Congestion Relief Research Articles

Congestion Relief Services by Vehicle-to-Grid Enabled Electric Vehicles Considering Battery Degradation

Battery electric vehicles (BEVs) offer substantial potential to enhance the electric grid through bi-directional charging technologies. In essence, BEVs, functioning as portable battery energy storage systems, play a pivotal role in enabling the seamless integration of renewable energy, grid optimization, and ancillary services. This article sets out to explore the value of BEVs equipped with Vehicle-to-Grid (V2G) for grid operators, particularly in the context of alleviating congestion. This valuable service, though not accompanied by direct monetary compensation for users, holds significant promise in minimizing congestion and renewable energy curtailment. This study utilizes the Day-Ahead Locational Marginal Price (LMP) data obtained from various locations within California Independent System Operator (CAISO) to ascertain the financial benefits to BEVs located on either side of congestion at different grid nodes, across various months. Similar analysis is performed on some of the largest solar energy plants in California. Mixed-integer linear programs are used to optimize the charging/discharging decisions for the BEV for maximizing revenue from LMP arbitrage and for minimizing the congestion component of LMP. Additionally, we take into account the impact of battery degradation, quantified as a cost per kilowatt-hour ($/kWh), and integrate this factor into our assessment to understand the evolving discharging behavior of BEVs. The article compares the benefits from the BEVs towards congestion minimization for the two different optimization scenarios, discusses seasonality, and addresses the importance of adequately compensating BEV users and incentivizing them to prioritize congestion relief during specific time intervals.

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

The dynamic charging behavior of electric vehicles (EVs) is causing frequent line-overloading problems and serious power security issues. Controlled and smart charging mechanisms for EVs incorporating demand response (DR) may provide significant operational flexibility to the grid operators and reduce charging costs for EV users. However, controlling EV charging may cause inconvenience to individual EV users. A market-based mechanism is proposed in this research work that allows EV users and households to participate in the network congestion alleviation DR program online while maintaining the power quality. Consumers submit their charging requirements (e.g., charging power and charging deadlines) and load curtailment tolerances to the aggregator. The aggregator is an entity assumed to have a long-term contract with the distribution system operator (DSO) and regularly receives network congestion information on behalf of retail energy users. Lyapunov optimization (LO) framework is used to reschedule the EVs, and household loads using DC optimal power flow (DCOPF) to get the dynamic congestion cost signal (CCS). The developed strategy is tested on a modified IEEE 33-bus radial active distribution network (ADN). Simulation results illustrate that the designed algorithm is promising in mitigating network congestion by ensuring significantly fewer violations of network constraints (i.e., line limits) both in terms of capacity and frequency. It also results in less energy cost as compared to other benchmark algorithms, e.g., greedy algorithm, and provides a guarantee of meeting EV charging, and flexible household loads’ (FHL) delay tolerance constraints. The average curtailment ratio of FHL and the service delay for EV-charging requests are converged to the user-defined limits, i.e., 0.25 for curtailment ratio tolerance and 10 times-lots for EV service delay. Unlike other counterparts, the developed algorithm is especially suitable for real-time applications as the per-slot average computational cost is negligible, i.e., 0.55 s.

Empirical evaluation of change in crash risk due to lane marking reallocation: A case study in Kochi City, Japan

Lane markings are considered an essential component of a road system. As highlighted in previous studies, they are directly linked to efficiency and safety. The rearrangement or reallocation of lane markings can be an economical way to improve efficiency. However, such changes could influence driver behavior. Thus, there is a tradeoff between efficiency and safety. Through a case study in Kochi City, Japan, this study evaluated the change in crash risk caused by a lane marking reallocation. Video data were collected before and after the implementation of a new road layout (achieved by reallocating lane markings) that was intended to mitigate traffic congestion at a signalized intersection. Based on the video data, PICUD (Possibility Index for Collision with Urgent Deceleration), a surrogate safety index used to estimate collision risk, was estimated for lane changes and conflicts between leading and following vehicles in the through lane. In particular, it was confirmed that the collision risk between a lane-changing vehicle and a leading vehicle in the through lane was reduced due to the reduction in traffic density caused by the new road layout. In addition, the results indicate that the PICUD value tends to decrease (i.e., the crash risk tends to increase) with increasing speed of the following vehicle relative to the leading vehicle. Overall, the improvement in safety after the implementation of the new road layout was marginal and statistically insignificant. Therefore, this study highlights the necessity of incorporating speed control measures, such as speed limits, along with congestion alleviation measures in order to enhance safety.

