TransCad analysis and GIS techniques to evaluate transportation network in Nasiriyah city

Qasim, Zaynab I.; Ziboon, Abdul-Razzak; Falih, Khaldoon T.

doi:10.1051/matecconf/201816203029

Cited by 10 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study evaluated the flow of traffic network patterns through several applications such as GIS, Global Positioning System (GPS), Transcad, in order to collect various data such as traffic volume and free flow rate. The results of this study indicated that the construction of new roads to divert foreign travel, as well as adding new bridges to get rid of traffic are applicable solutions to solve the traffic problem (Qasim et al, 2018). In Elfar, Talebpour, and Mahmassani (2018) predicted short-term traffic using the machine learning method.…”

Section: Introductionmentioning

confidence: 96%

“…The results also showed that the Fuzzy C-Means (FCM) clustering algorithm is improved in both fuzzy membership and weight degree (Behzadi, Mousavi, & Norouzi, 2019;Cheng et al, 2020;Mahjoobi & Behzadi, 2022). In Qasim, Ziboon, and Falih (2018) conducted a study to analyze Transcad and GIS techniques to evaluate the transportation network in Nasiriyah city, Deir ez-Zor province. This study evaluated the flow of traffic network patterns through several applications such as GIS, Global Positioning System (GPS), Transcad, in order to collect various data such as traffic volume and free flow rate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Traffic Data in GIS using Different Neural Network Methods

Ghasempoor

Behzadi

2022

View full text Add to dashboard Cite

Traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that a large percentage of people prefer a long but low traffic route than a short route with heavy traffic. Therefore, traffic is a very determining factor in societies, especially in metropolitan areas. The issue of traffic forecasting is another important factor in the field of traffic. In such a way that the traffic of the coming days can be predicted based on the traffic of the previous days. In this paper, traffic forecasting in the coming days is done using a neural network algorithm based on the collected traffic data. Traffic forecasting is performed using Basic Neural Network methods, Feed-forward Levenberg-Marquardt, Conjugate Gradient Neural Network and Bayesian Neural Network. The results of the forecast are then compared with real observations. The results show that the Feed-forward Levenberg-Marquardt method predicts traffic data with 81.59% accuracy, which is the most accurate method among the others. The accuracy of Bayesian Neural Network, Conjugate Gradient Neural Network and Basic Neural Network methods is 81.55, 81.50 and 75%, respectively. Regression values of 24 hours a day were also estimated and it was found that the proximity of input and output values in the Basic Neural Network method is approximately 80%. This parameter was obtained 69.69%, 69.71% and 69.87% for three Feed-forward Levenberg-Marquardt, Conjugate Gradient Neural Network and Bayesian Neural Network respectively.

show abstract

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Predicting Traffic Data in GIS using Different Neural Network Methods

Ghasempoor

Behzadi

2022

View full text Add to dashboard Cite

show abstract

“…The poor condition of the infrastructure uses the individual transport rather than mass transport. Lastly the existencemof services and government centers especially those that deal directly with the public, leads to the state of congestion, [2]. Determining the level of service (LOS) for urban street and intersections is very important, as the first step of analysis procedure, affects the planning, design, and operational phase of transportation projects as well as the allocation of limited financial resourcesmamong competing transportation projects, [3].…”

Section: Introductionmentioning

confidence: 99%

TRAFFIC PERFORMANCE EVALUATION AND ANALYSIS OF Al-FALLAH INTERSECTION IN BAGHDAD CITY UTILIZING SYNCHRO.10 SOFTWARE

Ziboon¹,

Iraq²,

Qasim³

et al. 2019

JEASD

View full text Add to dashboard Cite

Transportation is an essential element for the development of countries economically, socially, and culturally. The progress in countries is measured through the progress in transportation systems and urban road network. Al-Fallah intersection which lies in northeastern of Baghdad city considered one of the highly congested urban centers, suffers congestion, particularly during morning and evening peak hours. The objective of this study is to analyses the delay time, degree of saturation, level of service in Al-Fallah intersection in Baghdad city using Synchro.10 software in addition to evaluate and analyze the intersection formchoosing the best suggestion. The technique of video recording has been used for collection the traffic volume data for all approaches from 7a.m to 4 p.m. for three days: Monday, Tuesday, and Wednesday. The operation analysis of isolated signalized intersection provides valuable and important information about the performance of transportation system in the city.The through movements of Al-Fallah Street illustrate the movement from center of Al-Sadir city to Al-Rusafa and reverse, they represent the heaviest traffic volumes in Al-Fallah intersection. The Results of analysis specified that the Al-Fallah intersection works under level of service F with an average control delay of (322.1) seconds per vehicle and degree of saturation (2.56) v/c during the peak hours.

show abstract