Relevant bibliographies by topics / Highway on-ramp

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Highway on-ramp'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Highway on-ramp"

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Liu, Haobing, Shuyang Zhang, Guojun Chen, and Qian Gao. "Observed Characteristics and Modeled Emissions of Transit Buses on Ramps." Sustainability 12, no. 7 (April 1, 2020): 2770. http://dx.doi.org/10.3390/su12072770.

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On-ramps and off-ramps that serve as connections between high-speed facilities and arterials are potential hotspots for vehicle emissions. The engine load associated with grade and acceleration on uphill ramps can lead to significant emissions of criteria pollutants and greenhouse gases (GHGs) over a short distance. This study explores transit bus operations and emissions at ramps using Global Positioning System (GPS) data collected from Detroit transit buses. Ramp-associated operating data are extracted from the vehicle traces using ArcGIS and assigned to the applicable United States Environmental Protection Agency’s emission rates, i.e., EPA’s Motor Vehicle Emission Simulator (MOVES). The results show that transit bus emission rates for on-ramp operations at 40 mph (64.37 km/h) are about double the average emission rate on the MOVES highway cycles. For lower on-ramp speeds (< 64.37 km/h), as average speeds decrease, on-ramp emission rates drop roughly to the highway emission rates given the less aggressive acceleration noted in the data. Off-ramp emission rates are approximately half of the highway emission rates. The study also finds that post-ramp acceleration, right after buses enter the highway from the on-ramp, contributes to high emissions, because of the high-speed and high-power operations. This is true for the loop on-ramp, where the bus emission rate after entering the highway is higher than the emissions associated with driving on the ramp. On-ramp emissions are found to vary across a wide range of conditions, indicating that further study and more data are needed to explore the overall impacts of on-ramp and post-ramp activity in emissions modeling. A sensitivity analysis of ramp grade effect on emission indicates that ramp grade should be specifically considered in project-level analyses. The research results are useful for understanding ramp driving characteristics, the potential impacts of ramp grade on emissions, and the ramp hotspot analysis.
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Chiang, Tung-Chien, and Wen-June Wang. "Highway on-ramp Control using Fuzzy Decision Making." Journal of Vibration and Control 17, no. 2 (August 23, 2010): 205–13. http://dx.doi.org/10.1177/1077546309349854.

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Wang, Qiao-Ming, Rui Jiang, Xiao-Yan Sun, and Bing-Hong Wang. "Assigning on-ramp flows to maximize highway capacity." Physica A: Statistical Mechanics and its Applications 388, no. 18 (September 2009): 3931–38. http://dx.doi.org/10.1016/j.physa.2009.05.030.

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Xiao, Jinjian, Yubo Weng, and Yingna Xie. "Highway Ramp Radius Effects on Driving Safety Cognition." IOP Conference Series: Earth and Environmental Science 428 (January 17, 2020): 012079. http://dx.doi.org/10.1088/1755-1315/428/1/012079.

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Liu, Yugang, Hongbo Yi, Jiali Li, Bin Chen, and Hongtai Yang. "Design and Control Method of Switchable On- or Off-Ramp for Urban Highway." Journal of Advanced Transportation 2020 (September 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/7375416.

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High volume of traffic is loaded on urban highway during peak hours as well as the ramp connecting main road and auxiliary road of highway. Once the demand exceeds the capacity of the ramp, congestion may happen on both main road and the auxiliary road. To deal with this problem, an new urban highway ramp, switchable ramp (SR) that can switch between on and off condition, is proposed in this paper. A macroscopic time-space discrete model is built in this paper to model this process by dividing the studied time period into short time periods and the studied road segment into small sections. The impact of the type of ramp on upstream and downstream traffic flow is analyzed. A segment of urban highway in Chengdu, China, is used as a case study. The traffic flow data in peak hours is collected and used. Mathematical analysis proves that the SR system proposed in this paper can reduce the total travel time (TTT) in the studied area. Results show that the SR system is more useful when the traffic volume is high.
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Wang, Huanjie, Shihua Yuan, Mengyu Guo, Xueyuan Li, and Wei Lan. "A deep reinforcement learning-based approach for autonomous driving in highway on-ramp merge." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 10-11 (February 24, 2021): 2726–39. http://dx.doi.org/10.1177/0954407021999480.

