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Relevant bibliographies by topics / Traffic management systems

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Academic literature on the topic 'Traffic management systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Traffic management systems"

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Anikeev, E. "INFORMATION TRAFFIC MANAGEMENT SYSTEMS." Моделирование систем и процессов 9, no. 4 (2017): 5–11. http://dx.doi.org/10.12737/24574.

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M. Vijai Kishore, M. Vijai Kishore, Achal Garg, and Onika Arora. "Intelligent Transportation Systems for Traffic Management in Dehradun City." International Journal of Scientific Research 3, no. 4 (2012): 146–47. http://dx.doi.org/10.15373/22778179/apr2014/50.

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Magableh, Aws Abed Al Raheem, Mohanad A. Almakhadmeh, Nawaf Alsrehin, and Ahmad F. Klaib. "Smart Traffic Light Management Systems." International Journal of Technology Diffusion 11, no. 3 (2020): 22–47. http://dx.doi.org/10.4018/ijtd.2020070102.

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Traffic congestion is a major concern in many cities. Failure to heed signals, poor law enforcement, and bad traffic light management are main factors that have led to traffic congestion. One of the most important problems in cities is the difficulty of further expanding the existing infrastructures. Having that in mind, the main accessible and available alternatives that could provide better management of the traffic lights is to use technological systems. There are many methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, and a few other technologies. This research is focused on reviewing all these existing methods and studies using a systematic literature review (SLR). The SLR was intended to improve the synthesis of research by introducing a systematic process. This article aims at analyzing and assessing the existing studies against selected factors of comparison. The study achieves these aims by analyzing 78 main studies. The research outcomes indicated that there are decent numbers of studies that have been proposed in the area of smart traffic light management. However, less attention has been paid on the possibility of investigating the use of live traffic data to improve the accuracy of traffic management.

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Prandini, Maria, Vamsi Putta, and Jianghai Hu. "Air traffic complexity in future Air Traffic Management systems." Journal of Aerospace Operations 1, no. 3 (2012): 281–99. http://dx.doi.org/10.3233/aop-2012-0015.

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Ristov, Pančo, Ante Mrvica, and Pavao Komadina. "Security of Data and Information in Vessel Traffic Management Information Systems." Naše more 63, no. 1 (2016): 1–8. http://dx.doi.org/10.17818/nm/2016/1.7.

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Ristov, Pančo, Pavao Komadina, and Vinko Tomas. "Reliability and Availability of the Vessel Traffic Management and Information Systems." Transactions on Maritime Science 02, no. 02 (2013): 101–8. http://dx.doi.org/10.7225/toms.v02.n02.00.

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Banks, James H. "Performance Measurement for Traffic Management Systems." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (1998): 39–45. http://dx.doi.org/10.3141/1634-05.

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Performance measurement refers to attempts to quantify some aspect of the performance of an organization. A study was conducted to analyze needs, opportunities, and techniques for measuring performance of transportation management centers (TMCs). Opportunities and needs were identified by analyzing the interrelationships among performance measurement objectives, objects, and study designs. This analysis suggests that before-and-after evaluation studies of traffic management actions and monitoring of traffic data to detect system changes are the most appropriate forms of performance measurement for TMCs. Important potential measures of effectiveness for traffic management systems include travel time and related measures, ramp delay, traffic volumes, accident rates, traffic information accuracy, incident duration, and equipment status. Techniques are available for quantifying these measures, although there are a number of concerns with data accuracy, especially where travel times are estimated from loop-detector data. Case studies of two California TMCs suggest that, although sophisticated data collection systems are available or planned, the institutional infrastructure to carry out performance measurement may be lacking.

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Chin-Ping Chang, Edmond, and K. K. Ho. "Freeway Traffic Management Strategy Expert Systems." IFAC Proceedings Volumes 27, no. 12 (1994): 317–21. http://dx.doi.org/10.1016/s1474-6670(17)47490-8.

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Haver, D. A., and P. J. Tarnoff. "Future directions for traffic management systems." IEEE Transactions on Vehicular Technology 40, no. 1 (1991): 4–10. http://dx.doi.org/10.1109/25.69965.

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Аникеев, Евгений, and Evgeniy Anikeev. "URBAN AND HIGHWAY TRAFFIC MANAGEMENT SYSTEMS." Modeling of systems and processes 11, no. 4 (2019): 4–11. http://dx.doi.org/10.12737/article_5c79642b735693.94065713.

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Dissertations / Theses on the topic "Traffic management systems"

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Jaworski, P. "Cloud computing based adaptive traffic control and management." Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/d63ba84e-bd0c-4e00-8242-310dbbaa3b92/1.

