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Relevant bibliographies by topics / Area traffic signal control

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Academic literature on the topic 'Area traffic signal control'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Area traffic signal control"

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Liang, Zi Yi, Xin Nan Qu, and Xiang Hui Zhao. "Exploration Based on ZigBee Traffic Signals Area Control System." Applied Mechanics and Materials 135-136 (October 2011): 535–39. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.535.

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In order to solve the problem of traffic jams, this paper presents a solution about traffic signals area control system based on ZigBee wireless sensor network. The basic idea is to connect the area's traffic lights through ZigBee wireless sensor network according to the traffic flow during a time period where the system located. The command center coordinates and controls the length of the green signal, period and the time of the green signal on all roads through the ZigBee wireless sensor network, so that the motor vehicles which move from corresponding two-way intersections could get through with less red signal. The solution adopts TI's CC2430 RF transceiver chip and analog front-end chip CC2591 and some external circuits to design the hardware circuit of sensor nodes and the central control nodes. Then designs the applications about sensor nodes, central control nodes and management software of control center, which based on the ZigBee protocol stack. Test results show that the performance of the system, such as stability, response speed, could meet the actual demand. It can obviously improve the highway capacity.

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Kondratov, Ivan Vladimirovich. "DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS." Chronos 6, no. 7(57) (July 13, 2021): 16–18. http://dx.doi.org/10.52013/2658-7556-57-7-6.

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Real-time adaptive traffic control is an important problem in modern world. Historically, various optimization methods have been used to build adaptive traffic signal control systems. Recently, reinforcement learning has been advanced, and various papers showed efficiency of Deep-Q-Learning (DQN) in solving traffic control problems and providing real-time adaptive control for traffic, decreasing traffic pressure and lowering average travel time for drivers. In this paper we consider the problem of traffic signal control, present the basics of reinforcement learning and review the latest results in this area.

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Xia, Xiaomei, Xiaodan Ma, and Jin Wang. "Control Method for Signalized Intersection with Integrated Waiting Area." Applied Sciences 9, no. 5 (March 7, 2019): 968. http://dx.doi.org/10.3390/app9050968.

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To alleviate traffic congestion in the city, an integrated waiting area is introduced to the signalized intersection in this paper. After the design idea and the typical form of the integrated waiting area is proposed, the control method at the signalized intersection is discussed. The coordination control process of the main and pre-signal at the signalized intersection with the integrated waiting area is analyzed and modeled. To assess the operational performance of the integrated waiting area at intersections, a microscopic traffic simulation software (VISSIM) is utilized to simulate intersections with and without integrated waiting areas. Key issues concerning signal timing plans are then discussed. With comparisons between the operation of intersections with and without integrated waiting areas, the implementation effect is quantified based on the statistical data of the simulation result. The results confirm the potential benefits of the integrated waiting areas at the signalized intersections and show that integrated waiting areas work best in heavy traffic demand.

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Sekiyama, Kosuke, and Yasuhiro Ohashi. "Distributed Route Guidance Systems with Self-Organized Multi-Layered Vector Fields." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (March 20, 2005): 106–13. http://dx.doi.org/10.20965/jaciii.2005.p0106.

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This paper deals with novel distributed route guidance that cooperates with self-organizing control of traffic signal networks. Self-organizing control of traffic signals provides a fully distributed approach to coordinate a number of signals distributed in a wide area based on local information of traffic flows so that split and offset control parameters between traffic signals are adjusted for efficient traffic flow. The self-organizing route guidance systems (SRGS) concept is introduced for efficient route guidance to facilitate offset adjustment of the self-organizing control of signal networks by self-organizing multilayered vector fields. Simulation demonstrates the effectiveness of the proposal under nonstationary traffic conditions.

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Zhang, Xin Jie, and Jing Fei Yu. "A Study on Linear Coordinated Control of Intersection Signal in Youyi Street of Baotou City." Applied Mechanics and Materials 361-363 (August 2013): 2240–43. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2240.

