Framework model for time‐variant propagation speed and congestion boundary by incident on expressways

Fei, Wenpeng; Song, Guohua; Zang, Jinrui; Gao, Yifan; Sun, Jian; Yu, Lei

doi:10.1049/iet-its.2015.0222

Cited by 13 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All these efforts predicted traffic congestion status in one or multiple road segments in next time-step, but the propagation of traffic congestion is not predicted. Fei et al [6] predicted propagation speed and boundary based on shockwave model for a single congestion given an observed traffic incident. However, there is no prediction of propagation footprint in [6] and this work only focus on traffic incident.…”

Section: Related Workmentioning

confidence: 99%

“…This information is valuable in routing services as well as for public safety, to mitigate emerging congestion problems via real-time response and add in structural change planning. Past studies on traffic congestions have used various approaches, such as expert system [5], cell transmission model [18], and shock-wave model [6], based on simulated data or small amount of real-world observation data. To account for various situations in real-world, a large number of real-world observations is needed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Traffic Congestion Propagation Patterns

Xiong

Vahedian

Zhou

et al. 2018

Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science

View full text Add to dashboard Cite

A traffic congestion in a road network may propagate to upstream road segments. Such a congestion propagation may make a series of connected road segments congested in the near future. Given a spatial-temporal network and congested road segments in current time, the aim of predicting traffic congestion propagation pattern is to predict where those congestion will propagate to. This can provide users (e.g. city officials) with valuable information on how congestion will propagate in the near future to help mitigating emerging congestions. However, it is challenging to predict in realtime due to complex propagation process between roads and high computational intensity caused by large dataset. Recent studies have been focusing on finding frequent or most likely congestion propagation patterns in historical data. In contrast, this research will address the problem of predicting congestion propagation patterns in the near future. We predict the footprint of congestion propagation as Propagation Graphs (Pro-Graphs) where the root of each Pro-Graph is a set of congested roads propagating congestion to nearby roads. We propose an efficient algorithm called PPI_Fast to achieve this prediction. Our experiments on real-word dataset from Shenzhen, China shows that the PPI_Fast is able to predict near future propagations with AUC of 0.75 and improves the running time of the baseline algorithm. Two case studies have been done to show our work can find meaningful patterns.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Traffic Congestion Propagation Patterns

Xiong

Vahedian

Zhou

et al. 2018

Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science

View full text Add to dashboard Cite

show abstract

“…The floating cars accounted for 15-25% of the traffic flow on the road network. The sufficient sample ensures the quality of the FCD, which are widely used in recent studies [21,22]. Then, the average speed of floating cars and the TPI of the road network were calculated through a process of data quality control, real-time map matching, and travel speed calculations.…”

Section: Data Descriptionmentioning

confidence: 99%

Analysis of rainfall effects on road travel speed in Beijing, China

Zhang

Song

Gong

et al. 2017

IET Intelligent Transport Systems

Self Cite

View full text Add to dashboard Cite

“…A number of inserting lane changing vehicles (LCVs) disturb highway traffic, reduce roadway capacity, and threaten driving safety [1], [2]. The heaviest impact of a LCV on its adjacent car following vehicles (CFVs) appears when it changes from a lane to adjacent lane.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of Mutually Adaptive Vehicle Maneuvers During the Inserting Process of Lane Changing Vehicle in Urban Driving Context

et al. 2020

View full text Add to dashboard Cite

This study developed a pair of lane changing (LC) model and car following (CF) model. They are used to investigate mutually adaptive maneuver of LC vehicle (LCV) and its immediate CF vehicle during the LCV's inserting process. The two models are developed based on a multianticipative CF model to simulate both of the LC and LC-adaptive CF maneuvers. A group of pairwise parameters are applied in the models to reflect the stimuli perceived by the two vehicle drivers simultaneously. Based on the united model framework, the features of the two vehicle maneuvers which are mutually and intensively influenced can be compared and analyzed. Vehicle trajectories collected on the urban arterial are applied to calibrating and validating the two models. Results show that the developed models can fit the trajectories in a higher accuracy than the previous models. The estimates of the model parameters revealed that dynamics of lateral moving vehicle influence the LC and CV maneuvers in different ways. It is found that the lateral influence has the heaviest influence on the vehicle maneuvers than other stimuli. The vehicles also adjust their maneuvers along with the change of traffic signal or LC target. INDEX TERMS Microscopic traffic flow, lane changing, car following, vehicle trajectory.

show abstract

Framework model for time‐variant propagation speed and congestion boundary by incident on expressways

Cited by 13 publications

References 23 publications

Predicting Traffic Congestion Propagation Patterns

Predicting Traffic Congestion Propagation Patterns

Analysis of rainfall effects on road travel speed in Beijing, China

Modelling and Analysis of Mutually Adaptive Vehicle Maneuvers During the Inserting Process of Lane Changing Vehicle in Urban Driving Context

Contact Info

Product

Resources

About