigation, Data curation. **Elliot Massey:** Investigation, Data curation. **Alix Vermeulen:** Investigation, Data curation. **Alexandre Chasse:** Project administration, Funding acquisition.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work is funded by the French National Research Agency as part of the Mob Sci-Dat Factory project (ANR-23-PEMO-0004) under the France 2030 program.

We express our gratitude to Enrico Barchiesi for his constructive insights.

## Data availability

All data sources used in the study are explicitly mentioned.

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