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Recent years have shown a growing concern over increasing traffic volume worldwide. The insufficient road capacity and the resulting congestions have become major problems in many urban areas. Congestions negatively impact the economy, the environment and the health of the population as well as the drivers satisfaction. Current solutions to this topical and timely problem rely on the exploitation of Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management involves the collection and processing of a large amount of geographically distributed information to control distributed infrastructure and individual vehicles. The distributed nature of the problem prompted the development of a novel, scalable ITS-Cloud platform. The ITS-Cloud organises the processing and manages distributed data sources to provide traffic management methods with more accurate information about the state of the traffic. A new approach to service allocation, derived from the existing cloud and grid computing approaches, was created to address the unique needs of ITS traffic management. The ITS-Cloud hosts the collection of software services that form the Cloud based Traffic Management System (CTMS). CTMS combines intersection control algorithms with intersection approach advices to the vehicles and dynamic routing. The CTMS contains a novel Two-Step traffic management method that relies on the ITS-Cloud to deliver a detailed traffic simulation image and integrates an adaptive intersection control algorithm with a microscopic prediction mechanism. It is the first method able to perform simultaneous adaptive intersection control and intersection approach optimization. The Two-Step method builds on a novel pressure based adaptive intersection control algorithm as well as two new traffic prediction schemes. The developed traffic management system was evaluated using a new microscopic traffic simulation tool tightly integrated with the ITS-Cloud. The novel traffic management approaches were shown to outperform benchmark methods for a realistic range of traffic conditions and road network configurations. Unique to the work was the investigation of interactions between ITS components.

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Gariel, Maxime. "Toward a graceful degradation of air traffic management systems." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34799.

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Abstract: This thesis addresses the problem of graceful degradation for air traffic management systems (ATMS). The graceful degradation is the process by which the safety of the airspace is ensured in the event of failures or operational degradation in the system. After listing the main areas where failures and degradation can affect the ATMS, an ontology of the ATMS is proposed. The ontology allows to introduce failures at different levels, track their propagation throughout the system, and measure their operational impact. Then, two operational degradations are studied: The first degradation studied is a reduction in the landing capacity at San Francisco International Airport. The aircraft queueing process for terminal area is modeled and optimized to ensure a graceful degradation. The second degradation encompasses Communication, Navigation and Surveillance systems failures. The graceful degradation is ensured by increasing the spacing distance between aircraft, using novel algorithms of avoidance under uncertainties. Those algorithm also serve as probes to compare the degradation capabilities of different traffic configurations such as Miles-In-Trail and Free-Flight arrivals. Finally, this thesis focuses on monitoring the airspace for potential degradation. The ability and the difficulty of en-route traffic configuration are evaluated using degradation maps. Those maps can be used controller to rapidly and efficiently steer traffic from nominal mode of operations to mode of operations under abnormal conditions. Finally, a monitoring tool for terminal area is presented: the conformance of current flight to pre-identified typical operations is determined in real time. As the number of non-conforming aircraft increases, the complexity seen by air traffic controllers increases, and can become a threat for the airspace safety.

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Davies, Gary John. "Agent based impact analysis of air traffic management systems." Thesis, Queen's University Belfast, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.579707.

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Increasing travel demand sees projections of 11.5 million flights in Europe annually by 2016. The current system is not capable of managing these increases, introducing the need for a redevelopment initiative, the Single Sky, to reduce environmental impact, improve efficiency and manage the increased capacity with even better safety. This is a challenging goal as these systems are characteristically complex arising from numerous interacting objects whose behaviour is affected by history or feedback in an open system resulting in emergent, ordered and disordered behaviour through the structure. It is difficult to understand the system behaviour, which is key to modifying, developing and implementing the desired configurations effectively. Ascertaining the behaviour of such systems requires an understanding of the coupling and interdependencies on a system wide scale, which is not available in the traditional approaches to ATM system analysis. This research addresses the need by developing a simple, inexpensive, non data intensive, qualitative analysis for identifying these relationships and interdependencies through a technique of invoking a change in the system architecture and measuring the deviation experienced by the other elements, with the deviation referred to as impact. An agent based modelling approach was identified as the most suitable and the method was therefore implemented in an agent based environment. The model was verified in a system of 21 airports from the UK and Ireland and a user defined number of flights with at least a departure or arrival airport within the system. Critical review and validation of the model is performed using a combination of scenarios comprising real world events. System analysis and changes in protocol were used to identify phenomenon such as impact propagation and absorption. The combination of impact analysis, approach and the model is shown to provide a simple means for ascertaining the relationships and interdependencies as impact propagates through the system due to aircraft moving through the operational hierarchy. The research identifies the applicability of the technique toward the analysis of other complex systems including ecosystems and financial markets.

