Data-driven Multimodal Transportation Energy Consumption Prediction and Analysis Framework for Sustainable Transit and Transportation Planning

The transportation sector is a major energy consumer and contributor to air pollution. Public transportation authorities can often find it challenging to choose the best projects among a spectrum of candidate transportation projects to reduce energy consumption and mitigate emissions. The objective of this project was to develop a sketch environmental planning framework to integrate traffic simulation and regulatory transportation air quality modeling (i.e., MOVES) to provide robust statistical correlations of the energy and air quality impacts with changes in traffic activities for transit operations and planning. Firstly, a traffic simulation was conducted on the road network of the city of Columbia, South Carolina. The simulations were validated with real-world traffic data acquired from iPeMS in South Carolina to demonstrate the representativeness of the traffic simulation results. iPeMS is the real-time data analysis, visualization, and reporting platform for South Carolina statewide traffic information. Secondly, outputs of traffic simulation, i.e. link-level vehicle trajectory data are processed to prepare inputs for energy/emissions simulation using EPA’s Motor Vehicle Emissions Simulator. The processed inputs include vehicle mile traveled, operating mode distribution, etc. The results of emissions simulation are link-level energy/NOx emission rates on each link for transit bus driving of the whole transportation road network. Then, we utilized machine learning models to develop a statistical correlation between the aggregated traffic activity patterns and MOVES-calculated emissions. The test set predictions showed that our models can accurately estimate MOVES-based energy and emissions rates (within a 10% mean absolute percentage error, MAPE) given aggregated traffic activity data. We also improved prediction accuracy using our neural network model, then the regular regression model. We tested the spatial transferability of our developed models to predict MOVES energy/emissions results using the road network activities of a medium-sized city. Our neural network models can achieve a 15% mean absolute percentage error relative to MOVES results. This demonstrates the transferability of the model results. With traffic data of a whole network, we are able to generate a heat map to show the energy consumption rate of operating transit buses on any link of the road network.

• Record URL:
  https://cecas.clemson.edu/C2M2/data-driven-multimodal-transportation-energy-consumption-prediction-and-analysis-framework-for-sustainable-transit-and-transportation-planning-final-report/

Language

• English

Project

• Status: Completed
• Funding: $46464
• Contract Numbers:

  69A3551747117

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Center for Connected Multimodal Mobility

  Clemson University
  Clemson, SC  United States  29634

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204
• Managing Organizations:

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208
• Project Managers:

  Chen, Yuche

• Performing Organizations:

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204
• Principal Investigators:

  Chen, Yuche

  Comert, Gurcan

  Huynh, Nathan

  Zhang, Yunteng

• Start Date: 20181201
• Expected Completion Date: 20200831
• Actual Completion Date: 20200925
• USDOT Program: University Transportation Centers
• Source Data: https://cecas.clemson.edu/C2M2/data-driven-multimodal-transportation-energy-consumption-prediction-and-analysis-framework-for-sustainable-transit-and-transportation-planning-final-report/

Subject/Index Terms

• TRT Terms: Data analysis; Energy; Machine learning; Sustainable transportation; Transportation
• Subject Areas: Economics; Energy; Highways; Planning and Forecasting; Research;

Filing Info

• Accession Number: 01690757
• Record Type: Research project
• Source Agency: Center for Connected Multimodal Mobility
• Contract Numbers: 69A3551747117
• Files: UTC, RIP
• Created Date: Jan 11 2019 3:56PM