Queue discharge patterns at signalized intersections with green signal countdown device and long cycle length

Abstract

Two apparent features that prevail at signalized intersections in China are green signal countdown device and long cycle lengths. The objective of this study is to investigate the impacts of green signal countdown device and long cycle length on queue discharge patterns and to discuss its implications on capacity estimation in the context of China's traffic. At five typical large intersections in Shanghai and Tianjin, 11 through lanes were observed, and 9251 saturation headways were obtained as valid samples. Statistical analyses indicate that the discharge process of queuing vehicles can be divided into three distinct stages according to the discharge flow rate: a start-up stage, a steady stage, and a rush stage. The average time for queuing vehicles to reach a stationary saturation flow rate, that is, the start-up stage, was found to be approximately 20-30 seconds; the rush stage usually occurs during the phase transition period. The finding is contrary to the conventional assumption that the discharge rate reaches a maximum value after the fourth vehicle is discharged and then remains constant during the green time until the queue is completely dissolved. The capacity estimation errors that might arise from the conventional methods are discussed through a comparative study and a sensitivity analysis that are based on the identified queue discharge patterns. In addition, a piecewise linear regression method was proposed in order to reduce such errors. The proposed method can be used for capacity estimation at signalized intersections with the identified queue discharge patterns.

Modelling the saturation flow rate for continuous flow intersections based on field collected data

“…where, h 5 is the headway of vehicles enter the displaced left-turn lanes at the pre-signal of CFI; and d is the distance between the end of queue and the pre-stop line, m, which can be further determined by the length of displaced left-turn lane and the queue length. Since the queue length will continue growing from the start of green time of the pre-signal, d will change accordingly, which can be specified by Eq (6). Then, the average saturation Fig 7.…”

“…where, g is the green time of the phase that left-turn vehicles entering the displaced left-turn at the pre-signal, s. Substituting Eqs (5) and (6) into Eq 7, the adjustment coefficient of saturation flow rate for left-turn at the pre-signal of CFI can be obtained by using the saturation flow rate of Group 1 as a benchmark, as shown in Eq (8). As the coefficient f CP is dependent on the length of displaced left-turn lanes (L), the applicable range of L in Eq (8) can be obtained by the measured length of d, which is from 20 m to 120 m.…”

“…For some influencing factors with common cognition, such as the lane width, heavy vehicles, grade, parking, bus blockage, area type, lane utilization, right turns, left turns, and pedestrians and bicycles, have been subordinated to the widely used highway capacity manuals, such as HCM2016 [1]. Other adjustment factors, such as short-lane [2,3], driving behaviour [4], red light camera [5], green signal countdown device [6], work zone [7], Electric bicycles [8], and guideline markings [9] have also been discussed.…”

Impact of Guideline Markings on Saturation Flow Rate at Signalized Intersections

“…Sharma et al analyzed queue discharge characteristics at signalized intersections under heterogeneous traffic conditions and on the effect of a countdown timer on the headway distribution based on the data collected from two intersections in Chennai, India [31]. Tang et al investigated the impact of green signal countdown devices and long cycle lengths on queue discharge patterns and discussed their implications for capacity estimation in the context of China's traffic [32].…”

Analysis of saturation flow rate at tandem intersections using field data

“…In HCM2010 [30], the adjustment factors include lane width, heavy vehicles, grade, parking, bus blockage, area type, lane utilisation, right turns, left turns, and pedestrians and bicycles. Other adjustment factors, such as short lanes [31,32], weather conditions [33], lighting conditions [34], access flows [35], the stochastic nature of queue discharge headways [36], and signal countdown devices [37] are discussed. However, for tandem intersections, few or no studies have been conducted up to the present time regarding the effect of this control method on the saturation flow rate.…”