Development of Multiregime Speed–Density Relationships by Cluster Analysis

2005 ◽  
Vol 1934 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Lu Sun ◽  
Jie Zhou
Keyword(s):  
Cluster Analysis ◽  
Traffic Flow ◽  
Ad Hoc ◽  
Data Sets ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Data Segmentation ◽  
Essential Components ◽  
Real Traffic

Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.

scholarly journals Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment

2021 ◽  
Vol 11 (21) ◽  
pp. 9914
Author(s):  
Aleksandra Romanowska ◽  
Kazimierz Jamroz
Keyword(s):  
Traffic Flow ◽  
Deterministic Model ◽  
Empirical Relationship ◽  
Flow Speed ◽  
Traffic Data ◽  
Flow Models ◽  
Flow Parameters ◽  
Fundamental Relationship ◽  
Traffic Flow Models ◽  
Real Traffic

The fundamental relationship of traffic flow and bivariate relations between speed and flow, speed and density, and flow and density are of great importance in transportation engineering. Fundamental relationship models may be applied to assess and forecast traffic conditions at uninterrupted traffic flow facilities. The objective of the article was to analyze and compare existing models of the fundamental relationship. To that end, we proposed a universal and quantitative method for assessing models of the fundamental relationship based on real traffic data from a Polish expressway. The proposed methodology seeks to address the problem of finding the best deterministic model to describe the empirical relationship between fundamental traffic flow parameters: average speed, flow, and density based on simple and transparent criteria. Both single and multi-regime models were considered: a total of 17 models. For the given data, the results helped to identify the best performing models that meet the boundary conditions and ensure simplicity, empirical accuracy, and good estimation of traffic flow parameters.

scholarly journals Cellular Automata Intersection Model

2020 ◽  
Vol 5 ◽  
Author(s):  
Tim Peter Erich Vranken ◽  
Michael Schreckenberg
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Variable Number ◽  
Urban Traffic ◽  
Traffic Data ◽  
Traffic Lights ◽  
Car Following ◽  
Time Value ◽  
Intersection Model ◽  
Flow Through

This paper introduces a cellular automaton design of intersections and defines rules to model traffic flow through them, so that urban traffic can be simulated. The model is able to simulate an intersection of up to four streets crossing. Each street can have a variable number of lanes. Furthermore, each lane can serve multiple purposes at the same time, like allowing vehicles to keep going straight or turn left and/or right. The model also allows the simulation of intersections with or without traffic lights and slip lanes. A comparison to multiple empirical intersection traffic data shows that the model is able to realistically reproduce traffic flow through an intersection. In particular, car following times in free flow and the required time value for drivers that turn within the intersection or go straight through it are reproduced. At the same time, important empirical jam characteristics are retained.

scholarly journals Elevator Trip Distribution for Inconsistent Passenger Input-Output Data

2007 ◽  
Vol 1 (2) ◽  
pp. 175-190 ◽  
Author(s):  
Kiyoshi Yoneda
Keyword(s):  
Measurement Errors ◽  
Ad Hoc ◽  
Sensor Data ◽  
Data Sets ◽  
Traffic Data ◽  
Input Output ◽  
Output Data ◽  
Passenger Traffic ◽  
Alternative Algorithm ◽  
Data Adjustment

Accurate traffic data are the basis for group control of elevators and its performance evaluation by trace driven simulation. The present practice estimates a time series of inter-floor passenger traffic based on commonly available elevator sensor data. The method demands that the sensor data be transformed into sets of passenger input-output data which are consistent in the sense that the transportation preserves the number of passengers. Since observation involves various behavioral assumptions, which may actually be violated, as well as measurement errors, it has been necessary to apply data adjustment procedures to secure the consistency. This paper proposes an alternative algorithm which reconstructs elevator passenger origin-destination tables from inconsistent passenger input-output data sets, thus eliminating the ad hoc data adjustment.

scholarly journals Modelling and Simulation of the Car Following Behavior in Real Traffic Flow

2019 ◽  
Vol 8 (6) ◽  
pp. 580-586
Keyword(s):  
Traffic Flow ◽  
Modelling And Simulation ◽  
Highway Traffic ◽  
Car Following ◽  
Matlab Code ◽  
Simulink Model ◽  
Proposed Model ◽  
Traffic Scenario ◽  
Mathematical Equations ◽  
Real Traffic

