Traffic Flow Measurement Research Articles

Multi-models machine learning methods for traffic flow estimation from Floating Car Data

Traffic flow measurement is very important for traffic management systems. However, the existing traditional measurement approaches are highly time-consuming and expensive to continuously gather the required data and to maintain the corresponding equipment, such as loop detectors and video cameras. On the other hand, many services on the web propose to estimate automobile travel time taking into account traffic conditions thanks to crowd sourced data (Floating Car Data). This work proposes to reconstruct, from estimated travel time, traffic flows using machine learning method. In particular, we evaluate the capacity of Gaussian Process Regressor (GPR) to address this issue. After obtaining estimated travel time on a given route, a clustering process shows that travel duration profiles in each day can be associated to different “types of day”. Then, different regressors are trained in order to estimate traffic flows from travel duration. In the “multi-model” variant, we trained a Regressor for each type of day. Conversely, in the “single model” variant, only one Regressor is trained (the type of day is not taken into account). This is an innovative work to estimate and reconstruct the traffic flow in transportation networks with machine learning method from aggregated Floating Car Data (FCD). A series of experiments are conducted to compare the estimated traffic flows, obtained by the proposed single model and multi-model, and the real ones from actual sensors. The obtained results show that both single model and multi-models can capture the tendency of real traffic flows. Furthermore, the performance can be improved by regulating parameters in GPR machine learning model, such as half width of sample window and sample size (a whole week or only weekdays), and multi-models can highly increase the performance compared with the single model. Therefore, the proposed GPR machine learning and FCD based new method can replace those traditional loop detectors for the measurement of traffic flow.

An Adaptive Cutoff Frequency Selection Approach for Fast Fourier Transform Method and Its Application into Short-Term Traffic Flow Forecasting

Historical measurements are usually used to build assimilation models in sequential data assimilation (S-DA) systems. However, they are always disturbed by local noises. Simultaneously, the accuracy of assimilation model construction and assimilation forecasting results will be affected. The fast Fourier transform (FFT) method can be used to acquire de-noised historical traffic flow measurements to reduce the influence of local noises on constructed assimilation models and improve the accuracy of assimilation results. In the practical signal de-noising applications, the FFT method is commonly used to de-noise the noisy signal with known noise frequency. However, knowing the noise frequency is difficult. Thus, a proper cutoff frequency should be chosen to separate high-frequency information caused by noises from the low-frequency part of useful signals under the unknown noise frequency. If the cutoff frequency is too high, too much noisy information will be treated as useful information. Conversely, if the cutoff frequency is too low, part of the useful information will be lost. To solve this problem, this paper proposes an adaptive cutoff frequency selection (A-CFS) method based on cross-validation. The proposed method can determine a proper cutoff frequency and ensure the quality of de-noised outputs for a given dataset using the FFT method without noise frequency information. Experimental results of real-world traffic flow data measurements in a sub-area of a highway near Birmingham, England, demonstrate the superior performance of the proposed A-CFS method in noisy information separation using the FFT method. The differences between true and predicted traffic flow values are evaluated using the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage (MAPE) values. Compared to the results of the two commonly used de-noising methods, i.e., discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) methods, the short-term traffic flow forecasting results of the proposed A-CFS method are much more reliable. In terms of the MAE value, the average relative improvements of the assimilation model built using the proposed method are 19.26%, 3.47%, and 4.25%, compared to the model built using raw data, DWT method, and EEMD method, respectively; the corresponding average relative improvements in RMSE are 19.05%, 5.36%, and 3.02%, respectively; lastly, the corresponding average relative improvements in MAPE are 18.88%, 2.83%, and 2.28%, respectively. The test results show that the proposed method is effective in separating noises from historical measurements and can improve the accuracy of assimilation model construction and assimilation forecasting results.

