# Modeling traffic-related air and noise pollution: Multi-criteria assessment case study around schools

Michele Tirico, Yu Gao, Delphine Sengelin, Pascal Gastineau, Pierre Aumond, Perrine Charvolin-Volta, Cecile Becarie, Valentin Le Bescond, Romain Rumpler, Lionel Soulhac, et al.

► To cite this version:

Michele Tirico, Yu Gao, Delphine Sengelin, Pascal Gastineau, Pierre Aumond, et al. Modeling traffic-related air and noise pollution: Multi-criteria assessment case study around schools. Transportation Research Part D: Transport and Environment, 2025, 149, pp.105029. 10.1016/j.trd.2025.105029. hal-05329254

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# Modeling traffic-related air and noise pollution: Multi-criteria assessment case study around schools

Michele Tirico<sup>a, b, *</sup>, Yu Gao<sup>a, c, d</sup>, Delphine Sengelin<sup>a, b</sup>, Pascal Gastineau<sup>a, e</sup>, Pierre Aumond<sup>d</sup>, Perrine Charvolin-Volta<sup>a</sup>, Cecile Becarie<sup>a, b</sup>, Valentin Le Bescond<sup>a, d</sup>, Romain Rumpler<sup>c</sup>, Lionel Soulhac<sup>a</sup>, Arnaud Can<sup>a, d</sup>

## ARTICLE INFO

### Keywords:

- Co-exposure

- Multi-criteria assessment

- Chain modeling

- Traffic simulation

- Traffic-related noise pollution

- Traffic-related air pollution

## ABSTRACT

Reducing children's exposure to traffic-related noise and air pollution in urban areas is a critical challenge. Therefore, evaluating traffic management strategies in a computational environment offers a practical tool for planners, policymakers, and researchers. However, a key research gap remains: most studies evaluate traffic strategies on air pollution, noise, or traffic separately. Few quantify their combined impacts in a single framework. To address this, we propose a multi-criteria evaluation approach and apply it by comparing four scenarios against a baseline. Through a comprehensive panel of statistical, spatial, and temporal analyses of traffic conditions, air pollutant concentrations, and noise levels, we find that: (1) restricting vehicle access during student arrival times significantly reduces exposure to both noise and air pollution; and (2) speed limit reductions have only limited effects on noise and may, under certain conditions, increase air pollution levels.

## 1. Introduction

Children in urban areas are significantly exposed to air pollution and noise during their daily commutes to and from school, as well as during their time spent at school (Forns et al., 2016). Traffic-related air pollution has been associated with increased child mortality rates (EEI, 2022), impaired memory development (Sunyer et al., 2015), cognitive damage (Basagaña et al., 2016), and lower academic performance (Stenson et al., 2021). Similarly, traffic noise has been shown to reduce working memory and attention in primary school children (Foraster et al., 2022), disrupt the teaching-learning process (Mondal and Ghatak, 2014), and cause effects that worsen over time (Caviola et al., 2021). Combined exposure to high levels of noise and air pollution exacerbates these impacts, which can lead to greater multi-organ damage, especially with long-term or repetitive exposure (Kuntic et al., 2023; Chan et al., 2023; Stucki et al., 2024). This co-exposure is particularly acute in densely populated urban areas, notably during peak-traffic periods associated with school drop-off and pick-up.

Therefore, reducing children's exposure to traffic-related hazards is a critical societal objective. To this end, traffic management planning, such as optimizing transport networks to limit traffic nuisances, reducing speed limits, or expanding pedestrian areas,

<footnote><sup>a</sup> LMFA UMR5509, INSA Lyon CNRS, Ecole Centrale de Lyon, Univ. Claude Bernard Lyon 1, 69621, Villeurbanne, France</footnote>

<footnote><sup>b</sup> LICIT-ECO7, ENTPE, Univ. Gustave Eiffel, Lyon, France</footnote>

<footnote><sup>c</sup> The Marcus Wallenberg Laboratory for Sound and Vibration Research (MWL), Department of Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden</footnote>

<footnote><sup>d</sup> UMRAE, Université Gustave Eiffel, CEREMA, Bouguenais, France</footnote>

<footnote><sup>e</sup> AME-SPLOTT, Université Gustave Eiffel, Bouguenais, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: michele.tirico@insa-lyon.fr (M. Tirico).</footnote>
represents a solid approach (EEI, 2022; Chamberlain et al., 2023; Bigazzi and Rouleau, 2017; An et al., 2021). This highlights the need for a robust evaluation framework that, through a multi-criteria approach, can assess the effectiveness of different strategies in simultaneously reducing noise and air pollution human's exposure, while also ensuring the feasibility of the strategies without disregarding overall traffic conditions.

Two primary approaches exist for estimating traffic management strategies in terms of noise and air pollution. One approach is to measure and analyze real-world conditions, providing measurements of nuisances' indicators. For instance, exposure of cyclists to air pollution (Krecl et al., 2020), and exposure to both noise and air pollution in areas close to road infrastructures (Gemma et al., 2025), have been estimated through this approach. However, this method has limitations. Testing impacts of a specific strategy is often challenging, and evaluating multiple strategies may be impractical. Additionally, concerns about the reliability of these estimates and the ability to cover extensive study areas often arise. Moreover, since localized traffic re-planning can significantly impact areas outside the immediate study area, this approach cannot fully account for broader traffic effects.

The second approach relies on computational frameworks, which allows to design a large number of scenarios, without affecting real urban areas and while reducing the time required for assessment. These frameworks are based on modeling chain, where multiple models are linked sequentially: the output of one model becomes the input of the next (Fallah Shorshani et al., 2015; Gastineau et al., 2023). Typically, traffic models are used to estimate traffic condition indicators (such as vehicular flow, mean speeds, and total travel distance), which are then fed into noise and air pollution emission models. These models estimate the levels of noise (such as average and median noise level) and pollutant emissions (such as Nitrogen Dioxide and Particulate Matter) from vehicles, which are subsequently used as inputs for dispersion models to generate nuisance maps. Finally, the outputs of these models can be integrated into a multi-criteria analysis to assess the impacts of traffic in terms of traffic conditions, noise, and air pollution exposure.

Despite the importance of providing decision-makers with reliable modeling chain-based tools to estimate human impacts from traffic scenario planning (Khreis et al., 2023), few studies have addressed multiple exposures to traffic-related nuisances, especially regarding children near schools. Since the introduction of the first co-exposure indicator by the World Health Organization (WHO, 2011), significant efforts have been dedicated to developing co-exposure metrics at the metropolitan level. For example, the collaborative efforts of noise and air pollution french observatories in the Île-de-France region (AirParif, BruitParif, 2025) and in Auvergne-Rhone-Alpes (Orhane, 2025) region have led to promising advancements. An interesting study providing detailed street-level estimations was conducted by Joo et al. (2019), who developed a multi-criteria framework (including safety, traffic flow efficiency, environmental impact, and human health) to evaluate traffic-calming measures. Fernandes et al. (2017) proposed a computational framework which operates a very fine scale to design optimal crosswalk locations by considering noise and air pollutant levels and estimate the co-exposure in the vicinity of roundabouts. However, critical challenges remain unaddressed, such as the definition of a comprehensive panel of indicators that enables comparison of traffic management strategies against a reference scenario. Moreover, since traffic systems are inherently complex, strategies aimed at reducing health impacts should also be tested with regard to potential collateral effects, such as the risk of increased congestion even beyond the planning area. This underscores the need for integrated modeling chains that encompass both traffic and environmental dimensions.

We contribute in two ways. First, we introduce a methodology for evaluating the impacts of traffic management strategies on both air pollution and noise. Second, we apply this methodology to assess measures designed to reduce children's exposure during their morning routines. The article is structured as follows: Section 2 reviews the current state of the art in noise and air pollution chain modeling, Section 3 presents the multi-criteria traffic management framework, introducing two modeling chains for estimating traffic conditions, air pollution dispersion, and noise levels. In Section 4, we apply the framework to four different scenarios aimed at reducing children's exposure during and after school entry, comparing them to a baseline scenario. Section 5 explores the effects of simple traffic planning measures on mitigating risks both around schools and across the broader urban area. Finally, Section 6 summarizes the main findings and concludes the study.

