Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks

Abstract

The urban environment near the road infrastructure is particularly affected by traffic emissions. This problem is exacerbated at road junctions. The roadside concentration of particulate (PM2.5 and PM10) emissions depends on traffic parameters, meteorological conditions, the characteristics and condition of the road surface, and urban development, which affects air flow and turbulence. Continuous changes in the structure and conditions of the traffic flow directly affect the concentration of roadside emissions, which significantly complicates monitoring and forecasting the state of ambient air. Our study presents a hybrid model to estimate the amount, concentration, and spatio-temporal forecasting of particulate emissions, accounting for dynamic changes in road traffic structure and the influence of meteorological factors. The input module of the model is based on data received from street cameras and weather stations using a trained convolutional neural network. Based on the history of emission concentration data as input data, we used a self-learning Recurrent Neural Network (RNN) for forecasting. Through micromodeling, we found that the order in which vehicles enter and exit an intersection affects the concentration of vehicle-related emissions. Preliminary experimental results showed that the proposed model provides higher accuracy in forecasting emission concentration (83–97%) than existing approaches.

1. Introduction

The growing density of urban populations and the increasing number of vehicles have nearly incapacitated road networks, which are unable to provide proper mobility given urban infrastructural restrictions. This increases the number and severity of traffic jams, leading to negative social, environmental, and economic consequences. Real-time optimization and control of traffic signals is a key strategy for managing traffic congestion and improving traffic conditions in urban areas [1,2]. Adaptive control of traffic signals is based on real-time traffic data, allowing for proper adjustment to signal timing in response to changes in the traffic flow [3,4]. The Transport Demand Management (TDM) strategy is also focused on controlling traffic congestion and its negative effects. This strategy is based on the implementation of various measures to effectively redistribute travel demand, such as developing comfortable public transport; promoting the use of active transport facilities, paid parking, and entrance fees to high-traffic areas; developing alternative routes; etc. A distinctive advantage of TDM is its low cost and flexible implementation in various conditions. The introduction of TDM contributes to sustainable transport development, reduces costs (fuel, time), reduces traffic congestion, improves road safety, and reduces traffic-related harmful emissions [5,6,7].

Given the importance of adaptive traffic light control for urban mobility, many signal control methods have been developed and successfully implemented. However, these solutions are not always effective, since they do not take into account environmental aspects, which are largely determined by the dynamic structure of the traffic flow and meteorological factors [8,9]. At the same time, accurate situational forecasting of the road situation that accounts for environmental factors enables traffic control systems to make appropriate decisions. This is a key factor in effective traffic management. Given the influence and variability of different factors on the organization of sustainable traffic, a methodology for collecting, assessing, and forecasting the dynamic and environmental parameters of traffic flows must be developed.

This paper focuses on forecasting the concentration of particulate (PM2.5 and PM10) emissions at urban signal-controlled intersections. We propose and implement a new data collection system, using the latest advances in vehicle detection and tracking to assess traffic-related emissions, that takes into account vehicle type and driver behavior. The scientific novelty of our study is the real-time assessment and modeling of the concentrations of vehicle-related emissions based on a method that includes the parallel decomposition of artificial neural networks (hybrid model). The input module of the hybrid model based on the optimized You Only Look Once (YOLOv4) recurrent neural network is responsible for collecting, interpreting, and aggregating traffic data to model the formation of maximum ground level emission concentrations, taking into account weather factors. The hybrid model architecture uses the advantage of the rapid obtaining of a large bulk of data by a convolutional neural network on the influence of traffic parameters and meteorological factors on the formation of emission concentrations. The forecasting module based on the Long Short-Term Memory (LSTM) recurrent neural network received continuous feature extraction to improve the accuracy of forecasting the distribution of concentrations of harmful emissions, taking into account spatio-temporal correlations (urban development).

We verified our model through laboratory tests and simulations and provided empirical evidence that the proposed solution is sufficiently accurate and can be further used as a launching point for other high-level models.

In the Literature Review section, we review scientific publications on emissions assessments and forecasting. The Materials and Methods section describes our approach to assessing and collecting parametric data on traffic-related emissions. The Experiment and Verification section presents an evaluation report and details of the experiment. The Discussion and Results section analyzes and generalizes the obtained results. Finally, the Conclusions section reviews the main results of the study and highlights areas of further research.

Literature Review

Growing road traffic density has become the main source of serious negative economic and environmental impact in large cities [10,11]. Several studies have found that poor urban areas are exposed to a higher adverse environmental impact, which affects the health of residents [12,13,14]; environmental inequality has thus been identified. The problem is more complicated and related to urban planning (dense building development), as well as complex economic and social structures. It is relevant to use a system of effective road traffic management, taking into account environmental aspects, that does not require the deployment of expensive infrastructure for highly populated areas. However, there are traffic-related environmental problems even in small settlements that develop clear urban dynamics and, at the same time, devote insufficient effort and funds to sustainable traffic management [15].

Many studies have also identified the hottest spots in terms of vehicle-related emissions in the urban environment [16,17,18]. Signal-controlled intersections and road junctions are the main sources of emissions due to low traffic capacity, long idle periods, and the traffic patterns (acceleration, deceleration, waiting) in these areas.

