Bi-Directional Traffic Management with Multiple Data Feeds for Dynamic Route Computation and Prediction System

Rahman, Rahatur; Akhter, Shamim

doi:10.20533/ijicr.2042.4655.2016.0088

Cited by 8 publications

(10 citation statements)

References 3 publications

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“…A new intelligence traffic management system (ITMS) [8] [11] [20] [21] [22] [30] was proposed and implemented to find an optimum route from source to destination. Each route is segmented and called as road segment.…”

Section: Introductionmentioning

confidence: 99%

“…Each route is segmented and called as road segment. We investigate environmental, vehicles and road related information for each road segment and generate a road weight for this particular road segment using Deep-Neuro-Fuzzy model [8][20] [21] [22]. However, ITMS needs some simulation or emulation to provide that the system works accurately.…”

Section: Introductionmentioning

confidence: 99%

“…However, ITMS needs some simulation or emulation to provide that the system works accurately. In this paper, we are proposing a traffic simulation environment by integrating Sumo Urban Mobility Simulation (SUMO) tool and a realtime Intelligent Traffic Management System (ITMS) [8] [11] [20] [21] [22]. Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making on complex traffic managerial issues and corporate them to the end-user applications.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Akhter¹,

Ahsan²,

Quaderi³

2019

IJACSA

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This paper focuses on Sumo Urban Mobility Simulation (SUMO) and real-time Traffic Management System (TMS) simulation for evaluation, management, and design of Intelligent Transportation Systems (ITS). Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making. In these regards, a new Intelligent Traffic Management System (ITMS) was proposed and implemented-where a path from source to destination was selected by Dijkstra algorithm, and the road segment weights were calculated using real-time analyses (Deep-Neuro-Fuzzy framework) of data collected from infrastructure systems, mobile, distributed technologies, and socially-build systems. We aim to simulate the ITMS in pragmatic style with micro traffic, open-source traffic simulation model (SUMO), and discuss the challenges related to modeling and simulation for ITMS. Also, we expose a new model-Ant Colony Optimization (ACO) in SUMO tool to support a multi-agent-based collaborative decisionmaking environment for ITMS. Beside we evaluate ACO model performance with exiting built-in optimum route-finding SUMO models (Contraction Hierarchies Wrapper)-CHWrapper, A-star (A*), and Dijkstra) for optimum route choice. The results highlight that ACO performs better than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Akhter¹,

Ahsan²,

Quaderi³

2019

IJACSA

Self Cite

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“…A new low cost, flexible, maintainable, and secure internetbased traffic management system with real time bi-directional communication was proposed and implemented (in [1] [2][3] [4]) to assist and reduce the traffic situation. To determine the dynamic road weights in TMS, four (4) different environmental attributes -rain fall, temperature, wind, and humidity are considered.…”

Section: Introductionmentioning

confidence: 99%

Exploreing K-Means with Internal Validity Indexes for Data Clustering in Traffic Management System

Nawrin¹,

Rahman²,

Akhter³

2017

ijacsa

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Abstract-Traffic Management System (TMS) is used to improve traffic flow by integrating information from different data repositories and online sensors, detecting incidents and taking actions on traffic routing. In general, two decision making systems-weights updating and forecasting are integrated inside the TMS. The models need numerous data sets for making appropriate decisions. To determine the dynamic road weights in TMS, four (4) different environmental attributes are considered, which are directly or indirectly related to increase the traffic jam-rain fall, temperature, wind, and humidity. In addition, peak hour is taken as an additional attribute. Usually, the data sets are classified by instinct method. However, optimum classification on data sets is vital to improve the decision accuracy of the TMS. Collected data sets have no class label and thus, cluster based unsupervised classifications (partitioning, hierarchical, grid-based, density-based) can be used to find optimum number of classifications in each attribute, and expected to improve the performance of the TMS. Two most popular and frequently used classifiers are hierarchical clustering and partition clustering. K-means is simple, easy to implement, and easy to interpret the clustering results. It is also faster, because the order of time complexity is linear with the number of data. Thus, in this paper we are going to demonstrate the performance of partition k-means and hierarchical k-means with their implementations by Davies Boulder Index (DBI), Dunn Index (DI), Silhouette Coefficient (SC) methods to outline the optimal number classifications (features) inside each attribute of TMS data sets. Subsequently, the optimal classes are validated by using WSS (within sum of square) errors and correlation methods. The validation results conclude that k-means with DI performs better in all attributes of TMS data sets and provides more accurate optimum classification numbers. Thereafter, the dynamic road weights for TMS are generated and classified using the combined k-means and DI method.

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“…A new internet-based (WebSocket (HTML5 Web Sockets, 2015) over HTTP) traffic management support system with real time bi-directional communication is implemented in Rahman et al, (2015a;2015b;2015c) to assist the traffic system to reduce the traffic and create a more sustainable environment. It also supports low cost implementation, flexibility, maintainability and infrastructure security.…”

Section: Introductionmentioning

confidence: 99%

Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System

Akhter

Rahman²,

Islam

2016

International Journal of Applied Evolutionary Computation

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Low-cost, flexible, easily maintainable and secure traffic management support systems are in demand. Internet-based real time bi-directional communication provides significant benefits to monitor road traffic conditions. Dynamic route computation is a vital requirement to make the traffic management system more realistic and reliable. Therefore, an integrated approach with multiple data feeds and Backpropagation (BP) Neural Network (NN) with Levenberg-Marquardt (LM) optimization is applied to predict the road weights. The results indicate that the proposed traffic system/tool with NN based dynamic weights computation is much more effective to find the optimal routes. The BP NN with LM optimization achieves 96.67% accuracy.

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Bi-Directional Traffic Management with Multiple Data Feeds for Dynamic Route Computation and Prediction System

Cited by 8 publications

References 3 publications

Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System

Exploreing K-Means with Internal Validity Indexes for Data Clustering in Traffic Management System

Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System

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