In generally typical highway traffic scenario a vehicle, following vehicle ahead needs to maintain benign gap to avoid mishap. Accordingly speed of follower vehicle needs to be controlled keeping watch on variation of speed of vehicle ahead. In this paper a car follow model is designed and it is estimating the speed of follower vehicle with respect to that of vehicle ahead is presented. This paper brings out the details of mathematical equations of the proposed model along with implementation of same in Matlab Code as well using Simulink model

Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network

1997 ◽  
Vol 1588 (1) ◽  
pp. 110-119 ◽  
Author(s):  
Hongjun Zhang ◽  
Stephen G. Ritchie ◽  
Zhen-Ping Lo
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Flow Model ◽  
Traffic Data ◽  
Freeway Traffic ◽  
Traffic Flow Model ◽  
Traffic System ◽  
Real Traffic

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their behavior and often do not track real traffic data correctly. On the other hand, microscopic traffic flow models can yield more detailed and accurate representations of traffic flow but are computationally intensive and typically not suitable for real-time implementation. Nevertheless, such implementations are likely to be necessary for development and application of advanced traffic control concepts in intelligent vehicle-highway systems. The development of a multilayer feed-forward artificial neural network model to address the freeway traffic system identification problem is presented. The solution of this problem is viewed as an essential element of an effort to build an improved freeway traffic flow model for the purpose of developing real-time predictive control strategies for dynamic traffic systems. To study the initial feasibility of the proposed neural network approach for traffic system identification, a three-layer feed-forward neural network model has been developed to emulate an improved version of a well-known higher-order continuum traffic model. Simulation results show that the neural network model can capture the traffic dynamics of this model quite closely. Future research will attempt to attain similar levels of performance using real traffic data.

Analysis and Comparison of Microscopic Traffic Flow Models with Real Traffic Microscopic Data

2005 ◽  
Vol 1934 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Vincenzo Punzo ◽  
Fulvio Simonelli
Keyword(s):  
Traffic Flow ◽  
Simulation Models ◽  
Microscopic Traffic Simulation ◽  
Flow Models ◽  
Traffic Conditions ◽  
Measure Model ◽  
Road Systems ◽  
Traffic Flow Models ◽  
Real Traffic ◽  
Better Than

The evermore widespread use of microscopic traffic simulation in the analysis of road systems has refocused attention on submodels, including car-following models. The difficulties of microscopic-level simulation models in the accurate reproduction of real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacies of the submodels themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty with the gathering of accurate field data. In this study, the use of kinematic differential Global Positioning System instruments allowed the trajectories of four vehicles in a platoon to be accurately monitored under real traffic conditions on both urban and extraurban roads. Some of these data were used to analyze the behaviors of four microscopic traffic flow models that differed greatly in both approach and complexity. The effect of the choice of performance measures on the model calibration results was first investigated, and intervehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared with those from previous studies (with peaks in cross validations between urban and extraurban experiments). This confirms the need for real traffic data. On comparison of the models, all models showed similar performances (i.e., similar deviations in validation). Surprisingly, however, the simplest model performed on average better than the others, but the most complex one was the most robust, never reaching particularly high deviations.

scholarly journals An Automatic Calibration Procedure of Driving Behaviour Parameters in the Presence of High Bus Volume

2019 ◽  
Vol 31 (5) ◽  
pp. 491-502 ◽  
Author(s):  
Nima Dadashzadeh ◽  
Murat Ergun ◽  
Sercan Kesten ◽  
Marijan Žura
Keyword(s):  
Calibration Procedure ◽  
Automatic Calibration ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Change Models ◽  
Calibration Process ◽  
Car Following ◽  
Driving Behaviour ◽  
Optimization Stage ◽  
Behaviour Models

Most of the microscopic traffic simulation programs used today incorporate car-following and lane-change models to simulate driving behaviour across a given area. The main goal of this study has been to develop an automatic calibration process for the parameters of driving behaviour models using metaheuristic algorithms. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a combination of GA and PSO (i.e. hybrid GAPSO and hybrid PSOGA) were used during the optimization stage. In order to verify our proposed methodology, a suitable study area with high bus volume on-ramp from the O-1 Highway in Istanbul has been modelled in VISSIM. Traffic data have been gathered through detectors. The calibration procedure has been coded using MATLAB and implemented via the VISSIM-MATLAB COM interface. Using the proposed methodology, the results of the calibrated model showed that hybrid GAPSO and hybrid PSOGA techniques outperformed the GA-only and PSO-only techniques during the calibration process. Thus, both are recommended for use in the calibration of microsimulation traffic models, rather than GA-only and PSO-only techniques.

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