The driver’s behavior significantly affects the time and nature of crossing at an intersection. The situation is especially aggravated by mixed traffic, when the driver finds it difficult to decide on the nature of movement in the so-called dilemma area due to fear of a traffic accident occurring [19,20,21].

With the advent of new engineering structures and solutions, modern cities are growing not only in width, but also in height. High-rise buildings standing along highways form canyons with accumulating pollutants and a lack of natural ventilation, which contributes to the dispersion of harmful emissions [22,23,24]. Nevertheless, the levels of street pollution can vary significantly depending on various factors: wind direction and speed, vehicle composition and movement speed, the dimensions of street buildings, degree of landscaping, etc. One study [25] developed a set of urban indicators to quantify the spatial patterns of urban districts and better describe the urban structure.

Over the past decade, there has been a steady increase in the number of studies assessing the concentrations of ultrafine PM2.5 particles and their impact on the population in urban transport microenvironments [26]. In this paper, the authors conclude that air quality is not uniform in the urban environment; pedestrians and cyclists who choose a low traffic route are exposed to fewer pollutants than people traveling by car.

The main tool for assessing air pollution concentration in urban areas is the Operational Street Pollution Model (OSPM) [27]. However, OSPM has a substantial drawback—it requires hourly background concentration measurements, including those from households, industrial enterprises, etc. Mensink and Cosemans [28] combined the OSPM with the Gaussian Immission Frequency Distribution Model (IFDM) to obtain a more realistic map of pollutant concentrations in the urban environment. A comparison of the simulated and measured data (for NO₂ and PM10) showed an accuracy of up to 15%, which is in line with EU directives.

Some researchers have focused on completing an inventory of vehicle-related emissions, accounting for the emission of volatile organic compounds resulting from evaporation. Luo et al. [29] used the Comprehensive Air-quality Model with extensions (CAMx) to comprehensively quantify PM2.5 and O₃ concentrations in China. The study also assessed public health risks from long-term exposure to these pollutants.

The chemical mass balance (CMB) model consists of a set of linear equations and is used to determine the contribution of various emission sources at the measurement site. This method is based on the assumption that the composition of emissions is constant over time, chemicals do not react with each other, the number of sources is less than or equal to the number of emission types, measurement uncertainties are random, and all emission sources are known. The CMB model [30,31] was used to determine the chemical components of PM2.5 and PM10 in a typical Chinese coastal city. As a result, seasonal fluctuations in the dominant sources of pollution were detected: soil dust was more prevalent in winter, while exhaust gasses were more prevalent in spring.

Many sensor-based devices are being deployed in cities to collect data on the state of the ambient air, which constitute the Internet of Things (IoT) for smart cities [32,33]. These sensors are able to accumulate and store only the necessary data from the collected dataset. Hu et al. [34] focused on the lack of 100% availability of data from the Internet of Vehicles (IoV) related to accidental disconnections due to inclement weather (or for other reasons), which leads to incomplete and sparse traffic data. Incomplete data create a traffic planning and forecasting problem. Therefore, Hu et al. proposed a method for short-term traffic flow forecasting based on data from a digital twin of current road traffic.

Yu et al. [35] determined the distribution of pollutants from the traffic flow in a three-dimensional space in a tunnel and along a highway using unmanned aerial vehicles equipped with PM2.5 sensors. They modeled air quality using the California Gaussian Linear Source Dispersion Model (CALINE4) and the Computational Fluid Dynamics (CFD) fluid model. A comparison of the simulated and measured data showed that the proposed model for predicting pollutant emissions is effective.

The US Environmental Protection Agency (USEPA) [36] uses various models to study air pollution to understand the formation of air pollutant mixtures and their atmospheric movement: the Community Multiscale Air Quality Modeling System (CMAQ), Air Quality Dispersion Model (AERMOD), MOVES (Motor Vehicle Emission Simulator), mobile device emissions model for air quality analysis, and multimedia and multi-stress modeling to help understand the relationship between the environment and the distribution of pollutants.

The AERMOD is recommended by the USEPA for designing roads and intersections and is also actively used to assess the dispersion and concentration of vehicle-related harmful emissions [37,38,39]. Thus, the authors of [37] studied the impact of the introduction of cordon pricing on the concentration of PM 2.5 in a downtown area and used the AERMOD to reveal a positive effect both outdoors (the concentration decreased by 7–13%), and indoors. Researchers united the MOVES, AERMOD, and CALINE4 models into MOVES-Matrix [38], a high-performance emission dispersion modeling system. The highly computational model that was developed performs 200 times more calculations than the MOVES and can work with various data sources for hotspot analysis.

Dispersion models require a huge amount of different data for calculations and are time-consuming. Therefore, the authors of [39] developed a link screening methodology to reduce the time spent on calculations without affecting accuracy. Thus, the AERMOD simulation time with the proposed model is only 0.2–1.1% of the conventional AERMOD simulation. Despite all the advantages of the considered models, they are not designed for the real-time assessment of the concentrations of vehicle-related harmful emissions, which does not provide for quick environmental risk management.

