Traffic Congestion Saturation Flow Data 2018 SDCC

<div><div><font face=‘Calibri, sans-serif’ size=‘3’>SDCC Traffic Congestion Saturation Flow Data 2018. Traffic Volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this Dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ Dataset.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>A detailed description of each column heading can be referenced below;</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>scn: Site Serial number</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>region: A group of Nodes that are operated under scoot control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>system: scoot STC UTC (UTC-MX)</font></div><div><div><font face=‘Calibri, sans-serif’ size=‘3’>locn: locations</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>site: Site numbers<div>day: Days of the week Monday to Sunday. Abbreviations; Mo,TU,WE,TH,FR,SA,SU.</div><div>date: reflect correct actual Date of when data was collected.</div><div>start_time: Note — Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the ‘date’ column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflect start time of 15 minute intervals. </div><div>end_time: End time of 15 minute intervals.</div></font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>flow: A representation of demand (flow) for each link built over several minutes by the scoot model. Scoot has two profiles:</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>(1) Short — Raw data representing the actual values over the previous few minutes</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>(2) Long — A smoothed average of values over a longer period</font></div><div><font><font face=‘Calibri’, sans-serif’ size=‘3’>scoot will choose to use the appropriate profile depending on a number of factors.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>flow_pc: Same as above ref ref PC scoot</font><div><fiv><font face=‘Cbribri, sans-serif’ size=‘3’>cong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by Stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval of this is recorded. The number of intervals of congestion in any cycle is also recorded.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>The percentage congestion is calculated from:</font><div><font face=‘Calibri, sans-serif’ size=‘3’>No of congested intervals x 4 x 100 cycle time in seconds.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>This percentage of congestion is available to view and more importantly for the optimisers to take into account.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>cong_pc: Same as above ref PC scoot</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’>DSAT: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links Approaching the node.</font></div><div><font face=‘Calibri, sans-serif’ size=‘3’></font></div><div><font face=‘Calibri, sans-serif’ size=3‘></font><div style=’font-family:Calibri, sans-serif; font-size:medium;‘></div></div><div><span style='font-family:Calibri, sans-serif; background-color:rgb(255, 255, 255);‘><font size=’3‘></font></div><div><span style=’font-family:Calibri, sans-serif; background-color:rgb(255, 255, 255);‘><font size=’3‘></font></div><div><span style=’font-family:Calibri, sans-serif; background-color:rgb(255, 255, 255);‘><font size=’3‘></font></div><div><span style=’font-size:11.0pt; font-family: &amp;quot;Calibri &amp;,sans-serif;'></div>SDCC 交通拥堵与饱和流量数据集2018。本数据集包含南都柏林郡各点位的交通流量、交通饱和度及拥堵数据，供交通管理部门用于调控交叉口信号灯配时。建议结合《SDCC交通数据点位名称》数据集一同使用。 各列标题的详细说明可参考如下： scn：点位序列号（Site Serial number） region：指采用同一公共周期时长受SCOOT管控的节点群组，通常为需进行协调控制的交叉口节点。部分节点可能以区域周期时长的一半进行双周期运行。 system：SCOOT STC UTC（UTC-MX） locn：点位位置 site：点位编号 day：涵盖周一至周日，缩写分别为Mo、TU、WE、TH、FR、SA、SU date：准确记录数据实际采集的日期 start_time：注意——请忽略本列中的日期信息，实际数据采集日期已在`date`列中准确标注。本列显示的日期为报告生成并从系统导出的日期，但本列可准确反映15分钟间隔的开始时刻。 end_time：对应15分钟间隔的结束时刻 flow：指SCOOT模型基于数分钟数据生成的各路段通行需求（流量）表征。SCOOT包含两种数据剖面： (1) 短期剖面：反映前几分钟内的原始实测数值 (2) 长期剖面：对较长时段内的数值进行平滑平均后的结果 SCOOT会根据多种因素选择合适的剖面类型。 flow_pc：流量占比，与上述流量说明一致，参考SCOOT PC相关定义 cong：拥堵状态直接由检测器实测得到。若检测器安装于路段排队长度的常规末端以外区域，通常不会被静止车流覆盖，仅在发生拥堵时会检测到静止车流。若某一检测器在整个间隔时段内均检测到静止车流，则判定为拥堵时段，并记录单个周期内的拥堵时段总数。 拥堵百分比通过以下公式计算：拥堵百分比 =（拥堵时段数 × 4 × 100）/ 周期时长（秒） 该拥堵百分比可供查看，更重要的是可被信号灯配时优化算法纳入考量。 cong_pc：拥堵占比，与上述拥堵说明一致，参考SCOOT PC相关定义 DSAT：即需求流量与最大可能通行流量的比值，具体为需求流量与通行速率（饱和占用率）的乘积乘以有效绿灯时长。信号灯配时优化模块会尽可能降低交叉口进口路段的最大饱和度。