## 2. Literature review

Air and noise pollution estimates rely on traffic models to generate aggregated traffic indicators or individual vehicle trajectories (Pinto et al., 2020; Gastineau et al., 2023; Fontes et al., 2015). Regarding air pollution, traffic outputs feed pollutant emission models. These models estimate the quantity of pollutants from traffic-related and human degradation sources, such as Particulate Matter (PM) and Nitrogen Dioxide ($NO_2$) for given temporal and spatial resolutions. Temporal resolution typically ranges from 1 s to 1 h, while spatial resolution can refer to a specific geographical position, a road, or roads within a neighborhood.

The resulting emissions serve as input for air pollutant concentration models. Typically, three levels of representation are distinguished within urban areas: the urban scale, the district scale, and the street scale. Regarding noise, both aggregated traffic indicators and individual vehicle trajectories can be used as inputs for a noise emission model. This model estimates traffic-related sound power levels on discretized linear sound sources (corresponding to roads). A set of receivers is defined across the study area, and the sound level at each receiver is computed by modeling sound propagation from sources to receivers and summing the contributions of each source. Depending on the traffic representation, the temporal resolution ranges from 1 s to 1 h, while the spatial resolution is fine, with receivers typically spaced no more than 10 m apart.

Both modeling chains produce detailed spatio-temporal estimates of air pollutant concentrations and noise levels at the road level. However, several limitations must be taken into account. For instance, validating each individual model, as well as the entire modeling chain, against empirical measurements is essential. Moreover, selecting appropriate spatio-temporal scales when coupling models, and integrating dynamic mechanisms to identify highly polluted areas (hotspots), remain subjects of ongoing debate (Gastineau et al., 2023).
<figure><img src="image_3.png" /><figcaption>Fig. 1. General framework.</figcaption></figure>

## 2.1. Modeling chains for air pollution

Both chain modeling have been applied for estimation traffic planning strategies in policy makers’ decision-making processes to improve public health. Regarding air pollution (Bigazzi and Rouleau, 2017), Numerous studies have recently focused on using modeling chains, including a microscopic traffic model for the road traffic component, to estimate the impact of traffic regulation on pollutant emissions (Tirico et al., 2025; Fallah Shorshani et al., 2015; Santiago et al., 2021; Forehead and Huynh, 2018; Wang et al., 2016). However, very few have extended their analysis to estimate the impacts in terms of air pollution concentrations, including calculations of pollutant dispersion within a comprehensive modeling chain. Tang et al. (2020a) show that the change in traffic management strategies can lead to positive and negative outcomes in terms of greenhouse gas emissions and air pollutant concentrations. In Tang et al. (2020b), authors compared traffic scenarios based on speed regulation and different fleet vehicles consider to air pollutant concentration around a school, finding that reducing speed limits has often effect to increase vehicle emissions. Adamidis et al. (2020) proposed a simulated environment to estimate the impact of eco-driving behaviors on emissions at urban scale, showing that fewer driving changing consistently reduces traffic emissions. Soret et al. (2011) compared air pollutant estimations from a base scenario to three expected scenarios, each one representing a different set of traffic management measures.

## 2.2. Modeling chains for noise

Similarly, traffic management strategies are a key element in urban noise reduction (Ellebjerg and Bendtsen, 2007; Morillas et al., 2018). Recent studies have increasingly focused on assessing the noise impact of traffic regulation strategies by integrating either microscopic or mesoscopic traffic models within comprehensive modeling chains. The microscopic approach has been implemented using a variety of traffic simulation tools, such as Symuvia (Can et al., 2010a; Chevallier et al., 2009; Can et al., 2019), Paramics (De Coensel et al., 2010, 2012; Wei-li et al., 2012), Vissim (Estévez-Mauriz and Forssén, 2018), and HUTSIM (Heltimo et al., 2002). The mesoscopic approach commonly relies on MATSim (Le Bescond et al., 2021; Kaddoura et al., 2017; Hankach et al., 2024). The microscopic approach, which relies on individual vehicle trajectories, is well-regarded for its ability to estimate advanced traffic indicators (Can et al., 2009) as well as noise peaks (De Coensel et al., 2016). This approach is based on the principle that the traffic model captures the influence of a control strategy on vehicle kinematics, while the acoustic model translates these kinematic changes into vehicle emissions and resulting noise levels at selected receiver locations. The traffic control strategies assessed in the literature include comparisons between roundabouts and signalized intersections (Can et al., 2010a; Chevallier et al., 2009; Estévez-Mauriz and Forssén, 2018), speed reduction measures (Wei-li et al., 2012), the introduction of dedicated bus lanes (Can et al., 2010a), green wave coordination (Can et al., 2010a; De Coensel et al., 2010), traffic signal optimization (De Coensel et al., 2012), and demand management during the morning peak period (Can et al., 2019). Although various models have been employed, studies consistently show that reduced speeds and flows, combined with smoother traffic conditions, contribute to lowering noise levels. However, with some exceptions (Quintero et al., 2019; Can et al., 2019), which considers a network covering several square kilometers, these studies are generally limited to small areas and do not account for the potential reassignment of vehicles across the network resulting from the tested strategy.

# 3. Methodology

Our literature review highlights a significant research gap: while many studies examine the effects of traffic management strategies on air pollution concentration, noise levels, or traffic conditions individually, none systematically quantify their combined impacts
<table><caption>**Table 1**
Notations.</caption><thead><tr><th>Notation</th><th>Symbol</th><th>Meaning</th></tr></thead><tbody><tr><td>Road network</td><td>G(N, L)</td><td>Directed geometric graph composed of the set of nodes N and the set of links L</td></tr><tr><td>Zones</td><td>L<sub>0</sub>, L<sub>1</sub>, L<sub>2</sub></td><td>High-security zone, planning zone, influence zone</td></tr><tr><td>Vehicles</td><td>I</td><td>Set of vehicles, indexed by i</td></tr><tr><td>Time periods</td><td>t</td><td>Time intervals considered in the study (t = 1 s, 15 min, 1 h)</td></tr><tr><td>Origin-Destination matrix</td><td>OD</td><td>The list of trips, where for each vehicle i ∈ I is associated the origin location, a destination coordinate, and a start time instant</td></tr><tr><td>Vehicle coordinates</td><td>X(t)</td><td>Spatial coordinates of vehicles i ∈ I at interval t</td></tr><tr><td>Vehicle speeds</td><td>V(t)</td><td>Speeds of vehicles i ∈ I at interval t</td></tr><tr><td>Vehicle accelerations</td><td>a(t)</td><td>Accelerations of vehicles i ∈ I at interval t</td></tr><tr><td>Engine profiles</td><td>F(t)</td><td>Engine profile assigned to vehicles i ∈ I</td></tr><tr><td>Air pollutants</td><td>p</td><td>traffic-related air pollutants (p = NO<sub>x</sub>, NO<sub>2</sub>, PM<sub>10</sub>)</td></tr><tr><td>Vehicle emissions</td><td>E<sub>I,p</sub>(t)</td><td>Emissions of pollutant p from vehicle i ∈ I at each second (t = 1 s)</td></tr><tr><td>Link emissions</td><td>E<sub>L,p</sub>(t)</td><td>Emissions of pollutant p aggregated over links l ∈ L during the time intervals t = 15 min, 1 h</td></tr><tr><td>Square cell grid</td><td>XY</td><td>Regular grid of square cells (10 m resolution) for air pollution concentration analysis</td></tr><tr><td>Receiver coordinates</td><td>X<sub>n</sub>, X<sub>a</sub></td><td>Spatial coordinates of noise (X<sub>a</sub>) and air quality (X<sub>a</sub>) receivers</td></tr><tr><td>Cell air pollution</td><td>A<sub>xy,p</sub>(t)</td><td>Air pollution concentration of pollutant p in grid cell xy ∈ XY during time interval t = 1 h</td></tr><tr><td>Receiver air pollution</td><td>A<sub>r,p</sub>(t)</td><td>Air pollution concentration of pollutant p at air receiver r ∈ X<sub>a</sub> during time interval t = 1 h</td></tr><tr><td>Receiver noise level</td><td>N<sub>r</sub>(t)</td><td>Noise level at receiver r ∈ X<sub>a</sub> during time interval t = 1 h</td></tr><tr><td>Total travel distance</td><td>ttd(t)</td><td>Sum of distances traveled by vehicles passing through links l during time intervals t = 15 min, 1 h</td></tr><tr><td>Total travel time</td><td>ttt(t)</td><td>Sum of time spent by vehicles passing through links l during time intervals t = 15 min, 1 h</td></tr><tr><td>Spatial mean speed</td><td>sms(t)</td><td>Ratio between ttd(t) and ttt(t) for the links l during time intervals t = 15 min, 1 h</td></tr><tr><td>Traffic flow</td><td>Q(t)</td><td>Number of vehicles passing through links l during time intervals t = 15 min, 1 h</td></tr><tr><td>Averaged noise level</td><td>L<sub>Aeq,1h</sub></td><td>Equivalent continuous sound level with A-frequency weighting over 1 h</td></tr><tr><td>Median noise level</td><td>L<sub>A50,1h</sub></td><td>The median of the fluctuating noise level over 1 h</td></tr><tr><td>Noise proportion</td><td>T<sub>65,1h</sub></td><td>Proportion of receivers L<sub>Aeq,1h</sub> over 65 dB and 1 h</td></tr></tbody></table>