New technologies output large data volumes which are successfully and effectively processed by a neural network. The neural network has intelligent data processing functions and can learn, memorize, and forecast data. It is irreplaceable in complex systems with a high degree of unpredictable factors, such as road traffic. Therefore, neural networks have become widely used in research over the past decade.

Teng et al. [40] proposed the use of a new hybrid model combining a bidirectional neural network with long short-term memory to accurately forecast vehicle-related PM2.5 concentration. The developed model shows trends of PM2.5 levels over a period of 6 to 24 h and performs very well.

The forecast of vehicle-related pollutant emissions in the urban environment relies primarily on the road traffic forecast. Many studies develop predictive models of urban traffic. Shuai et al. [41] developed a model based on the information on vehicles collected using vehicle communications. Vehicle behavior is considered to be a stochastic process by the queue theory. The model helps to reduce the travel time and standing time of vehicles.

Shang et al. [42] proposed an interesting solution for building a high-precision traffic volume forecasting model using an ensemble deep graph reinforcement learning network. The final version of the model was obtained by combining two neural networks, which allowed the authors to achieve better results than several dozen models developed by various researchers.

The wavelet-attention-based traffic prediction method built on the analysis of weather conditions (temperature, wind speed, rain, visibility, and humidity) identified temporal correlations between road traffic and weather conditions and thereby improved the forecasting of traffic parameters [43].

Gu et al. [10] used a recurrent neural network in a traffic flow prediction model to better express the temporal and spatial characteristics of traffic flow. A deep learning method based on recurrent neural networks [44,45,46] produced more accurate traffic prediction based on big data.

Classification of recent publications is depicted in

Table 1. Classification of studies by emissions from traffic.

Models	Types of Models	Studies and Year	Input Data
Traffic management models	Adaptive control of traffic light signals	Lee et al. [1] Wang et al. [2] Fusco et al. [4]	Real-time traffic from surveillance cameras, Internet of Vehicles.
	Modeling Driver Behavior	Pathivada and Perumal [19] Najmi et al. [20] Calvi et al. [21]	Video recording of the driver’s decision.
Air quality models	Operational Street Pollution Model	Berkowicz et al. [27] Mensink and Cosemans [28]	Pollutants from the source to the receptor, The recirculation component the urban background.
	Comprehensive Air-quality Model	Luo et al. [21]	Surface characteristics, Initial and boundary conditions, Emission rates various meteorological fields.
	Chemical mass balance	Gao et al. [30] Song et al. [31]	Speciated profiles of sources, Corresponding ambient data from analyzed samples collected at a single receptor site.
Dispersion models for predicting air pollutant concentrations	CALINE4	Yu et al. [35] Air Quality Modeling [36]	Traffic emissions, Site geometry meteorology.
	AERMOD	Baghestani et al. [37] Liu et al. [38] Kim et al. [39]	Surface meteorological data, Upper air soundings, Data from on-site instrument towers.
	CMAQ	Hembeck et al. [47] Wang et al. [48]	Meteorology from a WRF38 simulation, Regional emission inputs.

.

After studying the relevant literature, we can conclude that measuring and accurately forecasting traffic flows is still a challenge due to the limited input data and high uncertainty of dynamic processes.

2. Materials and Methods

The entire developed program can be divided into two subsystems. The first is a monitoring system; the second is a forecasting system. The input data for the monitoring system is a video stream from a camera. The vehicle detection module receives this video stream and, using the YOLOv4 neural network and the Simple Online and Realtime Tracking (SORT) tracker, detects and tracks the vehicle over a series of frames. The movement path of these vehicles is broken down and imported into the Emissions module. The Emissions module calculates the average speed of traffic flows, idle time, and pollutant emissions by the movement directions and categories based on the obtained vehicle movement paths. It also calculates the maximum concentration, taking into account weather factors across the entire study area. The calculated data are aggregated into 20-min time intervals and stored in the PostgreSQL database. The LSTM subsystem for forecasting the number of vehicles and emissions trains the neural network based on the data obtained from the PostgreSQL database after their normalization. Training is performed in manual mode on command. The resulting weights are used for forecasting based on the normalized data also obtained from the database. Figure 1 shows a flowchart of the developed software system. More detailed information on emission calculation, concentration, and prediction algorithms is described in the following sections.

The quantitative assessment of traffic-related emissions is an important component of urban air quality design. Emissions levels change based on the number and type of vehicles and their dynamic performance (speed) [49,50]. Traffic-related emissions are primarily determined by the main characteristics of the traffic flow, such as the number of vehicles, speed, and standing time at the intersection [51,52]. Building prediction models on a dataset of many variables with relatively few observations can provide less accurate predictions and limit the performance of deep learning models [53].

In the first stage of this study, our primary goal was to develop a dynamic model to collect accurate data on road traffic parameters within the largest sections of the road network. We trained and modified the YOLOv4 convolutional neural network to collect high-quality dynamic data on individual vehicle movement parameters such as vehicle type, speed, and idling time [54,55,56,57]. We used low-resolution urban street cameras, allowing us to collect data within an area of up to 40,000 m². Figure 2 shows how the YOLOv4 neural network detects and tracks moving objects (vehicles) from street camera feeds.

