Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019]

A new multi-objective optimization approach based on layered objective is proposed.Two improvements on the non-dominated sorting genetic algorithm are given which are new layering method and the new selection mechanism.On the basis of the traditional non-dominated sorting genetic algorithm, a new objective layer approach is adapted and cycle copy through the first half layers.Another one is making a restriction mechanism before crossover on the classical non-dominated sorting genetic algorithm with elitist strategy.Through these operations, it can effectively improve the convergence rate of the optimal solution set.At last, the corresponding non-inferior solutions will be maintained.Several classic test functions commonly adopted in evolutionary multi-objective optimization and the network design problem are used.The results indicate that the non-dominated sorting genetic algorithm based on layered objective can effectively speed up the convergence rate.

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

This study examines India's initiatives to enhance road safety via the Integrated Road Accident Database (iRAD) and the e-Detailed Accident Report (e-DAR). The Ministry of Road Transport and Highways (MoRTH) has been executing many measures to improve road safety. Road accidents have become a significant threat in India. iRAD aims to address this issue by establishing a comprehensive road accident database to assist authorities in making proactive choices and ensuring 'Better and Safer Roads for All.' The main goals were to assess the efficacy of these activities in enhancing road safety measures, enabling precise data gathering, and optimizing accident reporting systems. The initiative will provide diverse insights by studying the national road accident data using data analytics techniques. The proposed system would provide analytical data via a Monitoring & Reporting Dashboard and an Analytics Dashboard to enhance understanding and support forecasting and decision-making by Apex Authorities for the formulation of new policies and strategies. The project's outcome will be enhanced road safety, namely 'Safe Road for All' in India

Enhancing road safety through advanced predictive analytics in V2X communication networks

This article explores the intricate relationship between mental health disorders and their potential impact on road safety, with a particular emphasis on Cameroon. Mental health conditions such as Intermittent Explosive Disorder, Bipolar Disorder, Schizophrenia, Substance Use Disorder, and borderline personality disorder are examined in the context of driving behaviors, road rage incidents, and overall road safety. The objective is not to stigmatize individuals with mental illness but to highlight the importance of screening, support, and attention to mental health in the context of increasing road accidents. The paper discusses the symptoms of these disorders and how they may increase the risk of road accidents. It also explores the roles of government, community, and individuals in enhancing road safety through mental health support. Evidence-based data specific to road safety in Cameroon and other developing countries are utilized to support the arguments. Recommendations for interventions and support mechanisms are provided, emphasizing a non-discriminatory and objective approach.

In response to escalating road safety challenges within Algeria, marked by rising traffic volumes and urban development, the "TariqAmn Algeria" initiative emerges as a groundbreaking approach in traffic management. This study delineates the development and deployment of this innovative system, which leverages intelligent traffic signs, LiDAR (Light Detection and Ranging, using laser light to measure distances) radar, and proactive brake control technologies, embedded within a robust traffic management framework. The system's dynamic adaptation to real-time traffic conditions and its enforcement of speed compliance are central to its design. Rigorous evaluations through SUMO (Simulation of Urban Mobility, a traffic simulation software) simulations and field tests in high-risk urban settings affirm its efficacy, demonstrating a 60% reduction in traffic incidents and a 50% reduction in speeding violations. By marrying local cultural insights with cutting-edge technology, "TariqAmn Algeria" not only enhances immediate road safety but also establishes a new paradigm in traffic management systems. The findings illuminate the significant scalability potential of the system, promising broader applications in enhancing road safety and adherence to traffic regulations across various regions, such as densely populated urban areas, high-traffic corridors, and accident-prone black spots. This initiative underscores the critical role of Intelligent Transportation Systems (ITS) in modern road safety strategies and sets a precedent for future technological advancements in the field.