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In this paper, we focus on the problem of highway merge via parallel-type on-ramp for autonomous vehicles (AVs) in a decentralized non-cooperative way. This problem is challenging because of the highly dynamic and complex road environments. A deep reinforcement learning-based approach is proposed. The kernel of this approach is a Deep Q-Network (DQN) that takes dynamic traffic state as input and outputs actions including longitudinal acceleration (or deceleration) and lane merge. The total reward for this on-ramp merge problem consists of three parts, which are the merge success reward, the merge safety reward, and the merge efficiency reward. For model training and testing, we construct a highway on-ramp merging simulation experiments with realistic driving parameters. The experimental results show that the proposed approach can make reasonable merging decisions based on the observation of the traffic environment. We also compare our approach with a state-of-the-art approach and the superior performance of our approach is demonstrated by making challenging merging decisions in complex highway parallel-type on-ramp merging scenarios.
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Poots, Jerry F. "Bumps on the entrance ramp to the information highway." Postgraduate Medicine 99, no. 6 (June 1996): 31–33. http://dx.doi.org/10.1080/00325481.1996.11946135.

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Lee, Ha Youn, Hyun-Woo Lee, and Doochul Kim. "Traffic states of a model highway with on-ramp." Physica A: Statistical Mechanics and its Applications 281, no. 1-4 (June 2000): 78–86. http://dx.doi.org/10.1016/s0378-4371(00)00021-2.

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Mufhidin, Amar, and Eviani Subiastuti. "Traffic Analysis Due To Development on Off Ramp Becakayu Highway." IJTI (International Journal of Transportation and Infrastructure) 4, no. 1 (September 30, 2020): 36–43. http://dx.doi.org/10.29138/ijti.v4i1.1163.

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The kalimalang highway is a connecting road between the city of Jakarta and Bekasi, the high mobility of passenger vehicles and goods is a problem of congestion that must be minimized. The volume of vehicles on the Kalimalang highway has increased quite high each year, but this has not been matched by growth in capacity. Therefore the government plants to build the Becakayu toll road to reduce congestion. This study aims to determine bottlenecks that occur due to the construction of an off-ramp on the Becakayu toll road, especially in section 2A, which is located on Jl. Veteran, while the location under review is the intersections affected by the construction of the off-ramp including the intersection of four signaling BCP, intersection of four signaling Veteran and Sarbini unsigned intersection, Data analysis in this study uses the MKJI 1997 method. From this method displays the behavior of traffic, for signal intersections will display signal time, capacity, vehicle stopped ratio, queue length, and average delay and for unsignalized intersections will display capacity, average delay and queue opportunity. From the results of this study it was found that the signaled intersection had an average value of DS > 1, so for the service level at the intersection is the current forced / jammed with low speed. Where the largest queue length occurred 1876.7 m, an average delay of 210.30 SMP/ sec, and vehicles stopped at an average of 7378 SMP/hour. And for unsignalized intersections having an average DS <0.45, the service level at the intersection is a stable flow with operating speed starting to be limited by traffic conditions, this intersection has the greatest queue probability (QP%) 8.48-20.35 in the Saturday time period - afternoon and intersection delay 9.5 SMP / sec.
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Du, Chuanhong, Licai Liu, Shuaishuai Shi, and Jiao An. "Prediction of Traffic Flow on Highway Ramp in Scenic Area." Journal of Physics: Conference Series 1646 (September 2020): 012057. http://dx.doi.org/10.1088/1742-6596/1646/1/012057.