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As the changing situations of the traffics, according to demanded traffic speed and the distance between two adjacent intersections, a multi-scheme continuous entrance system is taken. The method determine an appropriate time difference in order that vehicles travel in appropriate speed will continuous meet the green lights cross by cross. Furthermore, studies on linear coordinated intersection signal control is the basis of research of area traffic signal control.

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Daneshfar, Fatemeh, and Javad RavanJamJah. "A New Design of Intelligent Traffic Signal Control." International Journal of Fuzzy System Applications 3, no. 3 (July 2013): 51–67. http://dx.doi.org/10.4018/ijfsa.2013070103.

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Dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This paper proposed an adaptive and cooperative multi-agentfuzzy system for a decentralized traffic signal control. The proposed model has three levels of control, the current intersection traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the current intersection traffic pattern. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. Also every intersection flow is predicted in two different ways: 1- through a recursive algorithm. 2- based on a two stage fuzzy clustering algorithm. The proposed solution is tested with traffic control of a large connected junction and the result obtained is promising in comparison to the conventional fixed sequence traffic signal and to the vehicle actuated traffic signal control strategies which are the most applicable strategies in this area. Also to simulate the proposed traffic control solutions, a Netlogo-based traffic simulator has been developed as the agents’ world which simulates the roads, traffic flow and intersections.

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Pranevičius, Henrikas, and Tadas Kraujalis. "KNOWLEDGE BASED TRAFFIC SIGNAL CONTROL MODEL FOR SIGNALIZED INTERSECTION." TRANSPORT 27, no. 3 (September 19, 2012): 263–67. http://dx.doi.org/10.3846/16484142.2012.719545.

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Intelligent transportation systems have received increasing attention in academy and industry. Being able to handle uncertainties and complexity, expert systems are applied in vast areas of real life including intelligent transportation systems. This paper presents a traffic signal control method based on expert knowledge for an isolated signalized intersection. The proposed method has the adaptive signal timing ability to adjust its signal timing in response to changing traffic conditions. Based on the traffic conditions, the system determines to extend or terminate the current green signal group. Using the information from its traffic detectors of isolated intersection, the proposed controller gives optimal signals to adapt the phase lengths to the traffic conditions. A comparative analysis between proposed control algorithm, fuzzy logic (FLC) and fixed-timed (pre-timed) controllers has been made in traffic flows control, with varying traffic volume levels, by using simulation software ‘Arena’. Simulation results show that the proposed traffic signal control method (EKC) has better performance over fuzzy logic and conventional pre-time controllers under light and heavy traffic conditions.

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Wang, Yun Xia, and Dong Bo Liu. "Research on the Method of Setting Waiting Area for Non-motor Vehicle at Signal Control Intersection." E3S Web of Conferences 38 (2018): 03021. http://dx.doi.org/10.1051/e3sconf/20183803021.

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Electric bicycle has become an indispensable important component of the transportation system. The fact is that traffic organization and channelizing design of signal control intersection is not intensive, which cannot adapt to the current traffic demand of non-motor vehicle, such as unclear traffic rules and poor visibility, thus the traffic safety of non-motor vehicle is not optimistic. Therefore, it is necessary to study on traffic organization method based on the demand of non-motor vehicle, which can provide certain theoretical basis for traffic administrative department to make policy and traffic design. This article focuses on the method of setting waiting area for non-motor vehicle at signal control intersection, including the advantages, disadvantages and the applicable conditions.

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Gartner, Nathan H., and Mohammed Al-Malik. "Combined Model for Signal Control and Route Choice in Urban Traffic Networks." Transportation Research Record: Journal of the Transportation Research Board 1554, no. 1 (January 1996): 27–35. http://dx.doi.org/10.1177/0361198196155400104.

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Traffic signals have a significant effect on the choice of routes by motorists in urban areas. They are of primary importance in the development of advanced traffic management strategies that involve dynamic rerouting of traffic flows through signal-controlled street networks. A combined network model that simultaneously accounts for both the route choices made by motorists and the desired signal controls to match these choices is presented. Given origin-destination travel demand information, the model generates signal controls to optimize network performance and calculates the resulting traffic volumes in the network. This optimization model inherently reflects the mutual consistency between traffic flows and signal controls. The model is applicable to both fixed-time and demand-responsive signals. Computational procedures and sample network solutions are presented.