Collecting data from street cameras makes it possible to track the movement and individual dynamic parameters of vehicles over a long period and across large sections of the road network. This approach requires significantly fewer sensors and less server power while greatly improving the accuracy of vehicle-related emissions assessments.

2.1. A Methodology for Calculating the Amount of Finely Dispersed Pollutant Emissions from Traffic Flows

Studies and verification measurements were carried out near signal-controlled intersections, which are more highly exposed to harmful exhaust gases from traffic flows. In the initial stage of the study, we focused on collecting and analyzing data on the amount and concentration of emissions over 20-min intervals. This approach makes it possible to obtain a stable trend of the concentration in the control area and respond promptly to environmental changes to avoid reaching the maximum permissible concentrations of harmful substances in the ambient air. The amount of finely dispersed emissions of the i-th vehicle-related pollutant within a signal-controlled intersection over 20 min ($M_{I_i}^{Stop}$) can be calculated using the formula [58,59]:

$$M_{I_i}^{Stop}=\frac{P_c}{60}\cdot {\displaystyle \sum _1^{N_c}{\displaystyle \sum _1^k\left(M_{I_{i,k}}^{\prime }\cdot G_{kI}\right)}},$$

(1)

where P_c is the duration of red and yellow traffic lights over 20 min, in s; N_c is the number of red and yellow traffic light cycles over 20 min; k is the number of vehicle groups; $M_{I_{i,k}}^{\prime }$ is the specific emission of the i-th pollutant generated by vehicles of the k-th group in queue at the red light, in g/min; and $G_{kI}$ is the number of vehicles of the k-th group in the queue at the end of each red light cycle.

Table 2. Specific emissions of the i-th vehicle-related pollutant [58].

Vehicle Group Name	Group Number	Emissions (g/min)
		CO	PM2.5
Cars	I	0.17	0.011
Vans and minibuses weighing up to 3.5 tons	II	1.00	0.033
Trucks weighing 3.5 to 12 tons	III	1.00	0.220
Trucks weighing over 12 tons	IV	2.00	0.450
Buses weighing over 3.5 tons	V	0.90	0.120

shows the specific emissions of CO and PM2.5 pollutants by vehicle group.

The emission of the i-th pollutant generated over a 20 min period by vehicles moving in a given direction at an intersection when the traffic light turns green ($M_{L_i}^{Go}$) is calculated as:

$$M_{L_i}^{Go}=L^I{\displaystyle \sum _1^{N_c^{\prime }}{\displaystyle \sum _1^k{M}_{{}_{k,i}}^L\cdot G_{k_{go}}\cdot r_{V_{k,i}}}},$$

(2)

where $L^I$ is the distance traveled by vehicles in one direction at green traffic signals over 20 min, including the length of the vehicle queue formed at red and yellow traffic signals and the length of the corresponding intersection area, in km; $N_c^{\prime }$ is the number of cycles of the green traffic light over 20 min; $M_{{}_{k,i}}^L$ is the specific running exhaust emission of the i-th pollutant generated by the vehicles of the k-th group, in g/min; $G_{k_{go}}$ is the number of vehicles of each k-th group passing through the intersection in one direction during the green traffic light; $r_{V_{k,i}}$ is the correction factor accounting for the mean speed of the vehicle flow V_k,i (in km/h) on a particular road (or a section thereof) [58,59,60].

Table 3. Specific running exhaust emissions of the i-th vehicle-related pollutant [58].

Vehicle Group Name	Group Number	Emissions (g/min)
		CO	PM2.5
Cars	I	0.90	0.55 × 10−2
Vans and minibuses weighing up to 3.5 tons	II	4.60	3.70 × 10−2
Trucks weighing 3.5 to 12 tons	III	5.30	0.37
Trucks weighing over 12 tons	IV	5.60	0.44
Buses weighing over 3.5 tons	V	3.90	0.15

shows the specific running exhaust emission of the i-th vehicle-related pollutant [58,59,60].

The total amount of emissions in g/s of the i-th pollutant generated by vehicles moving in a particular direction at a signal-controlled intersection is calculated as the sum of the emissions produced at green and red/yellow traffic signals, divided by 1200. The presented method only calculates the amount of vehicle-related emissions from exhaust pipes. We used specific running coefficients according to the COPERT method [60] to determine particulate matter (PM2.5 and PM10) emissions produced from the road, brake pads, and tire wear.

The following coefficients are used in the method:

• I—passenger vehicles;
• II—vehicles with a load capacity up to 3.5 tons;
• III—heavy-duty vehicles (over 3.5 tons).

We classified the specific running coefficients for three types of vehicles according to the COPERT method into five groups based on their load capacity (

Table 4. Specific running coefficients by types of vehicles [58,59,60].

Vehicle Type	Vehicle Type, per COPERT	PM2.5 (g/min)			PM10 (g/min)
		Brake Pad Wear	Tire Wear	Road Surface Wear	Brake Pad Wear	Tire Wear	Road Surface Wear
I	I	0.00293	0.00449	0.00405	0.00735	0.00642	0.00750
II	II	0.00456	0.00710	0.00405	0.01147	0.01014	0.00750
III	III	0.01277	0.01887	0.02052	0.03209	0.02696	0.03800
IV	III	0.01277	0.01887	0.02052	0.03209	0.02696	0.03800
V	III	0.01277	0.01887	0.02052	0.03209	0.02696	0.03800

).