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Dissertations / Theses on the topic "Highway on-ramp"

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Kherroubi, Zine el abidine. "Novel off-board decision-making strategy for connected and autonomous vehicles (Use case highway : on-ramp merging)." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE1331.

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L'insertion sur autoroute est un défi pour réaliser une conduite entièrement automatisée (Niveau 4 de conduite autonome). La combinaison des technologies de communication et de conduite autonome, qui sous-tend la notion de Connected Autonomous Vehicles (CAV), peut améliorer considérablement les performances de sécurité lors de l'insertion sur autoroute. Cependant, même avec l'émergence des véhicules CAVs, certaines contraintes clés doivent être prises en compte afin de réaliser une insertion sécurisée sur autoroute. Tout d'abord, les véhicules conduits par des conducteurs humains seront toujours présents sur la route, et il faudra peut-être des décennies avant que tous les véhicules commercialisés ne soient entièrement autonomes et connectés. Aussi, les capteurs embarqués des véhicules peuvent fournir des données inexactes ou incomplètes en raison des limites des capteurs et des angles morts, en particulier dans de telles situations de conduite critiques. Afin de résoudre ces problèmes, la présente thèse propose une nouvelle solution utilisant une unité de bord de route (Road-Side Unit (RSU)) permettant une insertion entièrement automatisée sur autoroute pour véhicules connectés et automatisés. Notre approche est basée sur un réseau de neurones artificiels (ANN) pour prédire l'intention des conducteurs. Cette prédiction est utilisée comme état d'entrée pour un agent Deep Reinforcement Learning (DRL) qui fournit l'accélération longitudinale pour le véhicule qui s'insère. Afin d'y parvenir, nous montrons d'abord comment l'unité Road-Side Unit peut-être utilisée pour améliorer la perception dans la zone d'insertion sur autoroute. Ensuite, nous proposons un modèle de reconnaissance d'intention du conducteur qui peut prédire le comportement des véhicules conduits par des conducteurs humains sur la voie principale de l'autoroute, avec une précision de 99%. Nous utilisons la sortie de ce modèle comme état d'entrée pour entrainer un agent Twin Delayed Deep Deterministic Policy Gradients (TD3) qui apprend une politique de conduite « sûre » et « coopérative » pour effectuer l'insertion sur autoroute. Nous montrons que notre stratégie de prise de décision améliore les performances par rapport aux solutions proposées dans l'état de l'art
Merging in the highway on-ramp is a significant challenge toward realizing fully automated driving (level 4 of autonomous driving). The combination of communication technology and autonomous driving technology, which underpins the notion of Connected Autonomous Vehicles (CAVs), may improve greatly safety performances when performing highway on-ramp merging. However, even with the emergence of CAVs vehicles, some keys constraints should be considered to achieve a safe on-ramp merging. First, human-driven vehicles will still be present on the road, and it may take decades before all the commercialized vehicles will be fully autonomous and connected. Also, on-board vehicle sensors may provide inaccurate or incomplete data due to sensors limitations and blind spots, especially in such critical situations. To resolve these issues, the present thesis introduces a novel solution that uses an off-board Road-Side Unit (RSU) to realize fully automated highway on-ramp merging for connected and automated vehicles. Our proposed approach is based on an Artificial Neural Network (ANN) to predict drivers’ intentions. This prediction is used as an input state to a Deep Reinforcement Learning (DRL) agent that outputs the longitudinal acceleration for the merging vehicle. To achieve this, we first show how the road-side unit may be used to enhance perception in the on-ramp zone. We then propose a driver intention model that can predict the behavior of the human-driven vehicles in the main highway lane, with 99% accuracy. We use the output of this model as an input state to train a Twin Delayed Deep Deterministic Policy Gradients (TD3) agent that learns « safe » and « cooperative » driving policy to perform highway on-ramp merging. We show that our proposed decision-making strategy improves performance compared to the solutions proposed previously
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Walker, Jonathan Bearnarr. "An Empirical Method of Ascertaining the Null Points from a Dedicated Short-Range Communication (DSRC) Roadside Unit (RSU) at a Highway On/Off-Ramp." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85151.