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Fernández, Ruiz Bruno Miguel 1973. "Architecture for the integration of dynamic traffic management systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/80949.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.<br>Includes bibliographical references (p. 104-106).<br>by Bruno Miguel Fernández Ruiz.<br>S.M.

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Price, Joshua. "Human factors in the design of traffic management systems." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/388155/.

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This research seeks to investigate how application of Human Factors techniques could be used to improve performance resulting from the use of technical traffic management and SCOOT validation systems. The systems used in both domains have historically been developed without consideration given to the social factors important to their use, designs instead being based solely on technical constraints. In the first stages of the project traffic management is investigated through conduction of a literature review covering the objectives, functions and constraints acting upon Traffic Management Centres (TMCs) in road, rail, maritime and air domains. Congestion management is then considered in urban road TMCs through application of the Event Analysis of Systematic Teamwork (EAST) method based on observational data collected from four TMCs, Bristol, Cardiff, Dorset and Nottingham, in which the tasks, social agents, information and relationships between these elements are considered. The EAST method is then expanded to enable investigation into TMCs’ resilience, providing further knowledge about the domain. The later stages of the project are concerned with SCOOT validation, the process by which adaptively controlled traffic lights using SCOOT are set up to reflect real traffic conditions. The domain, using the current PC SCOOT Urban Traffic Control system, is assessed through Cognitive Work Analysis (CWA) with the findings used to propose areas suitable for development. One of these areas, STOC validation, is then developed further by applying Ecological Interface Design to develop an alternative display addressing limitations with PC SCOOT’s display. This concept display is then evaluated through two empirical experiments examining performance compared to traditional displays and investigating the role of experience within the domain. Finally, by using insights obtained into the STOC validation process an automated STOC selection algorithm is developed which has the potential to redefine how STOC validation is conducted.

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Lenkei, Zsolt. "Crowdsourced traffic information in traffic management : Evaluation of traffic information from Waze." Thesis, KTH, Transportplanering, ekonomi och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239178.

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The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

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Soto, Villacampa José Antonio. "Towards a blockchain-based private road traffic management implementation." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74287.

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Zhang, Tong. "Improving the performance of a traffic data management system." Ohio : Ohio University, 1999. http://www.ohiolink.edu/etd/view.cgi?ohiou1175198741.

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Choi, Myung Cheon. "Traffic flow management for RSVP/ATM edge devices." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15520.

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Almejalli, Khaled A., Keshav P. Dahal, and M. Alamgir Hossain. "Intelligent traffic control decision support system." Springer-Verlag, 2007. http://hdl.handle.net/10454/2554.

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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

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Books on the topic "Traffic management systems"

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Administration, United States Fire. Traffic incident management systems. U.S. Fire Administration, 2012.

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Coifman, Benjamin. System management and monitoring: Temporal evaluation of freeway management systems. Midwest Regional University Transportation Center, College of Engineering, Dept. of Civil and Environmental Engineering, University of Wisconsin, Madison, 2005.

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Hager, Rolf. Management of traffic information networks. Verlag Mainz, 1994.

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Masaki, Ichiro. Image-information systems for traffic management. Productivity From Information Technology, "PROFIT" Research Initiative, Sloan School of Management, Massachusetts Institute of Technology, 1994.

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Electronic Navigation Research Inst, ed. Air Traffic Management and Systems II. Springer Japan, 2017. http://dx.doi.org/10.1007/978-4-431-56423-2.

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Electronic Navigation Research Inst, ed. Air Traffic Management and Systems III. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7086-1.

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Electronic Navigation Research Inst, ed. Air Traffic Management and Systems IV. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4669-7.

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

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A, Mierzejewski Edward, National Cooperative Highway Research Program., National Research Council (U.S.). Transportation Research Board., American Association of State Highway and Transportation Officials., and United States. Federal Highway Administration., eds. Electronic Toll and Traffic Management (ETTM) systems. National Academy Press, 1993.

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Mannering, Fred L. Incident management systems framework: Impacts of service patrols : final summary report, Research Project GC 8719, Task 6, Incident Management Systems Framework. Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1991.

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Book chapters on the topic "Traffic management systems"

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Amador, António, Rui Dias, Tiago Dias, and Tomé Canas. "Traffic Management Systems." In Intelligent Transport Systems. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118894774.ch13.

