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Journal articles on the topic 'Macroscopic Fundamental Diagrams (MFD)'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Ambühl, Lukas, Allister Loder, Michiel C. J. Bliemer, Monica Menendez, and Kay W. Axhausen. "Introducing a Re-Sampling Methodology for the Estimation of Empirical Macroscopic Fundamental Diagrams." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (2018): 239–48. http://dx.doi.org/10.1177/0361198118788181.

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The uncertainty in the estimation of the macroscopic fundamental diagram (MFD) under real-world traffic conditions and urban dynamics might result in an inaccurate estimation of the MFD parameters—especially if congestion is rarely observed network-wide. For example, as data normally come from punctual observations out of the whole network, it is unclear how representative these observations might be (i.e., how much is the observed capacity affected by the network’s inhomogeneity). Similarly, if the observed data do not exhibit a distinct congested branch, it is hard to determine the network capacity and critical density. This, in turn, also leads to uncertainties and errors in the parametrization of the MFD for applications, for example traffic control. This paper introduces a novel methodology to estimate (i) the level of inhomogeneity in the network, and (ii) the critical density of the MFD, even when no congested branch is observed. The methodology is based on the idea of re-sampling the empirical data set. Using an extensive data set from Lucerne, Switzerland, and London, UK, insights are provided on the performance and the application of the proposed methodology. The proposed methodology is used to illustrate how the level of inhomogeneity is lower in Lucerne than in the three areas of the network of London that are investigated. The proposed measure of the level of inhomogeneity gives city planners the possibility to analyze and investigate how efficiently their road network is utilized. In addition, the analysis shows that, for the network of Lucerne, the proposed methodology allows accurate estimation of the critical density up to 16 times more often than would be possible otherwise. This simple and robust estimation of the critical density is crucial for the application of many traffic control algorithms.

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Hu, Guojing, Weike Lu, Feng Wang, and Robert W. Whalin. "Macroscopic Fundamental Diagram Based Discrete Transportation Network Design." Journal of Advanced Transportation 2020 (January 20, 2020): 1–13. http://dx.doi.org/10.1155/2020/4951953.

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The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and k-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results.

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Xu, Guanhao, Zhengyao Yu, and Vikash V. Gayah. "Analytical Method to Approximate the Impact of Turning on the Macroscopic Fundamental Diagram." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (2020): 933–47. http://dx.doi.org/10.1177/0361198120933274.

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Network macroscopic fundamental diagrams (MFDs) have recently been shown to exist in real-world urban traffic networks. The existence of an MFD facilitates the modeling of urban traffic network dynamics at a regional level, which can be used to identify and refine large-scale network-wide control strategies. To be useful, MFD-based modeling frameworks require an estimate of the functional form of a network’s MFD. Analytical methods have been proposed to estimate a network’s MFD by abstracting the network as a single ring-road or corridor and modeling the flow–density relationship on that simplified element. However, these existing methods cannot account for the impact of turning traffic, as only a single corridor is considered. This paper proposes a method to estimate a network’s MFD when vehicles are allowed to turn into or out of a corridor. A two-ring abstraction is first used to analyze how turning will affect vehicle travel in a more general network, and then the model is further approximated using a single ring-road or corridor. This approximation is useful as it facilitates the application of existing variational theory-based methods (the stochastic method of cuts) to estimate the flow–density relationship on the corridor, while accounting for the stochastic nature of turning. Results of the approximation compared with a more realistic simulation that includes features that cannot be captured using variational theory—such as internal origins and destinations—suggest that this approximation works to estimate a network’s MFD when turning traffic is present.

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Ji, Yangbeibei, Mingwei Xu, Jie Li, and Henk J. Van Zuylen. "Determining the Macroscopic Fundamental Diagram from Mixed and Partial Traffic Data." PROMET - Traffic&Transportation 30, no. 3 (2018): 267–79. http://dx.doi.org/10.7307/ptt.v30i3.2406.

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The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management. For the determination of an MFD, both traffic volumes and traffic densities are needed. This study introduces a methodology to determine an MFD using combined data from probe vehicles and loop detector counts. The probe vehicles in this study were taxis with GPS. The ratio of taxis in the total traffic was determined and used to convert taxi density to the density of all vehicles. This ratio changes over the day and between different links. We found evidence that the MFD was rather similar for days in the same year based on real data collected in Changsha, China. The difference between MFDs made of data from 2013 and 2015 reveals that the modification of traffic control can influence the MFD significantly. A macroscopic fundamental diagram could also be drawn for an area with incomplete data gained from a sample of loop detectors. An MFD based on incomplete data can also be used to monitor the emergence and disappearance of congestion, just as an MFD based on complete traffic data.

