Relevant bibliographies by topics / Traffic signals

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Traffic signals'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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Journal articles on the topic "Traffic signals"

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Woscholski, Rüdiger, and Peter J. Parker. "Inositol lipid 5-phosphatases-traffic signals and signal traffic." Trends in Biochemical Sciences 22, no. 11 (1997): 427–31. http://dx.doi.org/10.1016/s0968-0004(97)01120-1.

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Behzadi, Saeed. "AN INTELLIGENT LOCATION AND STATE REORGANIZATION OF TRAFFIC SIGNAL." Geodesy and cartography 46, no. 3 (2020): 145–50. http://dx.doi.org/10.3846/gac.2020.10806.

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In all geo-database related to traffic, beside storing roads data, the information associated to traffic signals such as location, types of traffic signals, side street name, and so on are also stored in that database. In reality, the reason of defining traffic signals for road is the situations and conditions which the roads have. So the existence of traffic signals in the network is related to the parameters of the road. In this paper, instead of storing traffic signal data in the database, a novel method is introduced which implemented on the road network. As a result, the spatial and non-spatial information of traffic signals in the network are extracted based on the location and attribute of the road network. The proposed method is implemented on the network; the result of the intelligent method is compared with the traffic signals information which stored in the database. By comparing the locations and states of proposed traffic signals and the real ones, the overall accuracy for recognizing locations of traffic signal is obtained 94% and the overall accuracy for recognizing states of traffic signal is obtained 89%.
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Patil, Vrushal. "Traffic Signal Pattern Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 126–28. http://dx.doi.org/10.22214/ijraset.2023.57249.

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Abstract: Every day we are witnessing a rapid increase in traffic volume on roads. Traffic signals are made to manage the traffic to get less disturbance during the journey and to avoid collisions. Sometimes these traffic signals might become a reason for a delay due to poor time management at signal timings. The old traffic signal patterns are the main cause of this issue and hence this project of new signalling patterns will help in using traffic signals more efficiently. In the traditional pattern at a crossover only one signal can be opened but using our pattern algorithm more than one signal can be opened and traffic could clear more easily. Even concepts of image processing are used to make the system more automated and intelligent.
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Cui, Naizhong. "Optimization Strategies for Traffic Signal and Identification Design." Frontiers in Science and Engineering 5, no. 2 (2025): 92–98. https://doi.org/10.54691/nvmq1d61.

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This article deeply studies how to improve the effectiveness of traffic signal and roadway signage design, pointing out some shortcomings in current design, including the lack of rationality in signal configuration, low recognition, and ineffective coordination with the surrounding road environment. In response to these issues, scientific layout and planning of traffic signals, enhancing the recognizability of signals and signs, improving the compatibility between signals and roads, and promoting the development and application of intelligent traffic signal systems have been proposed. Intended to increase traffic flow continuity, reduce traffic accident rates, and enhance road safety.
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Williams, Ruth. "Ubiquitin traffic signals." Journal of Cell Biology 185, no. 3 (2009): 372. http://dx.doi.org/10.1083/jcb.1853iti2.

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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 (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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Tomar, Ishu, Indu Sreedevi, and Neeta Pandey. "State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends." Electronics 11, no. 3 (2022): 465. http://dx.doi.org/10.3390/electronics11030465.

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The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.
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Rebezyuk, Leonid M., and Mykhailo Lytvynenko. "Expert information processing system for traffic light system decision-making with adaptive conditional tram priority." Management Information System and Devises, no. 179 (November 27, 2023): 17–24. http://dx.doi.org/10.30837/0135-1710.2023.179.017.

