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Journal articles on the topic 'Dynamic Traffic Information'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Hamerslag, Rudi. "Dynamic Traffic Information for Advanced Traveler Information Systems." IFAC Proceedings Volumes 27, no. 12 (August 1994): 609–14. http://dx.doi.org/10.1016/s1474-6670(17)47538-0.

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2

Fan, Shan Shan, Dian Ge Yang, Zhao Sheng Zhang, Ting Li, and Xiao Min Lian. "Fusion of Dynamic Traffic Information and Embedded GIS." Advanced Materials Research 433-440 (January 2012): 5654–58. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5654.

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Dynamic traffic information is an important data for Intelligent Transportation System(ITS). Fusion of dynamic traffic information and embedded GIS is a basis for dynamic path planning and dynamic navigation. The approach proposed in this paper designed a model of dynamic traffic information and a hierarchical sub-model of road condition, and established algorithms for fusion with road network. Experiments with embedded GIS show that the models and integration algorithms are validated and effective. We believe the newly developed approach will be of great potential in ITS and future applications of dynamic traffic information.

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3

Khoshyaran, Megan M., and Jean-Patrick Lebacque. "Reactive dynamic traffic assignment: impact of information." Transportation Research Procedia 47 (2020): 59–66. http://dx.doi.org/10.1016/j.trpro.2020.03.154.

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4

Yu, Guodong, and Yu Yang. "Dynamic routing with real-time traffic information." Operational Research 19, no. 4 (May 15, 2017): 1033–58. http://dx.doi.org/10.1007/s12351-017-0314-9.

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5

Fang, Yu, Dong Liang Zhang, Chun Gang Yan, Hong Zhong Chen, and Changjun Jiang. "Service and Management Oriented Traffic Information Grid." International Journal of Distributed Systems and Technologies 1, no. 4 (October 2010): 14–26. http://dx.doi.org/10.4018/jdst.2010100102.

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Traffic information service plays an important role in one’s daily life. However, traffic information processing is very complicated because of its dynamic, cooperative and distributed features. This paper presents the Service and Monitoring Oriented Traffic Information Grid. In this system, it is a remarkable characteristic to provide real-time, dynamic information services for travelers and traffic managers by grid technology. The system provides travelers with services of optimized route scheme, bus arrival prediction based on real-time route status, and route status forecast. For traffic managers, the system can provide vehicle tracing, traffic monitoring, history data analysis, and decision making on traffic control strategy. In this regard, key research includes large multi-source traffic data integration, route status forecast, and optimum dynamic travel scheme implementation based on massive GPS data.

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6

Xu, Bixia, and Zhulin Huang. "Information Traffic and Information Effectiveness." Journal of Emerging Technologies in Accounting 13, no. 2 (August 1, 2016): 21–36. http://dx.doi.org/10.2308/jeta-51548.

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ABSTRACT Search engines are among the most important information technology (IT) applications and platforms on which to conduct information search. This study contributes by investigating whether and how the search engine-enabled information search is related to accounting information effectiveness. We develop the concept of information traffic to conceptualize investor IT-enabled information search activities and to explore whether the searches captured by this concept provide any insights for understanding and enhancing accounting information effectiveness. Building upon the input-process-output model (Maines and McDaniel 2000) and with a sample of 59 accounting information items, we report that information items with higher information traffic have greater ability to explain and predict firm market value (i.e., higher information effectiveness). The impact of information traffic on information effectiveness is higher for economic upturns than for economic downturns and differs among different types of information. We propose a conceptual measure that integrates both information traffic and information effectiveness to capture information relative importance and to suggest empirically an order in importance of the ten types of information we investigate. Our dynamic analysis of information traffic reveals a significant increase of investor IT-enabled information search in the post-financial-crisis period. It also shows higher search increases for accounting items that received previously scant investor attention.

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7

Kim, Kwangsoo, Minseok Kwon, Jaegeun Park, and Yongsoon Eun. "Dynamic Vehicular Route Guidance Using Traffic Prediction Information." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3727865.

