Traffic Emission Modelling Using LiDAR Derived Parameters and Integrated Geospatial Model

Omer Saud Azeez, Biswajeet Pradhan, Ratiranjan Jena, Hyung Sup Jung, Ahmed Abdulkareem Ahmed

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara–Selatan Expressway–NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

Original languageEnglish
Pages (from-to)137-149
Number of pages13
JournalKorean Journal of Remote Sensing
Issue number1
StatePublished - 2019


  • GIS
  • SVR
  • prediction maps
  • remote sensing
  • traffic CO


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