Abstract
Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.
Highlights
Earth’s Atmosphere naturally consists of greenhouse gases (GHGs), which enable the transmission of visible light and the absorption of infrared radiation, passing solar radiation from the Sun and trapping the heat energy within Earth’s atmosphere
These navigation algorithms differ in the way that they address the changing traffic conditions over time, and they can be divided into the two main categories shown in Figure 4, namely, static and dynamic
When comparing the results of the three routing objectives, the dynamic eco-predictive routing objective outperforms the dynamic eco-real time routing and static routing objectives. In this scenario, where traffic conditions are highly dynamic, real-time dynamic routing is rendered less effective compared to the static routing methodology due to the rapidly changing traffic conditions
Summary
Earth’s Atmosphere naturally consists of greenhouse gases (GHGs), which enable the transmission of visible light and the absorption of infrared radiation, passing solar radiation from the Sun and trapping the heat energy within Earth’s atmosphere. In 1896, Arrhenius was the first to mathematically correlate increased CO2 concentration in the atmosphere to Earth’s increased surface temperature [2]. In. February 2007, 111 years later, thousands of scientific researchers on the Intergovernmental Panel on Climate Change (IPCC) collectively concluded that industrialization is causing global warming through the acceleration of CO2 emissions. Increased temperature on Earth’s surface is causing extreme weather changes, such as droughts, floods, heavy rain, and excessive heat, resulting in fires. [30] Network Simulator, ns-3, [Online], ”http://www.nsnam.org/”, [April 21, 2019]. S. degree in Electrical and Computer Engineering from University Of Michigan, Dearborn, MI in 2001. Mohamad held multiple Electrical Engineering positons at General Motors and Chrysler Corporation before joining Mercedes Benz Research & Development North America, Inc. in 2008 as a Senior Manager for the eDrive team
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