Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

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Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Remote Sensing ◽

10.3390/rs13122329 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2329

Author(s):

Elżbieta Macioszek ◽

Agata Kurek

Keyword(s):

Travel Behavior ◽

Traffic Management ◽

Road Traffic ◽

Traffic Volume ◽

The Road ◽

Traffic Characteristics ◽

Annual Average Daily Traffic ◽

On The Road ◽

The Impact ◽

The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

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An Exact Algorithm for Large-Scale Continuous Nonlinear Resource Allocation Problems with Minimax Regret Objectives

INFORMS Journal on Computing ◽

10.1287/ijoc.2020.0999 ◽

2021 ◽

Author(s):

Jungho Park ◽

Hadi El-Amine ◽

Nevin Mutlu

Keyword(s):

Resource Allocation ◽

Objective Function ◽

Large Scale ◽

Computational Study ◽

Optimal Solution ◽

Exact Algorithm ◽

Minimax Regret ◽

Optimal Solutions ◽

Heuristic Approaches ◽

Exact Approach

We study a large-scale resource allocation problem with a convex, separable, not necessarily differentiable objective function that includes uncertain parameters falling under an interval uncertainty set, considering a set of deterministic constraints. We devise an exact algorithm to solve the minimax regret formulation of this problem, which is NP-hard, and we show that the proposed Benders-type decomposition algorithm converges to an [Formula: see text]-optimal solution in finite time. We evaluate the performance of the proposed algorithm via an extensive computational study, and our results show that the proposed algorithm provides efficient solutions to large-scale problems, especially when the objective function is differentiable. Although the computation time takes longer for problems with nondifferentiable objective functions as expected, we show that good quality, near-optimal solutions can be achieved in shorter runtimes by using our exact approach. We also develop two heuristic approaches, which are partially based on our exact algorithm, and show that the merit of the proposed exact approach lies in both providing an [Formula: see text]-optimal solution and providing good quality near-optimal solutions by laying the foundation for efficient heuristic approaches.

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Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms ◽

10.4018/978-1-7998-8048-6.ch053 ◽

2021 ◽

pp. 1074-1115

Author(s):

Victer Paul ◽

Ganeshkumar C ◽

Jayakumar L

Keyword(s):

Genetic Algorithms ◽

Nearest Neighbor ◽

Optimization Technique ◽

Optimal Solution ◽

Computation Time ◽

Search Space ◽

Population Based ◽

Vital Role ◽

Traveling Salesman ◽

Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

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Ant Colony Optimization for Real Time Traffic Lights Control on a Single Intersection

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v14i02.10332 ◽

2020 ◽

Vol 14 (02) ◽

pp. 196 ◽

Cited By ~ 1

Author(s):

Nouha Rida ◽

Mohammed Ouadoud ◽

Aberrahim Hasbi

Keyword(s):

Road Traffic ◽

Green Light ◽

Optimal Solution ◽

Ant Colony ◽

Traffic Data ◽

Traffic Light ◽

Traffic Lights ◽

The Road ◽

Real Time Traffic ◽

On The Road

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

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Reconfigurable SRTM System for Road Traffic in Kingdom of Bahrain

Transport and Telecommunication Journal ◽

10.1515/ttj-2016-0026 ◽

2016 ◽

Vol 17 (4) ◽

pp. 298-306 ◽

Cited By ~ 1

Author(s):

Wael El-Medany ◽

Alauddin Al-Omary ◽

Riyadh Al-Hakim ◽

Taher Homeed

Keyword(s):

Traffic Management ◽

Road Traffic ◽

Traffic Signals ◽

Google Maps ◽

Traffic System ◽

The Road ◽

Field Programmable ◽

Hardware Description ◽

Traffic Management System ◽

On The Road

Abstract This paper presents reconfigurable hardware architecture for smart road traffic system based on Field Programmable Gate Array (FPGA). The design can be reconfigured for different timing of the traffic signals according to the received and collected data read by the different sensors on the road; the design has been described using VHDL (VHSIC Hardware Description Language). The SRTM (Smart Road Traffic Management) System has some more features that help passenger to avoid traffic jamming by sending the collected information through web/mobile applications to find the best road between the start and destination points, which will be displayed on Google maps, at the same time it will also shows the points of traffic jamming on Google maps. SRTM system can also manage emergency vehicles such as ambulance and fire fighter and also can send snapshots and video streaming for different roads and junctions to show the points of traffic jamming. The design has been simulated and tested using ModelSim PE student edition 10.4. Spartan 3 FPGA starter kit from Xilinx has been used for implementing and testing the design in a hardware level.