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The expert information processing system was proposed for a traffic signal system at a multimodal intersection with three types of traffic users: public transport (tram), pedestrians, and general traffic. The system considers the dynamics characteristics of the tram after it is detected by the entry detector of the intersection and is based on a set of fuzzy decision-making rules for decision-making on the time parameters adjustment of the traffic signal plan based on the compatibility of traffic direction signals and the importance of traffic requests. For general traffic signals, the solution is to extend or terminate the permissive signal for a certain group of traffic directions controlled by this signal. Decisions for tram signals, depending on the proximity of the planned permissive signal and the importance of the associated request, can affect both the time parameters of the immediate traffic signal plan and change the overall signal sequence and/or their duration. The conditional priority involves taking into account possible conflicts be-tween the decisions from the public transport signals or from public transport and general traffic signals, which are handled by the corresponding production rules proposed in the paper. The traffic light plan is changed iteratively in an acyclic manner, considering the degree of confidence of the decision obtained by a two-level fuzzy inference model based on the Mamdani algorithm, the second level of which is responsible for determining the signal sequence and the first level for their time parameters. The information processing model is implemented and validated using SUMO urban mobility simulation tool for moderate and saturated transport demands for a non-trivial intersection with synthetic traffic demand. The experimental results demonstrate the applicability of the system in conditions of a saturated transport demand, allowing to reduce the negative impact on the general traffic flows without a significant increase in the time losses of tram passengers compared to the adaptive unconditional way of implementing tram traffic light priority.
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DurgaDevi, Mrs S., Mr P. Senthil, and T. Keerthana. "Controlling Traffic Signals Through GPS for Ambulance." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (2018): 32–37. http://dx.doi.org/10.31142/ijtsrd14563.

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Savitha, A. C., Kumar K. M. Madhu, T. S. Sangeetha, M. Shashank, L. B. Sinchana, and R. Tejashwini. "Traffic Signal Management and Control System based on Density of Vehicles and Emergency Vehicles." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 11–18. https://doi.org/10.5281/zenodo.15387842.

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Traffic lights play such an important role in traffic management to control the traffic on the road. The traffic is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicles to cross the road which involves many junctions. Emergency vehicles, like ambulances, have responsibility to reach patients or those who are met with accidents have to quickly transfer them to hospital. Due to traffic signals, they may be delayed for rescue operations. Traffic signal Control for Emergency Vehicles is designed and developed to detect emergency vehicles, how to manipulate the traffic lights and how to provide a free way to emergency vehicles. Emergency vehicles include Ambulance, Fire Engine, Police vehicles… etc. This system creates a connection between emergency vehicles and traffic signals. This system uses Radio Frequency Identification (RFID) as its key component to implement the control system. The RFID tag assists in confirming the passage of emergency vehicles from the traffic signal. Based on that movement of emergency vehicle near the traffic signal reflects in changing the signal, this helps the emergency vehicle to clear all the junctions without wasting time at the signals. This is continued until the emergency vehicle reaches its destination. All these processes happened without any human intervention. The quantified output that this system is going to provide with the display of the following parameters are all the four ultrasonic sensor readings continuously, the timer count and the lane in which the emergency vehicle is detected. Along with the reading the system is going to change the signals accordingly. The timer readings display in milliseconds and the ultrasonic sensor readings, that is the distance, will be displayed in centimeters.  
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Dissertations / Theses on the topic "Traffic signals"

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Peiffer, John P. "Fatigue testing of stiffened traffic signal structures." Laramie, Wyo. : University of Wyoming, 2009. http://proquest.umi.com/pqdweb?did=1888253611&sid=11&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Franz, Mark L. "Local agency traffic sign retroreflectivity case study and model of observed traffic sign light intensity." Morgantown, W. Va. : [West Virginia University Libraries], 2009. http://hdl.handle.net/10450/10473.

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Thesis (M.S.)--West Virginia University, 2009.<br>Title from document title page. Document formatted into pages; contains viii, 85 p. : ill. (some col.), col. map. Includes abstract. Includes bibliographical references (p. 79-82).
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Ballman, Karla V. "Cost-effectiveness of smart traffic signals." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13829.