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Mocanu, Isabela, Philippe Nitsche, and Kerry Malone. "Impacts of Cooperative Traffic Systems on Safety, Environment and Travel Times: a Literature Survey." In Traffic Management. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307822.ch19.

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Gordon, Robert. "Active Traffic Management (ATM)." In Intelligent Transportation Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14768-0_10.

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Muhlrad, Nicole, Gilles Vallet, Ilona Butler, et al. "Analysis of Road Safety Management Systems in Europe." In Traffic Safety. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307853.ch1.

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Burrell, Anthony T., Titsa P. Papantoni-Kazakos, A. Lombardo, S. Palazzo, and G. Schembra. "Traffic Characterization & Management." In Third Generation Mobile Telecommunication Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56919-7_7.

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Baum, Michael S. "Unmanned Traffic Management (“UTM”)." In Introduction to Unmanned Aircraft Systems, 3rd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9780429347498-19.

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Guerrieri, Marco, and Raffaele Mauro. "Traffic Management and Control Systems." In Springer Tracts in Civil Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60723-4_6.

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Krautter, W., D. Manstetten, and T. Schwab. "Traffic Simulation for the Development of Traffic Management Systems." In Traffic and Mobility. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60236-8_13.

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Lalitha, K., and M. Pounambal. "IoT-Based Traffic Management." In Emerging Research in Data Engineering Systems and Computer Communications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0135-7_14.

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Blosseville, Jean-Marc. "Image Processing for Traffic Management." In Advanced Video-Based Surveillance Systems. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5085-3_7.

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Conference papers on the topic "Traffic management systems"

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Stogerer, Christoph, and Wolfgang Kastner. "System management standards for traffic management systems." In 2009 IEEE 14th International Conference on Emerging Technologies & Factory Automation. ETFA 2009. IEEE, 2009. http://dx.doi.org/10.1109/etfa.2009.5347139.

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Chang, Tang-Hsien, and Shen-Lin Chang. "Traffic game in smart city for traffic management." In 2017 Intelligent Systems Conference (IntelliSys). IEEE, 2017. http://dx.doi.org/10.1109/intellisys.2017.8324295.

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Davey, E. "Rail Traffic Management Systems (TMS)." In IET Professional Development Course on Railway Signalling and Control Systems (RSCS 2012). IET, 2012. http://dx.doi.org/10.1049/ic.2012.0048.

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Rupert, Robert L. "The TravTek Traffic Management Center and Traffic Information Network." In Vehicle Navigation & Instrument Systems. SAE International, 1991. http://dx.doi.org/10.4271/912822.

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Azzopardi, Marc Anthony, and James F. Whidborne. "Computational Air Traffic Management." In 2011 IEEE/AIAA 30th Digital Avionics Systems Conference (DASC). IEEE, 2011. http://dx.doi.org/10.1109/dasc.2011.6095967.

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"The TravTek traffic management center and traffic information network." In 1991 Vehicle Navigation and Information Systems Conference. IEEE, 1991. http://dx.doi.org/10.1109/vnis.1991.205820.

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Rungta, Neha, Eric G. Mercer, Franco Raimondi, Bjorn C. Krantz, Richard Stocker, and Andrew Wallace. "Modeling complex air traffic management systems." In the 8th International Workshop. ACM Press, 2016. http://dx.doi.org/10.1145/2896982.2896993.

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van Lint, Hans, Ramon Landman, Yufei Yuan, Chris van Hinsbergen, and Serge Hoogendoorn. "Traffic monitoring for coordinated traffic management—Experiences from the field trial integrated traffic management in Amsterdam." In 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2014. http://dx.doi.org/10.1109/itsc.2014.6957735.

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Karimi Shahri, Pouria, Shubhankar Chintamani Shindgikar, Baisravan HomChaudhuri, and Amir H. Ghasemi. "Optimal Lane Management in Heterogeneous Traffic Network." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9040.

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Abstract This paper aims to determine an optimal allocation of autonomous vehicles in a multi-lane heterogeneous traffic network where the road is shared between autonomous and human-driven vehicles. The fundamental traffic diagram for such heterogeneous traffic networks is developed wherein the capacity of the road is determined as a function of the penetration rate and the headways of autonomous and human-driven vehicles. In this paper, we define two cost functions to maximize the throughput of the network and minimize the variation between flow rates. To solve the proposed optimization problem, an exhaustive search optimization approach is performed. Several numerical examples are presented to highlight the different influence of different design parameters on the allocation of autonomous vehicles.