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We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonly used metric of mean travel time. Our simulation results show that our dynamic routing algorithm with prediction outperforms both Static and Dynamic without prediction routing algorithms under various traffic conditions and road configurations. We also include traffic scenarios where not all vehicles comply with our dynamic routing with prediction strategy, and the results suggest that more than half the benefit of the new routing algorithm is realized even when only 30% of the vehicles comply.

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8

Peng, Jun Long, and Da Wang. "Pre-Warning Information System of Traffic Safety." Advanced Materials Research 368-373 (October 2011): 3320–23. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.3320.

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Traffic safety dynamic trend pre-warning could effectively strengthenmonitoring strength of traffic management department, reduce the number of trafficaccident and ensure highway transportation orderly and smooth. This article analysestrend pre-warning system of traffic safety from the highway operation managementangle. Based on the above analysis, the trend dynamic pre-warning system composition and the information processing method are advanced. The processingmethods in according to the characteristics of road traffic safety, from trend impactsof traffic safety evaluation indicators use six indicators to reflect all aspects ofsecurity potential crises, such as average speed, traffic flow size, etc.

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9

Auld, Joshua, Omer Verbas, and Monique Stinson. "Agent-Based Dynamic Traffic Assignment with Information Mixing." Procedia Computer Science 151 (2019): 864–69. http://dx.doi.org/10.1016/j.procs.2019.04.119.

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10

Fleischmann, Bernhard, Stefan Gnutzmann, and Elke Sandvoß. "Dynamic Vehicle Routing Based on Online Traffic Information." Transportation Science 38, no. 4 (November 2004): 420–33. http://dx.doi.org/10.1287/trsc.1030.0074.

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11

Lee, Seungjae, Yongtaek Lim, Gyengchul Kim, and Kangwon Lim. "A Dynamic Traffic Management Model with Responsive Signal Control and Traffic Information." IFAC Proceedings Volumes 30, no. 8 (June 1997): 1345–50. http://dx.doi.org/10.1016/s1474-6670(17)44008-0.

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12

Yang, Fan, Henry X. Liu, Rachel R. He, Xuegang Ban, and Bin Ran. "Bilevel Formulation for Optimal Traffic-Information Dissemination." Transportation Research Record: Journal of the Transportation Research Board 1836, no. 1 (January 2003): 21–28. http://dx.doi.org/10.3141/1836-04.

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With the fast-growing telematics market and maturing traffic-information services, telematics devices provide a feasible means with which to manage traffic more efficiently. The provision of traffic information to travelers usually involves different parties that have distinctive objectives: travelers are concerned with benefits of travel-time savings at an affordable service charge, private information service providers (ISPs) seek to provide marketable information services from which they can derive a profit, and traffic management centers (TMCs) have the responsibility to maintain and improve system performance, especially to minimize the total system travel time. How transportation system managers can analyze the trade-offs among these objectives and adjust this new traffic-information flow diagram to improve system performance remains an open question. The trade-offs needed among the conflicting multiple objectives of different parties are studied, and traffic system performance is analyzed. The complex traffic network is formulated as a bilevel program. The upper level can be formulated by using various objective functions, such as the objectives for ISP and TMC. The lower level is a multiclass dynamic traffic-assignment model, which determines dynamic traffic flows in the network by considering the information dissemination strategies provided by the upper-level model. Numerical results of a small network are provided to illustrate the behavior of this model, and they prove that when there is congestion in the dynamic transportation network, appropriate subscribed rates benefit both all travelers and system performance, while the ISPs’ information influences little without congestion in the transportation network.

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13

HASEGAWA, Yasuhisa, Kazuhiro TAKEFUJI, Yusuke IKEMOTO, and Toshio FUKUDA. "Dynamic Routing System for Traffic Dispersion Using Global Traffic Information and Local Communication." Transactions of the Japan Society of Mechanical Engineers Series C 69, no. 677 (2003): 97–103. http://dx.doi.org/10.1299/kikaic.69.97.