through a comprehensive analysis. In this paper, we present a multi-criteria assessment to evaluate the effects of four traffic planning interventions compared to a baseline scenario. Our assessment considers varying spatial distances from the target planning area, as well as effects both during and after the implementation of traffic management measures. Traffic conditions are modeled using Symuvia (Leclercq, 2007b), noise levels are estimated with NoiseModelling (Bocher et al., 2019), traffic-related air pollutant emissions are derived from the PHEM model (Hausberger, 2003), and air pollutant dispersion is simulated using the Sirane model (Soulhac et al., 2017). The models are detailed in Sections 3.2–3.4. The general framework is presented in Fig. 1. The notations used in this paper are reported in Table 1.

### 3.1. The study case

The study area is located in the northeastern part of the metropolitan area of Lyon, France (Fig. 2), roughly corresponding to the city's 6th district. This area was selected due to its geometric and topological characteristics: it features a high density of perpendicular intersections, and link lengths are somewhat normal distributed. Among the 17 schools in the analyzed area, we selected three for this study. This decision was based on the main considerations that their entrances are located on three different but interconnected streets, allowing us to develop planning scenarios that cover a significant subarea and potentially influence a broader part of the city.

The streets surrounding the schools are initially categorized based on their relevance to reducing nuisance exposure around school entrances. The proposed methodology follows two guiding principles: (1) the classification should be clear, rigorous, and easy to implement, and (2) it should incorporate a notion of distance, here interpreted in terms of graph topology.

Let the street network within the analyzed area be represented by a directed graph G = (N, L), where N is the set of nodes (intersections) and L is the set of links (directed street segments). For any subset of links L<sub>x</sub> ⊆ L, the incident set of L<sub>x</sub> is defined as the set of all links in L that share at least one node with any link in L<sub>x</sub>. According to this definition, we define three subsets of the link set L (referred to as zones):

* Zone L<sub>0</sub> includes streets where school entrances are located. This is the high-security zone, where stricter planning and mitigation measures are implemented to reduce risks to children.

* Zone L<sub>1</sub> consists of streets incident to L<sub>0</sub>. This is the planning area that surrounds the high-security zone, where some traffic management actions are enforced.

* Zone L<sub>2</sub> includes streets incident to L<sub>1</sub>, excluding streets in L<sub>0</sub>. This is the influence zone, where changes in air pollution, noise, and traffic conditions are expected as a consequence of interventions in L<sub>0</sub> and L<sub>1</sub>.

#### 3.1.1. Scenarios: the rationale

A key preliminary consideration is that, since our goal is to reduce children's exposure during school entry, dynamic traffic control is preferable. In France, primary school children typically arrive between 8:00 AM and 8:30 AM; however, in some cases (due to accompanying adults' working schedules) drop-offs can begin as early as 7:30 AM. Accordingly, we define a traffic management period that encompasses both entry windows, during which we design and implement a set of traffic management strategies (see Section 3.1.2). Consequently, we distinguish three main time periods:
<figure><img src="image_4.png" /><figcaption>Fig. 2. The study area. (Background map: © OpenStreetMap).</figcaption></figure>

* Before the traffic management period (6:30 AM–7:30 AM): serves as the initialization phase of traffic conditions, with no control measures applied.

* During the traffic management period (7:30 AM–8:30 AM): the core time slot when interventions are enforced.

* After the traffic management period (8:30 AM–9:30 AM): a restoration phase during which traffic conditions are expected to stabilize, although residual effects of the interventions may still propagate.

Our analysis primarily focuses on the second and third periods, since the first period serves only to initialize the simulation, before any traffic management strategies are applied.

Two key principles motivated the design of our scenarios:

1. Simplicity and feasibility: the proposed traffic control strategies aim to avoid complex technical or technological requirements. This makes them readily implementable in the short term. We do not include real-time dynamic traffic management, such as live rerouting or real-time trip updates.

2. Variable impact on individual mobility: scenarios are designed with varying levels of impact on personal travel plans. Two scenarios involve temporary access restrictions for individual vehicles, while two others focus solely on speed reduction measures.

In France, two types of measures are commonly considered by planners when designing traffic management strategies to reduce human exposure around schools: lowering speed limits and restricting access for private vehicles. Since one of the aims of this work is to provide concrete support for policymakers and planners, and given that few studies have focused on the quantitative assessment of co-exposure reductions with these two actions, the strategies we propose explicitly take them into consideration.

### 3.1.2. Scenarios: the implementation

Simulations were conducted over a 3 h period (6:30 AM–9:30 AM) representing the morning peak traffic. Only vehicles that pass through zones $L_0$ or $L_1$ during the control period are subject to interventions.

Five scenarios have been considered in this study (see Fig. 3). One of them, named “Sc:Base”, is the baseline scenario, designed as a reference representing the current state of the streets. The remaining four scenarios are named according to the convention: Sc:“action in zone $L_0$”, “action in zone $L_1$”. For each zone, we use “xx” when no action is taken, “00” when vehicle access is forbidden, and “20” when speed is limited to 20 km/h. The five scenarios are described as follows:
<figure><img src="image_5.png" /><figcaption>Fig. 3. Scenarios. For each scenario, we specify the traffic regulations applied to vehicles that begin their trips before or after 7:30 and are expected to pass through zones $L_0$ or $L_1$.</figcaption></figure>

* Sc:Base: baseline scenario with no intervention in either $L_0$ or $L_1$, i.e. benchmark scenario without any interventions.

* Sc:00.20: access to zone $L_0$ is forbidden for vehicles that begin their trip during the control window. Vehicles that start their trip before 7:30 are not restricted. Affected vehicles are rerouted using the shortest-path principle based on distance. The speed limit in zone $L_1$ is reduced from 50 km/h to 20 km/h, regardless of trip start time. This speed reduction measure does not take trip start time into account, as it has minimal impact on overall travel time and can be easily integrated into individual routing behavior.

* Sc:00.xx: access to zone $L_0$ is forbidden for vehicles that begin their trip during the control window. Vehicles that start their trip before 7:30 are not restricted. Affected vehicles are rerouted using the shortest-path principle based on distance. No action is taken in zone $L_1$.

* Sc:20.20: the speed limit in zones $L_0$ and $L_1$ is reduced from 50 km/h to 20 km/h, regardless of trip start time.

* Sc:20.xx: the speed limit in zone $L_0$ is reduced from 50 km/h to 20 km/h, regardless of trip start time. No action is taken in zone $L_1$.