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The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle's onboard unit (OBU). The main research question asks: 'What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?' The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network's probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models.
Ph. D.
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Alkadri, Mohamed Yaser. "Freeway Control Via Ramp Metering: Development of a Basic Building Block for an On-Ramp, Discrete, Stochastic, Mesoscopic, Simulation Model within a Contextual Systems Approach." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/1308.

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One of the most effective measures of congestion control on freeways has been ramp metering, where vehicle entry to the freeway is regulated by traffic signals (meters). Meters are run with calibrated influx rates to prevent highway saturation. However, recent observations of some metering sites in San Diego, CA indicate that metering, during peak hour demand, is helping freeway flow while sometimes creating considerable traffic back-ups on local streets, transferring congestion problems from the freeway to intersections. Metering problems stem largely from the difficulty of designing an integrated, dynamic metering scheme that responds not only to changing freeway conditions but also to fluctuating demand throughout the ramp network; a scheme whose objective is to maintain adequate freeway throughput as well as minimize disproportionate ramp delays and queue overspills onto surface streets. Simulation modeling is a versatile, convenient, relatively inexpensive and safe systems analysis tool for evaluating alternative strategies to achieve the above objective. The objective of this research was to establish a basic building block for a discrete system simulation model, ONRAMP, based on a stochastic, mesoscopic, queueing approach. ONRAMP is for modeling entrance ramp geometry, vehicular generation, platooning and arrivals, queueing activities, meters and metering rates. The architecture of ONRAMP's molecular unit is designed in a fashion so that it can be, with some model calibration, duplicated for a number of ramps and, if necessary, integrated into some other larger freeway network models. SLAM.II simulation language is used for computer implementation. ONRAMP has been developed and partly validated using data from eight ramps at Interstate-B in San Diego. From a systems perspective, simulation will be short-sided and problem analysis is incomplete unless the other non-technical metering problems are explored and considered. These problems include the impacts of signalizing entrance ramps on the vitality of adjacent intersections, land use and development, "fair" geographic distribution of meters and metering rates throughout the freeway corridor, public acceptance and enforcement, and the role and influence of organizations in charge of decision making in this regard. Therefore, an outline of a contextual systems approach for problem analysis is suggested. Benefits and problems of freeway control via ramp metering, both operational short-term and strategic long-term, are discussed in two dimensions: global (freeway) and local (intersection). The results of a pilot study which includes interviews with field experts and law enforcement officials and a small motorist survey are presented.
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Rudjanakanoknad, Jittichai. "Increasing freeway merge capacity through on-ramp metering." 2005. http://repositories.cdlib.org/its/ds/ds2005rudjana/.

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Thesis (Ph. D. in Engineering)--University of California, Berkeley, 2005.
Includes bibliographical references (p. 64-66). Also available online via the University of California web site (http://repositories.cdlib.org/escholarship/).
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Lee, Hsing-Fei, and 李杏妃. "Fuzzy Decision Control on Highway''s Ramp Traffic Light." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/06717276012753821488.

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碩士
國立中央大學
電機工程研究所
88
Abstract This thesis utilizes the fuzzy control concept to design traffic light controls on the entrance ramp and on the intersection near exit ramp of highway, respectively, such that the traffic lights work well to maintain the smoothness of the traffic flow on the highway. In Chapter 2, the designed control includes fuzzy decision base (FDB) to estimate the probability of the light change and a decision function (DF) to decide the change making of the light. We only need one sensor to measure the speed and the speed change rate of the vehicles on the highway. Our simulation shows that the proposed control has a good performance. On the other hand, we know that the traffic jam on the highway is always caused from the heavy load in the exit ramp. Chapter 3 develops a fuzzy controller to decide both phase sequences and phase lengths of traffic signals adaptively according to the traffic conditions in the intersection near exit ramp. Moreover, it also guarantees the higher priority of vehicles in the exit ramp than those in the other intersection streets. Then, the traffic jam in the highway will be forbidden. Finally, we give a simulation to illustrate the proposed controller''s operation and its performance.
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Lu, Lukas, and 陸勇奇. "The Research of Speed Simulation and Ramp Management on Highway --- Taichung Area." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/41789991340832632411.