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Sullivan, Andrew J. "Developing a traffic signal design manual for Alabama." Birmingham, Ala. : University of Alabama at Birmingham, 2009. https://www.mhsl.uab.edu/dt/2009m/sullivan.pdf.

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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.<br>"ISSN 0781-5816." Includes bibliographical references (p. 65-71). Available online as a PDF file via the World Wide Web.
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Zhang, Li. "Optimizing Traffic Network Signals Around Railroad Crossings." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27750.

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The dissertation proposed an approach, named â Signal Optimization Under Rail Crossing sAfety cOnstraintsâ (SOURCAO), to the traffic signal control near a highway rail grade crossing (HRGC). SOURCAO targets two objectives: HRGC safety improvement (a high priority national transportation goal) and highway traffic delay reduction (a common desire for virtually all of us). Communication and data availability from ITS and the next generation train control are assumed available in SOURCAO. The first step in SOURCAO is to intelligently choose a proper preemption phase sequence to promote HRGC safety. An inference engine is designed in place of traditional traffic signal preemption calls to prevent the queue from backing onto HRGC. The potential hazard is dynamically examined as to whether any queuing vehicle stalls on railroad tracks. The inference engine chooses the appropriate phase sequence to eliminate the hazardous situation. The second step in SOURCAO is to find the optimized phase length. The optimization process uses the network traffic delay (close to the control delay) at the intersections within HRGC vicinities as an objective function. The delay function is approximated and represented by multilayer perceptron neural network (off-line). After the function was trained and obtained, an optimization algorithm named Successive Quadratic Programming (SQP) searches the length of phases (on-line) by minimizing the delay function. The inference engine and proposed delay model in optimization take the on-line surveillance detector data and HRGC closure information as input. By integrating artificial intelligence and optimization technologies, the independent simulation evaluation of SOURCAO by TSIS/CORSIM demonstrated that the objectives are reached. The average network delay for 20 runs of simulation evaluation is reduced over eight percent by a t-test while the safety of HRGC is promoted. The sensitivity tests demonstrate that SOURCAO works efficiently under light and heavy traffic conditions, as well as a wide range of HRGC closure times.<br>Ph. D.
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Amanzholov, Anuar. "Analysis of off-peak traffic signal operations." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 129 p, 2008. http://proquest.umi.com/pqdweb?did=1605156311&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Malek, Shahram. "EASINET : a procedural package for development and analysis of intersection control strategies." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/33612.

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Gopalan, Ganesh. "Improvement of traffic flow conditions using access management techniques : a netsim study /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426063.

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Shaik, Nawaz M. "Improving traffic flow conditions for interstate work-zones evaluation of three traffic control devices /." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4260.

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Thesis (M.S.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 11, 2006) Includes bibliographical references.
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Books on the topic "Traffic signals"

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Ireland. Department of the Environment. Traffic signs manual. Stationery Office, 1996.

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Great Britain. Department for Transport., ed. Traffic signs manual. TSO, 2003.

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National Research Council (U.S.). Transportation Research Board., ed. Communications, traffic signals, and traffic control devices, 1991. Transportation Research Board, National Research Council, 1991.

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Brilon, Werner, ed. Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1.

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Staunton, Michael M. Traffic signals maintenance manual. An Foras Forbartha, 1985.

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County Surveyors' Society. Traffic and Safety Committee. Traffic Management Working Group. TCUG traffic signals survey. County Surveyors' Society, 2001.

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Nepal. Dept. of Roads. Road and Traffic Unit., ed. Traffic signs manual. Road and Traffic Unit, Planning and Design Branch, Dept. of Roads, Ministry of Physical Planning and Works, 2007.

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Orcutt, Fred L. The traffic signal book. Prentice Hall, 1993.

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Gene, Hawkins H., Carlson Paul J, Texas Transportation Institute, and Texas. Dept. of Transportation., eds. Traffic signal warrants: Guidelines for conducting a traffic signal warrant analysis. Texas Dept. of Transportation, 1998.