In the two vehicle-restricted scenarios, trips are not altered in order to account for residential departures or destinations.

## 3.2. Traffic condition estimation

Symuvia is a microscopic traffic simulator based on the Lagrangian resolution of the Lighthill-Whitham-Richards (LWR) model (Leclercq, 2007b). It operates at the vehicle level, with route selection determined by a dynamic traffic assignment model aimed at minimizing total travel time and distance. Thanks to a comprehensive set of rules governing vehicle dynamics, including principles such as car-following laws (Leclercq, 2007a), lane changes (Laval and Leclercq, 2008), and intersection crossings (Chevallier and Leclercq, 2007), the simulator provides detailed traffic conditions. The origin-destination matrix was obtained from travel surveys and vehicle counts at loop detectors, and validated by comparing it with hourly traffic flow measurements for the same case study as our work (Ameli et al., 2020). Similarly, the model was validated for a subregion of the Paris metropolitan area (Lejri et al., 2018) by comparing estimated vehicle flows with observed measurements.

Symuvia has been used in various vehicular traffic studies, including the assessment of route guidance algorithms (Leclercq et al., 2021), the optimization of real-time ride-sharing services (Alisoltani et al., 2022), and multi-modal traffic algorithms (Ameli et al., 2021). Regarding air pollution emission assessment, it has been previously coupled with the PHEM and COPERT emission models (Lejri et al., 2018), to estimate emission discrepancies between COPERT and HBEFA and with the MATSim traffic model (Tirico et al., 2025). For acoustic studies, it has been previously combined with the NoiseModelling model to compare noise levels under different traffic management strategies (Can et al., 2010b, 2009).

In this study, the origin-destination matrix OD was obtained from travel surveys and vehicle counts at loop detectors, and validated by comparing with hourly traffic flow rate measurements (Ameli et al., 2020). Two types of simulation outputs were considered. First, we consider vehicle trajectories at a temporal resolution of $t = 1$s, capturing spatial coordinates $X_i(t)$, speed $V_i(t)$, and acceleration $a_i(t)$ of vehicles $i \in I$. These outputs were used as inputs for air pollution and noise level emission estimations. Second, we aggregated vehicle trajectories both spatially and temporally to obtain key traffic metrics: traffic flow $Q(t)$, total travel distance $ttd(t)$, total
travel time *ttt*(*t*), and spatial mean speed *sms*(*t*) = *ttd*(*t*) / *ttt*(*t*). The spatial mean speed was computed using Edie's formulation (Edie, 1965), which has been shown to ensure consistent traffic- and air pollution-impact estimates (Tirico et al., 2025; Lejri et al., 2018). Traffic conditions were assessed using outputs aggregated over 15 min intervals, while air pollution emissions and multi-criteria analyses were based on hourly aggregations. Spatially, all traffic metrics were computed per road link *l* ∈ *L*, for the entire study area *L*, as well as for the zones *L*₀, *L*₁, and *L*₂.

### 3.3. Air pollution estimation

#### 3.3.1. Fleet vehicle composition

To estimate pollutant emissions accurately, it is crucial to define the composition of the vehicle fleet in terms of engine types and technological standards present on the road network. Due to the lack of detailed local data, this study relies on national-level fleet information for the year 2021, as reported by the French Ministry of Ecological Transition.

The fleet composition used in our simulations is based on data provided by CITEPA (CITEPA, 2025), the French national agency responsible for air pollution and climate change emissions inventories (CITEPA, 2024). According to 2021 inventory, diesel vehicles accounted for 64% of the total distance traveled by light-duty vehicles, while petrol vehicles represented 35%. The most common vehicle type was medium-sized EURO5 diesel passenger cars, which made up 16% of the total fleet. Electric and hybrid vehicles remained relatively minor contributors, representing 0.64% and 3.5%, respectively. This fleet characterization enables the simulation model to incorporate emission factors weighted by the prevalence of different vehicle types, providing a realistic representation of pollutant emissions across both time and space within the network.

#### 3.3.2. Pollutant emission estimation

To estimate traffic emissions at the street level, two main approaches are commonly used. The first approach computes the mean speed of vehicles on a given street over a specified time period and derives emission factors using models such as COPERT (Ntziachristos et al., 2009) or HBEFA (Hausberger, 2009). In these models, for a given trip, the emission factor is expressed as a function of the average travel speed. For most engine profiles, this relationship follows a U-shaped curve, with higher emission factors observed at both low and high speeds. Emission factors are then multiplied by the total travel distance to estimate emissions at the given spatio-temporal resolution. The second approach relies on instantaneous emission models, which is using detailed vehicle kinematics (instantaneous speed and acceleration), environmental data (e.g., street gradient), mechanical parameters (e.g., current gear), and engine emission maps to calculate pollutants emissions at the fine temporal resolution of one second. These instantaneous emissions are then aggregated spatially and temporally. In this work, we adopt the second approach using the PHEM model (Hausberger, 2003) to calculate exhaust emissions (from the tailpipe of internal combustion engine vehicles) of Nitrogen Oxide (NOₓ) and Particulate Matter (PM₁₀). The model first calculates the engine power from the vehicle's speed profile and road gradient, then determines engine speed using transmission ratios and a gear-shifting model. PHEM has been widely applied for assessing road traffic management strategies (Smit et al., 2010), comparing emissions across traffic models (Quaassdorff et al., 2022; Lejri et al., 2018), and providing inputs for air pollutant dispersion models (Oettl et al., 2006). It was preferred for this study because it preserves the fine spatio-temporal resolution of microscopic traffic simulations, allowing accurate representation of short-term, high-intensity traffic phenomena (Vieira da Rocha et al., 2015; Lejri et al., 2018).

As observed in previous studies (Lejri et al., 2018; Quaassdorff et al., 2016; Vieira da Rocha et al., 2015), instantaneous models better capture detailed vehicle interactions than aggregated emission models, particularly under highly congested traffic conditions. By contrast, COPERT is more suitable when detailed vehicle kinematics are not available, as in mesoscopic traffic models such as MATSim (Borge et al., 2012; Tirico et al., 2025). Alternatively, it can be adapted through corrective factors that account for the features of microscopic traffic models (Lejri et al., 2018; De Nunzio et al., 2021).

The PHEM model requires assigning an appropriate engine profile $f \in F(I)$ to each vehicle in the simulation. In this work, as detailed information on individual vehicle engine types is not available, we proceeded by random drawing, conducting 10 simulations per scenario. In each run, vehicles were randomly assigned an engine profile $f$, weighted according to the probability distribution derived from the national fleet composition (Section 3.3.1). We estimated the exhaust emissions of two key pollutants (NOₓ and PM₁₀) using the emission function $e_{p,l}(X_l, V_l, a_l, t=1s)$, where $X_l(t) = (x, y)$ represents the geographic position of vehicle $i$, $V_l(t=1s)$ its speed, and $a_l(t=1s)$ its acceleration at each 1 s time step. For each simulation run, we aggregated the instantaneous emissions over a 1 h period $p$ for each road link $l$, resulting in total emissions $E_{p,l}(t=1h)$ for both NOₓ and PM₁₀.

#### 3.3.3. Pollutant dispersion estimation

SIRANE is an air quality model that transports and chemically transforms pollutants at the neighborhood level (Soulhac et al., 2017, 2012, 2017). The model is based on a geometrical representation of the urban canopy as a network of inter-connected street canyons. Flows in and above the canopy are driven by the synoptic meteorological conditions, provided as input of the model. Transport below the roof level is described considering some fundamental assumptions that allow to estimate concentrations, such as the modeling air flow and transport of traffic emissions in street and intersection boxes with corresponding balance (Soulhac et al., 2008). Above the roof level, the flow is modeled as a Monin-Obukhov boundary layer profile over a rough wall, as a simplified reconstruct of urban canopy (Salizzoni et al., 2008). Turbulent transport in this part is described with a modified Gaussian plume model, adapted to consider a large number of point, linear or area sources. A specific photochemical scheme has been added to model
the simplified NO – NO₂ – O₃ cycle (Soulhac et al., 2023), considering an emission ratio ϵ of NO₂/NOₓ (in this work, ϵ = 0.2) and reaction rates depending on the temperature, the solar flux and the age of the pollutants.