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Huang, Chongxuan, Nan Zheng, and Jun Zhang. "Investigation of Bimodal Macroscopic Fundamental Diagrams in Large-Scale Urban Networks: Empirical Study with GPS Data for Shenzhen City." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (2019): 114–28. http://dx.doi.org/10.1177/0361198119843472.

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This paper investigates traffic dynamics in bimodal urban networks utilizing the macroscopic fundamental diagram (MFD) and the three-dimensional macroscopic fundamental diagram (3D-MFD), which are network-level traffic flow modeling tools. Although the existence and the properties of the MFD have been extensively analyzed with field data in literature, few empirical studies examine these features of the 3D-MFDs for large-scale networks. For this work, GPS data for cars and buses running in the network of Shenzhen city in China are available for analysis and this offers a great opportunity for the investigation. Interestingly, both MFD and 3D-MFD dynamics are reflected in the data. Network partition is performed to reduce the hysteresis on the MFD and the network is split into two regions for further analysis. Then the investigation focuses on the MFD relationship for buses only. The average passenger occupancy is estimated and incorporated to generate a passenger MFD (pMFD) for buses. Moreover, bus operation on dedicated bus lanes is analyzed. Having understood traffic dynamics of cars, buses, and passengers respectively, the 3D-MFDs which illustrate the joint influence of car and bus accumulations on the global network-level traffic performance are presented. Given the scatter plot of the 3D-MFDs for the two partitioned regions, analytical approximations are provided, fitting by exponential functions. These results are promising, as they confirm the traffic features that were found from simulation-based studies in previous work.

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Lin, Xiaohui, Jianmin Xu, and Chengtao Cao. "Simulation and comparison of two fusion methods for macroscopic fundamental diagram estimation." Archives of Transport 51, no. 3 (2019): 35–48. http://dx.doi.org/10.5604/01.3001.0013.6161.

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Accurate estimation of macroscopic fundamental diagram (MFD) is the precondition of MFD’s application. At present, there are two traditional estimation methods of road network’s MFD, such as the loop detector data (LDD) estimation method and the floating car data (FCD) estimation method, but there are limitations in both traditional estimation methods. In order to improve the accuracy of road network MFD estimation, a few scholars have studied the fusion method of road network MFD estimation, but there are still some shortcomings on the whole. However, based on the research of adaptive weighted averaging (AWA) fusion method for MFD estimation of road network, I propose to use the MFD’s two parameters of road network obtained by LDD estimation method and FCD estimation method, and establish a back-propagation neural network data fusion model for MFD parameters of road network (BPNN estimation fusion method), and then the micro-traffic simulation model of connected-vehicle network based on Vissim software is established by taking the intersection group of the core road network in Tianhe District of Guangzhou as the simulation experimental area, finally, compared and analyzed two MFD estimation fusion methods of road network, in order to determine the best MFD estimation fusion method of road network. The results show that the mean absolute percent error (MAPE) of the parameters of road network’s MFD and the average absolute values of difference values of the state ratio of road network’s MFD are both the smallest after BPNN estimation fusion, which is the closest to the standard MFD of road network. It can be seen that the result of BPNN estimation fusion method is better than that of AWA estimation fusion method, which can improve the accuracy of road network MFD estimation effectively.

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Yu, Yi, Liang Wang, Xianglun Mo, Yao Yu, and Mei liu. "Macroscopic Fundamental Diagram based Road Network Characteristics Identification and Simulation." E3S Web of Conferences 165 (2020): 04051. http://dx.doi.org/10.1051/e3sconf/202016504051.

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As an inherent property of the road network, macroscopic fundamental diagram (MFD) method can effectively describe the traffic status of the urban roads and identify the relationship among key factors, such as traffic flow and occupancy. Currently, using MFD is easily affected by various network inner factors including topology and road density, so in this paper we propose a method to identify inner characteristic of road network and do a series of comparisons under different scenarios with fixed traffic input circumstance. The differential impact of data collector setting locations are discussed with a aim to reveal the respective location setting suitable for various networks conditions in initial; then road topology and density are designed in road network and simulated MFD performances with flow equilibrium affections. It is shown as the dispersion decreasing of link length or road density of network, the network exhibits better operation efficiency so as to increase the output of link flow and the dissipative ability of the road network. Meanwhile, the equivalent of entrances and exits is proved as another important factor has same impact on MFD.

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Yan, Fei, Fu-li Tian, and Zhong-ke Shi. "Iterative Learning Control Approach for Signaling Split in Urban Traffic Networks with Macroscopic Fundamental Diagrams." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/975328.

