scholarly journals Introducing a Re-Sampling Methodology for the Estimation of Empirical Macroscopic Fundamental Diagrams

2018 ◽  
Vol 2672 (20) ◽  
pp. 239-248 ◽  
Author(s):  
Lukas Ambühl ◽  
Allister Loder ◽  
Michiel C. J. Bliemer ◽  
Monica Menendez ◽  
Kay W. Axhausen
Keyword(s):  
Traffic Control ◽  
Critical Density ◽  
Network Capacity ◽  
Accurate Estimation ◽  
Fundamental Diagram ◽  
Data Set ◽  
Urban Dynamics ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram ◽  
Inaccurate Estimation

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.

scholarly journals Analysis of Traffic Conditions in Urban Region Based on Data from Fixed Detectors

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shidong Liang ◽  
Shuzhi Zhao ◽  
Minghui Ma ◽  
Huasheng Liu
Keyword(s):  
Traffic Control ◽  
Evaluation System ◽  
Travel Speed ◽  
Urban Region ◽  
Speed Estimation ◽  
Network Efficiency ◽  
Estimation Model ◽  
Fundamental Diagram ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram

To evaluate traffic conditions in an urban region, the average travel speed in the street network and the average traffic flow per lane are selected as indexes. The results for identifying traffic conditions are obtained using macroscopic fundamental diagram. Because of the difficulty of collecting the traffic parameter of travel speed from widely distributed fixed detectors directly, in this paper, the relationship between average travel speed and queue lengths at signalized intersections within the urban area is established based on an equivalent assumption. Finally, the average travel speed estimation model proposed in this paper is tested with microscopic simulation platform, and the results showed that the average travel speed estimation model performed well with satisfactory accuracy. In addition, the best performance of the network efficiency can be identified based on the reflections of macroscopic fundamental diagram, and the evaluation system is simple enough for application in engineering. Therefore, the result is instructive both for generating traffic control strategies and for making route choices.

scholarly journals Application of Macroscopic Fundamental Diagram under Flooding Situation to Traffic Management Measures

2021 ◽  
Vol 13 (20) ◽  
pp. 11227
Author(s):  
Piyapong Suwanno ◽  
Rattanaporn Kasemsri ◽  
Kaifeng Duan ◽  
Atsushi Fukuda
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Road Network ◽  
Road Traffic ◽  
Control Measures ◽  
Flow Density ◽  
Fundamental Diagram ◽  
Urban Flood ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram

Bangkok, Thailand is prone to flooding after heavy rain. Many road sections become impassable, causing severe traffic congestion and greatly impacting activities. Optimal vehicle management requires the knowledge of flooding impact on road traffic conditions in specific areas. A method is proposed to quantify urban flood situations by expressing traffic conditions in specific ranges using the concept of macroscopic fundamental diagram (MFD). MFD-based judgement allows for a road manager to understand the current traffic situation and take appropriate traffic control measures. MFD analysis identified traffic flow–density and density–velocity relationships by using the shape of the estimated MFD travel time-series plots. Then, results were applied to develop a traffic model with vehicle-flow parameters as a measuring method for road-network performance. The developed model improved road-network traffic-flow performance under different flood conditions. A method is also presented for traffic management evaluation on the assumption that flooding occurs.

scholarly journals Simulation and comparison of two fusion methods for macroscopic fundamental diagram estimation

2019 ◽  
Vol 51 (3) ◽  
pp. 35-48 ◽  
Author(s):  
Xiaohui Lin ◽  
Jianmin Xu ◽  
Chengtao Cao
Keyword(s):  
Road Network ◽  
Estimation Method ◽  
Weighted Averaging ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Fusion Method ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram ◽  
Fusion Methods ◽  
Estimation Fusion

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.

Macroscopic Fundamental Diagram Approach to Evaluating the Performance of Regional Traffic Controls

2020 ◽  
Vol 2674 (7) ◽  
pp. 420-430
Author(s):  
Weike Lu ◽  
Jun Liu ◽  
Jiannan Mao ◽  
Guojing Hu ◽  
Chuqiao Gao ◽  
...  
Keyword(s):  
Control System ◽  
Road Network ◽  
Evaluation Method ◽  
Load Capacity ◽  
Network Capacity ◽  
Maximum Throughput ◽  
Fundamental Diagram ◽  
Advantages And Disadvantages ◽  
Macroscopic Fundamental Diagram ◽  
Control Schemes

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.

scholarly journals Macroscopic Fundamental Diagram Based Discrete Transportation Network Design

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Guojing Hu ◽  
Weike Lu ◽  
Feng Wang ◽  
Robert W. Whalin
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Demand Uncertainty ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Network Capacity ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram

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.

scholarly journals Determining the Macroscopic Fundamental Diagram from Mixed and Partial Traffic Data

2018 ◽  
Vol 30 (3) ◽  
pp. 267-279 ◽  
Author(s):  
Yangbeibei Ji ◽  
Mingwei Xu ◽  
Jie Li ◽  
Henk J Van Zuylen
Keyword(s):  
Traffic Control ◽  
Incomplete Data ◽  
Traffic Management ◽  
Real Data ◽  
Traffic Data ◽  
Fundamental Diagram ◽  
Probe Vehicles ◽  
Loop Detectors ◽  
Macroscopic Fundamental Diagram ◽  
The Difference

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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