Modeling Traffic Congestion Based on Air Quality for Greener Environment: An Empirical Study

Anjum, Shaik Shabana; Noor, Rafidah Md; Aghamohammadi, Nasrin; Ahmedy, Ismail; Kiah, Miss Laiha Mat; Hussin, Nornazlita; Anisi, Mohammad Hossein; Qureshi, Muhammad Ahsan

doi:10.1109/access.2019.2914672

Cited by 27 publications

(11 citation statements)

References 29 publications

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“…The study reveals that air pollution level strongly depends on fuel consumption, traffic volume, vehicle speed, and waiting time on the road. The result also shows that lower traffic congestion reduces the level of air pollution in Kuala Lumpur [30].…”

Section: Introductionmentioning

confidence: 79%

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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Global warming and climate change have become universal issues recently. One of the leading sources of climate change is automobiles. Automobiles are the prime source of air pollution in urban areas globally. This has resulted in a problematic and chaotic state in the development of an automatic traffic management system for capturing and monitoring vehicles' hourly and daily passage. With the significant advancement of sensor technology, atmospheric information such as air pollution, meteorological, and motor vehicle data can be harvested and stored in databases. However, due to the complexity and non-linear associations between air quality, meteorological, and traffic variables, it is difficult for the traditional statistical and mathematical models to analyze them. Recently, machine learning algorithms in the field of traffic emissions prediction have become a popular tool. Meteorological and traffic variables influence the variation and the trend of the traffic pollutants. In this paper, an optimized artificial neural network (OANN) was developed to enhance the existing artificial neural network (ANN) model by updating the initial weights in the network using a Genetic Algorithm (GA). The OANN model was implemented to predict the concentration of CO, N O, N O2, and N Ox pollutants produced by motor vehicles in Kuala Lumpur, Malaysia. OANN was compared with Artificial Neural Network (ANN), Random Forest (RF), and Decision Tree (DT) models. The results show that the developed OANN model performed better than the ANN, RF, and DT models with the lowest MSE values of 0.0247 for CO, 0.0365 for N O, 0.0542 N O2, and 0.1128 for N Ox. It can be concluded that the developed OANN model is a better choice in predicting traffic emission concentrations. The developed OANN model can help environmental agencies monitor traffic-related air pollution levels efficiently and take necessary measures to ensure the effectiveness of traffic management policy. The OANN model can also help decision-makers mitigate traffic emissions to protect citizens living in the neighborhood of highways.

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Section: Introductionmentioning

confidence: 79%

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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“…The project construction should minimize the construction cost of the RPR facility in order to maximize its function and value. Besides, environmental protection should locate RPR facilities reasonably in order to attract more automobile travelers to public transport to reduce the environmental pollution caused by automobile exhaust [35], [36]. Therefore, an optimal RPR design should consider the generalized cost of the transport system, considering the budget and environmental limits of the upper model.…”

Section: Rpr Design Optimizationmentioning

confidence: 99%

A Combined Modal Split and Traffic Assignment Model With Capacity Constraints for Siting Remote Park-and-Ride Facilities

2020

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From the perspective of urbanization, commuters' long-distance and diversified trips are suitable for the connection between city and suburbs. The existing park-and-ride (P&R) facilities can influence user travel decisions, promoting the usage of public transportation and reducing the environmental pollution, but the P&R lots are usually located near train stations that the construction of lots at these locations is costly and complex. It is important to analyze the need for P&R and its capacity before its construction. Therefore, this paper proposes a bi-level model for determination of optimal location and capacity of a remote P&R (RPR) facility that allows a user to park a vehicle in a suburban area at lower cost and then take a bus to the nearby train station. A combined modal split and traffic assignment model with capacity constraints is developed as a lower-level model, and a nested-logit model is adopted to manage the mode similarity. The upper-level optimization is conducted based on the generalized cost of the transportation system according to the budget and environmental limits. The performance of the proposed model is experimentally verified that the optimal RPR scheme shifts commuters from the automobile mode to the transit and RPR modes, thus improving the social benefit dramatically. The model and analysis provide insights that may help operators determine how RPR facilities can be established to meet social cost improvement goals. INDEX TERMS Bi-level programming model, combined modal split and traffic assignment, multimodal transportation network, remote park-and-ride.

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“…The application shows the different routes with the AQI information wherein the user inputs the current location and destination address as shown in Figure 4, after which the selection of route with lesser AQI is chosen for the commuter to reach the destination through less congested route and also the approach of emergency vehicle, as depicted in Figure 5, ultimately resulting in balance of air pollution levels and mitigation of traffic-induced pollution. The experimental validation and analysis of the proposed methodology are carried out for different areas of greater KL, in Malaysia as part of the extension of the research presented in [15,16]. The various timings of the day (morning, afternoon and evening) are monitored for evaluation and explored for the usage of traffic parameter based on the mobile application.…”

Section: Mobile Application Frameworkmentioning

confidence: 99%

Development of Portable Air Quality Index (AQI) and Emergency Vehicles Preemption Prototype Based on Internet of Mobile Things (IoMT)

Anjum

Noor

Ahmedy

et al. 2020

Proceedings of the 2020 the 3rd International Conference on Information Science and System

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The technological advancements of the Internet of Things (IoT) in the recent past have facilitated immense progress towards mitigation of environmental pollution through smart transportation systems and solutions. In particular, communication to the commuters about the traffic ahead or occurrences of congestion has been envisioned to play a major role in outsmarting traffic through mobile applications giving rise to the emergence of the Internet of Mobile Things (IoMT). However, the existing mobile applications that serve as traffic reporting solutions still face major issues such as fixed route suggestions, longer delays during busy hours or emergencies, inefficient prompting of road accidents and heavy traffic en route to a particular destination. This research aims at providing solutions for notifying the commuters with updates on the traffic based upon the Air Quality Index (AQI) of the routes towards the destination and also about the approach of emergency vehicles. The cross-platform mobile application in this way enables the user to opt for a route with good air quality so that the more congested routes are avoided thereby mitigating the air pollution induced by road traffic. The experimental testing and validation of the proposed methodology are applied for areas belonging to Greater Kuala Lumpur. The various timings divided according to peak and non-peak hours are experimentally tested for analyzing the parameters of traffic usage and pattern through the mobile application. The outcome of the experiments has showed that when traffic flow is modelled and governed through vehicular emissions and concentrations of air pollutants, nearly 75% of the congested traffic is reduced thereby, giving rise to pollution-free environment as well as mitigation of urban heat island (UHI) effect that is formed through vehicular heat generation and difference in temperatures. On the other hand, the approach of emergency vehicles also prompts the commuters to avoid panic. CCS Concepts • Hardware➝Emerging tools and methodologies.

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Modeling Traffic Congestion Based on Air Quality for Greener Environment: An Empirical Study

Cited by 27 publications

References 29 publications

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

A Combined Modal Split and Traffic Assignment Model With Capacity Constraints for Siting Remote Park-and-Ride Facilities

Development of Portable Air Quality Index (AQI) and Emergency Vehicles Preemption Prototype Based on Internet of Mobile Things (IoMT)

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