2.2. Method Used to Calculate the Concentration of Finely Dispersed Pollutant Emissions from Traffic Flows

The maximum ground level one-time concentration of pollutants (C_M), when the gas-air mixture (GAM) is released from a single point source with a round mouth, is achieved at a dangerous wind speed u_m at a distance x_m from the emission source and determined by the formula [59]:

$$C_M=\frac{A\cdot M\cdot F\cdot m\cdot n\cdot \eta }{H^2\cdot \sqrt[3]{V_1\cdot \Delta T}},$$

(3)

where A is a coefficient which depends on the temperature stratification of the atmosphere and determines the conditions for horizontal and vertical pollutant dispersion in the air; M is the mass of pollutants emitted into the ambient air per unit of time (emission rate), in g/s; F is a dimensionless coefficient accounting for the settling rate of pollutants (gaseous and aerosols, including solid particles) in the ambient air; m and n are calculated dimensionless coefficients which account for the conditions of output from the mouth of the emission source; η is a dimensionless coefficient accounting for the influence of terrain features and urban development; H is the height of the emissions source, in m; V₁ is the gas-air mixture consumption, in m³/s; and ΔT is the difference between the temperature of the emitted GAM and the ambient air temperature.

The gas-air mixture consumption (V₁) is calculated as [59]:

$$V_1=\frac{\pi \cdot D^2}{4}\cdot {\omega }_0,$$

(4)

where D is the diameter of the mouth of the emission source, in m; and ω₀ is the mean speed of the GAM release from the mouth of the emission source, in m/s.

A dangerous wind speed u_m takes into account the following parameters: H, V₁, ω₀ and D [59].

The maximum ground level concentration (c_m.u) at a given wind speed (u) is calculated as:

$$\begin{gathered} c_{m.u}=r\cdot c_m \\ \begin{array}{l}r=0.67\cdot \frac{u}{u_m}+1.67\cdot {\left(\frac{u}{u_m}\right)}^2-1.34\cdot {\left(\frac{u}{u_m}\right)}^{3}when\frac{u}{u_m}\le 1,\\ r=\frac{3\cdot \left(u/u_m\right)}{2\cdot {\left(u/u_m\right)}^2-u/u_m+2}when\frac{u}{u_m}>1\end{array} \end{gathered}$$

(5)

The maximum ground level concentration at a given wind speed u at a distance x from the source along the emission plume axis and y perpendicular to the emission plume axis are calculated as:

$$c=s_1^{}\cdot c_m$$

(6)

$$c_y=s_2^{}\cdot c$$

(7)

where s₁ and s₂ are the coefficients dependent upon x and y.

The values of coefficient A depend on the geographical region and are given in

Table 5. Values of coefficient A [59].

#	Region	Coefficient A
1	Republic of Buryatia and Trans-Baikal Territory	250
2	Regions of the European part of the Russian Federation south of 50 °N, other regions of the Lower Volga territory, Asian part of the Russian Federation, except for those indicated in Items 1 and 3 of this Table	200
3	European part of the Russian Federation and the Urals from 50 °N to 52 °N inclusive, except for the areas falling into this zone, listed in Items 1 and 2 of this Table, as well as for the areas of the Asian part of the Russian Federation located north of the Arctic Circle and west of the meridian 108 °e.	180
4	European part of the Russian Federation and the Urals north of 52 °N (except for the center of the European part of the Russian Federation)	160
5	Vladimir, Ivanovo, Kaluga, Moscow, Ryazan, and Tula regions	140

.

3. Experiment and Verification

This section presents a methodology for determining the amount of emissions from vehicles in real time, taking into account meteorological conditions. A prediction model using a recurrent neural network is also proposed and its statistical reliability is verified.

3.1. Implementation of the Algorithm to Calculate the Amount and Concentration of Emissions from Mobile Emission Sources

To calculate the amount and concentration of emissions from a single mobile source, the movement of the object (a vehicle) is divided into intervals of 5 s (Figure 3). The coordinates of the mobile emission source are set as the center of the distance travelled in 5 s. The time the vehicle spends in queue at the traffic light is also taken into account. When a vehicle is in queue, the length of the resulting 5-s segment will be equal to 0. This method of segmentation was chosen because shorter time intervals and smoother paths provide higher computational capacity. This implementation is sufficient for the real-time calculation of emissions and concentration with the required accuracy. A series of screenshots (frames) are taken for each car. The frame time, current speed, and location of the vehicle is added to the frame each time the vehicle is detected by the neural network. When the difference in time between the first and last frame exceeds 5 s, the network builds the path of the vehicle and calculates the amount and concentration of emissions based on the entire series, after which the series is cleared and filled again.