DeepMatch: Fine-Grained Traffic Flow Measurement in SDN With Deep Dueling Neural Networks

In this paper, we propose a novel flow rule matching framework, DeepMatch, in Software-Defined Networking (SDN) to provide a fine-grained traffic flow measurement capability. Specifically, the flow rule matching control at a particular SDN switch is examined to maximize the traffic flow granularity degree while proactively protecting the flow-table in the switch from being overflowed. This control process is supervised by a control module referred to as DeepMatch instance. Regarding this instance, an optimization problem is formulated based on a Markov decision process (MDP) and a Partially Observable Markov decision process (POMDP), respectively. We develop a deep dueling neural network based flow rule matching control algorithm to solve the optimization problem, thereby quickly attaining a significant traffic flow granularity level and eliminating the switch flow-table overflow problem. Furthermore, we propose an experience data sharing (EDS) mechanism that enables a new instance to learn faster about the flow rule matching control. The results of our performance evaluation show that, by applying the DeepMatch framework in a highly dynamic traffic scenario, the traffic flow granularity degree at the access and the core switches increases by 24.0% and 31.63%, respectively, compared to the FlowStat method. DeepMatch is also highly outperforming the ReWiFlow, SDN-Mon, and Exact-Match approaches. In addition, by employing the EDS mechanism, a new instance can reduce its learning time up to 46.42% for supervising an access switch and up to 37.50% for supervising a core switch.

Effective and unburdensome forecast of highway traffic flow with adaptive computing

Given traffic flow measurements for one highway, how to forecast its flow in future periods? Recent works in traffic forecast propose burdensomeprocedures by depending on additional data that is not always available, like traffic measurements from other roads linked to the one of interest, social media, trajectory and car accident data, geographical and socio-demographic attributes, driver behavior information and weather forecast. The most accurate algorithms force anyone to monitor an entire network of highways, even when there is a single highway of interest. This procedure is commonly unaffordable. How to obtain highly accurate results without using any additional data? We answer the question with AdaptFlow: a novel, adaptive algorithm able to accurately forecast traffic flow by individually monitoring highways that are connected to each other in a complex network using local flow measurements only. We performed experiments on large datasets from highways in UK and USA. Our AdaptFlow notably outperformed well-known related works on many settings. For example, it achieved 95.5% accuracy on average when forecasting the next 15 minutes flow of the UK highways, leading to an error rate that is 36% smaller than the one of the most accurate related work that does not use additional data.

An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting

Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression.

Towards the coupling of a chemical transport model with a micro-scale Lagrangian modelling system for evaluation of urban NOx levels in a European hotspot

A multi-scale modelling system was developed to provide hourly NOx concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy), one of the most polluted areas in Europe. The WRF-Chem model was applied over three nested domains and employed with the aim of reproducing local background concentrations, taking into account meteorological and chemical transformation at the regional scale with nested resolutions of 15 km, 3 km and 1 km. Conversely, the PMSS modelling system was applied to simulate 3D air pollutant dispersion, due to traffic emissions, with a very high-resolution (4 m) on a 6 km × 6 km domain covering the city of Modena.The methodology employed to account for anthropogenic emissions relies on two different strategies. Traffic emissions were based on a bottom-up approach using emission factors suggested by the European Environmental Agency with traffic fluxes estimated by the PTV VISUM model in the urban area of Modena, combined with direct traffic flow measurements performed between October 28 and November 8, 2016 which was used for the hourly vehicle modulation. Other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model. Simulations were performed for the same period whereby the traffic measurement campaign was carried out.2 m temperature and 10 m wind speed were captured quite well by the WRF-Chem model with statistical metrics in line with similar case studies related to the Northern Italy. The NOx concentrations reproduced in the Po Valley area by WRF-Chem were on average simulated reasonably well with a general negative bias in almost all the examined rural background monitoring stations. Additionally, the deployment of an emission inventory at the original resolution (7 km) highlighted that increasing resolution from 3 km to 1 km does not generally improve the model performance.Nevertheless, simulated and observed NOx hourly concentrations in the urban area of Modena exhibit a large agreement in particular for urban traffic site where detailed traffic emission estimations proved to be very successful in reproducing the observed NOx trend. At urban background stations, despite a general underestimation of the observed concentrations, the combination of WRF-Chem with PMSS provided daily pattern in line with observations. The analysis of the modelled NOx daily cycle pointed out also that at both traffic and background urban stations the morning NOx peak concentration was on average underestimated. This could be explained with an overestimation of mixing phenomena between 07:30 a.m. and 10:00 a.m. by WRF-Chem which leads to a greater dispersion of NOx along the vertical and thus a morning underestimation.The statistical analysis showed finally that PMSS combined with WRF-Chem at both the resolutions (3 km and 1 km) and at both traffic and background sites fulfilled standard acceptance criteria for urban dispersion model evaluation, confirming that the proposed multi-modelling system can be employed as a tool to support environmental policies, epidemiological studies and urban mobility planning.