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14

Lu, M. L., Z. Zhang, and Y. Shi. "P2P Traffic Optimization Based on Dynamic Network Information Aware." International Journal of Communications, Network and System Sciences 10, no. 05 (2017): 90–104. http://dx.doi.org/10.4236/ijcns.2017.105b009.

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15

Yu, Jiangbo Gabriel, and R. Jayakrishnan. "Quantifying traveler information provision in dynamic heterogeneous traffic networks." Transportation Planning and Technology 42, no. 4 (April 5, 2019): 339–54. http://dx.doi.org/10.1080/03081060.2019.1600241.

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16

Hong, Chen. "Effective usage of global dynamic information for network traffic." Physica A: Statistical Mechanics and its Applications 424 (April 2015): 242–47. http://dx.doi.org/10.1016/j.physa.2015.01.027.

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17

Watling, David. "Urban traffic network models and dynamic driver information systems." Transport Reviews 14, no. 3 (July 1994): 219–46. http://dx.doi.org/10.1080/01441649408716881.

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18

Ru, Hua Suo, De Pin Peng, Lian Xiong Gao, and Yuan Zhou. "Short-Term Traffic State Forecasting for Dynamic Traffic Routing System." Advanced Engineering Forum 6-7 (September 2012): 506–12. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.506.

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The paper researches the problem of how to forecast traffic information in one minute interval. A kalman filter algorithm for short-term traffic forecasting based on the construction of dynamic traffic routing system of Nanning city of China was proposed in this paper, and selected National Road as field calibration, which compared with the real traffic conditions, and the error of predicted results is less than 10%. The results demonstrate the effectiveness of the proposed forecasting model in paper, and the research has a significant contribution for data process of Dynamic Traffic Routing System.

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19

Li, Dan, and Wen Ying Su. "Dynamic Maintenance Data Mining of Traffic Sign Based on Mobile Mapping System." Applied Mechanics and Materials 455 (November 2013): 438–41. http://dx.doi.org/10.4028/www.scientific.net/amm.455.438.

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A dynamic traffic information system is an essential component of an intelligent transportation system. Mobile mapping system (MMS) is a new technique that can be used to improve capability of the information gathering. Article describes the measurement system based on mobile mapping system increased relevant software and hardware that can be used to collect maintenance information of traffic sign. Build a dynamic traffic sign maintenance information system with the function of rapid acquisition and analysis, these improve the accuracy and immediacy of traffic sign maintenance information processing.

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20

Balakrishna, Ramachandran, Haris N. Koutsopoulos, Moshe Ben-Akiva, Bruno M. Fernandez Ruiz, and Manish Mehta. "Simulation-Based Evaluation of Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1910, no. 1 (January 2005): 90–98. http://dx.doi.org/10.1177/0361198105191000111.

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Traveler information has the potential to reduce travel times and improve their reliability. Studies have verified that driver overreaction from the dissemination of information can be eliminated through prediction-based route guidance that uses short-term forecasts of network state. Critical off-line tests of advanced dynamic traffic assignment–based prediction systems have been limited, since the system being evaluated has also been used as the test bed. This paper outlines a detailed simulation-based laboratory for the objective and independent evaluation of advanced traveler information systems, a laboratory with the flexibility to analyze the impacts of various design parameters and modeling errors on the quality of the generated guidance. MITSIMLab, a system for the evaluation of advanced traffic management systems, is integrated with Dynamic Network Assignment for the Management of Information to Travelers (DynaMIT), a simulation-based decision support system designed to generate prediction-based route guidance. Evaluation criteria and requirements for the closed-loop integration of MITSIMLab and DynaMIT are discussed. Detailed case studies demonstrating the evaluation methodology and sensitivity of DynaMIT's guidance are presented.