We have updated our method to track each vehicle throughout the entire field of view of the camera rather than following traffic light signals. The algorithm for calculating the amount of emissions has been adjusted accordingly. In the current implementation, the emission of the i-th pollutant from one vehicle is calculated as:

$$M=P\cdot M_p+L\cdot M_L\cdot r_V,$$

(8)

where M is the total amount of emissions for one vehicle (taking into account both the intervals when the vehicle is standing and when it is moving), in g; P is the idling time of the vehicle, in min; M_P is the specific emission of the i-th pollutant of the vehicle in the k-th group (used to calculate idle emissions), in g/min; L is the distance traveled by the vehicle, in km; M_L is the specific running exhaust emission of the i-th pollutant produced by the vehicle in the k-th group (the mileage emission used to calculate driving emissions), in g/km; and r_V is a correction factor accounting for the mean vehicle speed.

To convert the emissions into g/s, the resulting M value is divided by the vehicle tracking time [61]. Thus, emissions are calculated in real time for each vehicle every 5 s. The total amount of emissions produced by a vehicle is calculated as the sum of emissions at all intervals of its movement.

The following parameters were chosen to calculate the maximum ground level concentration of vehicle-related pollutants:

• A = 160 for Chelyabinsk;
• F = 1 (gaseous pollutants and fine aerosols with a diameter of no more than 10µm);
• η = 1 (terrain features and urban development are not taken into account);
• H = 2 m (minimum source height);
• T_GAM = 100 °C;
• D is the distance traveled by the vehicle;
• ω₀ = 0.01 m/s (since the vehicle exhaust pipe is directed horizontally, we used the minimum vertical speed of the air–water mixture).

Data on air temperature, wind speed, and wind direction were obtained from OpenWeather [62].

To calculate the emission concentration and distribution at the chosen intersection, we divided the intersection into a 20 × 20 grid of 20 × 20 m squares (Figure 4). Then, we calculated the concentration in each of the areas from each last unaccounted movement interval for each vehicle (Figure 5). The middle of the movement interval was taken as the GAM source. The concentration in each square was calculated at the moment when the emissions cloud reached the square. This delay was calculated by dividing the distance from the source to a square by the wind speed. Then the concentrations were summed up inside their areas. Figure 6 visualizes the distribution of the CO concentration at the intersection, accounting for the effects of wind. The maximum ground level pollutant concentration across the entire intersection is equal to the maximum pollutant concentration across all areas.

3.2. Forecasting the Number of Vehicles

Long short-term memory is a deep learning technique that can be used to obtain more accurate time series [44,45]. We implemented a serial encoder-decoder model with a Recurrent Neural Network (RNN) to forecast particulate matter in urban environments. The model was trained using emission concentration statistics and transport and meteorological data. The forecasts were in good agreement with the measurements, indicating the applicability of the system and its great potential for producing high-quality real-time forecasts of urban air pollutants. The recurrent neural network, unlike other networks, can highlight the features of each element, accounting for the connections between elements [63,64]. The vector H (Figure 7) of a cell in an RNN describes the current inner state of the network and contains memories of all the studied elements. These cells are collected in a sequence, representing a time series. Each cell transfers its inner state to a subsequent cell.

These neurons cannot memorize data vectors above a certain size. The LSTM model was proposed to solve this problem. The existing cell was supplemented with one more inner state S, as well as the ability to extract the most significant features from the input data and decide what has a greater influence on the inner state and what is sent to the output. An example of an LSTM cell is shown in Figure 8.

We implemented an RNN in the Keras library of the Python programming language. The dataset was formed from 1000 records from the database containing the aggregate number of vehicles in all directions and all categories over 20-min intervals. These 1000 20-min intervals amount to roughly two weeks. The data were divided into training and test samples in the ratio of 85% to 15%, respectively.

A vector of 72 24-h intervals with one parameter (the number of vehicles) was chosen as the input data of the neural network used as the basis for training and forecasting.

A vector of 24 8-h time intervals with one parameter (the number of vehicles) was chosen as the output data (forecasted value).

All input and output data were converted to the interval [0, 1] for normalization. The number of vehicles was divided by the maximum value of 2500 across the entire dataset for each 20-min time interval.

The mean absolute error (mae) of determining the number of vehicles was chosen to assess the quality of the neural network.

The following lawyers were used:

• LSTM (a recurrent layer);
• Dropout with rate = 0.2 (a layer that prevents retraining by ignoring randomly selected neurons during training);
• Dense (an output layer that changes the shape of the data into the desired form).

Eighteen options were tested with different parameters as the neural network configuration:

• Number of LSTM layers: 1, 2, 3;
• Number of neurons in each of the LSTM layers: 50, 100, 300, 500, 750, 1000.
• The number of learning epochs was 500.

The results of experimental training of 18 neural network configurations are shown as distribution fields of the error in calculating the number of vehicles (mean absolute error) and the network training time depending on the number of LSTM layers and neurons in each layer (Figure 9).

Testing showed that Configuration 15 with three layers and 300 neurons in each layer showed the minimum values of mean and maximum absolute errors. We found that configurations with fewer layers and neurons do not cope well with the task. Configurations of three layers with 750 and 1000 neurons are ineffective and spend too much time on training.

Figure 10 shows the forecasted number of vehicles produced by Configuration 15 for two conjugated 8-h time intervals.

Statistical analysis of the forecasted and actual number of vehicles showed a mean square deviation of 210 vehicles in a 20-min measurement interval, which is 34.7% (coefficient of variation) less than the mathematical forecast. Although this forecast deviation is rather high, it does not invalidate the model, but rather characterizes the amplitude of random deviations. The main significant factor is the cumulative number-of-vehicles basis during the experiment.

