Smartphone-based solutions to monitor and reduce fuel consumption and CO2 footprint : final report.

Details

• Creators:
  Cetin, Mecit ; Ustun, Ilyas ; Nadeem, Tamer ; Nguyen, Duc ; Rakha, Hesham A.
• Corporate Creators:
  Old Dominion University. Transportation Research Institute
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Environmental impacts
  • Fuel conservation
  • Fuel consumption
  • Greenhouse gases
  • Mobile applications
  • Mode choice
  • Smartphones
  • Travel time
  • Carbon dioxide Machine learning
  • CO2
• Publication/ Report Number:
  N16-01
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final report, Jan 1, 2014 - Jan 31, 2016
• Corporate Publisher:
  TranLIVE. University of Idaho
• Abstract:
  Smartphones equipped with GPS and several low-energy sensors (e.g., gyroscope, compass, and accelerometer) can provide ; a medium to collect probe data. As smartphone users navigate the transportation networks, their travel modes and ; trajectories can be inferred to estimate fuel consumption and CO2 footprint. The specific goals of the proposed research are: ; (i) Develop new algorithms to estimate the mode of travel (walking, biking, train, car, bus, etc.) and operating mode of a ; vehicle (e.g., idling) based on low-energy sensors available within smartphones; (2) Evaluate the effectiveness of FC and ; CO2 estimation from probe vehicles at various market penetration levels; and (3) Develop shortest paths algorithms for ; finding eco-friendly routes. To achieve these goals, various methodologies are developed and tested with both simulation ; and field data. For example, machine learning algorithms (e.g., support vector machines) are developed to predict the travel ; mode and to detect whether a vehicle has stopped. The results show that the travel mode can be detected accurately, about ; 94% on average, when considering all five travel modes within the sample data. Using the accelerometer data only, the ; results show that the models can accurately detect the times at which the vehicle stops and moves during its journey. To ; predict the impacts of probe vehicle market penetration on estimating fuel consumption, simulation data are created for an ; intersection. Lastly, the shortest path (SP) algorithm for static networks are modified so that the algorithm can find the SP ; for minimizing both the travel time and fuel consumption for a given network. Together, the models and algorithms ; developed in this study can be integrated to support various mobility and environmental applications.
  More +
• Format:
  PDF
• Funding:
  DTRT12GUTC17
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/32549/dot_32549_DS1.pdf
• File Type:
  [PDF - 798.03 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:3a786a1759e8df3b42c11a77bf516f271422adab67c1d6cdb2eea6234ce972cf06f4022e8857d32f8dc57df7e496a89aadaf2f8db7ababacd4d9f2de25b465a2