Modeling the Impacts of Traffic Flow Arrival Profiles on Ramp Metering Queues

2018 ◽  
Vol 2672 (15) ◽  
pp. 85-92 ◽  
Author(s):  
Guangchuan Yang ◽  
Rui Yue ◽  
Zong Tian ◽  
Hao Xu
Keyword(s):  
Traffic Flow ◽  
Queue Length ◽  
Ramp Metering ◽  
Signalized Intersection ◽  
Signal Timing ◽  
Microscopic Simulation ◽  
Length Estimation ◽  
Platoon Dispersion ◽  
Queue Lengths ◽  
Potential Impact

An adequate queue storage length is critical for a metered on-ramp to prevent ramp queue spillback to the upstream signalized intersection. Previous research on queue length estimation or queue storage length design at metered ramps has not taken into account the potential impact of various on-ramp traffic flow arrival profiles on ramp queue lengths. This paper depicts the traffic flow arrival profiles and queue generation processes at three different metered ramp categories. Based on a large number of microscopic simulation runs, it is found that, under a given demand-to-capacity scenario, the queue at a metered ramp with two on-ramp feeding movements is more likely to be cleared in a cycle than at a metered ramp with three on-ramp feeding movements. Also, the platoon dispersion effect significantly reduces the ramp queue length, and hence the queue storage needs at a metered ramp. In addition, this paper reveals that ramp queue length tends to increase linearly with upstream signal cycle length. The design of queue storage length for a metered on-ramp hence needs to fully consider the various ramp configurations and upstream signal timing settings.

Adequacy of TRANSYT-7F and Synchro models along a major arterial in Saudi Arabia

2009 ◽  
Vol 36 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Nedal T. Ratrout ◽  
Maen Abdullatif Abu Olba
Keyword(s):  
Saudi Arabia ◽  
Queue Length ◽  
Optimal Timing ◽  
Signal Timing ◽  
Calibration Process ◽  
Traffic Conditions ◽  
Platoon Dispersion ◽  
Queue Lengths ◽  
The City ◽  
Optimal Signal

The TRANSYT-7F and Synchro models are used in developing optimal timing plans in the city of Al-Khobar, Saudi Arabia. This paper evaluates the adequacy of both TRANSYT-7F and Synchro under local traffic conditions by comparing queue lengths observed along a major arterial in the study area with simulated queues. The models were then calibrated to produce simulated queue lengths which are as close as possible to the observed ones. A clear difference was found between queue lengths estimated by Synchro and TRANSYT-7F. A queue length calibration process was accomplished for TRANSYT-7F by using platoon dispersion factor values of 20 and 35 for through and left-turning traffic, respectively. Synchro calibration was unsatisfactory. The simulated queue lengths could not be calibrated in a meaningful way to resemble the observed queue lengths. Regardless of this, both models produced comparable optimal signal timing plans.

A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

2018 ◽  
Vol 2672 (45) ◽  
pp. 253-264 ◽  
Author(s):  
Juyuan Yin ◽  
Jian Sun ◽  
Keshuang Tang
Keyword(s):  
Kalman Filter ◽  
Queue Length ◽  
Estimation Method ◽  
Mobile Internet ◽  
Internet Technology ◽  
Sensor Data ◽  
Linear Regression Method ◽  
Mobile Sensor ◽  
Length Estimation ◽  
Queue Lengths

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

Organization Optimization of Traffic Engineering in Urban CBD

2013 ◽  
Vol 579-580 ◽  
pp. 890-893
Author(s):  
Mei Mei Huang ◽  
Qing Yang ◽  
Shang Lin Xiao
Keyword(s):  
Traffic Flow ◽  
Traffic Engineering ◽  
Queue Length ◽  
Motor Vehicle ◽  
Heavy Traffic ◽  
Optimization Methods ◽  
Difficult Problem ◽  
Signal Timing ◽  
Total Capacity ◽  
Business District

Orderly organization of traffic engineering in the urban CBD (Center Business District) is a difficult problem, with crowding people flow, heavy traffic flow and complex surrounding situation. This paper set CBD along Xinhua Street in Jinhua city center as an example, focused on the organization optimization process of traffic engineering in CBD. Through the survey on traffic engineering status of sections and intersections, it analyzed road congestion characteristics and intersection signal timing with Vissim software emulation, proposed traffic optimization methods as road channelization, intersection signal timing adjustment of Xinhua-Liberation Road. In Xinhua Street section, it can effectively canalized traffic flow by broadening 2 two-way lanes, adding four pedestrian crossing refuges, and separating Motor vehicle and non-motor vehicle separation barrier. It took queue length, number of stops, delay time three indicators as the objective function with the application of Synchro software adjusting the intersection signal timing. As a result, the total queue length could be reduced from 708.5m to 586.6m and total capacity from 2041 pcu/ h to 2838 pcu/ h.