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21

Wu, Wei Qiang, Bing Ni Luo, Pei Pei Chen, and Qin Yu Zhang. "QoS Routing Protocol Based on Resource Optimization for Aerospace Networks." Applied Mechanics and Materials 596 (July 2014): 856–60. http://dx.doi.org/10.4028/www.scientific.net/amm.596.856.

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To guarantee the QoS of the various traffics and take good advantage of the network resource, the QoS routing protocol based on resource optimization is proposed. By the network state information dynamic update and the QoS routing optimization model design, the QoS routing protocol can provide the optimal path for real-time traffic, broadband traffic and best effort traffic in time with low control message cost. Simulation shown that, compared to the traditional shortest path routing, the QoS routing can satisfy the demand of traffics and can improve the network performance.

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22

Liu, Shixu, Lidan Guo, Said M. Easa, Wensi Chen, Hao Yan, and Yingnuo Tang. "Chaotic Behavior of Traffic-Flow Evolution with Two Departure Intervals in Two-Link Transportation Network." Discrete Dynamics in Nature and Society 2018 (November 6, 2018): 1–11. http://dx.doi.org/10.1155/2018/1605717.

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In this study, the influence of traveler's departure time choice in day-to-day dynamic evolution of traffic flow in a transportation network is investigated. Combining historical information and real-time information, a dynamic evolution model of traffic flow with a study period divided into two intervals is proposed for a simple two-link network. Then, the evolution of network traffic flow is investigated using numerical experiments. Three types of information are considered: (1) only historical information, (2) only real-time information, and (3) both historical and real-time information. The results show that the dynamic evolution of network traffic flow under the three types of information is similar. However, the possibility of chaos occurrence under both historical and real-time information is smaller than that under two individual types of information. When chaos occurs, the chaotic behavior in traffic-flow evolution under only real-time information is relatively less complex than that under the other two types of information.

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23

董, 健. "The Bus Arrival Time Service Based on Dynamic Traffic Information." Hans Journal of Wireless Communications 02, no. 04 (2012): 90–96. http://dx.doi.org/10.12677/hjwc.2012.24017.

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24

Cheong, Taesu, and Chelsea C. White. "Dynamic Traveling Salesman Problem: Value of Real-Time Traffic Information." IEEE Transactions on Intelligent Transportation Systems 13, no. 2 (June 2012): 619–30. http://dx.doi.org/10.1109/tits.2011.2174050.

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25

Stathopoulos, Antony, and Theodore Tsekeris. "The Athens Dynamic Traffic Map for multimodal travel information services." Journal of Maps 4, no. 1 (January 2008): 119–33. http://dx.doi.org/10.4113/jom.2008.1001.

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26

Jiang, Ying, and Junyi Zhang. "How Drivers Adapt to Traffic Accidents and Dynamic Travel Information." Transportation Research Record: Journal of the Transportation Research Board 2413, no. 1 (January 2014): 74–83. http://dx.doi.org/10.3141/2413-08.

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27

Sharma, Vicky, Koushik Kar, Richard La, and Leandros Tassiulas. "Dynamic network provisioning for time-varying traffic." Journal of Communications and Networks 9, no. 4 (December 2007): 408–18. http://dx.doi.org/10.1109/jcn.2007.6182876.

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28

Wang, Xi Cang, and Pei Gang Jiao. "Research on Dynamic Display System of Highway Traffic Incident Information Based on GIS." Applied Mechanics and Materials 99-100 (September 2011): 264–67. http://dx.doi.org/10.4028/www.scientific.net/amm.99-100.264.

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With the development of domestic highway, people put forward higher and higher requirements for traffic capacity and service quality of the highway. This paper starts with traffic incident information management system functions, researches and develops a traffic incident information management system application platform based on the analysis of geographic information system software application. The feasibility of the system is proved with the valuable practical application and the program is implemented.

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29

Zhou, Xiong. "The Analysis and Design of the Computer Network Traffic Monitor." Applied Mechanics and Materials 556-562 (May 2014): 6791–94. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6791.