In this work, we assign traffic emissions $E_{l,p}(t = 1, h)$, computed using the PHEM model and aggregated for each road link over 1 h intervals. To account for meteorological variability, dispersion simulations are performed using a full year of weather data. The resulting pollutant concentrations are then averaged to obtain representative annual estimates. Hourly concentrations of pollutants (specifically NO₂ and PM₁₀) are estimated both at predefined receptor locations $X_a$ (placed at the midpoint of each road segment) and across a regular grid XY with a spatial resolution of 10 m.

### 3.4. Noise estimation

NoiseModelling is a tool for predicting environmental noise that can be interfaced with traffic models at various scales (Bocher et al., 2019). In this work, it uses vehicle trajectories from Symuvia(position, speed, and acceleration at each time step of 1 s) to estimate the sound power of each vehicle. The temporal evolution of noise levels is then computed at receiver locations through detailed propagation calculations. NoiseModelling relies on spatial databases such as H2GIS or PostGIS and was originally developed for large-scale urban noise mapping. The noise calculation method implemented in NoiseModelling is the standard European method CNOSSOS (Kephalopoulos et al., 2023), which is the official reference for environmental noise assessment across the EU. The calculation process is divided into three main modules:

* Noise Emission Module: Estimates the sound power level on a grid of emission points, typically spaced every 10 m. The estimation is based on inputs such as average flow rates, mean speeds, and traffic composition (e.g., proportions of light and heavy vehicles). When the input data consists of trajectories, as provided by SYMUVIA in this study, emissions are calculated at each time step for each vehicle on the network based on its speed and acceleration, then aggregated on the emission point grid. In addition, a modified version of CNOSSOS is used, since NoiseModelling implements the sound power level correction based on the acceleration provided by the IMAGINE model, as presented in Can et al. (2019).

* Path-Finding Algorithm: Identifies the acoustic paths between sources and receivers using a “rubber-band” approach. It considers factors such as propagation distance, number of reflections, topography and diffraction.

* Propagation Module: Computers attenuation along each identified path across a selected range of octave bands. The module also includes ground surface and meteorological conditions. The total sound level at each receiver is obtained by summing contributions from all relevant sources.

Unlike conventional static models, the modeling chain involving Symuvia and NoiseModelling allows for the computation of noise indicators that capture temporal variations, such as emergence, which is known to influence both perceptual and health outcomes (Lavandier et al., 2011; Basner et al., 2014). NoiseModelling has been coupled in the past with Symuvia to assess the impact of local transportation strategies on noise dynamics (Can et al., 2019), as well as individual noise exposure during commuting (Quintero et al., 2019).

The model outputs sound levels at each time step (1 s) for a grid of noise receivers $X_n$. For each street within zones $L_0, L_1$, and $L_2$, two rows of receivers were placed (one on each sidewalk) 1 m from the pavement edge. In our analysis, we computed the following noise indicators: the equivalent continuous sound level with A-frequency weighting over 1 h ($L_{Aeq,1h}$), the median noise level over 1 h ($L_{A50,1h}$), and the proportion of receivers for which $L_{Aeq,1h}$ exceeds 65 dB ($T_{65,1h}$). These outputs were then aggregated for each zone ($L_0, L_1$, and $L_2$).

### 3.5. Multi-criteria assessment

The multi-criteria assessment is based on the following indicators: spatial mean speed *sms(t)*, traffic flow *Q(t)*, total travel time *tt(t)*, total travel distance *ttd(t)*, average concentrations of pollutants *NO₂(t)* and *PM₁₀(t)*, hourly averaged noise level $L_{Aeq,1h}$, median fluctuating noise level over 1 h $L_{A50,1h}$, and the proportion of receptors where $L_{Aeq,1h}$ exceeds 65 dB, denoted as $T_{65,1h}$.

## 4. Results

Before addressing the multi-criteria assessment, which forms the core of this work, we present the results from the PHEM traffic emission model (Section 4.1). This section helps to contextualize the effectiveness of our findings and highlights the significant contribution that an instantaneous emission model like PHEM can provide in such analyses. Subsequently, the multi-criteria assessment is organized into three types of analyses: a temporal analysis of traffic conditions (Section 4.2), a spatial comparison of scenarios (Section 4.3), and a multi-dimensional analysis (Section 4.4).

### 4.1. Air pollutant emissions

We observed that, in each scenario, the total emissions computed from the 10 random simulations were very close to those obtained by averaging the time-period emissions of each simulation. The variation in total emissions ranged from -2.5% (Sc:20.xx) to +2.3% (Sc:20.20) for NOₓ, and from -4.4% (Sc:20.xx) to +3.3% (Sc:00.xx) for PM₁₀, relative to the average values. The highest variability occurred at 8:00 AM, which corresponds to the peak emission period. At this time, compared to the average, NOₓ emissions were
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Traffic condition analysis. The four panels depict the total travel time *ttt* (Fig. 4a), the traffic flow *Q* (Fig. 4b), the spatial mean speed *sms* (Fig. 4c) and the total travel distance *ttd* (Fig. 4d) for the three zones and the whole study area over 15 min time intervals. Vertical dotted lines mark the boundaries between the three defined time windows: before, during, and after the traffic management period.</figcaption></figure>

up to 5.7% lower (Sc:20.xx) and up to 6.2% higher (Sc:20.20). Similarly, PM₁₀ emissions were up to 10.7% lower and up to 10.8% higher (both in Sc:20.xx). Therefore, to account for the variability introduced by the randomized assignment of engine profiles, results were averaged across 10 simulations per scenario.

## 4.2. Temporal analysis of traffic condition

In all five scenarios, a total of 15,532 vehicles are generated during the morning rush hour within the *L* study zone. Among them, 310 vehicles (less than 2%) are rerouted in scenarios Sc:00.xx and Sc:00.20. Fig. 4 presents a detailed temporal analysis of four key traffic indicators: average speed (*sms*), traffic flow (*Q*), total travel time (*ttt*), and total travel distance (*ttd*). These indicators are computed in 15 min intervals for each of the three zones (*L*₀, *L*₁, and *L*₂) as well as the entire study area *L*.

As expected, three distinct phases, closely aligned with the implementation of traffic management strategies, can be identified in the temporal evolution of traffic indicators across all scenarios and zones. Specifically, (1) before the management period (6:30–7:30), no differences are observed between scenarios, as no traffic management measures are yet applied; (2) during the management period (7:30–8:30), scenarios diverge significantly in zones *L*₀, *L*₁, and *L*₂, reflecting the impact of the implemented strategies; and (3) after the management period (8:30–9:30), most indicators return to values similar to those observed in the scenario Sc:Base. Across all scenarios, the *L* zone shows minimal differences (with Pearson correlation coefficients between the four scenarios and the Sc:Base scenario reaching up to 0.97 for *ttd*, up to 0.99 for *Q*, up to 0.98 for *ttt*, and up to 0.93 for *sms*).

Table 2 reports the percentage differences of traffic indicators (*sms*, *Q*, *ttd*, *ttt*) between the baseline scenario and all other scenarios at peak hour (8:00) across zones (*L*₀, *L*₁, *L*₂, *L*). Total travel time *ttt* (defined as the sum of seconds spent by all vehicles traversing a given network subset within a 15 min slot) increases substantially in zones where speed limits are applied (Fig. 4a). In the slot starting at 8:00, *ttt* rises by 58.6% in scenario Sc:20.20 and 58.9% in Sc:20.xx. It is important to note that this increase does not concern the total travel time of entire trips for vehicles passing through zones *L*₀ and *L*₁, but only the portion of travel time within these zones—meaning the overall impact on travel time remains limited. In these scenarios, total travel distance *ttd* (analogously defined as the sum of kilometers traveled by vehicles crossing a network subset in a 15 min slot) remains unchanged relative to the baseline (Sc:Base) (Fig. 4d). By contrast, scenarios involving access restrictions to zone *L*₀ lead to sharp reductions in *ttd* across zones from
<caption>**Table 2**</caption>

Traffic condition. Percentage differences of traffic indicators (sms, Q, ttd, ttt) compared to the baseline scenario (Sc:Base) at 8:00, across different scenarios (Sc:20.20, Sc:00.xx, Sc:00.20, Sc:20.xx) and zones ($L_0, L_1, L_2, L$).

