Modeling Multiple User-Class Traffic

1998 ◽  
Vol 1644 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Piet H. L. Bovy
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Flow Characteristics ◽  
Traffic Density ◽  
Class Model ◽  
Specific Flow ◽  
Flow Models ◽  
Multiple User ◽  
Equilibrium Relationships ◽  
Traffic Composition

In contemporary traffic flow theory, the distinction between user classes is rarely made. However, we envisage that the accuracy and the descriptive power of the macroscopic traffic flow models can be improved significantly by separating user classes and their specific flow characteristics. As a consequence, the possibility of improved estimation and prediction of traffic flow conditions becomes available. Additionally, the availability of a realistic multiple user-class traffic flow model enables the automated generation of user-dedicated traffic control policies by means of mathematical optimal control theory. A macroscopic multiple user-class model is derived from mesoscopic principles. In opposition to earlier multiple user-class models, the model presented here implicitly defines equilibrium relationships between traffic density and equilibrium velocities as a function of current traffic conditions, traffic composition, and distribution of user-class-dependent desired velocities. Additionally, the velocity variance variable is introduced describing deviations from the average speed within the user classes. The multiple user-class model identifies competing processes. On the one hand, drivers attempt to traverse the freeway at their desired velocity; on the other hand, they adjust their velocity because of interaction with slower vehicles. These processes can result in self-formation of apparently random local structures. Finally, the proposed model satisfies the anisotropy condition and the invariant personality condition.

scholarly journals Developing a New Spatial Unit for Macroscopic Safety Evaluation Based on Traffic Density Homogeneity

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chen Wang ◽  
Lin Liu ◽  
Chengcheng Xu
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Flow Characteristics ◽  
Traffic Density ◽  
Minimization Method ◽  
Spatial Unit ◽  
Flow Parameters ◽  
Safety Knowledge ◽  
Traffic Operation ◽  
Crash Modeling

Macrolevel crash modeling has been extensively applied to investigate the safety effects of demographic, socioeconomic, and land use factors, in order to add safety knowledge into traffic planning and policy-making. In recent years, with the increasing attention to regional traffic management and control, the safety effects of macrolevel traffic flow parameters may also be of interest, in order to provide useful safety knowledge for regional traffic operation. In this paper, a new spatial unit was developed using a recursive half-cut partitioning procedure based on a normalized cut (NC) minimization method and traffic density homogeneity. Two Bayesian lognormal models with different conditional autoregressive (CAR) priors were applied to examine the safety effects of traffic flow characteristics at the NC level. It was found that safety effects of traffic flow exist at such macrolevel, indicating the necessity of considering safety for regional traffic control and management. Furthermore, traffic flow effects were also examined for another two spatial units: Traffic Analysis Zone (TAZ) and Census Tract (CT). It was found that ecological fallacy and atomic fallacy could exist without considering traffic flow parameters at those planning-based levels. In general, safety needs to be considered for regional traffic operation and the effects of traffic flow need to be considered for spatial crash modeling at various spatial levels.

scholarly journals Macroscopic Traffic Flow Control Via State Estimation

2015 ◽  
Vol 15 (5) ◽  
pp. 5-16
Author(s):  
H. Abouaïssa ◽  
H. Majid
Keyword(s):  
Flow Control ◽  
State Estimation ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Density ◽  
Traffic State ◽  
Loop Detectors ◽  
Traffic State Estimation ◽  
Estimation And Control ◽  
And Control

Abstract The studies presented in this paper deal with traffic control in case of missing data and/or when the loop detectors are faulty. We show that the traffic state estimation plays an important role in traffic prediction and control. Two approaches are presented for the estimation of the main traffic variables (traffic density and mean speed). The state constructors obtained are then used for traffic flow control. Several numerical simulations show very promising results for both traffic state estimation and control.

scholarly journals Effects of static bottlenecks on traffic flow in urban road network

2020 ◽  
Vol 12 (3) ◽  
pp. 1-15
Author(s):  
John N.P. Mahona ◽  
Cuthbert F. Mhilu ◽  
Joseph Kihedu ◽  
Hannibal Bwire
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Density ◽  
Urban Road Network ◽  
Peak Period ◽  
Flow Models ◽  
Urban Road ◽  
Stop And Go ◽  
Traffic Flow Models ◽  
Methodological Aspects

Existing traffic flow models do not consider the effects of road static bottlenecks on traffic flow. In this paper, a modified macroscopic continuum  model for traffic flow on urban road network with static bottlenecks is presented. The model takes into account the fluctuations of traffic flow considering static bottlenecks during the morning peak period. The model results show that existence of static road bottlenecks with various configurations cause traffic flow instabilities. This phenomenon lead into stop-and-go traffic flow conditions under the moderate density and reduction of the traffic system’s efficiency. Furthermore, results show that an increase in traffic density is accompanied by a significant decrease of speed which adversely influences performance of roadway and decrease the traffic system’s efficiency and thus resulting to the occurrence of congestions. The methodological aspects of the study and results will enable traffic engineers and planners to assess and improve existing urbanroad networks. Keywords: Traffic flow, Bottlenecks, stability, Stop-and-go traffic, System’s efficiency, Congestion.

