Reconstruction of delay distribution at signalized intersections based on traffic measurements

Zheng, Fangfang; Zuylen, Henk J van

doi:10.1109/itsc.2010.5625028

Cited by 5 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zheng and Van Zuylen [19] indicated that the parameters of a delay distribution could be properly estimated for both undersaturated and oversaturated intersections using the trajectories of vehicles. They used delay distribution functions at intersections (Equations (2) and (3)) to determine the parameters of the dynamic model of the traffic at intersections.…”

Section: Parameter Estimationmentioning

confidence: 99%

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Jonaghani

Honarparvar

Khademi

2018

IJGI

View full text Add to dashboard Cite

In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles' recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc.), the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions.

show abstract

Section: Parameter Estimationmentioning

confidence: 99%

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Jonaghani

Honarparvar

Khademi

2018

IJGI

View full text Add to dashboard Cite

show abstract

“…Derivation of these algorithms is usually accompanied with some fundamental assumptions, e.g., no lanechanging and/or known arrival distribution(s), which restrict their practicality. The data-driven algorithms, however, largely rely on fitting a significant amount of data with well-established probability distributions (Hofleitner, Herring, & Bayen, 2012a;Uno, Kurauchi, Tamura, & Iida, 2009;Zheng & van Zuylen, 2010). The major issue is the availability of enough data for model training.…”

Section: Introductionmentioning

confidence: 99%

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Yang

Boriboonsomsin

et al. 2017

Journal of Intelligent Transportation Systems

View full text Add to dashboard Cite

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.

show abstract

“…At present, there are two general trends in estimation of travel times using this probe data. One trend, from kinematic wave theory (see [18,7]), derives analytical probability distributions of travel times and infer their parameters with probe vehicle data. These approaches are computationally intensive, which limits their applicability for large scale networks.…”

Section: Introductionmentioning

confidence: 99%

Arriving on time: estimating travel time distributions on large-scale road networks

Hunter,

Hofleitner,

Reilly

et al. 2013

Preprint

View full text Add to dashboard Cite

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a 'stopand-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area.

show abstract

Reconstruction of delay distribution at signalized intersections based on traffic measurements

Cited by 5 publications

References 19 publications

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arriving on time: estimating travel time distributions on large-scale road networks

Contact Info

Product

Resources

About