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The important work of the network management in real-time network monitoring, network monitoring network traffic, the state, the collection of behavioral information, the information will be collected for statistical analysis, network traffic status data, and network traffic information collected includes dynamic information and static information, on this basis, conducted a study of network traffic monitoring and management system.

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Nourmohammadi, Fatemeh, Mohammadhadi Mansourianfar, Sajjad Shafiei, Ziyuan Gu, and Meead Saberi. "An Open GMNS Dataset of a Dynamic Multi-Modal Transportation Network Model of Melbourne, Australia." Data 6, no. 2 (February 19, 2021): 21. http://dx.doi.org/10.3390/data6020021.

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Simulation-based dynamic traffic assignment models are increasingly used in urban transportation systems analysis and planning. They replicate traffic dynamics across transportation networks by capturing the complex interactions between travel demand and supply. However, their applications particularly for large-scale networks have been hindered by the challenges associated with the collection, parsing, development, and sharing of data-intensive inputs. In this paper, we develop and share an open dataset for reproduction of a dynamic multi-modal transportation network model of Melbourne, Australia. The dataset is developed consistently with the General Modeling Network Specification (GMNS), enabling software-agnostic human and machine readability. GMNS is a standard readable format for sharing routable transportation network data that is designed to be used in multimodal static and dynamic transportation operations and planning models.

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31

Basu, Debasis, and Bhargab Maitra. "Evaluation of VMS-Based Traffic Information Using Multiclass Dynamic Traffic Assignment Model: Experience in Kolkata." Journal of Urban Planning and Development 136, no. 1 (March 2010): 104–13. http://dx.doi.org/10.1061/(asce)0733-9488(2010)136:1(104).

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32

Zheng, Shu Kang, Min Li, Qi Zhu, Xiao Min Liu, Hao Dong Shen, Xue Wu Zhang, Zhuo Zhang, and Xin Nan Fan. "Video-Based Traffic Flow Parameters Monitoring and Integrated Traffic Information System." Applied Mechanics and Materials 462-463 (November 2013): 77–84. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.77.

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Road traffic flow parameters are the important basic information for traffic safety management, traffic condition evaluation and decision-making. This project designs a video-based traffic flow parameters monitoring terminal (ITS monitoring terminal), which is based on MENLOW embedded platform and expands its hardware and software resources. CCD cameras are used to capture image in video sequences in traffic environment. Image processing and analysis technologies are used to track vehicles and analyze the vehicle conditions in real time, and a vision measurement model which computes the traffic flow parameters, such as length, width, speed, distance between two vehicles, traffic flow density, and occupancy ratios, etc. is constructed. Furthermore, BP neural network is used to classify vehicles. ITS terminals interconnected with each other through public network or private network (optical ring network of transport agency, WLAN, Internet, and 3G) and connected with monitoring center of transport agency, which achieves dynamic data exchange and share among ITS terminals. It realized a wide-area distributed and integrated transport information system which synthesizes transport information guidance, traffic tracking; condition evaluation, decision-making, and real time transport information release.

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33

Qu, Yanyu, Fangling Pu, Jianguo Yin, Lingzi Liu, and Xin Xu. "Dynamic Traffic Detection and Modeling for Beidou Satellite Networks." Journal of Sensors 2020 (January 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/4575721.

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Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.

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Lo, Hong K., Bin Ran, and Bruce Hongola. "Multiclass Dynamic Traffic Assignment Model: Formulation and Computational Experiences." Transportation Research Record: Journal of the Transportation Research Board 1537, no. 1 (January 1996): 74–82. http://dx.doi.org/10.1177/0361198196153700111.