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Recent analysis of field experiments in cities revealed that a macroscopic fundamental diagram (MFD) relating network outflow and network vehicle accumulation exists in the urban traffic networks. It has been further confirmed that an MFD is well defined if the network has regular network topology and homogeneous spatial distribution of vehicle accumulation. However, many real urban networks have different levels of heterogeneity in the spatial distribution of vehicle accumulation. In order to improve the mobility in heterogeneously congested networks, we propose an iterative learning control approach for signaling split, which aims at distributing the accumulation in the networks as homogeneously as possible and ensuring the networks have a larger outflow. The asymptotic convergence of the proposed approach is proved by rigorous analysis and the effectiveness is further demonstrated by extensive simulations.

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Paipuri, Mahendra, Ludovic Leclercq, and Jean Krug. "Validation of Macroscopic Fundamental Diagrams-Based Models with Microscopic Simulations on Real Networks: Importance of Production Hysteresis and Trip Lengths Estimation." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (2019): 478–92. http://dx.doi.org/10.1177/0361198119839340.

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This work focuses on the application of accumulation-based and trip-based macroscopic fundamental diagrams (MFD) approaches to real transportation networks and discusses the calibration of the MFD shape and trip lengths estimation using a thorough validation of the network dynamics with microsimulation data. This work not only investigates a classical unimodal approach to fit the production MFD, but also a bimodal MFD curve. Different methods of calibrating trip lengths in the reservoir are introduced to study the influence of trip lengths estimation on the accuracy of MFD models. MFD models are validated against microsimulations that are carried out using real origin–destination (OD) matrix and demand estimated from the data of Lyon city in France. The proposed bimodal production MFD curve captures the hysteresis in the production MFD to a good extent. Subsequently, it is shown that the refined description of trip lengths gives more accurate estimates of accumulation evolution for the trip-based approach. Finally, a case is presented with a modified OD matrix to study the effect of OD matrix changes on accuracy of MFD simulations.

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Ma, Yingying, Yuanqi Xie, and Yongjie Lin. "An Influence Analytical Model of Dedicated Bus Lane on Network Traffic by Macroscopic Fundamental Diagram." Journal of Advanced Transportation 2021 (August 24, 2021): 1–18. http://dx.doi.org/10.1155/2021/2617732.

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To study the influence mechanism of dedicated bus lanes on the urban road network, this paper proposes a novel analytical model of macroscopic fundamental diagram (MFD) and passenger macroscopic fundamental diagram (p-MFD) and the corresponding indicators based on MFD and p-MFD to evaluate the operation of the network. Taking the grid network as an example, this paper collects traffic flow to calibrate the developed MFD and p-MFD and evaluates the network performance under different proportions of dedicated bus lanes. The simulation results show that the larger the proportion of dedicated bus lanes, the greater the impact on the rising section and the stable section of MFD and the descending section and post-stable section of p-MFD. Further analysis for the sensitivity of simulation experiments found that the strategy of setting dedicated bus lanes will improve the efficiency of vehicle and passenger transport when the road network is in a smooth state and ensure the continuous output of passengers when the network is in a congested state.

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Hong, Rongrong, Chengchuan An, Zhenbo Lu, Jingxin Xia, Qinghui Nie, and Wenming Rao. "Characterizing Critical Transition State for Network Fundamental Diagram." Journal of Advanced Transportation 2018 (November 18, 2018): 1–13. http://dx.doi.org/10.1155/2018/4839729.

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Macroscopic Fundamental Diagram (MFD) reveals the relationship between network accumulation and flow at the macroscopic level. The network traffic flow state analysis is a fundamental problem for the MFD-based applications. Theoretical and experimental investigations have provided insights into the dynamics and characters of traffic flow states. Although many empirical studies had been conducted in the field of MFD, few studies were dedicated to investigate the network traffic flow states with field data. This study aims to develop a data-driven method based on time series analysis of MFD state points to characterize critical transition state (CTS) of network traffic flow using field data. The proposed method was tested in a real network of Kunshan City, China. The test results showed that the CTS points can be well captured by the proposed method. The identified CTS points distinguished the traffic states between free-flow state and optimal accumulation state, and the optimal accumulation state was characterized. The day-to-day pattern of CTS points was investigated by the Gaussian Mixture Model-based clustering model. An extended application of real-time identification of CTS points was also discussed. The proposed method is helpful to understand the temporal evolution process of network traffic flow and provides potentials for developing more reliable network traffic flow management strategies, such as optimizing traffic signal plans and developing strategies for congestion tooling.

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Lin, Xiaohui, Jianmin Xu, Peiqun Lin, Chengtao Cao, and Jiahui Liu. "Improved Road-Network-Flow Control Strategy Based on Macroscopic Fundamental Diagrams and Queuing Length in Connected-Vehicle Network." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/8784067.