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.

Using traffic simulation to quantify performance improvement due to vehicular traffic reduction at a university campus

The purpose of this study is to quantify the benefits of reducing vehicular traffic at a university campus resulting from adopting sustainable transport modes to travel to campus. To this end, a traffic simulation model was developed to estimate improvements in several traffic and environmental performance measures. A total of 11 traffic scenarios were simulated and their results were compared. These scenarios varied from a baseline situation, i.e., the current AM peak-hour traffic, to a variety of traffic-reduction scenarios ranging from 5% to 50% with an increment of 5%. The results showed considerable improvements in traffic flow and air-pollution performance measures. All performance measures witnessed steady improvement as more traffic reduction occurred; nevertheless, traffic measures have seen more improvement as opposed to environmental measures. Statistical significance tests showed that 5% traffic reductions did not show significant impacts on performance, 10% reductions will improve traffic flow, whereas 20% traffic reductions will improve both traffic and air emissions.

Investigation of Driver Route Choice Behaviour using Bluetooth Data

Many local authorities use small-scale transport models to manage their transportation networks. These may assume drivers’ behaviour to be rational in choosing the fastest route, and thus all drivers behave the same given an origin and destination, leading to simplified aggregate flow models, fitted to anonymous traffic flow measurements. Recent price falls in traffic sensors, data storage, and compute power now enable Data Science to empirically test such assumptions, by using per-driver data to infer route selection from sensor observations and compare with optimal route selection. A methodology is presented using per-driver data to analyse driver route choice behaviour in transportation networks. Traffic flows on multiple measurable routes for origin-destination pairs are compared based on the length of each route. A driver rationality index is defined by considering the shortest physical route between an origin-destination pair. The proposed method is intended to aid calibration of parameters used in traffic assignment models e.g. weights in generalized cost formulations or dispersion within stochastic user equilibrium models. The method is demonstrated using raw sensor datasets collected through Bluetooth sensors in the area of Chesterfield, Derbyshire, UK. The results for this region show that routes with a significant difference in lengths of their paths have the majority (71%) of drivers using the optimal path but as the difference in length decreases, the probability of optimal route choice decreases (27%). The methodology can be used for extended research considering the impact on route choice of other factors including travel time and road specific conditions.

STAM-based methodology to prevent concurrence events in a Multi-Airport System (MAS)

Pre-tactically managing the imbalances of the network and organizing the resources of the airspace is one of the main objectives of Dynamic Demand & Capacity Balancing (dDCB). Introducing Short-term Air Traffic Flow and Capacity Management (STAM) measures such as minor ground delays or slight speed adjustments applied to a selected number of flights on the day of operations, supports the dDCB process by reducing the traffic complexity and the probability of Air Traffic Controllers interventions. This paper introduces a rolling horizon methodology for detecting concurrence events, analysing their trajectory interdependencies and applying a strategic mitigation measure that shifts the Estimated Take-Off Time (ETOT) within the assigned Calculated Take-Off Time (CTOT) window. The methodology proposes a collaborative decision-making process for a better coordination of departures from airports feeding airspace volumes with high air traffic demand. The experimental part of this paper aims at showing the potential benefits of such a STAM procedure by applying the methodology to distribute the demand of a sector located in the London Terminal Manoeuvring Area (LTMA). Experiments have considered regulated traffic scenarios where ETOT were allocated in order to reduce departure time uncertainties. Lastly, it is analysed the impact of the parameters used by the methodology to calculate the mitigation measures: safe separation criteria, look-ahead time and number of coordinated airports.

