Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology

Abstract

The authors of this paper propose a Markov-chain-based method for the synthesis of naturalistic, high-sampling-rate driving cycles based on the route segment statistics extracted from low-sampling-rate vehicle-tracking data. In the considered case of a city bus transport system, the route segments correspond to sections between two consecutive bus stations. The route segment statistics include segment lengths and maps of average velocity, station stop time, and station-stopping probability, all given along the day on an hourly basis. In the process of driving cycle synthesis, the transition probability matrix is built up based on the high-sampling-rate driving cycles purposely recorded in a separate reference city. The particular emphasis of the synthesis process is on satisfying the route segment velocity and acceleration boundary conditions, which may be equal to or greater than zero depending on whether a bus stops or passes a station. This enables concatenating the synthesized consecutive micro-cycles into the full-trip driving cycle. The synthesis method was validated through an extensive statistical analysis of generated driving cycles, including computational efficiency aspects.