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Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

Mathematical Problems in Engineering ◽

10.1155/2014/809642 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

I. Hameem Shanavas ◽

R. K. Gnanamurthy

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithm ◽

Design Automation ◽

Large Scale ◽

Hybrid Approach ◽

Hybrid Genetic Algorithm ◽

Optimal Solution ◽

Physical Design ◽

Silicon Area ◽

Vlsi Physical Design

In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

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Development of a procedure for adapting a model of adjustable intersection

The Russian Automobile and Highway Industry Journal ◽

10.26518/2071-7296-2020-17-6-726-735 ◽

2020 ◽

Vol 17 (6) ◽

pp. 726-735

Author(s):

I. A. Novikov ◽

A. G. Shevtsova ◽

A. A. Kravchenko ◽

A. G. Burlutskaia

Keyword(s):

Traffic Management ◽

Road Traffic ◽

Transport Model ◽

Optimal Solution ◽

Cost Effective ◽

Transport Processes ◽

Transport Infrastructure ◽

Transport Systems ◽

Software Environment ◽

Software Products

Introduction. The article is devoted to the development of an algorithm for checking the assessment of the adequate operation of the transport model and the application of this algorithm in practice.The research has been carried out at a regulated intersection in Belgorod, the main parameters necessary for creating a transport model have been determined. A transport model of the investigated intersection was created, an assessment of the adequate operation of the model was made, measures were proposed to increase the capacity of the investigated section of the road network.Methods and materials. The main attention is paid to the use of the software that allows simulating transport processes at the initial stages of activities related to the organization or reorganization of road traffic. Simulation is a fast, convenient and cost-effective way to assess the effectiveness of traffic management. The modeling process helps to select the most optimal solution for the transport infrastructure.Results. The authors have developed the algorithm for checking the adaptation of the controlled crossing model in the Aimsun software environment, and proposed a new coordination plan for crossing. Conclusion. It is concluded that it is necessary to use the software products intended for modeling transport systems.

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Analysis of the advancements in real-life performance of highly automated vehicles' with regard to the road traffic safety

MATEC Web of Conferences ◽

10.1051/matecconf/201823105003 ◽

2018 ◽

Vol 231 ◽

pp. 05003 ◽

Cited By ~ 1

Author(s):

Arkadiusz Matysiak ◽

Paula Razin

Keyword(s):

Traffic Safety ◽

Large Scale ◽

Road Traffic ◽

Real Life ◽

Automated Vehicles ◽

Automated Driving ◽

The Road ◽

Road Traffic Safety ◽

On The Road ◽

The Impact

The article presents the analysis of the performance of the vehicles equipped with automated driving systems (ADS) which were tested in real-life road conditions from 2015 to 2017 in the state of California. It aims at the effort to assess the impact on the road safety the continuous technological advancements in driving automation might have, based on of the first large-scale, real-life test deployments. Vehicle manufacturers and other stakeholders testing the highly automated vehicles in California are obliged to issue yearly reports which provide an insight on the test scale as well as the technology maturity. The so-called 'disengagement reports' highlight the range and number of control takeovers between the ADS and driver, which are made either based on driver's decision or information provided by the vehicle itself. The analysis of these reports allowed to investigate the development made in automated driving technology throughout the years of tests, as well as the direct or indirect influence of the external factors (e.g. various weather conditions) on the ADS performance. The results show that there is still a significant gap in reliability and safety between human drivers and highly automated vehicles which has been yet steadily decreasing due to technology advancements made while driving in the specific infrastructure and traffic conditions of California.

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Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2019040103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 55-92 ◽

Cited By ~ 1

Author(s):

Victer Paul ◽

Ganeshkumar C ◽

Jayakumar L

Keyword(s):

Genetic Algorithms ◽

Nearest Neighbor ◽

Optimization Technique ◽

Optimal Solution ◽

Computation Time ◽

Search Space ◽

Population Based ◽

Vital Role ◽

Traveling Salesman ◽

Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

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