Reliability of Dynamap traffic noise prediction

Dynamap (DYNamic Acoustic MAPping) is an European Life project whose main scope is to implement “real-time” acoustic maps in a district of the city of Milan. Dynamap is based on the idea of finding a suitable sets of roads displaying similar traffic noise behavior so that one can group them together into single noise maps. Twenty-four monitoring sensors have been installed permanently in appropriate locations within the pilot zone (District 9) to obtain six dynamic noise maps. The project has come to its “Test and Fault Analysis” phase. A set of new traffic noise and traffic flow measurements have been recorded to shed light on the reliability of Dynamap prediction. Besides the expected error due to statistical fluctuations of traffic noise, our preliminary analysis show a systematic error inherent to the adopted traffic flow model, which is an essential ingredient of the whole approach. By correlating the traffic noise prediction error to the one due to the traffic flow model, we succeed in improving the Dynamap prediction thus limiting the overall error to within about 3 dB. We suggest that Dynamap scheme can be applied to large urban conglomerates in general, thus becoming a useful tool for monitoring traffic noise accurately and efficiently in large cities.

Impact of vehicular emissions in an urban area of the Po valley by microscale simulation with the GRAL dispersion model

This work sets out the test of the GRAL model (Graz Lagrangian Model, vs.18.1) in the urban area of Modena (Po valley, Northern Italy). The simulation domain sizes 2 000 x 3 000 m2 and it features ‘microscale’ cells of 4 x 4 m2. The simulation focuses on an intersection featured by large traffic flows next to a school and a regulatory air quality monitoring station classified as an urban traffic site. The model is a lagrangian particle dispersion model and it takes into account the presence of buildings as obstacles and generating microscale wind fields accordingly, making this class of model suitable for investigating spatial pattern of atmospheric pollution in urban areas where local accumulation might occur. The simulation investigates traffic emissions of nitrogen oxides (NOx) over the period October 29 to November 10, 2016, when direct measurements of traffic flow were collected by four one-channel doppler radar traffic counters. These counters provided continuous estimate of vehicle length, speed and number. These latter data were combined with available traffic flows at rush hour by PTV VISUM mobility software and the fleet composition of the municipality to estimate the total NOx emissions by vehicular traffic over the roads included in the simulation domain. NOx simulated concentrations showed a moderate correlation with the NOx observations at the nearby monitoring site. To have a better insight on the potential and the limitations of the GRAL model, its results will be compared with the output of the lagrangian particle dispersion model PMSS over the same area.

Deep learning based short-term air traffic flow prediction considering temporal–spatial correlation

In order to improve the accuracy and stability of air traffic flow prediction, an end-to-end deep learning-based model is proposed in this paper. By analyzing spatial correlations of adjacent areas and temporal correlations of historical traffic on given area, we firstly apply the gridded map method (including the flight levels) to encode the whole air traffic flow situation into a new data representation, i.e., traffic flow matrix (TFM). By the proposed data representation, inherent features of air traffic flow and their transition patterns on different cells and flight levels can be represented comprehensively. Learning from the powerful ability of convolutional neural network and recurrent neural network on modeling spatial and temporal correlations, the ConvLSTM module is proposed to build a trainable model for air traffic flow prediction. Since the output of the proposed model is also the TFM and shows the overall air traffic situation of studied regions, we call the proposed model as an end-to-end one. Experimental results on real data show the superior performance over existing approaches on prediction accuracy and stability. Furthermore, the proposed model can also predict the flow distribution on different flight levels in our application, which promotes the level of air traffic management. By analyzing the distribution of prediction errors on different cells, flight levels and predicting instants, we can draw the conclusion that spatial and temporal transition patterns of flight flow in air traffic system are fully learned by the proposed model. With the proposed model, more efficient air traffic flow measures are expected to be fulfilled to improve the operation efficiency.

