Estimating Real-Time Traffic Carbon Dioxide Emissions Based on Intelligent Transportation System Technologies

In this paper, a bottom-up vehicle emission model is proposed to estimate real-time CO2 emissions using intelligent transportation system (ITS) technologies. In the proposed model, traffic data that were collected by ITS are fully utilized to estimate detailed vehicle technology data (e.g., vehicle type) and driving pattern data (e.g., speed, acceleration, and road slope) in the road network. The road network is divided into a set of small road segments to consider the effects of heterogeneous speeds within a road link. A real-world case study in Beijing, China, is carried out to demonstrate the applicability of the proposed model. The spatiotemporal distributions of CO2 emissions in Beijing are analyzed and discussed. The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of CO2 emissions with a high spatiotemporal resolution.

[1]  Matthew Barth,et al.  Development and Application of an International Vehicle Emissions Model , 2005 .

[2]  A. Sumalee,et al.  Dynamic marginal cost, access control, and pollution charge: a comparison of bottleneck and whole link models , 2012 .

[3]  Matthew Barth,et al.  Part 3: Developing Countries: Development and Application of an International Vehicle Emissions Model , 2005 .

[4]  Doo-Kwon Baik,et al.  Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks , 2006, IEEE Transactions on Vehicular Technology.

[5]  C. Kroeze.  N2O from animal waste. Methodology according to IPCC Guidelines for National Greenhouse Gas Inventories. , 1997 .

[6]  Nancy L. Nihan,et al.  Can Single-Loop Detectors Do the Work of Dual-Loop Detectors? , 2003 .

[7]  M. Khare,et al.  Modelling of vehicular exhausts – a review , 2001 .

[8]  Elliot W. Martin,et al.  Greenhouse Gas Emission Impacts of Carsharing in North America , 2011, IEEE Transactions on Intelligent Transportation Systems.

[9]  Hesham Rakha,et al.  INTEGRATION MODELING FRAMEWORK FOR ESTIMATING MOBILE SOURCE EMISSIONS , 2004 .

[10]  Saad Abo-Qudais,et al.  Performance evaluation of vehicles emissions prediction models , 2005 .

[11]  Yf F. Tang,et al.  Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census , 2006 .

[12]  Shiliang Sun,et al.  Variational Inference for Infinite Mixtures of Gaussian Processes With Applications to Traffic Flow Prediction , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  Yinhai Wang,et al.  Dynamic Estimation of Freeway Large-Truck Volumes Based on Single-Loop Measurements , 2004, J. Intell. Transp. Syst..

[14]  Qingquan Li,et al.  An improved distance metric for the interpolation of link-based traffic data using kriging: a case study of a large-scale urban road network , 2012, Int. J. Geogr. Inf. Sci..

[15]  Zhiheng Li,et al.  Road-Segment-Based Vehicle Emission Model for Real-Time Traffic Greenhouse Gas Estimation , 2011 .

[16]  Dušan Teodorović,et al.  Data fusion for updating information in modelling drivers’ choice behaviour , 2009 .

[17]  S. Xie,et al.  Estimation of vehicular emission inventories in China from 1980 to 2005 , 2007 .

[18]  Kebin He,et al.  Comparison of vehicle activity and emission inventory between Beijing and Shanghai. , 2007, Journal of the Air & Waste Management Association.

[19]  Q. Li,et al.  Using cloud computing to process intensive floating car data for urban traffic surveillance , 2011, Int. J. Geogr. Inf. Sci..

[20]  José Luis Jiménez-Palacios,et al.  Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing , 1999 .

[21]  Francesc Soriguera,et al.  Requiem for Freeway Travel Time Estimation Methods Based on Blind Speed Interpolations Between Point Measurements , 2011, IEEE Transactions on Intelligent Transportation Systems.

[22]  S. Wong,et al.  Environmentally Sustainable Toll Design for Congested Road Networks with Uncertain Demand , 2012 .

[23]  Qingquan Li,et al.  Finding Reliable Shortest Paths in Road Networks Under Uncertainty , 2013 .

[24]  Bin Ran,et al.  Large-Scale Freeway Network Traffic Monitoring: A Map-Matching Algorithm Based on Low-Logging Frequency GPS Probe Data , 2011, J. Intell. Transp. Syst..

[25]  Bi Yu Chen,et al.  Reliable shortest path finding in stochastic networks with spatial correlated link travel times , 2012, Int. J. Geogr. Inf. Sci..

[26]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[27]  Gordon Mitchell,et al.  TEMMS: an integrated package for modelling and mapping urban traffic emissions and air quality , 2002, Environ. Model. Softw..

[28]  Jay C. Bell,et al.  Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling , 2001 .

[29]  Laurence R. Rilett,et al.  Analysis of Aggregation Effects in Vehicular Emission Estimation , 2001 .

[30]  Henk J. van Zuylen,et al.  Localized Extended Kalman Filter for Scalable Real-Time Traffic State Estimation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[31]  Hesham Rakha,et al.  Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions , 2003 .

[32]  A. Meyer.  Economics Of Climate Change , 1995, Nature.

[33]  Pedro M. d'Orey,et al.  On the Impact of Virtual Traffic Lights on Carbon Emissions Mitigation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[34]  N. Nakicenovic,et al.  Climate change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers. , 2007 .

[35]  William H. K. Lam,et al.  USING AUTOMATIC VEHICLE IDENIFICATION DATA FOR TRAVEL TIME ESTIMATION IN HONG KONG , 2008 .

[36]  Huan Liu,et al.  In-Use Vehicle Emissions in China: Beijing Study , 2009 .

[37]  Serge P. Hoogendoorn,et al.  Real-Time Lagrangian Traffic State Estimator for Freeways , 2012, IEEE Transactions on Intelligent Transportation Systems.

[38]  Zhipeng Li,et al.  An Approach to Urban Traffic State Estimation by Fusing Multisource Information , 2009, IEEE Transactions on Intelligent Transportation Systems.

[39]  T. Kram,et al.  Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials , 2009 .