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Hurst, Carolyn. Flagging & work zone traffic control. 4th ed. Outdoor Empire Publishing, 1996.

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Book chapters on the topic "Traffic signals"

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Ni, Daiheng. "Warrants of Traffic Signals." In Signalized Intersections. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38549-1_2.

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Salter, R. J. "Introduction to traffic signals." In Highway Traffic Analysis and Design. Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13423-6_28.

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Salter, R. J. "Introduction to traffic signals." In Highway Traffic Analysis and Design. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-20014-6_28.

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Salter, R. J. "Delay at Traffic Signals Illustrated by an Example." In Traffic Engineering. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_24.

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Cedersund, H. Å. "Traffic Safety at Roundabouts." In Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1_19.

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Troutbeck, R. J. "Current and Future Australian Practices for the Design of Unsignalized Intersections." In Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1_1.

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Prevedouros, Pano D. "A Model of Unsignalized Intersection Capacity Based on Erlang-3 Gap Distribution." In Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1_10.

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Zhang, Xiwen. "The Influence of Partial Constraint on Delay at Priority Junctions." In Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1_11.

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Gaca, Stanislaw, and Janusz Chodur. "Simulation Studies of the Effects of Some Geometrical and Traffic Factors on the Capacity of Priority Intersections." In Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1_12.

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Tudge, R. T. "INSECT - The Calibration and Validation of an Intersection Simulation Model." In Intersections without Traffic Signals. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83373-1_13.

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Conference papers on the topic "Traffic signals"

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Dhamane, Shreyash, Trisha Shishodiya, Omkar Rane, and Sudhir Dhage. "Smart Signals: Real-Time Traffic Adjuster." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724375.

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Liang, Shuo, and Fei Yan. "Iterative Fault-Tolerant Control Strategy for Urban Traffic Signals Under Signal Light Failure." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606774.

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Wang, Yongbin. "Real-time Detection and Recognition of Railway Traffic Signals." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696369.

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Dubey, Mayank. "Smart signals in heterogeneous traffic conditions." In 55th ISOCARP World Planning Congress, Beyond Metropolis, Jakarta-Bogor, Indonesia. ISOCARP, 2019. http://dx.doi.org/10.47472/nsde5701.

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Major urban corridors in Indian cities are carrying significantly high traffic leading to near saturated conditions for extended peak hours. As mixed landuse and major trip attracting/generating establishments are generally observed to be located along such corridors for better accessibility, significant side friction is also observed along these corridors. Among various measures to improve the throughput along such corridors, signalized intersections seem to be the most preferred intervention for intersection control. Although frequent occurrence of such traffic signals and non-coordinated signal phases have in turn made the whole situation more complex. To overcome this challenge, variations of smart signals are being proposed by technology and traffic enterprises globally. Generally, smart interventions in operation of signalised intersections require communication among vehicles and control system through various sensors and applications of Intelligent transport services (ITS). Smart signal operations require the sensors grouted in pavement or attached with camera to share the relevant data in real time basis with central command and control centre. With adaptive signal operations, it is attempted to schedule signal phases in such a way that green phase of every cycle generally experiences near saturated flow conditions. The smart cities mission (SCM) of India, covering around 100 cities also focuses upon improving the urban mobility through various measures including smart signals. Some of the popular proposals relating to smart operation of signalised intersection across shortlisted smart cities include adaptive and coordinated traffic signals. It is understood that traffic signal optimization is not a one-time action but rather a continuous process, as data archiving, data crunching, research and adaptations are indispensable for its success. As the geometry, location and setting of each intersection in every network is bound to be unique, the optimization process needs to consider the same. The literature and case study of Indian city Bhubaneswar (ranked first in nationwide smart city challenge) revealed that challenges specific to Indian driving conditions are major cause of worry for yielding stated benefits of smart signals. Factors like varying hierarchy and functions along major arterial corridors, fluctuating carriageway width and quality, considerable side friction within right of way, heterogeneity in vehicular mix, significant variation in peak hour directional flows leading to tidal flow, surrounding network characteristics and efficacy of optimisation techniques are responsible for limited rewards out of the whole process. The study reflects upon these challenges and concludes with recommendations to improve the performance of signalized intersections along corridors with heterogeneous traffic conditions.
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Young, William, and John Dovel. "Powering Traffic Signals in an Emergency With Alternative Power Sources." In ASME Solar 2002: International Solar Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/sed2002-1053.