Power Flow Optimization with Energy Storage - Sal Island Case Study

The correlation between energy costs and the country's economic competitiveness is an unquestionable reality also responsible for the improvement of the population's life conditions. In the past Cape Verdean electric power system (EPS), expansion was based on fossil-fuel power plants, nowadays it shifted to renewable energy (RE) which is abundant in the Cape Verde archipelago. However, no reduction in the electricity tariffs occurred, due to renewable curtailment and other pendent questions related to power transmission losses in the EPS. This paper presents an approach, that supports an implementation of a distributed electric energy storage system (ESS) on the Sal Island of Cape Verde archipelago, as a solution to increase the RE integration and power Transmission congestion relief. Thus, a power flow optimization is only achievable by storing excess RE as near as possible to consumption buses that can reduce overall transmission losses. The most advantageous allocation of ESSs along the EPS buses is combinational which faces a maximization of transmission loss reduction and minimization of ESS investment capital. The proposed tool to manage the “trade-off” between cost and avoided losses, is based on a genetic algorithm (GA) that is broadly applied to multi-objective problems like this.

Case Study Evaluation of a New Approach to Price Metropolitan Highways for Congestion Relief, Sustainability and Equity

This paper addresses the practically important challenge of devising efficient and politically feasible congestion pricing policies in the context of metropolitan highways. Congestion on metropolitan highways continues to grow while governments struggle to fund alternative modes of travel. The purpose of this paper is to explore the viability of a new approach to address highway congestion while also accommodating the mobility needs of those who don’t drive. It involves creating congestion-priced lanes on limited access highways without adding new lanes. The lanes would be taken from general use and reserved for high-occupancy vehicles (HOV), transit vehicles and toll-payers, with cash rewards paid to HOV and transit users to attract solo drivers to shared travel. Variable tolls charged to lower-occupancy vehicles on the dedicated lanes would limit traffic demand on the lanes, keep traffic flowing, and fund the cash rewards. Rewards would be high enough to attract a sufficient number of drivers to ride as passengers instead so that congestion would be eliminated on the toll lanes and reduced on the remaining toll-free lanes through mode shifts. The policy-level analysis using a real-world case study of a radial highway segment with directional peaking suggests that this congestion pricing/cash rewards strategy could generate surplus revenues and provide financial support for bus rapid transit operating on the congestion-free lanes.

Has the ecological civilization pilot promoted the transformation of industrial structure in China?

Industrial structure transformation is important for realizing Chinese modernization, and ecological civilization is necessary for building a beautiful China. How to use the construction of ecological civilization to realize industrial structure transformation is a major issue for the economic development of China and other developing countries. The difference-in-differences method was used to evaluate the impact of the ecological civilization pilot policy (ECPP) on industrial structure transformation. The three-stage mediating effect method is used to explore the mechanism channel. Through heterogeneity discussion and spatial spillover analysis method, the effects of ECPP were further understood. The study reveals the following: Overall, the ECPP has promoted the transformation of the industrial structure. Specifically, the ECPP has a significant positive impact on industrial structure rationalization but a negative impact on industrial structure advancement. Several robustness checks support this finding. Second, the mechanism channels are primarily achieved through environmental investment, green finance infusion, and congestion alleviation. Third, good central-local relationships can enhance the trend toward industrial structure rationalization, while unfavorable relationships may pose hindrances to industrial structure advancement. In addition, the effect of the ECPP varies depending on the population density and environmental pollution. Lastly, the ECPP has a spatial positive spillover effect promoting industrial structure rationalization. The results above provide additional information about the effects of ECPPs on industrial structure transformation, both positive and negative, and help the ecological civilization strategy system be adjusted promptly.