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To model the impact of advanced transportation management and information systems, especially route guidance and other traffic information systems, it is often necessary to develop traffic models that acknowledge the existence of different user classes and that acknowledge that each class may respond to traffic information differently. As a minimum the model should differentiate vehicles that receive real-time traffic information from those that do not. Most existing approaches that incorporate multiple user classes are based on simulation. Although there are advantages of doing so, their main disadvantage is lack of solution quality and property. Recognizing the importance of these, an analytic approach in which the solution properties and quality are well defined is developed. The users are divided into three classes: (a) fixed-route travelers, (b) stochastic dynamic user-optimal, and (c) dynamic user-optimal. The approach defines the property of each class and integrates each into one modeling framework through a variational inequality formulation. Subsequently, a solution algorithm for the formulation is provided and the results of the algorithm are verified through six scenarios.

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35

Yang, Qi, Haris N. Koutsopoulos, and Moshe E. Ben-Akiva. "Simulation Laboratory for Evaluating Dynamic Traffic Management Systems." Transportation Research Record: Journal of the Transportation Research Board 1710, no. 1 (January 2000): 122–30. http://dx.doi.org/10.3141/1710-14.

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Advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS) are promising technologies for achieving efficiency in the operation of transportation systems. A simulation-based laboratory environment, MITSIMLab, is presented that is designed for testing and evaluation of dynamic traffic management systems. The core of MITSIMLab is a microscopic traffic simulator (MITSIM) and a traffic management simulator (TMS). MITSIM represents traffic flows in the network, and the TMS represents the traffic management system under evaluation. An important feature of MITSIMLab is its ability to model ATMS or ATIS that generate traffic controls and route guidance based on predicted traffic conditions. A graphical user interface allows visualization of the simulation, including animation of vehicle movements. An ATIS case study with a realistic network is also presented to demonstrate the functionality of MITSIMLab.

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De Nunzio, Giovanni, Mohamed Laraki, and Laurent Thibault. "Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact." Atmosphere 12, no. 1 (December 31, 2020): 53. http://dx.doi.org/10.3390/atmos12010053.

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Air pollution poses a major threat to health and climate, yet cities lack simple tools to quantify the costs and effects of their measures and assess those that are most effective in improving air quality. In this work, a complete modeling framework to estimate road traffic microscopic pollutant emissions from common macroscopic road and traffic information is proposed. A machine learning model to estimate driving behavior as a function of traffic conditions and road infrastructure is coupled with a physics-based microscopic emissions model. The up-scaling of the individual vehicle emissions to the traffic-level contribution is simply performed via a meta-model using both statistical vehicles fleet composition and traffic volume data. Validation results with real-world driving data show that: the driving behavior model is able to maintain an estimation error below 10% for relevant boundary parameter of the speed profiles (i.e., mean, initial, and final speed) on any road segment; the traffic microscopic emissions model is able to reduce the estimation error by more than 50% with respect to reference macroscopic models for major pollutants such as NOx and CO2. Such a high-resolution road traffic emissions model at the scale of every road segment in the network proves to be highly beneficial as a source for air quality models and as a monitoring tool for cities.

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Aung, Nyothiri, Weidong Zhang, Sahraoui Dhelim, and Yibo Ai. "T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities." Information 11, no. 3 (March 9, 2020): 149. http://dx.doi.org/10.3390/info11030149.

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Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system.

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38

YOSHII, Toshio, and Masao KUWAHARA. "Travel Behavior Model and Dynamic Traffic Simulation toward ITS Society. An Evaluation Method on Effects of Dynamic Traffic Information Provision." Doboku Gakkai Ronbunshu, no. 653 (2000): 39–48. http://dx.doi.org/10.2208/jscej.2000.653_39.

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39

Marchal, Fabrice, and André de Palma. "Measurement of Uncertainty Costs with Dynamic Traffic Simulations." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (January 2008): 67–75. http://dx.doi.org/10.3141/2085-08.