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碩士
東海大學
資訊工程與科學系碩士在職專班
91
It has been an important issue in vehicle flow management as traffic was tending saturation on Chung-Sung highway. The situation is getting well after second highway starting service. But, it still congested on holidays and on some commutations. Thus, a simulation system to imitate the flow variations on highway is urged for government institutions in making traffic regulation policies. There are many techniques practiced for vehicle flow management, including neural network, fuzzy theory, Markov Chain, regression analysis and so on. The traffic between two ramps resembled the flow in a pipe section with much more interactive actions. Since the correlation between two cross-sections has quite high dependence, that Markov Chain is a good choice to simulate traveling speed on highway. Our research uses Monte Carlo method to simulate flow speed based on section density of vehicles. At each speed scale, we summarize the section densities with a distribution function. The factors in calculation of section density include current density and speed, speed at next time point and on-ramp flow rate. We found the observed data followed by a normal distribution. A set of Fuzzy rules is developed for controlling ramp metering. The input variable included current speed, change rate of density and on-ramp flow rate. The simulation results showed that Fuzzy ramp metering rendered better service quality than both of no control imposing and fixed ramp metering policies. Key Words: System Simulation、Markov Chain、Fuzzy Theory、Highway Traffic
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Hsueh, Po-Yuan, and 薛博元. "A Study on Optimal Control of Integrating Variable Speed Limit and Ramp Metering on Highway." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/50109808055845695462.

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Book chapters on the topic "Highway on-ramp"

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Alimardani, Fatemeh, Nilesh Suriyarachchi, Faizan M. Tariq, and John S. Baras. "Models and Methods for Intelligent Highway Routing of Human-Driven and Connected-and-Automated Vehicles." In Transportation Systems for Smart, Sustainable, Inclusive and Secure Cities [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94332.

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Connected and automated vehicles (CAVs) have seen a rapid surge in interest over the past few years. A lot of focus is being placed on improving the efficiency and robustness of transportation systems by leveraging the sensors and capabilities of CAVs. However, the integration of CAVs into existing traffic infrastructure would give rise to certain issues that must be addressed before the CAVs can be seen ubiquitously on public roads. Since the highway networks are considered permanent investments that are expensive to build and maintain, the priority is to improve the efficiency of the current traffic system. This chapter explores the integration of two of the most common traffic management strategies, namely, ramp metering (RM) and route guidance (RG), into existing highway networks with human-driven vehicles (HDVs). The introduction of CAVs to public roads will engender issues pertaining to safe interactions between CAVs and HDVs. The later part of the chapter addresses the specific problems of improving highway on-ramp merging efficiency by optimally coordinating CAVs. The chapter concludes by presenting a scenario that requires an explicit consideration of interactions between HDVs and CAVs.
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Yehia Zakaria, Mohamed. "Unsteady Aerodynamics of Highly Maneuvering Flyers." In Biomimetics. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94231.

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In this chapter, a set of analytical aerodynamic models, based on potential flow, that can be used to predict the unsteady lift response during pitching maneuvers are presented and assessed. The result examines the unsteady lift coefficients experienced by a flat plate in high-amplitude pitch ramp motion. The pitch ramps are chosen based on two ramp pitch maneuvers of a maximum amplitudes of 25 and 45 degrees starting from zero degree. The aim is investigate the use of such classical models in predicting the lift dynamics compared to a full physical-based model. Among all classical methods used, the unsteady vortex lattice method (without considering the leading edge vortex) is found to be a very good predictor of the motion lift dynamic response for the 25 ° ramp angle case. However, at high pitch maneuvers (i.e.,the 45 ° ramp angle case), could preserve the response pattern with attenuated amplitudes without high computational burden. These mathematical analytical models presented in this chapter can be used to obtain a fast estimate for aircraft unsteady lift during pitch maneuvers instead of high fidelity models, especially in the early design phases.
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Dey, Bishwajit, Biplab Bhattacharyya, and Sharmistha Sharma. "Robust Economic Dispatch of Microgrid With Highly Penetrated Renewables and Energy Storage System." In Research Anthology on Clean Energy Management and Solutions, 303–23. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9152-9.ch014.