Impact assessment of vehicular exhaust emissions by microscale simulation using automatic traffic flow measurements

In order to assess the impact of traffic on local air quality a microscale simulation of pollutant concentration fields was produced for two busy intersections, in Reggio Emilia and in Modena, Italy. The simulation was performed by the model suite Micro-Swift-Spray, a Lagrangian particle dispersion model accounting for buildings. Direct measurements of traffic flow were continuously collected in Reggio Emilia over the period January 13–24, 2014 by a two channel radar traffic counter and in Modena from October 28 to November 8, 2016 by four single channel radar traffic counters and used for the hourly modulation of vehicular emissions. Combining radar counts with vehicular fleet composition for each municipality, specific emission factors were obtained. For both cities, simulated concentration fields were compared to local air quality measurements at the nearest urban traffic and urban background sites. The simulated NOx showed large correlation with the observations, notwithstanding some underestimation. The results proved the reliability of the procedure and provided a fair estimate of the NO2 mass fraction of total NOx (primary NO2) due to vehicular emissions in the investigated traffic sites.

PRINCIPLES OF ENSURING ENVIRONMENTAL WELFARE OF THE POPULATION UNDER THE EXPOSURE OF THE NOISE OF MOVING MOTOR TRANSPORT

The article presents the results of theoretical research, field and instrumental measurements of traffic flow on the main streets of Vladikavkaz. It is shown that in the formation of noise pollution of the urban environment during the movement of vehicles on the roads of the Vladikavkaz city, the road covering material, vehicle speed, tire pattern wear and out-of-limit lifespan, etc., are of great importance. The economic analysis of noise shield devices of vehicles and the dependence of the decrease in the sound pressure of transport and the implementation costs are given. At the same time, the norms of permissible sound levels in residential areas depending on the time of day are shown, which is significant when assessing the environmental comfort of housing in noise-stressed areas of the city. At the end of the article, conclusions are given for various categories of institutions and control and supervisory bodies to ensure favorable sanitary and environmental conditions for the population.

Analysis of Urban Traffic for Various Sets of Vehicles

The paper analyses the results of traffic flow measurements recorded by road traffic and noise monitoring station. The station is located in a medium size city in Poland (Kielce) situated at the national road to Cracow. The traffic flow was measured over the period between January and December 2013 for twenty-four hours a day. The measurements were documented at one hour intervals throughout the 24-hour period (0:00-23:00). Statistical analysis methods were used to determine the variability and uncertainty of the results. The measurements from two vehicular lanes running towards the town (L12) and two lanes running towards Cracow (L34) were analysed. Different vehicle sets (G(i)) were taken into account: i = cars, trucks, delivery vans and motorcycles. The coefficients of variation and positional variation were proposed for the analysis of traffic data scattering. It was found that in some cases the distribution of the tested variable was not normal. Differences in traffic intensity on opposite road lanes for particular sets of vehicles were demonstrated. Box plots were used to assess whether outliers occurred in the recorded data. The times of the day at which the parameters describing the variability of the recorded traffic stabilized were identified. The results obtained for the data recorded on working and non-working days were compared. Type A uncertainty was evaluated.

Freeway Loop Detector Data Reconciliation Based on Vehicle Conservation

Loop detector is the most common source of traffic flow measurements on freeways. Though being a mature technology, loop detector is prone to miscounts even when it is properly deployed and adjusted. If a loop detectors systematically over- or under-counts, the estimation based on vehicle conservation leads to results that are not physically plausible such as negative density or density higher than the jam-density. Here we propose an optimization-based method in which plausible values of accumulation is guaranteed through explicit constraints. Compared to similar methods, the proposed method distinct in the following: (i) the optimization problem is convex therefore leads to shorter computational time; (ii) occupancy is also taken into account in order to estimate upper and lower bounds in the accumulation between stations; (iii) no modifications are required when data from one or multiple lanes are missing. The method is first validated using micro-simulation by disturbing the loop-detector from micro-simulation, applying the method and then comparing the results with the ground truth data. The results show the method is able to capture the average counts almost exactly while providing estimate of density with satisfactory accuracy. The method is also successfully applied in a scenario in which field-data is available.

Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models

Improving road safety requires accurate network screening methods to identify and prioritize sites in order to maximize the effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models and ranking criteria derived from observed crash data. However, collision databases are subject to errors, omissions, and underreporting. More importantly, crash-based methods are reactive and require years of crash data. With the arrival of new technologies including Global Positioning System (GPS) trajectory data, proactive surrogate safety methods have gained popularity as an alternative approach for screening. GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool. However, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The purpose of this paper is to propose a surrogate safety screening approach based on smartphone GPS data and a Full Bayesian modelling framework. After processing crash data and GPS data collected in Quebec City, Canada, several surrogate safety measures (SSMs), including vehicle manoeuvres (hard braking) and measures of traffic flow (congestion, average speed, and speed variation), were extracted. Then, spatial crash frequency models incorporating the extracted SSMs were proposed and validated. A Latent Gaussian Spatial Model was estimated using the Integrated Nested Laplace Approximation (INLA) technique. While the INLA Negative Binomial models outperformed alternative models, incorporating spatial correlations provided the greatest improvement in model fit. Relationships between SSMs and crash frequency established in previous studies were generally supported by the modelling results. For example, hard braking, congestion, and speed variation were all positively linked to crash counts at the intersection level. Network screening based on SSMs presents a substantial contribution to the field of road safety and works towards the elimination of crash data in evaluation and monitoring.

Coherent Signal Processing for Traffic Flow Measuring Radar Sensor

This paper presents a technological demonstrator with traffic applications, which carries out three typical intelligent transportation system (ITS) tasks: traffic flow measurement, speed estimation, and vehicles classification. The proposed system is a frequency-modulated continuous wave radar working at 24 GHz, with side-looking installation. Two signal-processing modes, 1-D and 2-D, are introduced as detection scheme. Unlike other existing commercial systems, this demonstrator makes use of phase information from target echoes in order to perform clutter cancellation and target speed measurement. To this end, two different speed-estimating methods are proposed and compared by means of experimental results obtained from a complex scenario.

Modeling of road traffic noise and traffic flow measures to reduce noise exposure in Antalya metropolitan municipality.

BackgroundRoad traffic noise influencing directly public health in the modern cities is a growing problem in both developing and developed countries. The objective of this study was to model traffic-induced noise in Antalya province, validate the model with noise emission data, and to run the model for the noise preventive scenarios.MethodsIn this study, modeling of traffic-induced noise was performed using SoundPLAN® software at Gazi Boulevard in the city of Antalya. Calculations were made according to NMPB-Routes 96, which have been accepted by environmental noise legislation of the European Union and Turkey. Fundamental data sets such as geographical, topographical and meteorological data, building information and population, traffic network, traffic volume and vehicle speed, and composition of types of vehicle were utilized for the development of noise prediction model. Eight preventive scenarios to reduce traffic-induced noise levels were simulated using the validated model considering traffic flow measures such as types of vehicles, vehicle speeds, types of road surface, redirecting portion of heavy vehicles to alternative routes and noise barrier usage.ResultsResults showed that increase in heavy vehicle speeds in smooth road surface conditions caused more increase in exposures than that of light vehicle speed. It was highlighted that it would be appropriate to use porous road surface to reduce exposures on population on high-speed roads. Furthermore, the number of people that are exposed to noise is significantly reduced by precautions such as alternative routes for heavy vehicles and speed restriction. These precautions reduced noise exposures by 25.5–63.8%. The results showed that the usage of noise barrier at the alternative routes in case of porous asphalt road reduced population, dwellings, and area exposed to traffic noise which is greater than 75 dB(A) as 63.8, 40.5, and 60.0%, respectively.ConclusionIt could be concluded that the outcomes of the noise prediction models based on the generated scenarios could be used for the purpose of decision support system and could be helpful for decision-makers on the noise legislations.