This article investigates the application of Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error.

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Recent advances in bottleneck studies have highlighted that different architectural adjustments at the exit may reduce the probability of clogging at the exit thereby enhancing the outflow of the individuals. However, those studies are mostly limited to the controlled experiments with non-human organisms or predictions from simulation models. Complementary data with human subjects to test the model's prediction is limited in literature. This study aims to examine the effect of different geometrical layouts at the exit towards the pedestrian flow via controlled laboratory experiments with human participants. The experimental setups involve pedestrian flow through 14 different geometrical configurations that include different exit locations and obstacles near exit under normal and slow running conditions. It was found that corner exit performed better than middle exit under same obstacle condition. Further, it was observed that the effectiveness of obstacle is sensitive to its size and distance from the exit. Thus, with careful architectural adjustment within a standard escape area, a substantial increase in outflow under normal and slow running conditions could be achieved. However, it was also observed that placing the obstacle too close to the exit can reduce outflow under both normal and slow running conditions. Moreover, we could not observe "faster-is-slower" effect under slow running condition and instead noticed "faster-is-faster" effect. In addition, the power law fitted headway distribution demonstrated that any architectural configurations that enhanced the outflow have higher exponent value compared to the other configuration that negates the outflow. The findings from this paper demonstrate that there is a scope to adjust the architectural elements to optimize the maximum outflow at the egress point. Further, the output from the experiments can be used to develop and verify mathematical models intended to simulate crowd evacuation.From the data in Table 3, we could conclude that for normal walking situation, comparing with standard design ( s J  =2.67 Ped/s/m, S.D =  0.55 Ped/s/m), the most efficient setup is #11 (Corner exit,  =60 cm, D =100 cm) with 19.4% R. E. ( s J  =3.18 Ped/s/m, S.D =  0.34 Ped/s/m), and the least efficient geometry is #2 (Middle exit,  =60 cm, D =60 cm) with -10.6% R. E. ( s J  =2.38 Ped/s/m, S.D =  0.12 Ped/s/m). For slow running condition that is more representative of emergency evacuation condition, comparing with standard design ( s J  =3.37 Ped/s/m, S.D =  0.22 Ped/s/m), the most efficient setup was also #11 with 32.7% R. E. ( s J  =4.47 Ped/s/m, S.D =  0.19 Ped/s/m), and the least one turned into #5 (Middle exit,  =100 cm, D =60 cm) with -5.6% R. E. ( s J  =3.18 Ped/s/m, S.D =  0.08 Ped/s/m).

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