Application of Catastrophe Theory in Traffic Flow of Highway Tunnel Group

2013 ◽  
Vol 734-737 ◽  
pp. 1609-1612
Author(s):  
Wei Zhan ◽  
Yue Quan Shang ◽  
Feng Xia Chi
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Catastrophe Theory ◽  
Critical Density ◽  
Flow Characteristics ◽  
Control Measures ◽  
Highway Tunnel ◽  
Relationship Of ◽  
The Relationship ◽  
Highway Management

Based on the investigation of traffic flow in a typical highway tunnel group, the traffic flow characteristics were analyzed by catastrophe theory with the relationship of the speed, volume and density. The discontinuous leaping change phenomenon of the traffic data under large traffic volume is better explained by the catastrophe model than the traditional ways. The value of critical density can be obtained by analyzing the critical state of traffic flow. Then the traffic flow warning can be realized in highway tunnel group region. The data and results can be used for the reference of taking traffic control measures by highway management.

scholarly journals Left-Side On-Ramp Metering for Improving Safety and Efficiency in Underground Expressway Systems

2019 ◽  
Vol 11 (12) ◽  
pp. 3247 ◽  
Author(s):  
Minhua Shao ◽  
Congcong Xie ◽  
Lijun Sun ◽  
Xiaomin Wan ◽  
Zhang Chen
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Traffic Control ◽  
Flow Characteristics ◽  
Ramp Metering ◽  
Accident Risk ◽  
Data Traffic ◽  
Traffic Efficiency ◽  
Traffic Characteristics ◽  
Two Parameter

As one of the effective measures of intelligent traffic control, on-ramp metering is often used to improve the traffic efficiency of expressways. Existing on-ramp metering research mainly discusses expressways with right-side on-ramps. However, for underground expressway systems (UESs), left-side on-ramps are frequently adopted to reduce the ground space occupied by ramp construction. Since traffic entering from the left and right sides of the mainline may have different traffic characteristics, on-ramp metering for UESs with left-side on-ramps should be explored specifically. This study examines the impacts of left-side on-ramps on the traffic safety and efficiency of UESs and proposes an effective on-ramp metering strategy. Firstly, using field data, traffic flow fundamental diagrams and speed dispersion are discussed to explore the traffic flow characteristics of the “left-in” UES. The results show that the capacity and critical occupancy are both reduced in left-side on-ramp compared to right-side on-ramp expressways. Meanwhile, the speed dispersion is higher in left-side on-ramp UESs, which means a higher accident risk. Based on this, considering traffic safety and efficiency, a novel two-parameter left-side on-ramp metering strategy for UESs is proposed, in which occupancy and speed are used as the control indicators simultaneously. Additionally, the mechanism of the metering strategy is explained. Finally, the proposed on-ramp metering strategy is simulated on a real UES. The results demonstrate the advantages of the proposed two-parameter on-ramp metering strategy for improving the traffic safety and efficiency of UESs.

scholarly journals Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xu Qu ◽  
Linheng Li ◽  
Ziwei Yi ◽  
Peipei Mao ◽  
Mofeng Yang
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Driving Behavior ◽  
Cellular Automaton Model ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Flow Models ◽  
Variable Speed Limit Control

Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model.

Gas-Kinetic Traffic Flow Modeling Including Continuous Driver Behavior Models

2003 ◽  
Vol 1852 (1) ◽  
pp. 231-238 ◽  
Author(s):  
C. M. J. Tampère ◽  
B. van Arem ◽  
S. P. Hoogendoorn
Keyword(s):  
Traffic Flow ◽  
Driver Behavior ◽  
Discrete Event ◽  
Traffic Density ◽  
Flow Density ◽  
Flow Equations ◽  
Flow Models ◽  
Explanatory Factors ◽  
Traffic Flow Models ◽  
Car Following Model

A modeling technique is presented that analytically bridges the gap between microscopic behavior of individual drivers and the macroscopic dynamics of traffic flow. The basis of this approach is the (gas-) kinetic or mesoscopic modeling principle that considers the dynamics of traffic density and generalizations thereof as a probability density function of vehicles in different driving states. In contrast to traditional kinetic models, deceleration of individual vehicles due to slower traffic is treated as a continuous adaptive process rather than a discrete event. An analytic procedure is proposed to aggregate arbitrarily refined individual driver behavior to a macroscopic expected acceleration or deceleration of flow as a whole that can be used in macroscopic differential equations for traffic flow. The procedure implicitly accounts for the anisotropy of information flow in traffic, for anticipation behavior of drivers, and for the finite space requirement of vehicles, as long as these properties have been specified at the level of individual driver behavior. The procedure is illustrated for a simple car-following model with overtaking opportunity. The results show that the procedure yields micro-based aggregate traffic flow models that capture the essential properties of traffic dynamics. The techniques presented can contribute to the development of traffic flow models with driver behavior and driver psychology as important explanatory factors of congestion formation and propagation. Moreover, the approach allows building macroscopic traffic flow equations from future traffic flows for which no empirical speed–flow–density relations are available yet.

Research on Bus Stop Based on Cellular Automaton Model

2014 ◽  
Vol 1008-1009 ◽  
pp. 1484-1488
Author(s):  
Jun Qian ◽  
Yong Ju Hu
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Simulation Analysis ◽  
Flow Characteristics ◽  
Negative Influence ◽  
Traffic Density ◽  
Matlab Simulation ◽  
Bus Stop ◽  
Local Roads ◽  
The Relationship

Based on the NaSch model,a cellular automaton mode with two lanes is proposed by computer simulation analysis,which considers the influence of passengers on bus stop.By using MatLab simulation,the paper compares the different traffic flow characteristics between harbor-shaped and nonharbor shaped bus stop.It also analyzes the relationship between the traffic flow and the number of bus,the scope and intensity of bus stop in different ways.The results show that harbor-shaped bus stop is the effective method to alleviate the crowd traffic in the case of larger traffic density;It takes more passengers in the system which used harbor shaped bus stop can accommodate more buses;Harbor-shaped bus stop produced better improvement in the negative influence of bus stopping on local roads.

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

Sign in / Sign up

Export Citation Format

Share Document