Calibration of the Platoon Dispersion Model by Considering the Impact of the Percentage of Buses at Signalized Intersections

2017 ◽  
Vol 2647 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Youan Wang ◽  
Xumei Chen ◽  
Lei Yu ◽  
Yi Qi
Keyword(s):  
Dispersion Model ◽  
Signalized Intersections ◽  
Signal Timing ◽  
Time Step ◽  
Traffic Conditions ◽  
Flow Profiles ◽  
Platoon Dispersion ◽  
Link Travel Time ◽  
Downstream Flow ◽  
The Impact

Despite the wide use of Robertson’s platoon dispersion model, few studies have calibrated this model under different traffic conditions at signalized intersections. This study calibrated the platoon dispersion model on the basis of field data collected from Beijing; the impact of the percentage of buses was considered in the calibration. First, a video was made of the platoon dispersion at an intersection. The link travel time of the vehicles in the platoon was extracted from the video. Then, the parameters of the platoon dispersion model were estimated with the average and standard deviations of the fleet link travel times. It was found that the derived parameters varied, with the observed percentage of buses ranging from 0% to 6% or 15% to 22%. This factor showed the impact of the percentage of buses on platoon dispersion under specific conditions. Regression models were developed to reflect such an impact. To evaluate the effectiveness of the calibrated platoon dispersion model, the downstream flow profiles derived from the calibrated model were compared with the field-observed downstream flow profiles within the dispersion process. Finally, the influence of the time step on the calibrated platoon dispersion model was analyzed. The results show that the calibrated model has high accuracy. The calibrated platoon dispersion model can be used to represent the process of platoon dispersion at signalized intersections where the impact is affected by the percentage of buses. It can contribute to signal timing optimization of the intersections.

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.

scholarly journals The impact of different saturation headway values on intersection capacity

2021 ◽  
Author(s):  
Dražen Cvitanić ◽  
Biljana Maljković
Keyword(s):  
Traffic Flow ◽  
Flow Structure ◽  
Road Network ◽  
Weather Conditions ◽  
Traffic Volume ◽  
Signalized Intersections ◽  
Signal Timing ◽  
Bad Weather ◽  
The Impact ◽  
The City

Elements of the city road network that determine its capacity are signalized intersections. Their capacity depends of many factors: traffic volume and distribution, traffic flow structure, signal timing, and number of bicyclists and pedestrians. However, the starting parameter for calculation of intersection capacity is saturation headway. This research explores the influence of weather conditions and purpose of trip on saturation headway. Saturation headways were determined on few intersections in the morning peak hour of working and weekend day, in good and bad weather conditions. The impact of different trip purposes and different weather conditions on intersection capacity is analysed, as well as the influence of using mean and median values of saturation headway when calculating the intersection capacity.

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.

Dynamic platoon dispersion model based on real-time link travel time

2019 ◽  
Vol 13 (11) ◽  
pp. 1694-1700 ◽  
Author(s):  
Zhihong Yao ◽  
Taorang Xu ◽  
Yang Cheng ◽  
Lingqiao Qin ◽  
Yangsheng Jiang ◽  
...  
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Dispersion Model ◽  
Model Based ◽  
Platoon Dispersion ◽  
Link Travel Time

Passing Stage Division of Bicycle Groups at Signalized Intersection

2011 ◽  
Vol 243-249 ◽  
pp. 4408-4412
Author(s):  
Quan Yu ◽  
Li Ping Shi ◽  
Ning Li ◽  
Xiao Hui Deng
Keyword(s):  
Signalized Intersections ◽  
Signalized Intersection ◽  
Signal Timing ◽  
Beijing City ◽  
Video Cameras ◽  
Second Stage ◽  
Third Stage ◽  
Cluster Analysis Method ◽  
Tianjin City ◽  
The Impact

This paper presents a simple identifying method of bike group ,then takes bicycle groups of signalized intersection as the research object, investigations and video cameras are carried out at two signalized intersections in Beijing and Tianjin city. For the data analysis,by using K-means cluster analysis method ,this paper selects three criterions which are the average speed of bicycle group,the width of bicycle group and the length of bicycle group. Based on three indexes , the passing stage of the bicycle groups at two signalized intersections of Beijing city and Tianjin city are divided into three stages :the first stage for bicycle group gradually spreading ,the second stage for expanding ,the third stage for gradually shrinking.The research on the passing stage of bicycle group starting and running through the intersection at the signalized intersection are useful for determining the capacity of vehicles with the impact of bicycle , and for calculating the signal timing of bicycle under the mixed traffic flow condition at signalized intersection.

scholarly journals Real-Time Prediction of Lane-Based Queue Lengths for Signalized Intersections