Figure 11 compares the forecasted and measured total traffic flow volume (number of vehicles) over 48 20-min intervals (16 h in total). The calculation showed an increase in the forecast values of the number of vehicles from the actual number of 42,345 to 44,120 units, which is only 4.2% of the deviation.

A check of the differences using the non-parametric chi-square test (SPSS package) confirmed that there are no statistically significant differences between the forecast and actual daily emissions (error level = 54%, which considerably exceeds the difference significance threshold of 5%). Based on the deviation analysis, we can conclude that the RNN produces statistically reliable predictions of the number of vehicles passing through an intersection over a given period.

3.3. Forecasting the Amount of Emissions

An array of 72 intervals of 20 min was chosen as the input data of the neural network for training and forecasting. The input data have three parameters: PM2.5 emissions, the day of the week, and the time interval index. Thus, the shape of the two-dimensional array of input data was (72, 3). An array of 72 20-min intervals with one parameter (amount of PM2.5 emissions) was chosen as the output data (forecast value). The shape of the two-dimensional array of output data was (72, 1).

All input and output data were converted to the interval [0, 1] for normalization. The amount of PM2.5 emissions was divided by the maximum value of 30 across the entire dataset for each 20-min time interval. Each day of the week was assigned a value between zero and six, where zero was Monday and six was Sunday, and divided by six. Each 20-min interval was assigned a value between 0 and 71, where 0 was the time interval of 0:00–0:20, and 71 was the interval of 23:40–00:00, and divided by 71.

To find the best neural network configuration, we implemented a program that generates several configurations, trains each of them, conducts tests, and evaluates performance.

The mean squared error (mse), the average absolute error (mae), and the maximum absolute error (Mae) were chosen to assess the quality of the neural network.

The following layers were chosen to create configurations:

• LSTM (a recurrent layer);
• Dropout with rate = 0.2 (a layer that prevents retraining by ignoring randomly selected neurons during training). This layer follows each LSTM layer;
• Dense (an output layer that changes the shape of the data into the desired form).

We tested 32 configuration options with different parameters:

• Number of LSTM layers: 1, 2, 3, 4;
• Number of neurons in each of the LSTM layers: 50, 100, 250, 400, 600, 800, 1000, and 1200.

The number of learning epochs was 500.

Figure 12 shows the results of experimental training for 32 neural network configurations as distribution fields of the forecasting error and the training time depending on the number of LSTM layers and neurons in each layer.

Configuration 24, with three layers and 1200 neurons in each layer (mse = 0.081; time = 527 s) showed the minimum mse errors. However, this configuration spends too much time on learning and operation. Configuration 29, with four layers and 600 neurons in each layer (mse = 0.083; time = 212 s) is one of the most optimal. It produces predictions with nearly the same quality as Configuration 1, but 2.5 times faster.

Statistical analysis of the forecasting accuracy (mse) of various network configurations showed that the area of significant differences begins with errors of more than 0.108 (Pearson’s nonparametric chi-square test). Fifteen out of the thirty-two tested configurations fall into this area of statistically equal accuracy values. Configuration 29 again has the minimum forecast time, which is the main criterion for a real-time system. We ultimately chose Configuration 29 of the RNN, with four layers and 600 neurons, to forecast PM2.5 emissions.

Figure 13 presents the PM2.5 emissions forecasted by Configuration 29 with the original data in grams over 24 h (72 intervals of 20 min).

Statistical analysis of the forecast and measured emissions data in the neural network showed a 1.26-g mean square deviation in the 20-min measurement interval. This is, on average, 18.5% per day (coefficient of variation) less than the forecasted value. Although this deviation is quite a high deviation, the daily amount of emissions remains the main significant factor.

Figure 14 compares the total emissions of PM2.5 particulate matter on a cumulative daily basis. The daily forecast value was only 6.25% less than the actual recorded value (448 g and 477.5 g, respectively).

A non-parametric chi-square test confirmed that the difference between the forecast and actual daily volume of emissions was not statistically significant (error level = 34%, which considerably exceeds the difference significance threshold of 5%). We can conclude that the recurrent neural network produces statistically reliable forecasts of PM2.5 emissions.

4. Discussion and Results

To assess the reliability and quality of the above system for monitoring the amount of emissions and the atmospheric concentration of harmful substances from traffic flows, analytical work was performed to compare the model calculations of emissions with laboratory measurements made by the Regional State Institution Center for Environmental Monitoring of the Chelyabinsk Region.

The results of the laboratory measurements of pollutant emissions were read from the reports of the Center for Environmental Monitoring (CEM). The measurements were made within the intersection of Engelsa Street and Lenina Avenue. The laboratory measurements of PM2.5 and PM10 emissions were recorded using a standard meter—a DustTrack 8533 dust analyzer [65]. The device measures only one type of emissions and features a rigid, calculated connection of the other type with it.

Notably, the CEM reports also record meteorological conditions corresponding to the measurement time, which can serve as a basis for further studying the influence of meteorological conditions on the concentration of pollutants within the intersection, provided that the volume of measurements is sufficient. However, unfortunately, instrumental measurements are a rather laborious procedure; three 20-min measurements are routinely performed within one day in the morning and afternoon, no more than once a week.