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Disasters, whether man-made or natural, destroy buildings, structures, lives and natural surroundings. As an example, Hurricane Andrew devastated South Florida with winds up to 140 miles per hour leaving more than 250,000 people homeless and severely damaging at least 85,000 buildings, in addition to traffic signals and other roadway devices. Traveling was hazardous with debris in the roadway, power lines down, traffic signals damaged or not working, and road signs missing. With so many traffic signals not working, normal traffic flow was disrupted and roadways became congested. The importance of maintaining traffic flow in a disaster was evident for effective movement of emergency vehicles and to support recovery efforts. The same effect is realized, but to a smaller degree, during brown-outs, severe storms, accidents and other power outages for whatever the cause. During power outages caused by disasters or other events, there are many traffic signals that are still functional, but not operational due to loss of electrical power. Recent advances in power electronics, lighting and alternative energy sources provide a means of making these functional traffic signals operational during power outages. Updating signal heads with new light emitting diode (LED) lamps will lower the energy consumption by 60 to 80 percent of that of existing incandescent lights. With lower power requirements, renewable energy sources such as photovoltaics, become capable of providing the needed electric power. Redesigning traffic signals to incorporate new low-energy technologies make renewables a more viable source of power. This paper addresses these issues with respect to energy consumption and describes a new design that uses renewables to power these new lighting technologies.
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Sanders, Grant. "VDOT: Are My Signals Sick?" In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316034.

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Sandhya, K. S., and B. Karthikeyan. "Automatic traffic diversion system using traffic signals." In 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2). IEEE, 2017. http://dx.doi.org/10.1109/icnets2.2017.8067914.

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Siraut, J. "Economics of traffic signals." In IET Road Transport Information and Control Conference and the ITS United Kingdom Members' Conference (RTIC 2010). Better transport through technology. IET, 2010. http://dx.doi.org/10.1049/cp.2010.0388.

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Royle, M. C. "Optical aspects of traffic signals and variable message signs." In Eighth International Conference on Road Traffic Monitoring and Control. IEE, 1996. http://dx.doi.org/10.1049/cp:19960323.

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M S, Sneha Maria, Sreyas Raj P, Vivek Viswanathan, Saniya Sajan, and Soosan George T. "Vehicle Actuated Traffic Signal using AI." In Second International Conference in Civil Engineering for a Sustainable Planet. AIJR Publisher, 2025. https://doi.org/10.21467/proceedings.179.41.

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Traffic congestion in urban areas leads to capacity issues, intersection delays, increased congestion, fuel consumption, and air pollution. Advanced traffic management systems, including adaptive signals and intelligent transportation systems, offer solutions to mitigate congestion and improve road network efficiency. Utilizing live camera imagery and AI for real-time traffic density assessment, alongside adaptive signal control algorithms, reduces congestion and optimizes traffic flow, aligning with the trend of technology-driven transportation systems for environmental benefits. YOLO (You Only Look Once) is a renowned AI object detection algorithm, enabling a vehicle-activated traffic signal to adjust timing based on traffic density, reducing congestion, wait times, and pollution. A simulation and prototype demonstrate the proposed system's effectiveness compared to fixed-time signals. It dynamically adjusts green signal durations based on traffic density, prioritizing high-traffic directions to minimize delays, congestion, and fuel consumption. Results show significant improvements in vehicles crossing intersections, with potential enhancements through real-world data calibration.
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Reports on the topic "Traffic signals"

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Fries, Ryan, Yan Qi, Anne Werner, et al. Optimum Traffic Signal Condition Assessment and Strategic Maintenance Planning. Illinois Center for Transportation, 2024. http://dx.doi.org/10.36501/0197-9191/24-019.

