Chapter 4 Case Study
4.2 Result Analysis
4.2.1 Scenario 1: 2015/12/28~2015/12/31
The purpose of scenario 1 is to investigate the congestion propagation pattern during normal weekdays and a day with a special event, which is the New Year’s celebration events in this case study. During this time period, the bike lane on Fu-Xing S. road and Xin-Sheng S. road was still under construction. The VD data from 12/28 to 12/31 in 2015 are extracted. The kernel density estimation of congestion is calculated for each road segments in our ROI.
Threshold of LOS C – Overview
The visualization of a KDE plain view is shown in Figure 4.2. Larger circle and darker color represents relatively higher density. Based on the threshold of LOS C (30km/hr), we can observe that the VDs with relatively high density are located on Xin-Sheng S. road, especially for the segment between the two largest arterials of Taipei City:
Jen-Ai road and Xin-Yi road. For other road segments, generally they can still maintain level LOS C within peak hours. However, comparing Figure 4.2 with Figure 4.3 (for 12/31), only the color saturation at the locations of hot spots on the heat map become slightly higher, indicating higher probability of the occurrence of congestion.
Figure 4.2 KDE of Scenario 1 with LOS C (2015/12/28~30)
Figure 4.3 KDE of Scenario 1 with LOS C (2015/12/31)
Threshold of LOS C - Segment-wise
For road segments with relatively high density, further investigation and observation are needed, since they can be the potential sources of congestion propagation. Road segments 43 shows the highest density among all segments. However, since segment 43 is located at the north edge of our ROI, none of its upstream segments are accounted in this study. The possible congestion propagation to the upstream segments from the congestion of the origins of road segment 33, 43 and 44 is visualized in Figures 4.4, 4.5, and 4.6, respectively. The thickness and darkness of the color mark represents the degree of influence. Segments without a ramp can be either providing minor contributions or indicating the situation of no data obtained. The color red represents the congestion source road segments, while the upstream road segments are shown in gray scale. Darker color and thicker line segment indicates larger impact. White arrows indicate the travel directions.
For the analysis of road segment 33, the effect of congestion propagation to the upstream road segments are too minor to be observed.
For the analysis of road segment 44, road segments of the 1st order adjacency are more likely to be affected, while road segments of the 2nd order adjacency are influenced less. Among all road segments of the 2nd order adjacency, the one that enters road segment 44 by a left turn may be receiving more contribution from the congestion source.
Figure 4.4 Upstream Influence from The Congestion of Segment 33 (S1_C)
Figure 4.5 Upstream Influence from The Congestion of Segment 43 (S1_C)
Figure 4.6 Upstream Influence from The Congestion of Segment 44 (S1_C)
Average Travel Speed - Overview
The visualization result presenting kernel density within the whole week of 2015/12/28 to 2015/12/31 is shown in Figure 4.7. Another figure specifically presenting the kernel density on 2015/12/31 is shown in Figure 4.8. The locations with larger circles and darker colors are road segments with higher kernel density. Similar locations of hot spots of congestion can be observed through Figure 4.7 and Figure 4.8. We can observe that the road segments on Xin-Sheng S. road, Jian-Guo S. road and Fu-Xing S. road near He-Ping E. road has relatively higher density than other road segments, indicating higher
Figure 4.7 KDE of Scenario 1 with Average Travel Speed (2015/12/28~30)
Figure 4.8 KDE of Scenario 1 with Average Travel Speed (2015/12/31)
Average Travel Speed - Segment-wise
Road segments 40, 44 and 56 show the highest density among all segments. Hence, segment-wise analysis is performed. The possible propagation to the upstream segments from road segment 40, 44 and 56 is visualized in Figure 4.9, 4.10 and 4.11 respectively.
For the analysis of road segment 40, road segments of the 1st order adjacency are still more likely to be influenced. Road segments of the 2nd order adjacency are not affected as much as the road segment of the 1st order adjacency. Among those adjacent upstream road segments, the ones without a turn has higher density.
For the analysis of road segment 44, road segments of the 1st order adjacency are still more likely to be influenced. Since Xin-Yi road only allows one way traffic, there is no road segments entering road segment 44 by left turn. Among the two road segments of the 1st order adjacency, the effects are almost the same. Road segments of the 2nd order adjacency are not affected as much as the road segment of the 1st order adjacency. Among the three upstream road segments of the 2nd order adjacency, the effects are almost the same.
For the analysis of road segment 56, road segments of the 1st order adjacency are more likely to be affected. Among road segments of the 1st order adjacency, the one that enters by a left turn has higher density than the other that enters by a right turn. Road segments of the 2nd order adjacency following the road segment of the 1st order adjacency enter by a right turn are influenced less. Among all road segments of the 2nd order adjacency, the one does not enter by a left turn may receive more contribution to congestion from the source road segment 56 than the other.
Figure 4.9 Upstream Influence from The Congestion of Segment 40 (S1_avg)
Figure 4.10 Upstream Influence from The Congestion of Segment 44 (S1_avg)
Figure 4.11 Upstream Influence from The Congestion of Segment 56 (S1_avg)