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Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD) can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1) no control strategy, (2) boundary control, and (3) boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two.

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Lu, Weike, Jun Liu, Jiannan Mao, Guojing Hu, Chuqiao Gao, and Lan Liu. "Macroscopic Fundamental Diagram Approach to Evaluating the Performance of Regional Traffic Controls." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 7 (2020): 420–30. http://dx.doi.org/10.1177/0361198120923359.

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Evaluating a regional traffic control system requires understanding both the advantages and disadvantages of control schemes as well as the interrelated characteristics of the system. To assess the efficiency of regional signal control schemes in a road network, this study, which is based on the macroscopic fundamental diagram (MFD) concept, proposes four evaluative indicators: maximum throughput, critical accumulation, gridlock accumulation and the degree of homogeneity. The maximum throughput and gridlock accumulation can be used to reflect the road network capacity and load capacity, respectively. The degree of homogeneity quantifies the spatial variations of traffic flows in the network. Combined with the gridlock accumulation, the critical accumulation values the durability of a regional control system in managing congestion in the network. This study used the regional road network in Qingyang District of Chengdu, China, as a real-world example to demonstrate the proposed MFD-based approach. In the demonstration, the MFD-based evaluation method was compared to the traditional travel time-based method. The demonstration evaluated the control effect and characteristic values of the network under four control modes: fixed-time, actuated, adaptive and adaptive coordinated control.

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Ji, YangBeibei, Chao Mo, Wanjing Ma, and Dabin Liao. "Feedback Gating Control for Network Based on Macroscopic Fundamental Diagram." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3528952.

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Empirical data from Yokohama, Japan, showed that a macroscopic fundamental diagram (MFD) of urban traffic provides for different network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. This provides new tools for network congestion control. Based on MFD, this paper proposed a feedback gating control policy which can be used to mitigate network congestion by adjusting signal timings of gating intersections. The objective of the feedback gating control model is to maximize the outflow and distribute the allowed inflows properly according to external demand and capacity of each gating intersection. An example network is used to test the performance of proposed feedback gating control model. Two types of background signalization types for the intersections within the test network, fixed-time and actuated control, are considered. The results of extensive simulation validate that the proposed feedback gating control model can get a Pareto improvement since the performance of both gating intersections and the whole network can be improved significantly especially under heavy demand situations. The inflows and outflows can be improved to a higher level, and the delay and queue length at all gating intersections are decreased dramatically.

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Wei, Bangyang, and Daniel(Jian) Sun. "A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram." Journal of Advanced Transportation 2018 (August 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/8616120.

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Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

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Yan, Fei, Fuli Tian, and Zhongke Shi. "Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks." International Journal of Modern Physics C 27, no. 04 (2016): 1650045. http://dx.doi.org/10.1142/s0129183116500455.

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Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

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Lin, Xiaohui. "A Road Network Traffic State Identification Method Based on Macroscopic Fundamental Diagram and Spectral Clustering and Support Vector Machine." Mathematical Problems in Engineering 2019 (April 21, 2019): 1–10. http://dx.doi.org/10.1155/2019/6571237.

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Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network’s MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network’s MFD. Secondly, SVM multiclassifier is trained with the partitioned road network’s MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel.

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Zyryanov, Vladimir. "Simulation network-level relationships of traffic flow." MATEC Web of Conferences 334 (2021): 01005. http://dx.doi.org/10.1051/matecconf/202133401005.

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This paper describes possibilities of the macroscopic fundamental diagram (MFD) of traffic flow to predict the conditions of operation of the road network in urban areas. This study examines relationships between traffic flow parameters on the network level. Microscopic traffic simulation has provided important data on the estimation of road capacity, velocity, trip time, and detection of congestions reasons. Data of spatial distribution density in network useful for implementing approach based on gating policy on subnetwork using MFD. It presents the results of a simulation using the example of central area of Rostov-on-Don

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Kim, Sunghoon, Sehyun Tak, and Hwasoo Yeo. "Investigating Transfer Flow between Urban Networks Based on a Macroscopic Fundamental Diagram." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (2018): 75–85. http://dx.doi.org/10.1177/0361198118778927.

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The aim of the concept of perimeter control is to manage the transfer flow between urban road networks. However, there is a lack of description on the behavior of transfer flow between networks in relation to the macroscopic fundamental diagram (MFD). Hence, this paper suggests a description of transfer flow and investigates it with microscopic simulation experiments. The results found that the network outbound demand strongly influences the outflow regardless of spatial inhomogeneity. Another finding is that the boundary capacity influences the network outflow. The effect of restriction on outflow of a network due to limited supply level of a neighboring network was also checked. Furthermore, it was found that a network’s inflow and outflow in each direction (north, south, east, and west) is proportional to the demand ratio. Finally, the suggested description of transfer flow has been confirmed, and this can be useful for various analyses on inter-network traffic dynamics.