<table><thead><tr><th rowspan="2">Scenario</th><th rowspan="2">Indicator</th><th colspan="4">Dif. (%) to Sc:Base at 8:00</th></tr><tr><th>L0</th><th>L1</th><th>L2</th><th>L</th></tr></thead><tbody><tr><td rowspan="4">Sc:20.20</td><td>sms</td><td>-25.8</td><td>-39.7</td><td>-7.8</td><td>-8.9</td></tr><tr><td>Q</td><td>6.9</td><td>12.6</td><td>4.2</td><td>-1.1</td></tr><tr><td>ttd</td><td>17.6</td><td>7.6</td><td>2.6</td><td>-3.4</td></tr><tr><td>ttt</td><td>58.6</td><td>78.4</td><td>11.2</td><td>6.0</td></tr><tr><td rowspan="4">Sc:00.xx</td><td>sms</td><td>18.2</td><td>1.9</td><td>7.6</td><td>-6.3</td></tr><tr><td>Q</td><td>-84.5</td><td>-65.6</td><td>-22.2</td><td>-0.6</td></tr><tr><td>ttd</td><td>-96.8</td><td>-66.0</td><td>-21.3</td><td>-5.2</td></tr><tr><td>ttt</td><td>-97.3</td><td>-66.6</td><td>-26.9</td><td>1.2</td></tr><tr><td rowspan="4">Sc:00.20</td><td>sms</td><td>12.7</td><td>-28.8</td><td>8.0</td><td>-2.6</td></tr><tr><td>Q</td><td>-84.5</td><td>-63.4</td><td>-20.3</td><td>-0.2</td></tr><tr><td>ttd</td><td>-96.8</td><td>-64.3</td><td>-19.8</td><td>-2.2</td></tr><tr><td>ttt</td><td>-97.1</td><td>-49.9</td><td>-25.8</td><td>0.4</td></tr><tr><td rowspan="4">Sc:20.xx</td><td>sms</td><td>-31.4</td><td>-6.2</td><td>-0.8</td><td>-2.3</td></tr><tr><td>Q</td><td>5.2</td><td>5.5</td><td>3.1</td><td>0.3</td></tr><tr><td>ttd</td><td>9.0</td><td>5.4</td><td>2.7</td><td>-1.2</td></tr><tr><td>ttt</td><td>58.9</td><td>12.4</td><td>3.6</td><td>1.1</td></tr></tbody></table>

$L_0$ to $L_2$, as expected given the associated reduction in traffic flow (Fig. 4b). For example, at 8:00, ttd in $L_0$ drops by approximately 96%. In $L_1$, reductions reach 66.0% (Sc:00.xx) and 64.3% (Sc:00.20), while in $L_2$ decreases are more moderate: 21.3% (Sc:00.xx) and 19.8% (Sc:00.20). This overall decrease in ttd mirrors the observed reductions in traffic flow Q: 84.5% in $L_0$ (in both Sc:00.xx and Sc:00.20), 65.6% in $L_1$ (Sc:00.xx) and 63.4% (Sc:00.20), and 22.2% in $L_2$ (Sc:00.xx) and 20.3% (Sc:00.20). The phenomenon can be explained by the fact that re-planned trips of affected vehicles differ significantly from those in the baseline scenario, with rerouted vehicles largely avoiding the core area ($L_0 + L_1 + L_2$) and instead taking longer detours beyond the monitored zones. As discussed in Section 3.1.2, rerouted vehicles follow the shortest available path, without being subject to the optimization algorithms implemented in Symuvia, which continues to govern the routing of all other vehicles.

Spatial mean speed (sms) exhibits varying behaviors across scenarios when compared to the baseline (Fig. 4c). In the base scenario (Sc:Base), sms remains relatively stable throughout the simulation. For example, in zone $L_0$, it ranges from 16.70 km/h (Sc:20.20) to 19.23 km/h (Sc:00.20); in $L_1$, from 19.29 km/h (Sc:00.20) to 22.67 km/h (Sc:20.xx); and in $L_2$, from 27.63 km/h (Sc:20.20) to 29.29 km/h (Sc:00.20). In contrast, sms shows notable temporal fluctuations in the other scenarios. At 8:00, variations in $L_0$ range from -31.4% (Sc:20.xx) to +18.2% (Sc:00.xx); in $L_1$, from -39.7% (Sc:20.20) to -1.9% (Sc:00.xx); and in $L_2$, from -7.8% (Sc:20.20) to +8.0% (Sc:00.20). Scenarios implementing a 20 km/h speed limit in $L_0$ see a drop in sms during the control period, consistent with an increase in total travel time (ttt), while traffic flow (Q) and total travel distance (ttd) remain largely unchanged.

In terms of traffic conditions, no significant differences were observed across the whole study area. At this level, local and time-limited interventions of only one hour show minimal impact, confirming the resilience of the traffic system. At planned ($L_0$ and $L_1$) and influence zone level ($L_2$), the most noticeable changes occur in scenarios where street access is restricted during the management period. This indicates that reducing speed limits in a few secondary streets has little effect on traffic, while access restrictions can cause more noticeable variations as vehicles are forced to re-plan their routes. These rerouted vehicles tend to follow the shortest available path, leading to traffic variations even in areas distant from the controlled zone. However, these impacts remain limited.

## 4.3. The spatial analysis

Figs. 5 and 6 illustrate the pairwise differences in traffic (ttt), air pollutant concentrations ($NO_2$), and noise ($L_{A,eq}$) levels between two selected scenarios (Sc:00.20 and Sc:20.20), compared to the baseline scenario Sc:Base, both during (from 7:30 to 8:30) and after (from 8:30 to 9:30) the traffic management window. For each scenario, we compute differences with respect to the baseline using a common spatial reference: traffic condition differences are calculated on each network link, air pollution differences on a regular grid XY, and noise level differences at the set of receptors $X_n$.

Total travel time (ttt) exhibits markedly different spatial behaviors in zone $L_0$ between the two selected scenarios. While ttt generally increases in scenario Sc:20.20, it decreases in scenario Sc:00.20 across both during and after the traffic management windows. During the traffic management window, ttt increases in all three roads of zone $L_0$ under scenario Sc:20.20, with a maximum increase of 41.6% on one road. In contrast, under scenario Sc:00.20, ttt decreases across all three roads, reaching a reduction of up to 92.6%. After the window, the differences are less pronounced: in Sc:20.20, ttt increases slightly on only one road (12.1%), while in Sc:00.20, significant decreases are observed on two roads, with reductions of up to 50.7%.

These traffic changes are also reflected in the air pollutant and noise level maps. According to our spatial analysis, speed limit reductions have only limited effects on $NO_2$ concentrations: during the control period, slight increases and decreases are observed,
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Spatial difference analysis between scenarios Sc:20.20 and Sc:Base. The six panels show the pairwise differences for three indicators: total travel distance *td* (first row), NO₂ concentration (second row), and Lₐeq, 1h level (third row), during the traffic management window (first column) and after (second column).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Spatial difference analysis between scenarios Sc:00.20 and Sc:Base. The six panels show the pairwise differences for three indicators: total travel distance *ttt* (first row), NO₂ concentration (second row), and Lₐeq, 1h level (third row), during the traffic management window (first column) and after (second column).</figcaption></figure>
while after the management window, variations remain below 1.0 µg/m³. More significant effects are observed in scenarios where access to the planned area is restricted. In these cases, NO₂ concentrations decrease by up to 26.0 µg/m³ across the three zones, with a notable reduction in pollutant concentrations in zone L₀: -45.3% during the traffic management window. Even after the management period, substantial reductions are still observed (up to -35.5%).