A dynamic multi-region MFD model for ride-sourcing with ridesplitting

Dynamic network-level models directly addressing ride-sourcing services can support the development of efficient strategies for both congestion alleviation and promotion of more sustainable mobility. Recent developments presented models focusing on ride-hailing (solo rides) and traditional ride-sharing, but no work addressed ridesplitting (a type of ride-sourcing service with shared rides) in dynamic contexts. Here, we sought to develop a dynamic aggregated traffic network model capable of representing ride-sourcing services and background traffic in a macroscopic multi-region urban network. We combined the Macroscopic Fundamental Diagram (MFD) with detailed state-space and transition descriptions of background traffic and ride-sourcing vehicles in their activities to formulate mass conservation equations. Accumulation-based MFD models might experience additional errors due to the variation profile of trip lengths, e.g., when vehicles cruise for passengers. We integrate the so-called M-model that utilizes the total remaining distance to capture dynamics of regional and inter-regional flows and accumulations for different vehicle (private or ride-sourcing) states. This aggregated model is capable to reproduce the dynamics of complex systems without using resource-expensive simulations. We also show that the model can accurately forecast the vehicles’ conditions in near-future predictions (e.g., 30 min ahead). Later, a comparison with benchmark models shows lower errors in the proposed model in all states. Finally, we evaluate the model’s robustness to noises in its inputs, and forecast errors remain below 15% even where inputs are 20% off the actual values for ride-sourcing vehicles. The development of this model prepares the path for developing real-time feedback-based management policies such as priority-based perimeter control or repositioning strategies for idle ride-sourcing vehicles and developing regulations over ride-sourcing in congested areas.

An alternative solution to congestion relief of U.S. seaports by container-on-barge: A simulation study

Port congestion has become a big challenge for many seaports in the United States (U.S.). There are multiple factors contributing to port congestion, such as the booming market of international container trading, limited space and capacity at a seaport, labor shortages, equipment failures, and supply chain disruptions (e.g., COVID-19 pandemic). Without appropriate management, port congestion will increase transportation costs, cause delays in goods movement and cargo delivery, reduce port operation efficiency, and lead to supply chain disruptions. Traditional strategies, such as infrastructure investment, truck appointments optimization, and party cooperation enhancement, have been implemented by ports for years, but the results are often unsatisfactory. To learn the needs of U.S. seaports and seek an efficient method for congestion relief, we develop a discrete event simulation model incorporating Container-on-Barge (COB) as an alternative transportation mode in this study. The simulation model mimics port operations involving COB and investigates congestion times at berth, yard, and gates. A case study on the Port of New Orleans (Port NOLA) is conducted to demonstrate the implementation of the simulation model and to evaluate the potential of COB for seaport congestion relief. The simulation results suggest that with a good level of COB development, the congestion at berth, yard and entrances of a seaport can be decreased significantly.

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

AbstractThe millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.

ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas.
 The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues.

A Novel Adaptive Restarting Genetic Algorithm for Transmission Congestion Alleviation in the Deregulated Power Market

A key concern in a deregulated power market (DPM) scenario is congestion on electricity transmission lines, which is one of the most pressing problems. The independent system operator (ISO) manages congestion to ensure the power system framework operates reliably and securely. Considering the context of the restructured power system’s inherent uncertainties, congestion management (CM) is critical in the operation and security of DPM. CM aims to ease congestion on the transmission lines while meeting all system constraints at the lowest possible congestion cost. This study employs a novel adaptive restarting genetic algorithm (ARGA) for rescheduling the generation to alleviate transmission congestion in the energy market. This work uses the proposed ARGA CM technique to reduce congestion costs by rescheduling generators’ active power output most cost-effectively. The participation of generation units in the CM is made possible by considering the generator sensitivity factors (GSF). IEEE 57-bus and IEEE 118-bus test systems are employed to evaluate the suggested methodology’s accuracy. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The proposed algorithm was tested in Python, and power flow analysis was done using the PANDAPOWER tool.

