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Problem Solving of Gasoline and Electric Vehicles Traffic Assignment with Variable Demand

Davazdah Emami, Behnam | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50930 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Zakaei Aashtiani, Hedayat
  7. Abstract:
  8. During the past decade the ever increasing volume of greenhouse gases due to fossil fuels consumption have made humans to seek for alternative, non-polluting fuels as an effective strategy to reduce pollution and prevent environmental issues thereof. Electric cars are today known as one of the most effective solutions for this purpose. Of course, transition from gasoline-powered cars to electric cars in a wide scale is not technically possible in short term due to technological and infrastructural limitations. As a result, this lengthy process would lead to emergence of a combinational transportation system including both gas and electric-powered vehicles. The major differences between these two types of vehicles involve their travelling range as well as their combined trip costs. The relatively limited travelling range of electric vehicles along with their relatively long charging time (compared to the re-fueling time of gasoline-based vehicles) may possibly affect the consumers’ choice of vehicle as well as travelling paths as consequently influence the performance of transportation systems. The present study seeks to provide a solution method to the assignment problem in the networks involving both gasoline and electric vehicles, assuming that the demand for each vehicle type depends on the paths characteristics and availability of electric charging stations at the path intersection nodes. To this end, an advanced traffic assignment algorithm has been employed to solve the mentioned assignment problem. In addition, we used a label-setting algorithm for solving the constraint shortest path problem. The results showed that our proposed algorithm outperforms existing algorithms in terms of both solution time and accuracy across multiple networks. For instance, for the Anaheim network given an accuracy of 10-4 and travelling range of 20 miles, the complementary algorithm reached a solution time less than 0.3 seconds compared to 3500 and 700 seconds resulted from FW-MLS and PG-PLS algorithms, respectively
  9. Keywords:
  10. Traffic Assignment ; Electric Motor Vehicles ; Gasoline Engines ; Shortest Path ; Mode Choice ; Path Finding ; Variable Demand

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