Nazmul Haque

Adaptive neuro-fuzzy approach for modeling equilibrium speed–density relationship

ABSTRACT This paper endeavors to model the equilibrium speed–density relationship using a new fuzzy logic-based approach. To capture the randomness in traffic flow dynamics, it develops a single-input Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with a hybrid algorithm. Furthermore, it proposes a new Premise-Consequent Conjugate Effect (PCCE) relationship to estimate fundamental diagram (FD) parameters from the ANFIS model. Several options (i.e. optimal split of data into training and testing data, selection of suitable membership function) offered by ANFIS are then illustrated. An experiment is performed to determine the maximum achievable goodness of fit without the occurrence of overfitting phenomenon by changing the number of clusters. The calibrated ANFIS model is compared against traditional and advanced speed–density models for five different freeway locations. Results show that the proposed model outperforms other preceding models by attaining goodness-of-fit values within a range of 0.82–0.92. Finally, the proposed PCCE relationship shows the ANFIS models robustness in accurate estimation of FD parameters for all freeway locations.

Pixel-based heterogeneous traffic measurement considering shadow and illumination variation

This paper endeavors to develop a new background estimation model for foreground segmentation using traditional background subtraction technique. Particularly, incorporation of luminance- and pollution-controlling parameters enables the model to address illumination variation and tail backs from vehicles, respectively. To classify each foreground pixel, a new heuristic dynamic threshold-difference function is proposed for determining individual threshold. Moreover, newly presented Positive Negative Segmentation technique removes shadow considering its physical characteristics from the foreground. After shadow correction, impulse flow waves and aggregated pictorial speed are computed accordingly. Impulse flow waves are eventually rectified and cumulated into actual flow. Pictorial speed is converted into actual speed using calibration equation considering perspective error. To facilitate video-based traffic measurement, a user-friendly tool PARTS (Pixel-Based Heterogeneous Traffic Measurement) is developed which incorporates the whole system. The data collected at different roadway locations are compared with that generated by the tool. It shows 90 and 97% correlations in measuring flow and speed, respectively.