DeepGAL: Intelligent Vehicle Control for Traffic Congestion Alleviation at Intersections

Intersections are prone to congestion in urban areas and making competent speed plans for vehicles to efficiently utilize green time resources is significant for congestion alleviation and driving experience improvement. The key is to increase the chance of going through green lights and avoid idling at red lights. Existing works for velocity planning fail to fully consider various realistic traffic conditions, such as interfering traffic, free lane switch, and group efficiency, thus compromising effectiveness and limiting feasibility. To address all the aforementioned issues, we propose a novel method DeepGAL for vehicle control by dividing vehicles into groups, assigning a leader in each group and delivering intelligent control on leaders with deep reinforcement learning. Extensive experiments are conducted based on two real-world datasets with distinct traffic flow rates in Hangzhou, China, and the results demonstrate that DeepGAL achieves outstanding improvement over various performance metrics applied in five classic car following models considering realistic traffic conditions. Moreover, DeepGAL outperforms four state-of-the-art baseline methods over various metrics under both light and heavy real-world traffic flows. The test of DeepGAL with indeterministic traffic-signal phase and timing (SPAT) information under an adaptive traffic signal control indicates its effectiveness even with partial SPAT information. In addition, considering the practical issue that only some of the vehicles can be controlled by our scheme, we conduct simulations on different penetration levels of controlled leader vehicles, which demonstrate that even with only 10% penetration, DeepGAL can notably alleviate congestion and enhance driving experience at intersections, validating its great feasibility.

Dynamic Planning of Active Distribution Network's Wire and Nonwire Alternatives Considering Ancillary Services Market Participation

This article proposes a comprehensive ADN expansion planning model that includes both WAs and NWAs. The primary motivation is to find the optimal sizing and siting of NWAs while considering WAs such that the ADN's dynamically growing electrical load and EV demand are met while maintaining network limits. Given the flexibility of NWA resources, they can provide the support needed to the ADN (e.g., congestion relief and voltage support) while providing ASs to the grid. The proposed model is posed as a mixed-integer linear program to maximize the overall profits of the DSO. WAs considered include adding/upgrading lines and upgrading the substation by installing transformers, while NWAs include sizing and siting of distributed RESs, TGs, and BESSs with considering EVCSs supply the EVs. Simulation results demonstrate the financial benefits of simultaneously considering WAs and NWAs. They also verify the feasibility of having the DSO participate in the ASs market utilizing its flexible resources. To further assess the proposed model characteristics, additional cases and sensitivity analyses are conducted.

Travel demand management: The solution to public transit congestion? An ex-ante evaluation of staggered work hours schemes for the Paris region

This paper investigates the congestion relief potential of staggered work hours (SWH) schemes for public transit. An ex-ante evaluation framework is developed, which combines a hybrid assignment model with a travel demand management module to simulate the impact of SWH schemes on travel demand and public transit congestion. The key performance indicators capture not only congestion relief benefits, but also rescheduling costs for users. The methodology is applied to the RER A heavy rail line in Paris, the busiest public transit line in Europe. We find that SWH schemes generate congestion relief benefits, as intended, even matching up to telework policies. Yet, such benefits remain moderate, adding up to 20% of total crowding costs for the morning peak period at most. Moreover, substantial rescheduling costs are involved: decreasing the total time standing by 1 h implies shifting fifteen trips by the same amount of time. Regarding policy design, our results suggest that shifting few users by a large amount of time is usually more efficient than shifting many users by a smaller amount of time; the latter may even prove counterproductive by merely transferring the peak to some other time in the morning. We also find for the RER A that focusing the SWH scheme on a single station - the biggest trip attractor - yields benefits similar to applying the scheme over the whole line (for a similar level of effort in terms of total timeshift). The developed framework is intended to provide guidance as how to improve the design of future SWH schemes, in particular in times of social distancing and need for reduced crowding.