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Lin, Xiaohui, and Jianmin Xu. "Feedforward feedback iterative learning control method for the multilayer boundaries of oversaturated intersections based on the macroscopic fundamental diagram." Archives of Transport 53, no. 1 (2020): 67–87. http://dx.doi.org/10.5604/01.3001.0014.1745.

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The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feedforward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.

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Li, Yudi, Lei Zhu, Jian Sun, and Ye Tian. "Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram." Journal of Advanced Transportation 2019 (July 31, 2019): 1–15. http://dx.doi.org/10.1155/2019/9540386.

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Transportation simulation and analysis projects that utilize maps with inappropriate fidelity levels carry a significant risk of having poor runtime or poor prediction performance. To address this, researchers use map abstraction method to abstract out a simplified map with fewer links and nodes based on the original full detailed map. Traditional static abstraction methods produce analysis maps with a single fidelity across the entire planning horizon, which cannot reflect the dynamic changes of daily traffic. This paper proposes a spatiotemporal dynamic map abstraction approach that adopts a time series clustering method to segment the analysis time horizon adaptively based on a Macroscopic Fundamental Diagram (MFD) curve, which describes network-wide dynamic traffic states. Time periods with similar macro-performance are grouped into one subinterval. A map with a dedicated fidelity is produced for each subinterval. Furthermore, a simulation is run on multiple abstracted maps with different fidelities in a sequence according to their temporal order. A numerical experiment ascertains that the proposed approach has promising results in both analysis accuracy and efficiency for resource-constrained modeling agents.

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Gao, Yuan, Jiandong Zhao, Ziyan Qin, Yingzi Feng, Zhenzhen Yang, and Bin Jia. "Traffic Speed Forecast in Adjacent Region between Highway and Urban Expressway: Based on MFD and GRU Model." Journal of Advanced Transportation 2020 (December 2, 2020): 1–18. http://dx.doi.org/10.1155/2020/8897325.

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Traffic congestion in the adjacent region between the highway and urban expressway is becoming more and more serious. This paper proposes a traffic speed forecast method based on the Macroscopic Fundamental Diagram (MFD) and Gated Recurrent Unit (GRU) model to provide the necessary traffic guidance information for travelers in this region. Firstly, considering that the road traffic speed is affected by the macroscopic traffic state, the adjacent region between the highway and expressway is divided into subareas based on the MFD. Secondly, the spatial-temporal correlation coefficient is proposed to measure the correlation between subareas. Then, the matrix of regional traffic speed data is constructed. Thirdly, the matrix is input into the GRU prediction model to get the predicted traffic speed. The proposed algorithm’s prediction performance is verified based on the GPS data collected from the adjacent region between Beijing Highways and Expressway.

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Guo, Yajuan, Licai Yang, and Jun Gao. "Coordinated Perimeter Control for Multiregion Heterogeneous Networks Based on Optimized Transfer Flows." Mathematical Problems in Engineering 2020 (September 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/3926265.

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Exploring efficient control strategies for heterogeneously congested urban networks remains a big research challenge. The theory of macroscopic fundamental diagram (MFD) provides a new perspective for network-wide congestion control decisions. This paper proposes a coordinated perimeter control strategy for multiregion heterogeneous networks based on optimized transfer flows. First, a two-layer network partitioning method is presented to capture spatial heterogeneity dynamics of urban networks. For this partition, traffic flow equilibrium model based on MFD and multiagent based hierarchical traffic management scheme are built. Then, an improved multinomial logit model is developed for deriving optimized transfer flows among multiple congested regions. A coordinated perimeter control strategy using model predictive control is further proposed, which is aimed at tracking desired accumulations of each congested region. As a case study, the proposed control strategy is applied to the downtown network of Jinan, China, using simulation analysis. The results demonstrate that it can achieve balanced network flow distribution and increased mobility.

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Ambühl, Lukas, Allister Loder, Nan Zheng, Kay W. Axhausen, and Monica Menendez. "Approximative Network Partitioning for MFDs from Stationary Sensor Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (2019): 94–103. http://dx.doi.org/10.1177/0361198119843264.