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Zhang, Zhenghua, Jin Qian, Chongxin Fang, Guoshu Liu, and Quan Su. "Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game." Wireless Communications and Mobile Computing 2021 (June 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/6693636.

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In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its learning ability. The distributed multiagent RL (MARL) can avoid this kind of problem by observing some areas of each local RL in the complex plane traffic area. However, due to the limited communication capabilities between each agent, the environment becomes partially visible. This paper proposes multiagent reinforcement learning based on cooperative game (CG-MARL) to design the intersection as an agent structure. The method considers not only the communication and coordination between agents but also the game between agents. Each agent observes its own area to learn the RL strategy and value function, then concentrates the Q function from different agents through a hybrid network, and finally forms its own final Q function in the entire large-scale transportation network. The results show that the proposed method is superior to the traditional control method.

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Dissertations / Theses on the topic "Area traffic signal control"

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Chiou, Suh-Wen. "Optimisation of area traffic control for equilibrium network flows." Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299926.

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Wong, Sze Chun. "Phase-based optimisation of signal timings for area traffic control." Thesis, University College London (University of London), 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262573.

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Al-Mudhaffar, Azhar. "Impacts of Traffic Signal Control Strategies." Doctoral thesis, Stockholm : Division of transports and logistics, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4268.

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Niittymäki, Jarkko. "Fuzzy traffic signal control principles and applications /." Espoo, Finland : Helsinki University of Technology, 2002. http://lib.hut.fi/Diss/2002/isbn9512257017/isbn9512257017.pdf.

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Dissertation for the degree of Doctor of Science in Technology--Helsinki University of Technology, Espoo, 2002.
"ISSN 0781-5816." Includes bibliographical references (p. 65-71). Available online as a PDF file via the World Wide Web.

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Renfrew, David T. "TRAFFIC SIGNAL CONTROL WITH ANT COLONY OPTIMIZATION." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/190.

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Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users’ delays. Ant Colony Optimization (ACO) is a metaheuristic based on the behavior of ant colonies searching for food. ACO has successfully been used to solve many NP-hard combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic flow networks. This thesis investigates the application of ACO to minimize user delay at traffic intersections. Computer simulation results show that this new approach outperforms conventional fully actuated control under the condition of high traffic demand.

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Cadet, Gerard Nivard. "Traffic signal control - a neural network approach." FIU Digital Commons, 1996. http://digitalcommons.fiu.edu/etd/1963.

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Artificial Neural Networks (ANNs) have been proven to be an important development in a variety of problem solving areas. Increasing research activity in ANN applications has been accompanied by equally rapid growth in the commercial mainstream use of ANNs. However, there is relatively little research of practical application of ANNs taking place in the field of transportation engineering. The central idea of this thesis is to use Artificial Neural Network Software Autonet in connection with Highway Capacity Software to estimate delay. Currently existing signal control system are briefly discussed and their short coming presented. As a relative new mathematical model, Neural Network offers an attractive alternative and hold considerable potential for use in traffic signal control. It is more adaptive to the change in traffic patterns that take place at isolated intersections. ANN also provides the traffic engineer more flexibility in term of optimizing different measures of effectiveness. This thesis focuses on a better quality signal control system for traffic engineering using Artificial Neural Networks. An analysis in terms of mean, variance and standard deviation of the traffic data is also presented.

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Coeymans-Avaria, Juan Enrique. "Traffic signal systems in a developing country." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305939.

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Chow, Lee-Fang. "Integrating adaptive queue-responsive traffic signal control with dynamic traffic assignment." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001280.

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Cai, C. "Adaptive traffic signal control using approximate dynamic programming." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/20164/.