A Survey on Traffic Modeling and Mitigation Techniques

Abstract—The growing road traffic congestion in India is becoming more prevalent nowadays, the worst hit being the metropolitan and developed cities. Its ever-increasing nature poses a threat to development and requires quick action to help ease the troubles faced by people on a day-to-day basis. Modern technology offers a great aid to these efforts by providing mechanisms to model the travel patterns of a region and provide quick congestion relief by introducing adaptive traffic signals that operate based on the current traffic density of the road. In this paper, we present a survey of the most popular techniques involving travel demand modeling and adaptive signal control and compare the various methods employed by researchers to tackle both of these issues separately. The survey focuses on analyzing these methods from the perspective of implementation in an Indian scenario and offering a theoretical solution that involves the use of both these techniques to ease the problem of traffic congestion. Index Terms—road traffic congestion mitigation, adaptive traf- fic signal, travel demand modeling,

Continuous Network Design Using Partial Linearized Subgradient Methods

Although demand management has shown to be a vital tool in managing congestion, many metropolitan planning organizations (MPOs) and departments of transportation (DOTs) still pursue network modification and capacity increase as a congestion relief and a mitigation measure. The network design problem (NDP) still has, therefore, an essential and significant role in shaping and sizing urban transportation networks. The literature has traditionally treated the NDP as a bi-level mathematical programming problem or a mathematical program with equilibrium constraints (MPEC). In the bi-level optimization setting, the problem is approached as a leader-follower problem in which the lower level is a user equilibrium (UE) assignment problem as the follower, and the upper level is the network sizing problem as the leader problem. NDP has long been known as a challenging problem, and many solution algorithms have been proposed to solve it. This study proposed an efficient solution algorithm for the continuous network design problem (CNDP). The solution algorithm has been shown empirically to solve the CNDP in a shorter time using partial linearized subgradient methods. The proposed method was applied to a small network that was traditionally used to evaluate the performance of solution algorithms versus Braess’s paradox. It was then applied to the Sioux Falls network as a well-known benchmark network to compare the results with previous studies. The proposed method has been shown to run much faster than all the previous studies reviewed in this paper with minimal degradation of accuracy (0.52% lower than the best solution).

Development and Validation of a Load Flow Based Scheme for Optimum Placing and Quantifying of Distributed Generation for Alleviation of Congestion in Interconnected Power Systems

The energy supply entities widely adopt distributed generators (DG) to meet the additional power requirement due to scheduled or unscheduled interruptions. The expansion of transmission and distribution systems via the inclusion of loads and generators and the occurrence of line interruptions are significant causes of congestion of transmission lines in interconnected systems. The management and alleviation of congested lines is a primary requirement for a power system network’s reliable and efficient operation. The researchers investigated the potential scope of distributed generation (DG) to alleviate the congested branches in interconnected transmission systems. The development of a reliable scheme to arrive at the best location and size of local generators for alleviating congestion deserves considerable importance. This paper attempted to develop a simple and reliable strategy for the optimum placement and sizing of DGs to be integrated with a transmission line system of DGs for congestion relief in transmission lines by analyzing power flow solutions. This research work considered the 14-bus system of IEEE for the preliminary analysis to identify the parameters employed for assessing the severity of line congestion and the best placement and sizing of DGs for congestion relief. This work analyzed power flows by load flow algorithms using ETAP software in the 14-bus IEEE system for different line outage cases. The analysis of power flow solutions of the 14-bus system of IEEE revealed that the percentage violation of the system can be regarded as an essential parameter to assess the extent of congestion in an interconnected system. A detailed power flow analysis of the system with various capacities of DG integration at several buses in the system revealed the application of two indices, namely the index of severity (SI) and sensitivity factor (SF), for optimum placement with the best capacity of DGs for congestion alleviation in the system. This work proposed a reliable algorithm for the best siting and sizing of DGs for congestion relief by using the identified indices. The proposed methodology is system indices allied load flow-based algorithm. This work produced a fast simulation solution without any mismatch through this developed scheme. The approximations linked with the algorithm were very minute, resulting in comprehensive bests instead of inexact limited bests with less simulation time and more convergence probability and availing the benefits of the mathematical approach. The work investigated the feasibility of the proposed methodology for optimum placing and quantifying DGs for congestion solutions for a practical interconnected bus system in the supply entity of the Kerala grid with many buses. Any transmission system operator can adopt this method in similar connected systems anywhere. The proposed algorithm determined the most severe cases of congestion and the optimum site and size of DGs for managing congested feeders in the grid system. The analysis of the losses in the system for different cases of DG penetration by load flow analysis validated the suitability of the obtained results.