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The macroscopic fundamental diagram (MFD) measures network-level traffic performance of urban road networks. Large-scale networks are normally partitioned into homogeneous regions in relation to road network topology and traffic dynamics. Existing partitioning algorithms rely on unbiased data. Unfortunately, widely available stationary traffic sensors introduce a spatial bias and may fail to identify meaningful regions for MFD estimations. Thus, it is crucial to revisit and develop stationary-sensor-based partitioning algorithm. This paper proposes an alternative two-step partitioning algorithm for MFD estimations based on information collected solely from stationary sensors. In a first step, possible partitioning outcomes are generated in the road networks using random walks. In a second step, the regions’ MFDs are estimated under every possible partitioning outcome. Based on previous work, an indicator is proposed to evaluate the traffic heterogeneity in regions. The proposed partitioning approach is tested with an abstract grid network and empirical data from Zurich. In addition, the results are compared with an algorithm that disregards stationary detectors’ biases. The results demonstrate that the proposed approach performs well for obtaining the quasi-optimal network partitions yielding the lowest heterogeneity among all possible partition outcomes. The presented approach not only complements existing literature, but also offers practice-oriented solutions for transport authorities to estimate the MFDs with their available data.

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Kumarage, Sakitha, Mehmet Yildirimoglu, Mohsen Ramezani, and Zuduo Zheng. "Schedule-Constrained Demand Management in Two-Region Urban Networks." Transportation Science 55, no. 4 (2021): 857–82. http://dx.doi.org/10.1287/trsc.2021.1052.

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Demand management aiming to optimize system cost while ensuring user compliance in an urban traffic network is a challenging task. This paper introduces a cooperative demand redistribution strategy to optimize network performance through the retiming of departure times within a limited time window. The proposed model minimizes the total time spent in a two-region urban network by incurring minimal disruption to travelers’ departure schedules. Two traffic models based on the macroscopic fundamental diagram (MFD) are jointly implemented to redistribute demand and analyze travelers’ reaction. First, we establish equilibrium conditions via a day-to-day assignment process, which allows travelers to find their preferred departure times. The trip-based MFD model that incorporates individual traveler attributes is implemented in the day-to-day assignment, and it is conjugated with a network-level detour ratio model to incorporate the effect of congestion in individual traveler route choice. This allows us to consider travelers with individual preferences on departure times influenced by desired arrival times, trip lengths, and earliness and lateness costs. Second, we develop a nonlinear optimization problem to minimize the total time spent considering both observed and unobserved demand—that is, travelers opting in and out of the demand management platform. The accumulation-based MFD model that builds on aggregated system representation is implemented as part of the constraints in the nonlinear optimization problem. The results confirm the resourcefulness of the model to address complex two-region traffic dynamics and to increase overall performance by reaching a constrained system optimum scenario while ensuring the applicability at both full and partial user compliance conditions.

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Yang, Xin, Juncheng Chen, Mantun Yan, Zhao He, Ziyan Qin, and Jiandong Zhao. "Regional Boundary Control of Traffic Network Based on MFD and FR-PID." Journal of Advanced Transportation 2021 (September 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/9730813.

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In recent years, urban traffic congestion has become more serious and the capacity of roads has declined, resulting in frequent traffic accidents. In order to effectively alleviate the traffic congestion of the regional road network, aiming at the problem of lack of accurate OD data of the road network, a regional boundary control method of the traffic network based on fuzzy RBF neural network PID (FR-PID) is proposed by combining the theory of macroscopic fundamental diagram (MFD). Firstly, based on the traffic survey, the simulation model of the study area is built, and the basic data such as the traffic flow and the time occupation rate of each road section are obtained. Secondly, the simulation data are used to test the existence of MFD in the road network, and the controlled area is defined. Then, the vehicle change model of the road network area is established. Then, in view of the problem of poor adaptive ability of traditional PID control, the FR-PID control structure is designed. Finally, an example is verified by VISSIM software. In the simulation, different control methods are used for comparison and verification, and the simulation results are analyzed. The results show that the control effect of the proposed method is better than that of the traditional method, and the regional average accumulative vehicle number, regional average completed volume, regional accumulative delays, and total vehicle travel time are optimized by 28.21%, 41.19%, 27.06%, and 32.73%, respectively. The research results can provide reference for the management of urban congestion, thereby reducing the number of traffic accidents and improving urban traffic safety.

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Soltani-Sarvestani, Mohammad Amin, Zohreh Azimifar, Alexander Wong, and Ali Akbar Safavi. "An Innovative Eigenvector-Based Method for Traffic Light Scheduling." Journal of Advanced Transportation 2020 (September 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/1462430.

