Abstract
This research intends to develop a simulation model that predicts vehicular delay, emissions and fuel consumption of individual vehicles travelling in a selected urban signal controlled road network.
ON-ROAD emission and fuel consumption data are collected by instrumented test vehicles travelling along selected routes in the urban areas. Average and modal emission and fuel consumption factors are derived. Based on the collected emission and fuel consumption data, an ON-ROAD modal emission and fuel consumption model for vehicles travelling in the urban areas has also been developed. Piecewise interpolation and negative exponential functions are employed to model instantaneous emission and fuel consumption rates. In the mean time an urban driving cycle has been for the first time developed for Hong Kong in the present study based on on-the-road speed data.
To simulate the modal movements of vehicles at signal controlled junctions, a new approach adopting the neural network technique was employed. A discharge headway model (NNDHM) was developed. The NNDHM model estimates discharge headway of individual vehicle queued at signal controlled junctions. It is an ideal tool to investigate the effect of variables on saturation flows and capacities that would not be easily characterised by direct measurement.
Finally, a microscopic traffic simulation model, TREFSIM, was developed for the estimation of vehicle journey time, average speed, delay, fuel consumption and emissions m urban traffic signal controlled road network. It is a time-based microscopic traffic simulation model. The TREFSIM model incorporated the ONROAD emission and fuel consumption model as well as the NNDHM model and other algorithms governing vehicle movements. The microscopic structure of the model makes the simulated vehicle emissions and fuel consumption sensitive to changes in vehicle behaviours due to traffic signals.
The TREFSIM model was compared with SATURN and PARAMICS in a selected urban network. It produces comparative flow, delay, emissions and fuel consumption results.
ON-ROAD emission and fuel consumption data are collected by instrumented test vehicles travelling along selected routes in the urban areas. Average and modal emission and fuel consumption factors are derived. Based on the collected emission and fuel consumption data, an ON-ROAD modal emission and fuel consumption model for vehicles travelling in the urban areas has also been developed. Piecewise interpolation and negative exponential functions are employed to model instantaneous emission and fuel consumption rates. In the mean time an urban driving cycle has been for the first time developed for Hong Kong in the present study based on on-the-road speed data.
To simulate the modal movements of vehicles at signal controlled junctions, a new approach adopting the neural network technique was employed. A discharge headway model (NNDHM) was developed. The NNDHM model estimates discharge headway of individual vehicle queued at signal controlled junctions. It is an ideal tool to investigate the effect of variables on saturation flows and capacities that would not be easily characterised by direct measurement.
Finally, a microscopic traffic simulation model, TREFSIM, was developed for the estimation of vehicle journey time, average speed, delay, fuel consumption and emissions m urban traffic signal controlled road network. It is a time-based microscopic traffic simulation model. The TREFSIM model incorporated the ONROAD emission and fuel consumption model as well as the NNDHM model and other algorithms governing vehicle movements. The microscopic structure of the model makes the simulated vehicle emissions and fuel consumption sensitive to changes in vehicle behaviours due to traffic signals.
The TREFSIM model was compared with SATURN and PARAMICS in a selected urban network. It produces comparative flow, delay, emissions and fuel consumption results.
| Original language | English |
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| Awarding Institution |
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| Publication status | Published - Mar 2001 |