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Bell, M. C. "Integration of traffic and environmental monitoring and management systems." In Eighth International Conference on Road Traffic Monitoring and Control. IEE, 1996. http://dx.doi.org/10.1049/cp:19960285.

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Reports on the topic "Traffic management systems"

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Mears, Brad. Advanced Traffic Management Systems (ATMS) Research Analysis Database System. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada388177.

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Stevens, S. S., and S. M. Chin. Cost estimates for near-term depolyment of advanced traffic management systems. Final report. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10155687.

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Bo, Wang, Mariya Grygorak, Viktor Voitsehovskiy, et al. Assessment of the Implementation of a Cargo Traffic Management System. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.04.10.

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Hubbard, Sarah M., and Bryan Hubbard. Investigation of Strategic Deployment Opportunities for Unmanned Aerial Systems (UAS) at INDOT. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317126.

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Unmanned aerial systems (UAS) are increasingly used for a variety of applications related to INDOT’s mission including bridge inspection, traffic management, incident response, construction and roadway mapping. UAS have the potential to reduce costs and increase capabilities. Other state DOTs and transportation agencies have deployed UAS for an increasing number of applications due to technology advances that provide increased capabilities and lower costs, resulting from regulatory changes that simplified operations for small UAS under 55 pounds (aka, sUAS). This document provides an overview of UAS applications that may be appropriate for INDOT, as well as a description of the regulations that affect UAS operation as described in 14 CFR Part 107. The potential applications were prioritized using Quality Function Deployment (QFD), a methodology used in the aerospace industry that clearly communicates qualitative and ambiguous information with a transparent framework for decision making. The factors considered included technical feasibility, ease of adoption and stakeholder acceptance, activities underway at INDOT, and contribution to INDOT mission and goals. Dozens of interviews with INDOT personnel and stakeholders were held to get an accurate and varied perspective of potential for UAVs at INDOT. The initial prioritization was completed in early 2019 and identified three key areas: UAS for bridge inspection safety as a part of regular operations, UAS for construction with deliverables provided via construction contracts, and UAS for emergency management. Descriptions of current practices and opportunities for INDOT are provided for each of these applications. An estimate of the benefits and costs is identified, based on findings from other agencies as well as projections for INDOT. A benefit cost analysis for the application of UAS for bridge inspection safety suggests a benefit cost over one for the analysis period.

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Andrews, Ted, and Kumares Sinha. The Components and Benefits of an Advanced Surface Arterial Traffic Management System. Purdue University, 1994. http://dx.doi.org/10.5703/1288284313328.

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Cassidy, Michael, and Kumares Sinha. An Electronic Surveillance and Control System for Traffic Management on the Borman Expressway, Part I. Purdue University, 1990. http://dx.doi.org/10.5703/1288284314184.

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Wang, Mu-Han, and Michael Cassidy. An Electronic Surveillance and Control System for the Management of Traffic on the Borman Expressway. Purdue University, 1996. http://dx.doi.org/10.5703/1288284313138.

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Cassidy, Michael, and Kumares Sinha. An Electronic Surveillance and Control System for Traffic Management on the Borman Expressway, Part I. Purdue University Press, 1990. http://dx.doi.org/10.5703/1288284313426.

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Cassidy, Michael, and Kumares Sinha. An Electronic Surveillance and Control System for Traffic Management on the Borman Expressway, Part I : Executive Summary. Purdue University, 1990. http://dx.doi.org/10.5703/1288284314185.

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Hunter, Margaret, Jijo K. Mathew, Ed Cox, Matthew Blackwell, and Darcy M. Bullock. Estimation of Connected Vehicle Penetration Rate on Indiana Roadways. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317343.

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Abstract:

Over 400 billion passenger vehicle trajectory waypoints are collected each month in the United States. This data creates many new opportunities for agencies to assess operational characteristics of roadways for more agile management of resources. This study compared traffic counts obtained from 24 Indiana Department of Transportation traffic counts stations with counts derived by the vehicle trajectories during the same periods. These stations were geographically distributed throughout Indiana with 13 locations on interstates and 11 locations on state or US roads. A Wednesday and a Saturday in January, August, and September 2020 are analyzed. The results show that the analyzed interstates had an average penetration of 4.3% with a standard deviation of 1.0. The non-interstate roads had an average penetration of 5.0% with a standard deviation of 1.36. These penetration levels suggest that connected vehicle data can provide a valuable data source for developing scalable roadway performance measures. Since all agencies currently have a highway monitoring system using fixed infrastructure, this paper concludes by recommending agencies integrate a connected vehicle penetration monitoring program into their traditional highway count station program to monitor the growing penetration of connected cars and trucks.

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