Analysis and Priority of Slow Traffic Flow at Signalized Intersection

2014 ◽  
Vol 543-547 ◽  
pp. 4169-4172
Author(s):  
Li Yu
Keyword(s):  
Traffic Flow ◽  
Design Flow ◽  
Signalized Intersection ◽  
Signal Timing ◽  
Slow Speed ◽  
Geometric Characteristics ◽  
Traffic Efficiency ◽  
Different Types ◽  
Traffic Conflict ◽  
Fast Start

The slow traffic flow is composed by walking and bike driving. Characteristics of slow traffic flow are fast start and slow speed. The slow traffic and vehicle flow are driving at the same time will cause traffic conflict. The slow traffic priority will be to improve safety and traffic efficiency at the signalized intersection. In this paper, the current problems of slow traffic are analyzed. Combined with the analysis of geometric characteristics of different types of intersection, the principles and implementation conditions of slow traffic priority at the signalized intersection are proposed. From the space priority, the measures of slow traffic priority are proposed at the signalized intersection. From the time priority, slow traffic priority signal timing design flow is proposed.

Real-Time Queue Length Estimation for Signalized Intersections Using Vehicle Trajectory Data

2017 ◽  
Vol 2623 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Fuliang Li ◽  
Keshuang Tang ◽  
Jiarong Yao ◽  
Keping Li
Keyword(s):  
Shock Waves ◽  
Queue Length ◽  
Penetration Rate ◽  
Signalized Intersections ◽  
Signal Timing ◽  
Probe Vehicle ◽  
Vehicle Trajectories ◽  
Length Estimation ◽  
Inflection Points ◽  
Sampling Intervals

Queue length is one of the most important performance measures for signalized intersections. Many methods for queue length estimation based on various data sources have been proposed in the literature. With the latest developments and applications of probe vehicle systems, cycle-by-cycle queue length estimation based only on probe data has become a promising research topic. However, most existing methods assume that information such as signal timing, arrival pattern, and penetration rate is known, an assumption that constrains their applicability in practice. The objective of this study was to propose a cycle-by-cycle queue length estimation method using only probe data without the foregoing assumption. Based on the shock wave theory, the proposed method is capable of reproducing the dynamic queue forming and dissipating process cycles at signalized intersections by using probe vehicle trajectories. To reproduce the queuing processes, the inflection points of probe vehicle trajectories representing the changes of arrival patterns are identified and extracted from the trajectory points of vehicles joining and leaving the queue. A piecewise linear function is then used to fit all the inflection points to estimate the stopping and discharging shock waves. Finally, signal timing data and queue lengths can be calculated on the basis of the estimated shock waves. Under both saturated and oversaturated traffic conditions, the performance of the method is comprehensively evaluated through 60 simulation scenarios, which cover sampling intervals from 5 s to 60 s and penetration rates ranging from 5% to 100%. Results show that compared with the method proposed by Ramezani and Geroliminis in 2015, the proposed method has more robustness for all the sampling intervals and displays more estimation accuracy of queue length and a higher success rate under conditions of low penetration rate.

scholarly journals High-Resolution Traffic Flow Prediction Model Based on Deep Learning

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhihong Yao
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Flow Rate ◽  
Dispersion Model ◽  
Signal Timing ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model ◽  
Platoon Dispersion ◽  
Adaptive Signal

The traditional platoon dispersion model is based on the hypothesis of probability distribution, and the time resolution of the existing traffic flow prediction model is too big to be applied to the adaptive signal timing optimization. Based on the view of the platoon dispersion model, the relationship between vehicle arrival at downstream intersection and vehicle departure from the upstream intersection was analyzed. Then, the high-resolution traffic flow prediction model based on deep learning was proposed. The departure flow rate at the upstream was taking as the input and the arrival flow rate at downstream intersection was taking as the output in this model. Finally, the parameters of the proposed model were trained by the field survey data, and this model was implemented to predict the arrival flow rate of the downstream intersection. The result shows that the proposed model can better reflect the fluctuant characteristics of traffic flow and reduced the sum of the squared errors (SSE), MSE, and MAE by 13.17%, 13.21%, and 14.24%, compared with Robertson’s model. Thus, the proposed model can be applied for real-time adaptive signal timing optimization.

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