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Nonrecurrent congestion in transportation networks occurs as a consequence of stochastic factors affecting demand and supply. Intelligent transportation systems such as advanced traveler information systems and advanced traffic management systems are designed to reduce the impacts of nonrecurrent congestion by providing information to a fraction of users or by controlling the variability of traffic flows. For these reasons, the design of these systems requires a reliable forecast of nonrecurrent congestion. A new method is proposed to measure the impacts of nonrecurrent congestion on travel costs by taking risk aversion into account. The traffic model is based on the dynamic traffic simulation model METROPOLIS. Incidents are generated randomly by reducing the capacity of the network. Users can instantaneously adapt to the unexpected travel conditions or can also change their behavior through a day-to-day adjustment process. Comparisons with incident-free simulations provide a benchmark for potential travel time savings that can be brought about by a state-of-the-art information system. The impact of variable travel conditions is measured by describing the willingness to pay to avoid risky or unreliable journeys. Indeed, for risk-averse drivers, any uncertainty corresponds to a utility loss. This utility loss is computed for several levels of network disruption. The main result of the study is that the utility loss due to uncertainty is of the same order of magnitude as the total travel costs.

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40

Wei, Chong, and Chun Fu Shao. "Minimizing Traffic Delays for Parallel Road Networks Using an Information-Based Control Strategy." Advanced Materials Research 838-841 (November 2013): 2140–44. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.2140.

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This study attempted to develop a control strategy in order to minimize the traffic delay caused by the traffic incident. The proposed strategy controls traffic in terms of providing a combination of different types of incident information. We used the cell transmission model as the underlying traffic flow model to take into accounts the traffic dynamic and use the simulated annealing algorithm to seek the optimal combination of the incident information. The numerical examples confirmed that providing incident information through the proposed control strategy can significantly reduce the traffic delay.

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41

Yu, Yang, Ke Han, and Washington Ochieng. "Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing." Transportation Research Part C: Emerging Technologies 114 (May 2020): 59–83. http://dx.doi.org/10.1016/j.trc.2020.02.004.

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De Palma, André, Fabrice Marchal, and Yurii Nesterov. "METROPOLIS: Modular System for Dynamic Traffic Simulation." Transportation Research Record: Journal of the Transportation Research Board 1607, no. 1 (January 1997): 178–84. http://dx.doi.org/10.3141/1607-24.

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METROPOLIS proposes an interactive environment that simulates automobile traffic in large urban areas. The core of the system is a dynamic simulator that integrates commuters’ departure time and route choice behaviors over large networks: Drivers are assumed to minimize a generalized travel cost function that depends on travel time and schedule delay. This simulator is based on a behavioral driver information process. It allows real-time and off-line simulations. The system also includes a scenario builder and a graphical results viewer. The main ideas underlying METROPOLIS are presented, and preliminary computer simulation experiments are discussed for Geneva, Switzerland.

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Madanat, Samer M., Shou-Ren Hu, and James V. Krogmeier. "Dynamic Estimation and Prediction of Freeway O-D Matrices with Route Switching Considerations and Time-Dependent Model Parameters." Transportation Research Record: Journal of the Transportation Research Board 1537, no. 1 (January 1996): 98–105. http://dx.doi.org/10.1177/0361198196153700114.

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An enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway origin-and-destination (O-D) matrices is presented. The effects of traffic congestion and traffic diversion information on the O-D distribution pattern are explicitly captured through a behavioral model of route switching. Moreover, in view of the time-varying nature of traffic variables, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation and prediction of dynamic freeway O-D demands.

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Lu, Yuan Zhang, and Bing Zhang. "Based on Data Fusion Intelligent Traffic Information Analysis and Optimization." Applied Mechanics and Materials 568-570 (June 2014): 831–34. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.831.

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In this paper, we propose an analysis refine scheme based on data fusion towards some existing problems in data analysis of intelligent transportation systems .This method constructed the data into a plurality of time-series according to the characteristics of each attribute data. Providing an objective scientific basis for dynamic traffic management through intelligent analysis of traffic information based on the gray advantage analysis among data and system model of Intelligent Traffic Information decision support and auxiliary decision analysis.

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Wu, Zhijun, Shengyan Zhou, Liang Liu, and Jin Lei. "Research on SWIM Services Dynamic Migration Method." Future Internet 11, no. 9 (August 27, 2019): 187. http://dx.doi.org/10.3390/fi11090187.