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This thesis presents a study on an adaptive traffic signal controller for real-time operation. An approximate dynamic programming (ADP) algorithm is developed for controlling traffic signals at isolated intersection and in distributed traffic networks. This approach is derived from the premise that classic dynamic programming is computationally difficult to solve, and approximation is the second-best option for establishing sequential decision-making for complex process. The proposed ADP algorithm substantially reduces computational burden by using a linear approximation function to replace the exact value function of dynamic programming solution. Machine-learning techniques are used to improve the approximation progressively. Not knowing the ideal response for the approximation to learn from, we use the paradigm of unsupervised learning, and reinforcement learning in particular. Temporal-difference learning and perturbation learning are investigated as appropriate candidates in the family of unsupervised learning. We find in computer simulation that the proposed method achieves substantial reduction in vehicle delays in comparison with optimised fixed-time plans, and is competitive against other adaptive methods in computational efficiency and effectiveness in managing varying traffic. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised. The proposed ADP algorithm is in compliance with a range of discrete systems of resolution from 0.5 to 5 seconds per temporal step. This study demonstrates the readiness of the proposed approach for real-time operations at isolated intersections and the potentials for distributed network control.

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Wang, Qichao. "Street Traffic Signal Optimal Control for NEMA Controllers." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101552.

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This dissertation aims to reduce urban traffic congestion with street traffic signal control. The traffic signal controllers in the U.S. follow the National Electrical Manufacturing Association Standards (NEMA Standards). In a NEMA controller, the control parameters for a coordinated control are cycle, green splits, and offset. This dissertation proposed a virtual phase-link concept and developed a macroscopic model to describe the dynamics of a traffic network. The coordinated optimal splits control problem was solved using model predictive control. The outputs of the solution are the green splits that can be used in NEMA controllers. I compared the proposed method with a state-of-the-practice signal timing software under coordinated-actuated control settings. It was found that the proposed method significantly outperformed the benchmarking method. I compared the proposed NEMA-based virtual phase-link model and a Max Pressure controller model using Vissim. It was found that the virtual phase-link method outperformed two control strategies and performed close, but not as good as, the Max Pressure control strategy. The disadvantage of the virtual phase-link method stemmed from the waste of green time during a fixed control cycle length and the delay which comes from the slowing down of platoon during a road link to allow vehicles to switch lanes. Compared to the Max Pressure control strategy, the virtual phase-link method can be implemented by any traffic controller that follows the NEMA standards. The real-time requirement of the virtual phase-link method is not as strict as the Max Pressure control strategy. I introduced the offsets optimization into the virtual phase-link method. I modeled the traffic arrival pattern based on the optimization results from the virtual phase-link control method. I then derived a phase delay function based on the traffic arrival pattern. The phase delay function is a function of the offset between two consecutive intersections. This phase delay function was then used for offsets optimization along an arterial. I tested the offsets optimization method against a base case using microscopic simulations. It was found that the proposed offset optimization method can significantly reduce vehicle delays.
Doctor of Philosophy

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Books on the topic "Area traffic signal control"

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Balke, Kevin N. Operational and institutional agreements that facilitate regional traffic signal operations. Washington, D.C: Transportation Research Board, 2011.

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Souleyrette, Reginald R. Guidelines for removal of traffic control devices in rural areas. Ames, Iowa: Center for Transportation Research and Education, Iowa State University, 2005.

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Kraft, Walter H. Traffic signal control systems maintenance management pratices. Washington, D.C: National Academy Press, 1997.

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Yauch, Peter J. Traffic signal control equipment: State of the art. Washington, D.C: Transportation Research Board, National Research Council, 1990.

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Dirck, Van Vliet, ed. Route choice and signal control: The potential for integrated route guidance. Aldershot, Hants: Avebury, 1992.

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Vincent, R. A. 'MOVA': Traffic responsive, self-optimising signal control for isolated intersections. Crowthorne: Transport and Road Research Laboratory, 1988.

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Vincent, R. A. 'Mova': Traffic responsive, self-optimising signal control for isolated intersections. Crowthorne, Berks: Transport and Road Research Laboratory, Traffic Group, Traffic Management Division, 1988.

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Gordon, Robert L. Traffic signal retiming practices in the United States. Washington, D.C: Transportation Research Board, 2010.

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Park, Byungkyu. Evaluation of pre-emption and transition strategies for Northern Virginia Smart Traffic Signal Systems (NVSTSS). Charlottesville, Va: Virginia Transportation Research Council, 2008.