Both reducing traffic flow (as in Sc:00.20) and imposing speed limits (as in Sc:20.20) show beneficial effects on noise levels, albeit to different extents. While speed limits noticeably reduce noise, restricting vehicle access leads to even more substantial noise reductions. In zone L₀, during the first time window, noise levels are reduced by up to 7.0 dB in scenario Sc:00.xx and 1.7 dB in Sc:20.20. In the second time window, reductions reach 1.8 dB in Sc:00.xx and 0.3 dB in Sc:20.20. Notably, improvements extend beyond the core zone. In L₁, during the traffic management period, noise reductions reach up to 5.7 dB in Sc:00.xx and 2.4 dB in Sc:20.20. After the window, reductions are no longer or insignificantly observed: -0.3 dB in Sc:00.xx and 0.1 dB in Sc:20.20. These results indicate that traffic interventions, especially access restrictions, have broader spatial impacts on environmental noise, extending beyond the targeted zones.

## 4.4. The multi-dimensional analysis

Fig. 7 presents a multi-dimensional chart analysis (based on polar bar chart visualizations) of the different scenarios, jointly assessing their effects on traffic (Q, sms), air pollution (NO₂, PM₁₀), and noise (Lₐ₅₀,₁h, and T₆₅,₁h) across the three zones (L₀, L₁, L₂) and during and after the traffic management window.

To reduce potential graphical distortion and facilitate interpretation, we manually fixed the scale for each indicator across all six panels, based on the observed minimum and maximum values. This approach allows each panel to be read both in terms of relative differences between scenarios across indicators and, conversely, for each indicator across zones and time periods. For completeness, the reference values from the baseline scenario (Sc:Base) are also displayed for each panel and indicator, enabling a more precise quantification of the impact of each traffic management strategy.

Across zones, we observe that the impact of traffic control strategies generally diminishes from L₀ to L₂. Stronger effects are seen during the management window than after it. While traffic-related indicators in L₂ show only marginal changes (e.g., traffic flow reductions of less than 1 vehicle per hour), improvements in air and noise pollution indicators, such as NO₂ concentrations and Lₐ₅₀,₁h noise levels, remain noticeable even in this outer zone. Remarkably, in zone L₀, after the intervention in scenarios Sc:00.xx and Sc:00.20, no receivers are expected to exceed 65 dB. Similarly, from a temporal perspective, the effects of the strategies tend to fade outside the traffic control period.

While air pollution levels may either increase or decrease depending on the scenario or the zone considered, noise levels generally decrease across all three zones in response to the traffic strategies. Improvements are less significant in scenarios involving speed limit reductions compared to those restricting access. Specifically, in the speed limit scenarios, only a small number of noise sensors recorded reductions greater than 3 dB during the control period, and no significant changes were observed after the control window. In contrast, in the access restriction scenarios, almost all sensors showed reductions of more than 3 dB during the control period, and a substantial portion continued to show improvements even after the control window. Through the multi-dimensional analysis, we identify two scenarios clusters. In scenarios Sc:00.20 and Sc:00.xx, restricting access around schools, especially when combined with reduced speed limits, consistently results in improvements across all indicators, making it the most effective overall strategy, particularly in zone L₀. In contrast, the scenarios Sc:20.20 and Sc:20.xx, based on speed limit strategies, yields more modest and sometimes ambiguous effects, with less consistent improvements in noise and air pollution and occasional minor degradations.

# 5. Discussion

This work highlights key discussion points regarding evaluating strategies to reduce co-exposure around schools (Section 5.1) the challenges of estimating traffic-related exposure (Section 5.2), conceptualizing a comprehensive value function (Section 5.3), transferring the multi-criteria approach to other contexts (Section 5.4) and evaluating strategies to reduce risk accidents (Section 5.5).

## 5.1. Strategies to reduce co-exposure around schools

Several types of strategies can be considered to reduce traffic-related air pollution and noise exposure in sensitive urban areas, such as around schools. A non-exhaustive list includes: promoting fleet renewal with less-polluting vehicles, encouraging the use of active transport modes to reduce the contribution of individual vehicles to traffic, re-planning urban areas to prevent the formation of congested zones, and implementing targeted traffic management strategies. Individuals today are exposed to unacceptable levels of traffic-related pollution, and the associated health effects are particularly pronounced in young populations. Consequently, rapid and effective interventions are needed. Among available policies, traffic management often represents the most adaptable solution. In this paper, we demonstrate that easily implementable traffic strategies can significantly reduce children's exposure around schools.

Reducing traffic flow during short but critical periods of the day, such as school entry or exit times, has been shown to significantly decrease daily children's exposure to air pollution and noise. Notably, this reduction is strong near school entrances: as we showed, children may be exposed to reduced levels near school entrances, with decreases of up to 26.0 µg/m³ of NO₂ and 7 dB, respectively. Moreover, this reduction, while somewhat less pronounced, remains appreciable even in the surrounding areas of schools (17.1 µg/m³ for NO₂ and 2.4 dB for noise). This indicates that beneficial effects are not limited to the immediate vicinity of the school entrance
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** The six polar bar charts illustrate the average differences between the baseline scenario (Sc:Base) and the four alternative scenarios across zones $L_0$, $L_1$, and $L_2$, during the traffic management window (07:30–08:30) and the post-intervention period (08:30–09:30). Each axis of the radar represents one indicator: spatial mean speed (*sms*), traffic flow (*Q*), nitrogen dioxide concentration (*NO₂*), particulate matter concentration (*PM₁₀*), the median of the fluctuating noise level over 1 h (*L*<sub>Aeq,1h</sub>), and the proportion of receivers where *L*<sub>Aeq,1h</sub> exceeds 65 dB (*T*<sub>65,1h</sub>). For each indicator, the reference value from the Sc:Base scenario is provided in brackets.</figcaption></figure>
but extend along portions of the children's commute. Given that schools often occupy large areas, pollution reductions are likely observed on streets adjacent to classrooms and windows. Finally, these measures may also benefit accompanying adults: by maintaining vehicle restrictions near school entrances and reorganizing drop-off and pick-up zones, traffic-related emissions around schools can be reduced, lowering exposure for everyone involved.

Imposing lower speed limits, while significantly reducing accident risk (see Section 5.5), proves less effective than restricting vehicle access in terms of co-exposure (with reductions limited to below 1.0 µg/m³ of NO₂ and 5.7 dB of noise near school entrances). According to the classic U-shaped relationship between mean speed and emission factors, when average speed is low due to congestion or city-center conditions, reducing speed limits will consequently increase emission factors. Noise reductions remain appreciable, but changes in air pollution are negligible. According to our results, this strategy appears more suitable for noise-sensitive areas where air pollution concentrations already close to, or below, critical health thresholds.

## 5.2. Towards a complete panel of estimations

Reducing exposure to traffic-related pollution remains a critical challenge for both urban planning and scientific research. On the one hand, planners and policymakers need reliable tools to quantitatively assess impacts of management strategies on traffic-nuisances exposure, while ensuring their feasibility from a traffic operation perspective. On the other hand, several open questions remain regarding the complex relationship between urban mobility dynamics and associated traffic nuisances (Gastineau et al., 2023). To address these challenges, we propose a comprehensive evaluation framework based on spatial, temporal, and multi-dimensional analyses.