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This paper introduces two traffic light strategies to control traffic and avoid traffic jam in urban networks. One strategy is a new traffic light scheduling system, which controls traffic light using local variables (waiting time and number of vehicle on links) but has a global impact on the traffic, using shared variables between neighbour intersections. The proposed traffic light scheduling system is designed based on eigenvector centrality of intersection relation matrix. The intersection relation matrix is a new representation of a junction which indicates the traffic relation between intersection’s links and adjacent intersections. The second contribution is expanding a new dual mode traffic light strategy (namely, Exit Status Traffic Light (ETL)), which notifies the drivers whether they are allowed to exit a street or not. In other words, vehicles are allowed to enter a street in both red and green ETL, but they are not allowed to exit the street for a long time in red ETL (while traffic is heavy in the subnetwork). The ETL gives a chance to relax traffic in a subnetwork and avoid traffic jam. The effectiveness of the proposed strategy is analysed and evaluated by a number of simulations on three-way grid networks. Two-way rectangular grid networks are modelled via a cell transmission model (CTM). The macroscopic fundamental diagram (MFD) and the number of jammed cells are compared with two state-of-the-art methods.

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Liu, Mianfang, Dongchu Han, Dongmei Li, and Ming Wang. "Route guidance during evacuations integrated with perimeter control strategy in large-scale mixed traffic flow networks." International Journal of Modern Physics C 29, no. 11 (2018): 1850112. http://dx.doi.org/10.1142/s0129183118501127.

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Recent efficient monitoring and traffic management of large-scale mixed traffic networks still remain a challenge for both traffic researchers and practitioners. The difficulty in modeling route guidance evacuation of pedestrian-vehicle mixed flow lies in mixed flow and uneven or heterogeneous network flow. Existing studies have demonstrated that multi-region control can display different layers of traffic state measurement and control, and incorporate heterogeneity effect in the large-scale network dynamics. The optimal perimeter control can manipulate the percentages of flows that transfer between two regions, offering real-time traffic management strategies to improve the network performance. However, the effect of route guidance evacuation integrated with perimeter control strategies in case of heterogeneous traffic networks is still unexplored. The paper advances in this direction by firstly extending route choice behavior aggregation with perimeter control. For an evacuation study, we consider a campus and its surrounding traffic network that can be classified into two types of networks: the first includes emergency areas that involve a large number of evacuees, and the second includes roads that lead to different destinations. The second network consists of some regions with different evacuation directions. Based on the configuration, this paper proposes a route evacuation guidance control strategy that addresses traffic flow first assignment between regions by controlling perimeter flow with the help of Macroscopic fundamental diagram (MFD) representation and to guide evacuates’ route choice at intersections by LOGIT model in regions. In addition, comparison results show that the proposed route guidance strategy has considerable potential to improve performances and equilibrium conditions (i.e. system optimum and user equilibrium) on the overall network.

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Ji, Yangbeibei, Rui Jiang, Edward Chung, and Xiaoning Zhang. "The impact of incidents on macroscopic fundamental diagrams." Proceedings of the Institution of Civil Engineers - Transport 168, no. 5 (2015): 396–405. http://dx.doi.org/10.1680/jtran.13.00026.

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Ji, Yangbeibei, Edward Chung, Rui Jiang, and Xiaoning Zhang. "The impact of incidents on macroscopic fundamental diagrams." Proceedings of the ICE - Transport 168, no. 5 (2015): 396–405. http://dx.doi.org/10.1680/tran.13.00026.

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Yildirimoglu, Mehmet, and Nikolas Geroliminis. "Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams." Transportation Research Part B: Methodological 70 (December 2014): 186–200. http://dx.doi.org/10.1016/j.trb.2014.09.002.

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Stamos, Iraklis, Josep Maria Salanova Grau, Evangelos Mitsakis, and Socratis Mamarikas. "MACROSCOPIC FUNDAMENTAL DIAGRAMS: SIMULATION FINDINGS FOR THESSALONIKI’S ROAD NETWORK." INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING 5, no. 3 (2015): 225–37. http://dx.doi.org/10.7708/ijtte.2015.5(3).01.

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Leclercq, Ludovic, Nicolas Chiabaut, and Béatrice Trinquier. "Macroscopic Fundamental Diagrams: A cross-comparison of estimation methods." Transportation Research Part B: Methodological 62 (April 2014): 1–12. http://dx.doi.org/10.1016/j.trb.2014.01.007.

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Cassidy, Michael J., Kitae Jang, and Carlos F. Daganzo. "Macroscopic Fundamental Diagrams for Freeway Networks: Theory and Observation." Transportation Research Record: Journal of the Transportation Research Board 2260, no. 1 (2011): 8–15. http://dx.doi.org/10.3141/2260-02.

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Geroliminis, Nikolas, and Carlos F. Daganzo. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings." Transportation Research Part B: Methodological 42, no. 9 (2008): 759–70. http://dx.doi.org/10.1016/j.trb.2008.02.002.