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Initiative, Indonesia Infrastructure. Tinjauan mobilitas perkotaan dan penerapan area traffic control system di Surabaya. [Jakarta]: Indonesia Infrastructure Initiative, 2010.

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Book chapters on the topic "Area traffic signal control"

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Salter, R. J. "Introduction to Traffic Signal Control." In Traffic Engineering, 79–85. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_20.

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Salter, R. J. "Signal control strategies." In Highway Traffic Analysis and Design, 277–79. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-20014-6_32.

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Salter, R. J. "Signal control strategies." In Highway Traffic Analysis and Design, 286–91. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13423-6_32.

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Kosonen, Iisakki, and Xiaoliang Ma. "Traffic Signal Control with Autonomic Features." In Autonomic Road Transport Support Systems, 253–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25808-9_15.

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Yamane, Satoshi, Kazuhiro Okada, Kenji Shinoda, and Oshima Kenji. "Traffic Signal Control Using Multi-layered Fuzzy Control." In Rough Sets and Current Trends in Computing, 171–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-69115-4_24.

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Schweiger, Benno, Regina Glas, Christian Raubitschek, and Johann Schlichter. "Traffic Signal Information in a Real Residential Area." In Lecture Notes in Electrical Engineering, 101–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33838-0_9.

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Cantarella, G. E., and A. Sforza. "Methods for Equilibrium Network Traffic Signal Setting." In Flow Control of Congested Networks, 69–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-86726-2_5.

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Al-Malik, Mohammed, and Nathan H. Gartner. "Development of a Combined Traffic Signal Control-Traffic Assignment Model." In Urban Traffic Networks, 155–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79641-8_6.

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Zeng, Xiaoqing, Chaoyang Wu, Yujia Chen, Qipeng Xiong, and Cong Wei. "Research on the Model of Traffic Signal Control and Signal Coordinated Control." In International Symposium for Intelligent Transportation and Smart City (ITASC) 2017 Proceedings, 64–76. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3575-3_7.

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Chen, Yong, Juncheng Yao, Chunjiang He, Hanhua Chen, and Hai Jin. "Adaptive Traffic Signal Control with Network-Wide Coordination." In Algorithms and Architectures for Parallel Processing, 180–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65482-9_12.

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Conference papers on the topic "Area traffic signal control"

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Krylatov, Alexander Y., Victor V. Zakharov, and Igor G. Malygin. "Signal control in a congested traffic area." In 2015 International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP). IEEE, 2015. http://dx.doi.org/10.1109/scp.2015.7342176.

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Guo, Jingjing, Qipeng Xiong, and Sheng Chen. "Division Approach of Traffic Signal Control Sub-Area." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5363143.

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Muraki, Yuji, and Hitoshi Kanoh. "Wide-area Traffic Signal Control Using Predicted Traffic Based on Real-time Information." In 2008 11th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2008. http://dx.doi.org/10.1109/itsc.2008.4732610.

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Thunig, Theresa, and Kai Nagel. "Towards a robust and wide-area traffic signal control for inner-city areas." In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2017. http://dx.doi.org/10.1109/mtits.2017.8005622.

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Hu, Xizhen, Chongchao Huang, and Aihua Luo. "An Area Traffic Signal Optimum Timing Control Model in Mixed Traffic Flows and Algorithm." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.913.

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Kiseok Sung. "A genetic algorithm to optimise signal phasing in area traffic control." In Second International Conference on Genetic Algorithms in Engineering Systems. IEE, 1997. http://dx.doi.org/10.1049/cp:19971214.

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Tan, Kai Liang, Subhadipto Poddar, Soumik Sarkar, and Anuj Sharma. "Deep Reinforcement Learning for Adaptive Traffic Signal Control." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9076.

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Abstract Many existing traffic signal controllers are either simple adaptive controllers based on sensors placed around traffic intersections, or optimized by traffic engineers on a fixed schedule. Optimizing traffic controllers is time consuming and usually require experienced traffic engineers. Recent research has demonstrated the potential of using deep reinforcement learning (DRL) in this context. However, most of the studies do not consider realistic settings that could seamlessly transition into deployment. In this paper, we propose a DRL-based adaptive traffic signal control framework that explicitly considers realistic traffic scenarios, sensors, and physical constraints. In this framework, we also propose a novel reward function that shows significantly improved traffic performance compared to the typical baseline pre-timed and fully-actuated traffic signals controllers. The framework is implemented and validated on a simulation platform emulating real-life traffic scenarios and sensor data streams.

