In terms of air quality management in the urban setting, an on-road vehicle emissions inventory is important, particularly in growing economies as private vehicle ownership increases. The basis of vehicle emissions inventory is an estimate of Vehicle Kilometres Travelled (VKT) for every model grid cell. This has proven difficult in South Africa as well as internationally with methodologies ranging from generalized spatial surrogate application (leading to many assumptions being incorporated) to detailed use of available traffic counts (leading to spatially limited inventories as count information is sparse). A Travel Demand Model (TDM) is used to simulate peak demand through road networks such that for example changes in infrastructure, changes in mode choice and preferential routing may be explored. It is a growing trend to use a TDM for estimating vehicle emissions inventories for air quality modeling. A TDM is used to estimate VKT and speed of different vehicle types. Here the Gauteng Transport Model (based upon an EMME/4 framework), as applied by the CSIR Built Environment’s Transport Group, is modified appropriately to estimate VKT on a 1km resolution grid within the City of Johannesburg such that realistic emission estimates are possible for use in an air quality model.
Reference:
Naidoo, M., Mokonyama, M. and Nkosi, M. 2016. Towards a detailed on-road vehicle emissions inventory: The use of a travel demand model. Proceedings of the 2016 Conference of the National Association for Clean Air, 5-7 October 2016, Emnotweni Arena, Mbombela
Naidoo, M., Mokonyama, M. T., & Nkosi, M. (2016). Towards a detailed on-road vehicle emissions inventory: The use of a travel demand model. http://hdl.handle.net/10204/9277
Naidoo, Mogesh, Mathetha T Mokonyama, and Muzi Nkosi. "Towards a detailed on-road vehicle emissions inventory: The use of a travel demand model." (2016): http://hdl.handle.net/10204/9277
Naidoo M, Mokonyama MT, Nkosi M, Towards a detailed on-road vehicle emissions inventory: The use of a travel demand model; 2016. http://hdl.handle.net/10204/9277 .