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Economic dispatch (ED) of a grid-connected and renewable integrated microgrid is considered in this article. Here, the renewable energy sources (RES) taken into consideration are wind farms. A parameter worst-case-transaction-cost which arises due to the stochastic availability and uncontrollable nature of wind farms is also emphasised and efforts have been taken to minimize it too. Hence, the article focuses on separately optimizing the generation costs and the worst-case transaction costs. It also optimises the net microgrid cost as a whole, which is the summation of generation costs and the worst-case transaction costs. Two different cases with highly varying transaction prices are studied. Various types of loads, ramp rates of generators, charging and discharging limits of the batteries are taken into consideration while minimizing the microgrid cost. Four meta-heuristic soft computing algorithms are applied for optimization and a comparative analysis among them is studied. Numerical results are tabulated to justify the effectiveness of the novel approach.
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Conference papers on the topic "Highway on-ramp"

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LEI, JIA, and UMIT OZGUNER. "HIGHWAY ON-RAMP CONTROL USING SLIDING MODES." In Proceedings of the 6th IEEE International Workshop on Variable Structure Systems. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812792082_0042.

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Gou, Chen-xi, Bai-gen Cai, and ShangGuan Wei. "Research on highway ramp control based on Cellular Automata." In 2010 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2010. http://dx.doi.org/10.1109/icma.2010.5589098.

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Wang, Tengjiao, Xinrong Liang, and Chaojun Dong. "Highway Ramp Metering Based on CMAC-PID Compound Control." In 2018 International Conference on Engineering Simulation and Intelligent Control (ESAIC). IEEE, 2018. http://dx.doi.org/10.1109/esaic.2018.00037.

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Shen, Minghao, Hanyang Hu, Bohua Sun, and Weiwen Deng. "Heuristics Based Cooperative Planning for Highway On-Ramp Merge." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569341.

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Wang, Ziran, Guoyuan Wu, and Matthew Barth. "Distributed Consensus-Based Cooperative Highway On-Ramp Merging Using V2X Communications." In WCX World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1177.

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Lee, H. Y. "Phase diagram of congested traffic flow on a highway with one on-ramp." In Third tohwa university international conference on statistical physics. AIP, 2000. http://dx.doi.org/10.1063/1.1291617.

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Xin Ci Wong, Jacqueline Anna James, Agileswari K. Ramasamy, and Syed Khaleel Ahmed. "Effect of highway ramp separation on traffic flow: An investigation using Monte Carlo simulation." In 2010 Student Conference on Research and Development (SCOReD). IEEE, 2010. http://dx.doi.org/10.1109/scored.2010.5704004.

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Schester, Larry, and Luis E. Ortiz. "Longitudinal Position Control for Highway On-Ramp Merging: A Multi-Agent Approach to Automated Driving." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8916951.

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Hadded, Mohamed, Pierre Merdrignac, Sacha Duhamel, and Oyunchimeg Shagdar. "Security attacks impact for collective perception based roadside assistance: A study of a highway on-ramp merging case." In 2020 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020. http://dx.doi.org/10.1109/iwcmc48107.2020.9148235.

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Yoshida, Masashi, Hiromu Asahina, Hiroshi Shigeno, and Iwao Sasase. "A Scheduling Scheme for Cooperative Merging at a Highway On-Ramp with Maximizing Average Speed of Automated Vehicles." In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-fall49728.2020.9348793.

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