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Trans. Planning Journal
| Title | Motorcycle-Following Models of General Motors (GM) and Adaptive |
|---|---|
| Author | Lawrence W. Lan, Chiung-Wen Chang |
| Summary | The main purposes of this paper are to investigate the characteristics of motorcycle flow in a mixed traffic, to identify significant factors affecting the motorcycle-following behaviors, and to construct models that can properly describe the relationship between motorcycle acceleration rates and these factors. A field observation is conducted and outcomes shows that only 13.8% of the overall samples reveal a motorcycle-following phenomenon. Statistical tests show that the significant factors affecting motorcycle-following behaviors include relative speed, space headway between a motorcycle and its leading vehicle, and acceleration rate of leading vehicle. General Motors (GM) five- generation models are firstly attempted to explain the motorcycle’s following behaviors in two cases: (1) only one leading vehicle in front; (2) two or more leading vehicles in front and neighboring-front (including either left-front, right-front, or both). The rather low values of the coefficient of multiple regression determination (R2=0.20~0.29 and 0.06~0.14 for both cases) and relative large root-mean-square-error values (RMSE 0.73~0.81 and 0.89~0.97 for both cases) imply that all of the GM models have poorly described the motorcycle-following behaviors. Therefore, we further propose a motorcycle-following model by incorporating the adaptive neuro-fuzzy inference system (ANFIS) with those significant factors that affect the motorcycle- following behaviors. Compared with the GM models, the ANFIS model outperforms with much smaller RMSE values (0.16 and 0.34 for both cases). Moreover, the Q-Q plot correlation coefficient tests also reveal that the predicted acceleration rates have a highly strong positive correlation with the observed acceleration rates in case (1) and a strong positive correlation in case (2). It suggests that our proposed ANFIS model can satisfactorily capture the nature of motorcycle-following behaviors in a mixed traffic. |
| Vol. | 33 |
| No. | 3 |
| Page | 511 |
| Year | 2004 |
| Month | 9 |
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