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

In this paper, a two-dimensional transonic cascade of German Aerospace Center (DLR) was redesigned with multi-objective optimization. The aerodynamic performance of the cascade was optimized with inlet Mach number of 1.1 by using the blade design method with thickness distribution superposed on the camber line. The non-dominated sorting genetic algorithm, (NSGA) II multi-objective optimization algorithm, was used to determine higher performance cascades. Cascade optimization was carried out within a unique inlet Mach number by varying the shapes of both the camber line and the thickness distribution curve. To enable the cascade to operate under specific conditions, we used an inlet Mach number control loop (IMCL) to regulate the outlet boundary conditions. Optimization was conducted to minimize the cascade total pressure loss coefficient under the three inlet conditions. The optimization results indicated that the total pressure loss coefficient can be reduced by 22% under the design condition, and the range of working conditions was widened.

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.

This research examines the utility of combined crash modification factors (CMFs) in minimizing crash severity at urban roundabouts. Conventional CMFs typically assess the influence of singular interventions on road safety. However, traffic dynamics and the interactions among multiple safety measures necessitate a more comprehensive strategy. This study introduces a novel methodology for developing integrated CMFs that accounts for the interactive effects of multiple safety measures, providing a comprehensive understanding of their collective impact on road safety. The investigation utilized data from 16 urban roundabouts in Amman, Jordan, from 2015 to 2021. Regression models have been employed to develop individual and combined CMFs for various geometric and traffic characteristics, such as entry and exit widths, speed limits, and weaving patterns. Key findings indicate that interventions, such as reducing speed limits, modifying entry and exit widths, and adjusting weaving lengths, significantly improve safety. The analysis identifies hazardous roundabouts and proposes effective single and combined treatments to mitigate crash risks. This study highlights the importance of considering multiple treatments simultaneously to improve the predictive accuracy of safety assessments and supports the development of more effective road safety strategies tailored to specific crash types and severities. The approach demonstrates a significant potential to enhance road safety analysis and decision-making, ultimately contributing to safer road environments.

Reducing vehicle lane widths has been proposed as an effective strategy to decrease vehicle speeds and enhance road safety. However, the safety benefits of narrower travel lanes remain a topic of debate due to mixed findings in the literature. This study examines the relationship between lane width, vehicle speed, and crash occurrence to comprehensively understand their impact on road safety and transportation planning. Using data from 320 urban arterial sections in Utah, the analysis reveals that narrower lane widths are associated with reduced vehicle speeds. For every additional foot of lane width, 85th and 95th percentile speeds increase by 1.012 mph and 1.088 mph, respectively. Furthermore, injury crash modeling indicates that a one-foot increase in lane width is associated with a 38.3% increase in the odds of an injury crash on a roadway section. These findings contribute to the growing evidence supporting the implementation of narrower lane widths as a strategy to improve road safety, foster multimodal infrastructure, and promote sustainable urban transportation systems. We recommend that UDOT adopt a minimum lane width of 10 or 11 feet for arterials in highly urbanized areas, such as downtowns and major activity centers.

Parametrized multi-objective seismic optimization for precast concrete frame with a novel post-tensioned energy dissipation beam-column joint

With the aim of enhancing both reliability and fatigue life of gasket, this study combines finite element analysis, orthogonal experimental design, dynamically-guided multi-objective optimization, and the non-dominated sorting genetic algorithm with elitist strategy to optimize the geometric parameters of the cylinder gasket. The finite element method was used to analyze the temperature field, thermal-mechanical coupling stress field, and deformation of cylinder gasket. The calculation results were experimentally validated by measured temperature data, and comparison results show that the maximum error between calculated value and experiment value is 7.1%, which is acceptable in engineering problems. Based on above results and orthogonal experiment design method, the effects of five factors, including diameter of combustion chamber circle, diameter of coolant flow hole, length of the insulation zone between third and fourth cylinders, thickness of gasket, and bolt preload, on three indexes: temperature, stress, and deformation of gasket, were examined in depth. Through the variance analysis of the results, three important factors were identified to proceed later calculation. The dynamically guided multi-objective optimization strategy and the non-dominated sorting genetic algorithm were effectively used and combined to determine the optimal geometric parameters of cylinder gasket. Furthermore, calculation results suggest that temperature, stress, and deformation of the optimized cylinder gasket have been improved by 27.88 K, 16.84 MPa, and 0.0542 mm, respectively when compared with the origin object, which shows the excellent performance of gasket optimization and effectiveness of the proposed optimization strategy.