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The objectives of this study include developing condition assessment procedures, condition thresholds, and improvement plans for traffic signals in Illinois. This project first synthesized previous research and current practices of transportation agencies related to the management and lifespan expectations of traffic signals and their components. Interviews were then conducted with personnel familiar with traffic signal management practices in Illinois, and field work was conducted to identify refinements to then-proposed methods and condition thresholds. The project’s technical review panel also provided important guidance throughout the development of the assessment methods. The final recommendations included 34 assessments of traffic signal components. Pictures were included to demonstrate condition levels, and hyperlinks were included to guide reviewers to important online resources. The developed procedures support consistent evaluation of traffic signals throughout Illinois, provide a systematic process for public agencies to identify components in critical condition, and create the foundation to including traffic signals into asset management frameworks. Overall, implementing these procedures and standards should improve traffic signal performance due to reduced failures related to poor component conditions. Improved traffic signal performance is expected to reduce traffic signal life-cycle costs to Illinois Department of Transportation and improve traffic signal performance and safety for the traveling public.
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Lee, Kevin, and Darcy Bullock. Traffic Signals in School Zones. Purdue University, 2003. http://dx.doi.org/10.5703/1288284313335.

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Wasson, Jay, Montasir Abbas, and Darcy Bullock. Reconciled Platoon Accommodations at Traffic Signals. Purdue University, 1999. http://dx.doi.org/10.5703/1288284313301.

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Rescot, Robert, Shuo Qu, Rebecca Noteboom, and Ahmad Nafakh. Evaluation of Flashing Yellow Arrow Traffic Signals in Indiana. Purdue University, 2015. http://dx.doi.org/10.5703/1288284315530.

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Smith, Jijo K., Howell Li, and Darcy M. Bullock. Populating SAE J2735 Message Confidence Values for Traffic Signal Transitions Along a Signalized Corridor. Purdue University, 2019. http://dx.doi.org/10.5703/1288284317322.

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The communication between connected vehicles and traffic signal controllers is defined in SAE Surface Vehicle Standard J2735. SAE J2735 defines traffic signal status messages and a series of 16 confidence levels for traffic signal transitions. This paper discusses a statistical method for tabulating traffic signal data by phase and time of day and populating the SAE J2735 messages. Graphical representation of the red-green and green-yellow transitions are presented from six intersections along a 4-mile corridor for five different time of day timing plans. The case study provided illustrates the importance of characterizing the stochastic variation of traffic signals to understand locations, phases, and time of day when traffic indications operate with high predictability, and periods when there are large variations in traffic signal change times. Specific cases, such as low vehicle demand and occasional actuation of pedestrian phases are highlighted as situations that may reduce the predictability of traffic signal change intervals. The results from this study also opens up discussion among transportation professionals on the importance of consistent tabulation of confidence values for both beginning and end of green signal states. We believe this paper will initiate dialog on how to consistently tabulate important data elements transmitted in SAE J2735 and perhaps refine those definitions. The paper concludes by highlighting the importance of traffic engineers and connected vehicle developers to work together to develop shared visions on traffic signal change characteristics so that the in-vehicle use cases and human-machine interface (HMI) meet user expectations.
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Bullock, Darcy, and Montasir Abbas. A Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals. Purdue University, 2001. http://dx.doi.org/10.5703/1288284313130.