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Gkiotsalitis, Konstantinos, and Andy Chow. "Significance of Fundamental Diagrams to First-Order Macroscopic Traffic Modelling." International Journal of Transportation 2, no. 2 (2014): 15–32. http://dx.doi.org/10.14257/ijt.2014.2.2.02.

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Zhao, Ting-ting, Yi Zhang, Zhi-heng Li, Bei-peng Mu, and Bing-yan Huang. "Exploring the influence of traveller information on macroscopic fundamental diagrams." IET Intelligent Transport Systems 8, no. 1 (2014): 58–67. http://dx.doi.org/10.1049/iet-its.2011.0234.

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WANG, Peng fei, Kentaro WADA, Takashi AKAMATSU, and Yusuke HARA. "An Empirical Analysis of Macroscopic Fundamental Diagrams for Sendai Road Networks." Interdisciplinary Information Sciences 21, no. 1 (2015): 49–61. http://dx.doi.org/10.4036/iis.2015.49.

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Shim, Jisup, Jiho Yeo, Sujin Lee, Samer H. Hamdar, and Kitae Jang. "Empirical evaluation of influential factors on bifurcation in macroscopic fundamental diagrams." Transportation Research Part C: Emerging Technologies 102 (May 2019): 509–20. http://dx.doi.org/10.1016/j.trc.2019.03.005.

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Du, Jianhe, Hesham Rakha, and Vikash V. Gayah. "Deriving macroscopic fundamental diagrams from probe data: Issues and proposed solutions." Transportation Research Part C: Emerging Technologies 66 (May 2016): 136–49. http://dx.doi.org/10.1016/j.trc.2015.08.015.

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Shim, Jisup, Jiho Yeo, Sujin Lee, and Kitae Jang. "Deriving Macroscopic Fundamental Diagrams Using Probe Vehicle Data Based on DSRC." Journal of The Korea Institute of Intelligent Transport Systems 16, no. 6 (2017): 29–41. http://dx.doi.org/10.12815/kits.2017.16.6.29.

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Hao, Jingyi, and Zhengbing He. "A Day-to-day Invariant Macroscopic Fundamental Diagrams for Probe Vehicles." IOP Conference Series: Earth and Environmental Science 189, no. 6 (2018): 062017. http://dx.doi.org/10.1088/1755-1315/189/6/062017.

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He, Z., S. He, and W. Guan. "A figure-eight hysteresis pattern in macroscopic fundamental diagrams and its microscopic causes." Transportation Letters 7, no. 3 (2014): 133–42. http://dx.doi.org/10.1179/1942787514y.0000000041.

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SAKAI, Takara, and Takashi AKAMATSU. "STABILITY OF TRAFFIC CONGESTION PATTERNS AND MACROSCOPIC FUNDAMENTAL DIAGRAMS IN TOKYO METROPOLITAN EXPRESSWAY NETWORK." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 75, no. 2 (2019): 97–108. http://dx.doi.org/10.2208/jscejipm.75.97.

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(Shirley) Gao, Xueyu, and Vikash V. Gayah. "An analytical framework to model uncertainty in urban network dynamics using Macroscopic Fundamental Diagrams." Transportation Research Procedia 23 (2017): 497–516. http://dx.doi.org/10.1016/j.trpro.2017.05.028.

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Gao, Xueyu (Shirley), and Vikash V. Gayah. "An analytical framework to model uncertainty in urban network dynamics using Macroscopic Fundamental Diagrams." Transportation Research Part B: Methodological 117 (November 2018): 660–75. http://dx.doi.org/10.1016/j.trb.2017.08.015.

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Buisson, Christine, and Cyril Ladier. "Exploring the Impact of Homogeneity of Traffic Measurements on the Existence of Macroscopic Fundamental Diagrams." Transportation Research Record: Journal of the Transportation Research Board 2124, no. 1 (2009): 127–36. http://dx.doi.org/10.3141/2124-12.

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Geroliminis, Nikolas, Jack Haddad, and Mohsen Ramezani. "Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach." IEEE Transactions on Intelligent Transportation Systems 14, no. 1 (2013): 348–59. http://dx.doi.org/10.1109/tits.2012.2216877.

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Poole, Adam, and Apostolos Kotsialos. "Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams." Applied Soft Computing 38 (January 2016): 134–50. http://dx.doi.org/10.1016/j.asoc.2015.09.011.

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Zhong, R. X., C. Chen, Y. P. Huang, A. Sumalee, W. H. K. Lam, and D. B. Xu. "Robust Perimeter Control for Two Urban Regions with Macroscopic Fundamental Diagrams: A Control-Lyapunov Function Approach." Transportation Research Procedia 23 (2017): 922–41. http://dx.doi.org/10.1016/j.trpro.2017.05.051.

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