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Yu, Chenmu, Shuhua Liu, Yu Zhang, and Jie Liu. "Research on AI Planning Based Modeling Method for Area Traffic Signal Control." In 2009 International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2009. http://dx.doi.org/10.1109/icmtma.2009.224.

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Ciyun Lin, Zhaosheng Yang, and Bowen Gong. "Dynamically combined and separated of sub-zone in adaptive traffic signal control area." In 2008 IEEE International Conference on Automation and Logistics (ICAL). IEEE, 2008. http://dx.doi.org/10.1109/ical.2008.4636469.

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Chen, Dan, and Xiaohong Gao. "Study on Intelligent Control of Traffic Signal of Urban Area and Microscopic Simulation." In Eighth International Conference of Chinese Logistics and Transportation Professionals (ICCLTP). Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/40996(330)671.

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Reports on the topic "Area traffic signal control"

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Lafferriere, Gerardo. Traffic Signal Consensus Control. Transportation Research and Education Center (TREC), 2019. http://dx.doi.org/10.15760/trec.213.

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Lafferriere, Gerardo. Traffic Signal Consensus Control. Transportation Research and Education Center (TREC), 2019. http://dx.doi.org/10.15760/trec.221.

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Aziz, H. M. Abdul, Hong Wang, Stanley Young, and SMA Bin al islam. Investigating the Impact of Connected Vehicle Market Share on the Performance of Reinforcement-Learning Based Traffic Signal Control. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1566974.

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Simms, Janet, Benjamin Breland, and William Doll. Geophysical investigation to assess condition of grouted scour hole : Old River Control Complex—Low Sill Concordia Parish, Louisiana. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41863.

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Abstract:

Geophysical surveys, both land-based and water-borne, were conducted at the Old River Control Complex‒Low Sill, Concordia Parish, LA. The purpose of the surveys was to assess the condition of the grout within the scour region resulting from the 1973 flood event, including identification of potential voids within the grout. Information from the ground studies will also be used for calibration of subsequent marine geophysical data and used in stability analysis studies. The water-borne survey consisted of towed low frequency (16-80 MHz) ground penetrating radar (GPR), whereas the land-based surveys used electrical resistivity and seismic refraction. The GPR survey was conducted in the Old River Channel on the upstream side of the Low Sill structure. The high electrical conductivity of the water (~50 mS/m) precluded penetration of the GPR signal; thus, no useful data were obtained. The land-based surveys were performed on both northeast and southeast sides of the Low Sill structure. Both resistivity and seismic surveys identify a layered subsurface stratigraphy that corresponds, in general, with available borehole data and constructed geologic profiles. In addition, an anomalous area on the southeast side was identified that warrants future investigation and monitoring.

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Li, Howell, Enrique Saldivar-Carranza, Jijo K. Mathew, Woosung Kim, Jairaj Desai, Timothy Wells, and Darcy M. Bullock. Extraction of Vehicle CAN Bus Data for Roadway Condition Monitoring. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317212.

Full text

Abstract:

Obtaining timely information across the state roadway network is important for monitoring the condition of the roads and operating characteristics of traffic. One of the most significant challenges in winter roadway maintenance is identifying emerging or deteriorating conditions before significant crashes occur. For instance, almost all modern vehicles have accelerometers, anti-lock brake (ABS) and traction control systems. This data can be read from the Controller Area Network (CAN) of the vehicle, and combined with GPS coordinates and cellular connectivity, can provide valuable on-the-ground sampling of vehicle dynamics at the onset of a storm. We are rapidly entering an era where this vehicle data can provide an agency with opportunities to more effectively manage their systems than traditional procedures that rely on fixed infrastructure sensors and telephone reports. This data could also reduce the density of roadway weather information systems (RWIS), similar to how probe vehicle data has reduced the need for micro loop or side fire sensors for collecting traffic speeds.

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