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Air traffic management (ATM) plays an important role in maintaining and promoting air traffic safety, maintaining air traffic order and ensuring smooth air traffic. As the core of air traffic management, it is essential to ensure the safe and stable operation of system-wide information management (SWIM). Facing the complex and ever-changing network environment, a SWIM services dynamic migration method is proposed in this paper. This method combines SWIM core services to select destination nodes and migrate services. The experiment proves that the method can hide the service node while ensuring service continuity and increase the difficulty of malicious detection. By comparing with others, this method is more suitable for SWIM in terms of invulnerability. The throughput and delay performance of the method can meet the needs of SWIM.

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Zhao, Xin, Gilles Goncalves, and Remy Dupas. "A dynamic vehicle routing problem based on real-time traffic information." International Journal of Innovative Computing and Applications 2, no. 4 (2010): 215. http://dx.doi.org/10.1504/ijica.2010.036809.

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Lee, Seungjae. "Multimodal, multiclass stochastic dynamic traffic assignment for evaluating information provision strategies." Journal of Advanced Transportation 42, no. 1 (January 2008): 45–64. http://dx.doi.org/10.1002/atr.5670420105.

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Li, Manman, Jian Lu, and Jiahui Sun. "Day-to-Day Traffic Assignment Model considering Information Fusion and Dynamic Route Adjustment Ratio." Discrete Dynamics in Nature and Society 2020 (June 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/5751349.

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A new day-to-day traffic assignment model is proposed to describe travelers’ day-to-day behavioral changes with advanced traffic information system. In the model, travelers’ perception is updated by a double exponential-smoothing learning process combining experience and traffic information that is explicitly modelled. Route adjustment ratio is dynamically determined by the difference between perceived and expected utilities. Through theoretical analyses, we investigate the existence of its fixed point and the influence factors of uniqueness of the fixed point. An iterative-based algorithm that can solve the fixed point is also given. Numerical experiments are then conducted to investigate effects of several main parameters on its convergence, which provides insights for traffic management. In addition, we compare the system efficiencies under the static route adjustment ratio and dynamic route adjustment ratio and show the application of the model.

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Zhou, Xu, Zhao Liu, Xiao Xiao Zhao, and Jian Hua Guo. "Framework for Dynamic OD Matrix Estimation Based on Multi-Source Traffic Data Fusion." Applied Mechanics and Materials 505-506 (January 2014): 1153–56. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1153.

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The advanced transportation management and information systems (ATMIS) are strengthening the capability of collecting multi-source traffic data constantly from the road networks. Considering the fundamental role of dynamic Origin-Destination data for many advanced traffic management systems, it is promising to apply the multi-source traffic data to improve the dynamic OD estimation. Targeting dynamic OD data estimation, the classical OD data estimation approaches are discussed, and a framework of dynamic OD estimation based on multi-source traffic data is proposed and analyzed. Future researches are recommended in the end.

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ZHUANG, YONG, DONG-MEI SHI, WEN-BO DU, HAI-FENG ZHANG, and BING-HONG WANG. "INTEGRATING LOCAL DYNAMIC AND GLOBAL STATIC INFORMATION FOR ROUTING TRAFFIC ON NETWORKS." International Journal of Modern Physics C 22, no. 06 (June 2011): 649–59. http://dx.doi.org/10.1142/s012918311101652x.

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Congestion in communication networks is a topic of theoretical interest and practical importance. In this work, we propose a mixed routing strategy by considering the global static information (topology of the network) and local dynamic information (queue length of neighbor nodes). Under this routing strategy, the traffic capacity can be remarkably promoted compared with that by former efficient routing strategy [G. Yan et al., Phys. Rev. E73, 046108 (2006)]. Besides, the traffic capacity, the average packet number as well as the travel time are almost independent of a time delay in updating the local dynamic information.

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