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).

The technique to forecast available parking spaces (APSs) is the foundation theory of parking guidance information systems (PGISs). This study utilises data collected on parking availability at several off-street parking garages in Newcastle upon Tyne, England, to investigate the changing characteristics of APS. Using these baseline data the research reported here aims to build up a short-term APS forecasting model and applies the wavelet neural network (WNN) method to the PGIS problem. After selecting optimal preferences, including training set size, delay time and embedding dimension, the APS short-term forecasting model based on WNN is built and tested using the real-world dataset. By experimental tests conducted using the same dataset, WNN's prediction performance is compared with the largest Lyapunov exponents (LEs) method in the aspects of accuracy, efficiency and robustness. It is found that WNN prevails through a more efficient structure and employs, barely half of the computational cost compared to the largest LEs method, which could be significant if applied to real-time operation. Moreover, WNN enjoys a more accurate performance, for its prediction average mean square error (MSE) is 6.4 ± 3.1 (in a parking building with 492 parking lots) for workdays and 8.5 ± 6.2 for weekends, compared to the MSE of largest LEs method, 18.7 and 29.0, respectively.

No abstract

No abstract

Understanding drivers' perspective on parking guidance information.