scholarly journals Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Serge P. Hoogendoorn
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Estimation Error ◽  
Ground Truth ◽  
Traffic Information ◽  
Estimation Errors ◽  
Sensing Data ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

scholarly journals Traffic State Estimation Using Connected Vehicles and Stationary Detectors

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ellen F. Grumert ◽  
Andreas Tapani
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Effective Control ◽  
Accurate Estimation ◽  
Connected Vehicles ◽  
Density Estimates ◽  
Promising Alternative ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.

scholarly journals Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1996
Author(s):  
Hoe Kyoung Kim ◽  
Younshik Chung ◽  
Minjeong Kim
Keyword(s):  
State Estimation ◽  
Traffic Engineering ◽  
Flow Density ◽  
Microscopic Simulation ◽  
Traffic Demand ◽  
Traffic System ◽  
Traffic Conditions ◽  
Traffic State ◽  
Vehicle Identification ◽  
Traffic State Estimation

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

Traffic State Estimation in Heterogeneous Networks with Stochastic Demand and Supply: Mixed Lagrangian–Eulerian Approach

2019 ◽  
Vol 2673 (11) ◽  
pp. 114-126 ◽  
Author(s):  
Mohammadreza Kavianipour ◽  
Ramin Saedi ◽  
Ali Zockaie ◽  
Meead Saberi
Keyword(s):  
State Estimation ◽  
Network Traffic ◽  
Ground Truth ◽  
Solution Algorithm ◽  
Estimation Problem ◽  
Demand And Supply ◽  
Traffic State ◽  
Probe Vehicles ◽  
Loop Detectors ◽  
Traffic State Estimation

A network fundamental diagram (NFD) represents the relationship between network-wide average flow and average density. Network traffic state estimation to observe NFD when congestion is heterogeneously distributed, as a result of a time-varying and asymmetric demand matrix, is a challenging problem. Recent studies have formulated the NFD estimation problem using both fixed measurements and probe trajectories. They are often based on a given ground-truth NFD for a single day demand. Stochastic variations in network demand and supply may significantly affect the approximation of an NFD. This study proposes a modified framework to estimate network traffic states to observe NFD while capturing the stochasticity in transportation networks. A mixed integer problem with non-linear constraints is formulated to address stochasticity in the NFD estimation problem. To solve this Nondeterministic Polynomial-hard (NP-hard) problem, a solution algorithm based on the simulated annealing method is applied. The problem is formulated and the solution algorithm is implemented to find an optimal configuration of loop detectors and probe vehicles to estimate the NFD of the Chicago downtown network and capture its day-to-day variations, considering a given available budget. Ground-truth NFDs and estimated NFDs based on a subset of loop detectors and probe vehicles are calculated using a simulation-based dynamic traffic assignment model, which is the best surrogate available to replicate real-world conditions. The main contribution of this study is to capture stochasticity in the demand and supply sides to find a more robust subset of links and trajectories to be acquired for the NFD estimation.

scholarly journals Particle Filter for Estimating Freeway Traffic State in Beijing

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Jun Bi ◽  
Can Chang ◽  
Yang Fan
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Estimation Error ◽  
Nonlinear Problems ◽  
The West ◽  
Freeway Traffic ◽  
Traffic State ◽  
Traffic Characteristics ◽  
Traffic State Estimation ◽  
Traffic Guidance

Freeway traffic state estimation is useful for intelligent traffic guidance, control, and management. The freeway traffic state is featured with rapid and dramatic fluctuations, which presents a strong nonlinear feature. In theory, a particle filter has good performance in solving nonlinear problems. This paper proposes a particle filter based approach to estimate freeway traffic state. The freeway link between the west of Peace Bridge and the west of San Yuan Bridge of the third ring in Beijing is used as the experimental object. According to the traffic characteristics and measurement mode of the link, the second-order validated macroscopic traffic flow model is adopted to set up the link model. The implementation steps of the particle filter for freeway traffic state estimation are described in detail. The estimation error analysis for the experiments proves that the proposed approach has an encouraging estimation performance.

scholarly journals Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

2020 ◽  
Vol 12 (18) ◽  
pp. 7278
Author(s):  
Leo Tišljarić ◽  
Tonči Carić ◽  
Borna Abramović ◽  
Tomislav Fratrović
Keyword(s):  
State Estimation ◽  
Urban Areas ◽  
Traffic Management ◽  
Data Representation ◽  
Traffic Data ◽  
Transition Matrices ◽  
Positive Effects ◽  
Traffic State ◽  
Traffic State Estimation ◽  
The City

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

A Traffic State Estimation Method Based on the Road Traffic Information Templates

ICTIS 2013 ◽  
2013 ◽  
Author(s):  
Dong-Wei Xu ◽  
Hong-Hui Dong ◽  
Li-Min Jia ◽  
Yin Tian
Keyword(s):  
State Estimation ◽  
Road Traffic ◽  
Estimation Method ◽  
Traffic Information ◽  
The Road ◽  
Traffic State ◽  
Traffic State Estimation ◽  
On The Road

Real-time Freeway Traffic State Estimation with Fixed and Mobile Sensing Data

2020 ◽  
Author(s):  
Mingming Zhao ◽  
Xianghua Yu ◽  
Yonghui Hu ◽  
Jingnan Cao ◽  
Simon Hu ◽  
...  
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Mobile Sensing ◽  
Freeway Traffic ◽  
Sensing Data ◽  
Traffic State ◽  
Traffic State Estimation

Freeway Traffic State Estimation with Floating-Car-Data

2001 ◽  
Vol 34 (9) ◽  
pp. 291-296 ◽  
Author(s):  
Georg Böker ◽  
Jan Lunze
Keyword(s):  
State Estimation ◽  
Freeway Traffic ◽  
Traffic State ◽  
Floating Car Data ◽  
Traffic State Estimation

Efficient Traffic State Estimation for Large-Scale Urban Road Networks

2013 ◽  
Vol 14 (1) ◽  
pp. 398-407 ◽  
Author(s):  
Qing-Jie Kong ◽  
Qiankun Zhao ◽  
Chao Wei ◽  
Yuncai Liu
Keyword(s):  
State Estimation ◽  
Large Scale ◽  
Road Networks ◽  
Urban Road ◽  
Traffic State ◽  
Traffic State Estimation
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