A Study of Loop Detectors Based on Vehicular Trajectories

2014 ◽  
Vol 505-506 ◽  
pp. 1118-1121
Author(s):  
Han Yang
Keyword(s):  
Traffic Flow ◽  
Traffic Data ◽  
Time And Space ◽  
Fundamental Diagram ◽  
Time Intervals ◽  
Loop Detectors ◽  
Data Collecting ◽  
Detector Location

Vehicular trajectories are firstly achieved on the basis of vehicle time and space information obtained from VISSIM simulation. Via simulating setting loop detectors at different locations and collecting traffic data in different time intervals, different traffic flow fundamental diagrams on the basis of the detect data are then generated. Finally, comparing these fundamental diagrams, two conclusions are achieved. The loop detective interval has a significant impact on fundamental diagrams while the detector location has an extremely limited influence. Particularly, fundamental diagram is more aggregated with longer data collecting interval and capacity is more easily to be obtained with longer distance between neighboring loop detectors.

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.

TRAFFIC FORECAST IN LARGE SCALE FREEWAY NETWORKS

2004 ◽  
Vol 14 (06) ◽  
pp. 1995-2004 ◽  
Author(s):  
ROLAND CHROBOK ◽  
ANDREAS POTTMEIER ◽  
SIGURDUR F. HAFSTEIN ◽  
MICHAEL SCHRECKENBERG
Keyword(s):  
Traffic Flow ◽  
Large Scale ◽  
Nonlinear Phenomenon ◽  
Traffic Data ◽  
German State ◽  
Traffic Forecast ◽  
Traffic State ◽  
Loop Detectors ◽  
Highly Nonlinear ◽  
Freeway Networks

Traffic flow in large and complex freeway networks is a highly nonlinear phenomenon, which makes traffic forecast a difficult task. In this article an approach to traffic forecast is presented, which uses a micro-simulator for traffic flow combined with current and historical traffic data. The micro-simulator and locally measured current traffic data are used to reconstruct the current network-wide traffic state. Then, this state is combined with historical traffic data to forecast the traffic development. This framework is applied to the freeway network of the German state North Rhine-Westphalia. The micro-simulator uses an advanced cellular-automaton model for traffic flow, and the data are supplied from more than 4,000 locally installed loop-detectors, which deliver information on the (local) traffic state online minute by minute.

Field Theory for Some Traffic Phenomena and Fundamental Diagram

2021 ◽  
pp. 036119812110064
Author(s):  
Daiheng Ni
Keyword(s):  
Field Theory ◽  
Traffic Flow ◽  
Causal Effect ◽  
Microscopic Approach ◽  
Fundamental Diagram ◽  
Longitudinal Control ◽  
Effect Analysis ◽  
Vehicle Trajectories ◽  
Vehicle Motion ◽  
Cross Reference

A fundamental diagram consists of a scatter of traffic flow data sampled at a specific location and aggregated from vehicle trajectories. These trajectories, if presented equivalently, constitute a microscopic version of the (conventional) fundamental diagram. The cross-reference between vehicle trajectories and the microscopic fundamental diagram provides details of vehicle motion dynamics which allow causal-effect analysis on some traffic phenomena and further reveal the microscopic basis of the conventional fundamental diagram. This observation inspires theoretical modeling by a microscopic approach to address traffic phenomena and the conventional fundamental diagram. Derived from the field theory of traffic flow, the longitudinal control model is capable of serving the purpose without the modifications or exceptions used by other approaches.

scholarly journals A Three-Phase Fundamental Diagram from Three-Dimensional Traffic Data

Axioms ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Maria Laura Delle Delle Monache ◽  
Karen Chi ◽  
Yong Chen ◽  
Paola Goatin ◽  
Ke Han ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Traffic Data ◽  
Fundamental Diagram ◽  
Model Based Clustering ◽  
Three Phase ◽  
Characterization Of Solutions ◽  
Two Phases

This paper uses empirical traffic data collected from three locations in Europe and the US to reveal a three-phase fundamental diagram with two phases located in the uncongested regime. Model-based clustering, hypothesis testing and regression analyses are applied to the speed–flow–occupancy relationship represented in the three-dimensional space to rigorously validate the three phases and identify their gaps. The finding is consistent across the aforementioned different geographical locations. Accordingly, we propose a three-phase macroscopic traffic flow model and a characterization of solutions to the Riemann problems. This work identifies critical structures in the fundamental diagram that are typically ignored in first- and higher-order models and could significantly impact travel time estimation on highways.

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

scholarly journals A Cellular Automaton Traffic Flow Model with Advanced Decelerations

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Yingdong Liu
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Flow Model ◽  
Stable State ◽  
Practical Significance ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
Short Time ◽  
Vehicle Deceleration ◽  
Real Traffic

A one-dimensional cellular automaton traffic flow model, which considers the deceleration in advance, is addressed in this paper. The model reflects the situation in the real traffic that drivers usually adjust the current velocity by forecasting its velocities in a short time of future, in order to avoid the sharp deceleration. The fundamental diagram obtained by simulation shows the ability of this model to capture the essential features of traffic flow, for example, synchronized flow, meta-stable state, and phase separation at the high density. Contrasting with the simulation results of the VE model, this model shows a higher maximum flux closer to the measured data, more stability, more efficient dissolving blockage, lower vehicle deceleration, and more reasonable distribution of vehicles. The results indicate that advanced deceleration has an important impact on traffic flow, and this model has some practical significance as the result matching to the actual situation.

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.

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

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