The proposed system based on a recurrent neural network has the following undeniable advantages: calculation results are provided in real time; there is no grouping of data into 20-min intervals, since the calculations are linked only to instantaneous data of the recorded traffic flow at the intersection. However, this gives rise to the main drawback of the calculation system—it does not take into account the general background of atmospheric pollution by industrial emissions. Notably, in our case, the analyzed intersection is located in an environmentally friendly urban area with minimum industrial emissions.

Figure 15 and Figure 16 present the results of comparing the predicted emissions of PM2.5 and PM10 particulate matter with the CEM instrumental data based on 14 control measurements.

According to a visual comparison of the concentrations, a stable lower value of the predicted emissions of particulate matter indicates the presence of similar emissions from the urban industrial enterprises. These deviations can act as their evaluation measure.

The significant unstable deviations observed in the graphs are explained by several random factors in instrumental measurements, such as the single passage of heavy vehicles with an unbalanced engine; sharply changing weather conditions; measuring instrument error floating in time. These factors cannot be minimized by averaging statistical algorithms due to the complexity and laboriousness of instrumental measurements.

The main trend seen in the graphs is a similar nature of changes in emissions both during instrumental measurements and computational forecasting, which is a good confirmation of the quality of the developed system for the dynamic monitoring of emissions.

The problem of separating emissions generated by various sources—industrial enterprises and motor traffic flows—is relevant and highly-demanded for identifying the reasons for exceeding maximum pollutant concentrations. In the results of the study (see Figure 12), a visible decrease in the predicted values of vehicle-related emissions versus the actual total emissions is observed in the time interval from 13.00 to 16.00 local time (from the 40th to the 50th twenty-minute interval), which exactly corresponds to the maximum industrial emissions. Nevertheless, at urban intersections, the main share of pollutions is still attributed to urban vehicles, which is confirmed by a perfect match of the actual and predicted emissions in the morning and evening. This is in good agreement with a priori estimates of minimum industrial emissions at that time. Notably, the analyzed intersection belongs to an environmentally friendly urban area with minimum industrial emissions.

With the wider commercialization of vehicle to vehicle (V2V), intelligent transportation systems are better able to reduce the negative impact of emissions on the urban environment by structuring and shaping traffic flows. Various sensors and systems designed to monitor urban transport and emissions are appearing. These devices often have overlapping functions, which require major computational capability and maintenance costs. Our approach is based on using the existing network of urban street cameras as sensors for data collection. Considering the worsening environmental situation in cities, monitoring tools alone are no longer sufficient; accurate forecasting methods and solutions to manage environmental risks are crucial. The presented hybrid model can effectively improve the accuracy of deep learning neural network models to forecast the amount and concentration of air pollutants. The optimized architecture of the combined YOLOv4 and LSTM neural networks uses far fewer computational resources and imposes minimal requirements on the number and characteristics of outdoor cameras used as sensors. Future research will expand this methodology to not just assess and forecast emissions, but also to model the dynamic structure of road traffic to reduce the negative environmental impact of vehicles.

5. Conclusions

Signal-controlled intersections are one of the most popular urban intersection structures. The traffic congestion which inherently occurs at such intersections leads to increased emissions concentrations.

Most applicable methods for assessing and forecasting emission concentrations do not fully consider the influence of road traffic heterogeneity and the behavior exhibited by drivers when following another type of vehicle. Many models do not account for the road infrastructure either. These omissions lead to major differences in forecasted emissions.

This study proposes a hybrid model that combines convolutional and recurrent neural networks to produce more accurate evaluations and forecasts of the amount and concentration of vehicle-related pollutants. The advantage of the proposed model is that it collects, interprets, and aggregates individual vehicle characteristics as mobile emission sources. This allows the model to account for driver behavior, including how drivers behave when following vehicles of various types. This aspect has been ignored in previous studies. The accuracy of the model is supported by high-quality monitoring of road traffic and by selecting features which account for spatiotemporal correlations (urban development). We collected 12,000 video frames from over 650,000 marked vehicles to train and evaluate the detector. The collected data covers more than 20 intersections with different viewing angles, occlusion levels, and major scale differences. The proposed system can count and classify vehicles by the direction of movement with an average percentage error of less than 6%. The module of the hybrid model for data collection and interpretation is a good solution for extracting the necessary data to forecast the amount of emissions while accounting for individual vehicle parameters. The hybrid model uses the advantages of the fast data extraction of a convolutional neural network and incorporates the efficiency of the long-term feature extraction of an LSTM recurrent neural network.

The proposed model successfully forecasted the amount and maximum ground level concentration of finely dispersed emissions and exceeded the forecasting accuracy of competing models.

Our methodology represents an evolution in modeling and assessment methods. The proposed model assesses driver behavior, vehicle type, and environmental factors (the influence of meteorological predictors and the features of transport infrastructure) on vehicle-related emissions. Our model can build next-generation datasets, which will support the development of sustainable transport systems. The proposed approach makes a qualitative transition from measuring emissions with expensive sensors to more informative, accurate, and efficient digital solutions.