Algorithm for VKT and VHT in Subdivided Speed Intervals Based on Driving Cycle Estimation
The driving cycle is one of the important parameters for vehicle emission evaluation,however the traditional data collection methods for driving cycle establishment are costly,time-consuming and lack of sample representativeness.To overcome the shortcomings of traditional data collection methods,and obtain the traffic flow parameters covering the whole road network required to develop an accurate driving cycle,the algorithm for dynamic vehicle kilometers traveled(VKT)and vehicle hours traveled(VHT)in subdivided speed intervals for driving cycle development was proposed.First,the macroscopic traffic fundamental diagrams were constructed based on the multi-source traffic flow data of various roads to estimate the traffic flow data of entire road network by using the segment speed data.Second,the algorithm for road network dynamic VKT and VHT in subdivided speed intervals was developed based on the C++algorithm.The influences of road type and city scale on the VKT and VHT distribution characteristics were studied.Finally,the dynamic VKT and VHT data analysis system was established to visually display the calculation results.The result indicates that the macroscopic traffic fundamental diagram model can well simulate the urban road traffic flow for various roads.The traffic volume measurement error is less than 10%.The VKT and VHT distribution in subdivided speed intervals are affected by the road types and city scales,especially the road types.It is necessary to establish the corresponding driving cycles for various road types and city scales.The VKT and VHT algorithms and data analysis system for driving cycle can quickly calculate the dynamic VKT and VHT in subdivided speed internals,and visually compare the results for various cities.The study result improves the calculation precision of dynamic VKT in subdivided speed intervals.It provides the data support for driving cycle,which can accurately describe the driving conditions.
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