Application of whale algorithm optimizer for unified power flow controller optimization with consideration of renewable energy sources uncertainty

Purpose. In this paper an allocation methodology of Flexible Alternating Current Transmission Systems (FACTS) controllers, more specifically, the Unified Power Flow Controller (UPFC) is proposed. As the penetration of Renewable Energy Sources (RESs) into the conventional electric grid increases, its effect on this location must be investigated. Research studies have shown that the uncertainty of RESs in power generation influences the reactive power of a power system network and consequently its overall transmission losses. The novelty of the proposed work consists in the improvement of voltage profile and the minimization of active power loss by considering renewable energy sources intermittency in the network via optimal location of UPFC device. The allocation strategy associates the steady-state analysis of the electrical network, with the location and adjustment of controller parameters using the Whale Optimization Algorithm (WOA) technique. Methodology. In order to determine the location of UPFC, approaches are proposed based on identification of a line which is the most sensitive and effective with respect to voltage security enhancement, congestion alleviation as well as direct optimization approach. The optimum location of UPFC in the power system is discussed in this paper using line loading index, line stability index and optimization method. The objective function is solved using the WOA algorithm and its performance is evaluated by comparison with Particle Swarm Optimization (PSO) algorithm. Results. The effectiveness of the proposed allocation methodology is verified through the analysis of simulations performed on standard IEEE 30 bus test system considering different load conditions. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used to overcome the optimum location issue. Additionally, the results show that when UPFC device is strategically positioned in the electrical network and uncertainty of RES is considered, there is a significant influence on the overall transmission loss and voltage profile enhancements of the network.

Conspicuity of Various Colored Express Lane Delineators: Matching or Contrast?

Express lanes (ELs) are an increasingly accepted traffic management tool for congestion relief and traffic efficiency. White, yellow, and orange vertical delineators are typically used for lane separation and channelization applications. However, upcoming changes to sections of the Manual on Uniform Traffic Control Devices that would affect the current use of the orange retroreflective delineators will have a statewide impact on current and future ELs. The availability of a wide array of delineator colors and the potential range of application purposes brings into question this blanket requirement. The ineffective use of color in roadway design could lead to an increase in driving errors/response time, driver fatigue, loss of situational awareness, and increased workload. Driver perception and reaction time is shown to be affected by visual factors, such as color, luminance, distractions, and speed. The objective of this study is to determine the impact of color usage and other environmental and demographic factors on driver perception. A fractional factorial experiment was designed to study the EL delineator color selection using a driving simulator and an eye-tracking device. The potential factors included time of day (night versus day), visibility (high versus low), traffic density (high versus low), and road surface type (concrete versus asphalt). Some 176 subjects were selected to participate in the experiment. It was found that, in general, white was the quickest to be noticed, while black took the longest time to perceive. Furthermore, it was found that delineators were generally more noticeable under night-time conditions, likely because of the standard use of retroreflective striping on delineators.

Methods and Methodologies for Congestion Alleviation in the DPS: A Comprehensive Review

The modern power system has reached its present state after wading a long path facing several changes in strategies and the implementation of several reforms. Economic and geographical constraints led to reforms and deregulations in the power system to utilize resources optimally within the existing framework. The major hindrance in the efficient operation of the deregulated power system (DPS) is congestion, which is the result of the participation of private players under deregulation policies. This paper reviews different setbacks introduced by congestion and the methods applied/proposed to mitigate it. Technical and non-technical methods are reviewed and detailed. Major optimization techniques proposed to achieve congestion alleviation are presented comprehensively. This paper combines major publications in the field of congestion management and presents their contribution towards the alleviation of congestion.