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Bing Li ◽  
Wei Cheng ◽  
Lishan Li
Keyword(s):  
Queue Length ◽  
Dispersion Model ◽  
Signalized Intersections ◽  
Traffic Signal Control ◽  
Evaluation Indexes ◽  
Time Prediction ◽  
Proposed Model ◽  
Platoon Dispersion ◽  
Queue Lengths ◽  
The Relationship

Queue length is one of the most important traffic evaluation indexes for traffic signal control at signalized intersections. Most previous studies have focused on estimating queue length, which cannot be predicted effectively. In this paper, we applied the Lighthill–Whitham–Richards shockwave theory and Robertson’s platoon dispersion model to predict the arrival of vehicles in advance at intervals of 5 seconds. This approach fully described the relationship between disparate upstream traffic arrivals (as a result of vehicles making different turns) and the variation of incremental queue accumulation. It also addressed the shortcomings of the uniform arrival assumption in previous research. In addition, to predict the queue length of multiple lanes at the same time, we integrated the prediction of the traffic volume proportions in each lane using the Kalman filter. We tested this model in a field experiment, and the results showed that the model had satisfactory accuracy. We also discussed the limitations of the proposed model in this paper.

Calibration of Platoon Dispersion Parameters on the Basis of Link Travel Time Statistics

2000 ◽  
Vol 1727 (1) ◽  
pp. 89-94 ◽  
Author(s):  
Lei Yu
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Dispersion Model ◽  
Time Data ◽  
Dispersion Parameters ◽  
Smoothing Factor ◽  
Travel Time Data ◽  
Platoon Dispersion ◽  
Advanced Traffic Management System ◽  
Link Travel Time

A calibration technique for platoon dispersion parameters for the widely used TRANSYT platoon dispersion model is presented. This technique calibrates platoon dispersion factor, travel time factor, and smoothing factor directly from the average link travel time and its standard deviation and can capture practically all of the roadway and traffic conditions in the field such as road grades, curvature, parking, opposing flow interference, traffic volume, and other sources of impedance. The technique is especially suited for applications in advanced traffic management system networks in which the required link travel time data could be obtained on a real-time basis. TRANSYT’s implementation of two scenarios is examined. The first scenario inputs the calibrated platoon dispersion parameter, with the result being that the smoothing factor used by TRANSYT is different from the calibrated parameter. The second scenario inputs a revised platoon dispersion factor, which is designed to make the smoothing factor used by TRANSYT identical to the calibrated parameter. This examination induces a recommendation that the TRANSYT input card or its internal calculation procedure for platoon dispersion be revised so that the average link travel time in the geometric distributed platoon dispersion model is consistent with those from the same model. The calibration of platoon dispersion parameters with field-collected link travel time data shows that platoon dispersion parameters are different for different standard deviations of link travel times even on the same street, and, therefore, the platoon dispersion parameters must be calibrated on a site-specific basis.

scholarly journals Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model

2021 ◽  
Author(s):  
Bernd Brüggenjürgen ◽  
Hans-Peter Stricker ◽  
Lilian Krist ◽  
Miriam Ortiz ◽  
Thomas Reinhold ◽  
...  
Keyword(s):  
Public Health ◽  
Transmission Rate ◽  
Latency Period ◽  
Policy Decision ◽  
Sensitivity Analyses ◽  
Delphi Panel ◽  
Time Step ◽  
Seir Model ◽  
Health Policy Decision ◽  
The Impact

Abstract Aim To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). Methods We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. Results Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. Conclusion Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures.

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

2021 ◽  
pp. 036119812199866 ◽  
Author(s):  
Elise Henry ◽  
Angelo Furno ◽  
Nour-Eddin El Faouzi
Keyword(s):  
Three Dimensional ◽  
Traffic Load ◽  
Transport Networks ◽  
Traffic Conditions ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Network Operations ◽  
Network States ◽  
The Impact ◽  
Proper Operation

Transport networks are essential for societies. Their proper operation has to be preserved to face any perturbation or disruption. It is therefore of paramount importance that the modeling and quantification of the resilience of such networks are addressed to ensure an acceptable level of service even in the presence of disruptions. The paper aims at characterizing network resilience through weighted degree centrality. To do so, a real dataset issued from probe vehicle data is used to weight the graph by the traffic load. In particular, a set of disrupted situations retrieved from the study dataset is analyzed to quantify the impact on network operations. Results demonstrate the ability of the proposed metrics to capture traffic dynamics as well as their utility for quantifying the resilience of the network. The proposed methodology combines different metrics from the complex networks theory (i.e., heterogeneity, density, and symmetry) computed on temporal and weighted graphs. Time-varying traffic conditions and disruptions are analyzed by providing relevant insights on the network states via three-dimensional maps.

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