A new Solution based on Inter-Vehicle Communication to Reduce Traffic jam in Highway Environment

Traffic congestion is an urban mobility problem, which generates stress to drivers and economic losses. In 2012, greenhouse gas emissions from transportation accounted for about 28% of total U.S. greenhouse gas emissions. Intelligent transportation systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this work proposes an intelligent traffic information system based on inter-vehicle communication to avoid vehicle traffic congestion. The main goal of the proposed solution is to decrease CO2 emissions, the average trip time and fuel consumption by avoiding congested roads. Simulation results show that our proposed solution can reduce the average trip time, and the overall CO2 emission and fuel consumption. In particular, the trip time was decreased approximately 86%, the fuel consumption 40% and the CO2 emission 55%. This shows the potential of the proposed solution.

Made available in DSpace on 2022-04-28T19:03:55Z (GMT). No. of bitstreams: 0\n Previous issue date: 2016-04-01

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions

Traffic congestion is handled by government agencies as an urban mobility problem, which generates stress to drivers and economic loss. Intelligent transport systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a protocol for collaborative identification and minimization of vehicular congestion. The protocol uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, consolidated information dissemination may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally, CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (vehicle density etc). The protocol also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related work) and minimize it.

Traffic jam in urban centers is a mobility problem responsible for significant economic losses and the degradation of the environment. However, a wide range of technologies, in particular, intelligent transportation systems, may be used to predict, identify and reduce vehicular traffic congestion. In this context, this work presents an Intelligent Traffic Information System (ITIS) which relies on inter-vehicle communication in order to avoid traffic jams in urban centers. The main goal of the proposed system is to reduce the average trip time, the fuel consumption and CO 2 emissions, thus increasing vehicular traffic efficiency, reducing economic losses and making cities more environmental friendly. Simulation results for a synthetic scenario have shown that our proposed system is able to accomplish this goal. In particular, the average trip time was decreased ≈47%, the fuel consumption ≈22% and the CO 2 emission ≈24%.

Intelligent Transport Systems (ITS) can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a proposal for collaborative identification and minimization of vehicular congestion. CARTIM uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, the dissemination of consolidated information may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally (in vehicles), CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (e.g., vehicle density etc). The proposed technique also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related works) and minimize it (based on a heuristic).

Cities with a large number of people are currently facing urban mobility problems, especially the problem of traffic congestions. This not only has an adverse effect on the economy of the city, but also impairs the quality of life of its citizens. One measure that can be adopted to mitigate these problems is the use of systems that help identify, reduce, and/or avoid these traffic jams, such as intelligent transport systems. In this context, we propose an intelligent traffic information system called UCONDES, which is based on inter-vehicle communications and can be applied to detect and reduce congestion in urban centers. Simulation results shows that, when compared to original vehicular mobility trace, our solution reduces the average trip time, and the overall CO2 emission and fuel consumption. More specifically, the average travel time for drivers was reduced by approximately 26%, resulting in a reduction of fuel consumption by 23% and the CO2 emission by 25%.

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

In this paper we present simulation results for our implementation of Adaptive Route Change (ARC) application for cooperative Intelligent Transportation Systems (ITS). The general purpose of the application is to generate recommendations for alternative driving routes in order to avoid traffic congestion. The Adaptive Route Change (ARC) application is implemented in an integrated cooperative ITS simulation platform. For the evaluation we chose a reference scenario defining two distinct traffic flows through an urban area that provides four crossings with traffic light controls. We were interested to evaluate the impacts of ARC on fuel and traffic efficiency. For that we introduced five performance metrics (average trip duration, average fuel consumption, average stop duration, maximum queue size and average queue size behind traffic lights) and evaluated ARC in a series of simulations with varied application penetration rates and traffic volume. The results indicate that ARC systems could reduce traffic congestion in intersections and improve fuel consumption. We observe up to one quarter reduction in average trip time and almost one third reduction in average stop time. Fuel consumption is also reduced by up to 17.3%, while average queue size and maximum queue size reduce more than 50%.

Urban transportation is considered one of the main sources of Greenhouse Gas (GHG) emissions. Therefore, there has been an essential need to develop a sustainable transportation system that could mitigate the environmental impacts by using high-capacity transportations modes, such as public buses. This study has aimed to assess the expected sustainability of the public bus sector in Westbank, Palestine, in case of developing this sector and increasing the number of buses to meet the minimum global requirements (number of buses/1000 population), by developing prediction models for number of buses and passenger cars. Then, the expected reduction in total travelled kilometers by passenger cars has been quantified. After that, the expected reduction in GHG emissions has been determined and the effects on traffic congestion have been investigated. After analyzing the results, the study has concluded that the public bus transport sector in Palestine suffers from the lack of number of buses compared to the number of population, with a value of 0.38 bus/1000 population, which is considered one of the lowest values among the world’s countries. Moreover, by increasing the number of buses to meet the minimum global requirement, there has been a significant expected reduction in CO2 emissions (94,628.56 ton) compared to the total CO2 emissions from other sectors in Palestine, and there has been an expected reduction in traffic congestion up to 5.84%.

The dependence of oil production in the Gulf Cooperation Council (GCC) region may have environmental consequences. This research explores the nonlinear effects of oil rents and the economic growth of six GCC countries on their per capita CO2, CH4, N2O, and Greenhouse Gas (GHG) emissions, considering spatial linkages through 1980–2014. We apply fixed effects (FE) and corroborate the spatial dependency in all estimated pollution models. Spatial Durbin model (SDM) is utilized to estimate the direct and spillover effects. We find the inverted U-shaped relationship of economic growth with CO2, CH4, N2O and GHG emissions, and of oil rents with CH4 and GHG emissions. Monotonic positive effects of oil rents on CO2 emissions and U-shaped relationship between oil rents and N2O emissions are also found. Urbanization has positive effect on the CO2, CH4 and GHG emissions and has negative effect on N2O emissions. Financial market development (FMD) has negative effects on all types of investigated emissions. Foreign direct investment (FDI) has negative effects on CO2 and N2O emissions. Energy use has positive effects on CO2 and N2O emissions. Further, the neighboring spillover effects of economic growth, oil rents, urbanization, FDI, energy use and FMD are found statistically significant for some investigated emissions. Hence, oil rents, energy use, urbanization and economic growth are responsible for environmental degradation of home and neighboring countries in the GCC region, and we recommend implementing tighter laws to protect the environment.

Micro/nanoplastic pollution heterogeneously increased greenhouse gas emissions from wetlands: A multilevel meta-analysis

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

Do soil conservation practices exceed their relevance as a countermeasure to greenhouse gases emissions and increase crop productivity in agriculture?

This is a research on traffic congestion identification and algorithm collusion prediction in the era of big data. We propose a new traffic congestion prediction model, which considers big data and predicts future traffic congestion by considering the past history and current situation, so as to reduce or eliminate the impact of collusion between algorithms. The model can be used in real-time systems, such as intelligent transportation system, intelligent transportation system and so on.