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Saldivar-Carranza, Enrique D., Tom Platte, Terri Wills, James Sturdevant, and Darcy M. Bullock. Business Processes to Prioritize Traffic Signal Retiming and Assess the Impact of Retiming Activities. Purdue University, 2025. https://doi.org/10.5703/1288284317807.

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The Indiana Department of Transportation (INDOT) manages over 2,000 traffic signals in the State. It is unfeasible to identify systemwide improvement opportunities by evaluating the performance of thousands of traffic signals on monitoring dashboards. This report describes how a scalable technique based on connected vehicle (CV) trajectory data systematically evaluates performance at the movement level to prioritize retiming and maintenance activities. In total, eleven timing changes were implemented at nine traffic signals over various time-of-day (TOD) periods using the proposed approach. The method achieved average control delay and split failure reductions of up to 53 sec/veh and 30%, respectively. A before-after performance analysis is presented for each intersection and the business processes implemented to achieve these results are discussed. The business process analysis resulted in a companion study, SPR-4857, which identified signals with operational performance problems that should be further evaluated for potential capital investments, such as turn lane additions.
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Saldivar-Carranza, Enrique D., Howell Li, Jijo K. Mathew, et al. Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data. Purdue University Press, 2023. http://dx.doi.org/10.5703/1288284317625.

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High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to relevant roadways and movements are presented. This allows for performance analyses that can range from the movement- to the system-level. A comprehensive suite of methodologies to evaluate signal performance using vehicle trajectories is then provided. Finally, uses of CV hard-braking and hard-acceleration event data to assess safety and driver behavior are discussed. To evaluate scalability and test the proposed techniques, performance measures for over 4,700 traffic signals were estimated using more than 910 million vehicle trajectories and 14 billion GPS points in all 50 states and Washington, D.C. The contents of this report will help the industry transition towards a hybrid blend of detector- and CV-based signal performance measures with rigorously defined performance measures that have been peer-reviewed by both academics and industry leaders.
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Monsere, Christopher. Operational Guidance For Bicycle-Specific Traffic Signals in the United States. Portland State University Library, 2012. http://dx.doi.org/10.15760/trec.146.

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Sakulneya, Apidej, and Jeffery Roesler. Smart Construction Work-Zone Safety with V2I Passive Material Sensing. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-027.

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This study explored new vehicle to infrastructure (V2I) technology in construction work zones (CWZ), where speeding, unsafe driving behaviors, and drivers' failure to obey traffic signs contribute significantly to elevated accident rates and fatalities. The objective of this research to advance CWZ safety by evaluating the potential of 3-axis magnetometers attached to a moving cart and traversing over a pavement-assisted passive sensing system can improve vehicle lateral positioning and warning in CWZ. Secondly, to develop a process to implement a programmable ferromagnetic oxide material for roadway coatings to interface with vehicles containing magnetometers on a field site. The research testing used a custom-built cart equipped with multiple 3-axis magnetometer to detect EM signals from invisible markings composed of 10% and 20% CrO₂, that were created to alert for speed, lane merges, and lane-keeping. The invisible marking strips were oriented and positioned in various ways to test the repeatability and ability to reliable detect a signal and signature that could be interpreted with automated algorithm. The experimental test results were acquired in a parking and signal-processing technique was established that normalized the raw signals, removed background EM signals not related to the created EM signatures, filtered high- and low-frequency noise, and took the derivative of the EM flux density with respect to the number of points. The V2I signals in the Y and Z-axes occasionally failed to exceed the minimum threshold set for the experiments, but the X-axis signals consistently exceeded the minimum value of ±200nT throughout the testing. The minimum threshold signals were used to calculate the speed of the cart, indicate a lane merge, and determine the lateral lane position of the cart. The detected speed signals closely correlated with the GPS speed measurements on the cart as well as provided accurate cart positioning and maneuvering actions. This pilot study demonstrated the potential of V2I communication specifically EM pavement signatures to enhance CWZ safety and provide detectable and actionable feedback to the vehicle.
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