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.

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.

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.

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.

Towards a Customized¬ Awareness Campaign to Educate the Public about COVID-19: Development and Validation of a Messaging Campaign to Promote Precautionary Behavior Using Geospatial Intelligence in Saudi Arabia (Preprint)

2020 ◽  
Author(s):  
Mayda Alrige ◽  
Hind Bitar Bitar ◽  
Maram Meccawi ◽  
Balakrishnan Mullachery
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Content Validity ◽  
Awareness Campaign ◽  
Artificial Intelligence Technique ◽  
The Public ◽  
High Infection ◽  
Local Map ◽  
The City ◽  
Intelligence Technique

BACKGROUND Designing a health promotion campaign is never an easy task, especially during a pandemic of a highly infectious disease, such as Covid-19. In Saudi Arabia, many attempts have been made toward raising the public awareness about Covid-19 infection-level and its precautionary health measures that have to be taken. Although this is useful, most of the health information delivered through the national dashboard and the awareness campaign are very generic and not necessarily make the impact we like to see on individuals’ behavior. OBJECTIVE The objective of this study is to build and validate a customized awareness campaign to promote precautionary health behavior during the COVID-19 pandemic. The customization is realized by utilizing a geospatial artificial intelligence technique called Space-Time Cube (STC) technique. METHODS This research has been conducted in two sequential phases. In the first phase, an initial library of thirty-two messages was developed and validated to promote precautionary messages during the COVID-19 pandemic. This phase was guided by the Fogg Behavior Model (FBM) for behavior change. In phase 2, we applied STC as a Geospatial Artificial Intelligence technique to create a local map for one city representing three different profiles for the city districts. The model was built using COVID-19 clinical data. RESULTS Thirty-two messages were developed based on resources from the World Health Organization and the Ministry of Health in Saudi Arabia. The enumerated content validity of the messages was established through the utilization of Content Validity Index (CVI). Thirty-two messages were found to have acceptable content validity (I-CVI=.87). The geospatial intelligence technique that we used showed three profiles for the districts of Jeddah city: one for high infection, another for moderate infection, and the third for low infection. Combining the results from the first and second phases, a customized awareness campaign was created. This awareness campaign would be used to educate the public regarding the precautionary health behaviors that should be taken, and hence help in reducing the number of positive cases in the city of Jeddah. CONCLUSIONS This research delineates the two main phases to developing a health awareness messaging campaign. The messaging campaign, grounded in FBM, was customized by utilizing Geospatial Artificial Intelligence to create a local map with three district profiles: high-infection, moderate-infection, and low-infection. Locals of each district will be targeted by the campaign based on the level of infection in their district as well as other shared characteristics. Customizing health messages is very prominent in health communication research. This research provides a legitimate approach to customize health messages during the pandemic of COVID-19.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

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.

Site response assessment at the city of Al Khobar, eastern Saudi Arabia, from microtremor and borehole data

2015 ◽  
Vol 8 (11) ◽  
pp. 10015-10030
Author(s):  
M. Alharbi ◽  
M. Fnais ◽  
A. Al-Amri ◽  
Kamal Abdelrahman ◽  
Meinrat O. Andreae ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Site Response ◽  
Response Assessment ◽  
Borehole Data ◽  
The City

scholarly journals Major Factors Causing Construction Delays in Mecca

2015 ◽  
Vol 7 (1) ◽  
pp. 75 ◽  
Author(s):  
Ghazi Saad A Elawi ◽  
Mohammed Algahtany ◽  
Dean Kashiwagi ◽  
Kenneth Sullivan
Keyword(s):  
Saudi Arabia ◽  
Infrastructure Projects ◽  
Average Delay ◽  
Gulf Countries ◽  
Underground Utilities ◽  
Research Findings ◽  
Research Gap ◽  
Major Factors ◽  
The City ◽  
Holy City

Delays are a major cause for concern in the construction industry in Saudi Arabia. This paper identifies the main causes of delay in infrastructure projects in Mecca, Saudi Arabia, and compares these with projects around the country and other Gulf countries. Data was obtained from 49 infrastructure projects undertaken by the owner and were analyzed quantitatively to understand the causes and severity of delay. 10 risk factors were identified and were grouped into four categories. Average delay in infrastructure projects in Mecca was found to be 39% of the estimated projects schedules. The most severe cause of delay was found to be the land acquisition factor. This highlights the critical land ownership and acquisition issues that are prevailing in the city. Additionally, other factors that contribute to delay include contractors’ lack of expertise, haphazard underground utilities (line services), and re-designing. It is concluded that the majority of project delays were caused from the owner’s side as compared to contractors, consultants, and other project’s stakeholders. This finding matched with the research findings of the Gulf Countries Construction (GCC) industry’s literature. This study fills an important practice and research gap for improving the efficiency in delivering infrastructure projects in the holy city of Mecca and Gulf countries at large.

Sign in / Sign up

Export Citation Format

Share Document