Two crucial considerations must be addressed when tackling this challenge: (1) the spatial variability across a sufficiently large study area, (2) the temporal shift effects of traffic strategies on traffic-related nuisances, and (3) relationship between detailed vehicle kinematics and emissions. Regarding the first point, defining appropriate spatial boundaries is essential. In this study, we assessed impacts not only within the targeted intervention areas (zones $L_0$ and $L_1$) but also in the surrounding zone ($L_2$) and at the district level ($L$). Notably, variations were observed in the surrounding zone, particularly in NO₂ concentrations during the traffic management window. However, after this period, indicators generally returned to normal levels in zone $L_2$. As for the second point, we found that residual effects on traffic, noise, and air pollution persist even after the traffic management window ends. These effects are particularly pronounced in scenarios involving restricted access, especially within zone $L_0$, compared to others. However, in general, indicators across all scenarios tend to converge back to baseline conditions during the hour after the intervention. Regarding the third point, recent studies have shown that simplifying vehicle trajectories in microscopic traffic models has a limited impact on pollutant emission calculations (Vieira da Rocha et al., 2015). For acoustics, previous work has highlighted the benefit of refining emission models to estimate more detailed indicators related to noise peaks, which can be attributed to strong accelerations or particularly noisy vehicles, which is beyond the scope of the present study (De Coensel et al., 2016). Nevertheless, a detailed analysis of the kinematic outputs from our simulations indicates that the share of time vehicles spend in acceleration mode is nearly identical across scenarios. Therefore, it can be assumed that the acoustic impact is primarily due to variations in speed.

## 5.3. Towards multi-criteria value functions

Our objective is to quantitatively assess traffic conditions, and air and noise pollution variations without assigning relative weights to each of the considered nuisances. As such, this work serves as a preliminary step for future research aiming to integrate a multi-criteria value function into a panel of indicators.

Various strategies for constructing such a value function can be considered. For instance, one approach, recently applied for noise exposure assessment (Nygren et al., 2024), involves using Disability-Adjusted Life Years (DALYs) to estimate health impacts, thereby enabling the assignment of relative weights to different dimensions. Another method, proposed by Joo et al. (2019) for air pollution assessment, involves designing a multi-criteria value function based on weights derived through an Analytical Hierarchy Process (AHP). This kind of analysis brings new challenges. One important consideration is that the impact of mitigation strategies depends on the pre-intervention levels of each nuisance. This is particularly relevant for noise: for example, reducing traffic noise by the same number of decibels will have a more substantial impact in already noisy areas than in quieter ones. Additionally, future assessments should be aligned with health-based optimal thresholds. In this context, the multidimensional analysis proposed here can be complemented by threshold-based evaluations. For example, despite a significant reduction in NO₂ concentrations (up to 27.2% in zone $L_0$), the resulting concentration of 70.21 µg/m³ remains far above the recommended European Union (40.0 µg/m³) and World Health Organization (10.0 µg/m³) daily average limit.

## 5.4. Applicability of the multi-criteria assessment in broader contexts

In this work, our methodology has been applied to evaluate traffic management strategies aimed at reducing children's co-exposure to air pollution and noise. However, the proposed multi-criteria assessment approach is adaptable and can be applied to a wider range of contexts.

For example, while our study focused on a single, spatially limited intervention zone, the methodology is equally suitable for larger or multiple planned areas. In such cases, the multi-criteria assessment can support a more comprehensive understanding of impacts across broader spatial and temporal scales. For metropolitan areas, the use of microscopic traffic models such as Symuvia may pose limitations in terms of computational resources and scalability. In these situations, mesoscopic traffic models such as
MATSim, capable of estimating network, wide traffic conditions with reduced computational resources requirement—can offer more appropriate support for impact assessment.

Beyond simulation-based applications, this methodology also holds potential in observational and forecasting contexts. As the demand grows for indicators capable of tracking the temporal evolution of environmental nuisances, our framework could be applied to estimate exposure trends and in evaluating the long-term effects of public policies. In such a context, the baseline condition used in this study can be redefined as a target reference state, enabling comparative analyses over time. Moreover, when complemented with health- or environment-based thresholds, such as those established by the World Health Organization or the European Union, our approach can provide a valuable tool for assessing the degree to which long-term policy objectives are being met.

### 5.5. Strategies to reduce accident risk

Although pedestrian crashes have multiple contributing factors, vehicle speed and traffic flow are primary determinants of the severity of accidents involving pedestrians (Islam, 2023). In high-traffic pedestrian areas, each 1 km/h increase in vehicle speed raises the odds of a pedestrian fatality by 11% (Hussain et al., 2019). At speeds above 40 km/h, the risk of severe injuries increases markedly (Oikawa et al., 2016). While elderly pedestrians face a higher fatality risk than children (Niebuhr et al., 2016), this danger cannot be overlooked when planning urban spaces around schools (Kendi et al., 2023). Nearly half of pedestrian injuries among children aged 0–9 result from “ dart-out” incidents (Swedler et al., 2024), underscoring the importance of strategies that limit or eliminate the possibility of contact between vehicles and children. Therefore, alleviating congestion and reducing average speeds near schools (not only to decrease noise and air pollution) also contributes significantly to reducing accident risks. This can be achieved by reducing the number of vehicles and lowering speed limits.

In this study, the proposed traffic management strategies show promise in addressing this challenge, and the multi-criteria assessment methodology can be enhanced by incorporating accident risk indicators. Traffic flow does not vary significantly in the planning zones when only speed limit reductions are applied, but it decreases substantially when vehicle access is restricted. At the same time, vehicle speeds drop by up to 9.8% during the control window when speed limits are reduced to 20 km/h. A comprehensive evaluation of these potential safety benefits is therefore essential and should be integrated as an additional criterion within a more complete multi-criteria assessment.

## 6. Conclusion

In this work, we present a multi-criteria assessment framework to evaluate traffic management strategies, considering their impacts on noise, air pollution, and traffic conditions. One key improvement is the development of a comprehensive analytical panel that can quantitatively assess and relate changes in each type of exposure across varying spatial scales and over an appropriate temporal horizon. To this end, we designed four readily implementable strategies with low technological complexity, and we compared them spatially, temporally, and statistically in terms of air pollution concentrations, noise levels, and traffic conditions against a baseline scenario.

Based on our results and in light of existing literature, we conclude that:

* A comprehensive evaluation panel, incorporating temporal, spatial, and multi-zonal analyses, is essential to fully understand the effects and trade-offs of traffic management strategies.

* Simple and readily implementable measures, such as vehicle access restrictions and speed limits, can meaningfully reduce exposure to traffic-related nuisances during critical time windows.

* Restricting vehicle access, particularly in sensitive areas, is highly effective in reducing both noise levels and air pollution concentrations.

* Speed limit reductions, while somewhat beneficial, particularly for noise reduction, show limited overall effectiveness and may, in some cases, inadvertently increase air pollution exposure due to longer time spent by vehicles within the planned area.

As a forward-looking perspective derived from this study, the outlook is threefold. First, the applicability of the proposed methodology can be extended beyond traffic management at the district level, allowing broader exposure assessments. This framework could be used to quantitatively evaluate the temporal evolution of traffic nuisances at larger spatial and temporal scales, considering long-term policy strategies at the city level. Second, recognizing that national guidelines and economic dynamics critically shape traffic demand and fleet composition, the framework can be applied to assess alternative fleet scenarios and evolving mobility patterns. Third, the approach can be integrated into a decision-making process for evaluating multiple traffic management scenarios. The next phase of research will focus on developing a value function that consolidates the impacts of each strategy on noise, air pollution, and traffic conditions into a single, comprehensive indicator.

### CRediT authorship contribution statement

**Michele Tirico:** Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization; **Yu Gao:** Writing – review & editing, Validation, Software, Resources, Methodology, Formal analysis, Data curation; **Delphine Sengelin:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization; **Pascal Gastineau:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization; **Pierre Aumond:**
Writing – review & editing, Validation, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization; Perrine Charvolin-Volta: Supervision, Software, Resources, Investigation, Data curation; Cecile Becarie: Validation, Software, Resources, Methodology, Data curation; Valentin Le Bescond: Software, Resources, Formal analysis, Data curation; Romain Rumpler: Supervision, Project administration, Funding acquisition; Lionel Soulhac: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization; Arnaud Can: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Data availability

Data will be made available on request.

## Acknowledgments

This work was supported by the French National Research Agency (ANR) as part of the ANR-21-CE22-0022-01 SYMEXPO project.

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