Passing Rates to Measure Relaxation and Impact of Lane-Changing in Congestion

Duret, Aurélien; Ahn, Soyoung; Buisson, Christine

doi:10.1111/j.1467-8667.2010.00675.x

Cited by 56 publications

(43 citation statements)

References 44 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, a very large empirical data collection was carried out, showing the fine details of individual lane changes, including their reasons and their duration [19]. Many studies (e.g., [20]) have studied the impact of lane changes at the level of platoons. Note that, in the U.K., slower vehicles drive on the left.…”

Section: Literature Reviewmentioning

confidence: 99%

Number of Lane Changes Determined by Splashover Effects in Loop Detector Counts

Knoop

Wilson

Buisson³

et al. 2012

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

Lane changes are important in quantifying traffic for both operational and planning purposes. Traditional in-lane loop detectors do not count lane changes; hence, historically, traffic engineers have estimated them using other data sources. This paper provides a method for estimating the number of lane changes based on observations of "straddling" vehicles that are simultaneously detected by the loops in adjacent lanes. In the data considered here, such "straddles" almost always correspond to vehicles that are in the process of changing lanes. However, many lane changes take place between detector sites and, hence, do not result in straddles. The methods developed here estimate probability distribution for the number of lane changes given an observed number of straddles. The efficacy of this approach depends on calibration issues and on the size of the aggregation period. In the evaluation study presented here, the results are good: The proposed method gives the number of lane changes with approximately 10% error, even though the number of lane changes per aggregation period varies by a factor of 10 over time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Number of Lane Changes Determined by Splashover Effects in Loop Detector Counts

Knoop

Wilson

Buisson³

et al. 2012

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As one of the fundamental driver behaviors, lane changing significantly affects traffic operations, and it has been identified in previous studies as a key trigger in freeway breakdown (Adeli and Karim, 2000; Adeli and Samant, 2000; Ghosh‐Dastidar and Adeli, 2003; Jiang and Adeli, 2004a, b; Adeli and Jiang, 2003; Ahn and Cassidy, 2007; Duret et al, 2011). The drivers’ decision to change lanes is associated with driver characteristics and driver attitudes (such as aggressive behavior) and depends on many factors.…”

Section: Introductionmentioning

confidence: 99%

Lane‐Changing Behavior on Urban Streets: An “In‐Vehicle” Field Experiment‐Based Study

Sun

Elefteriadou

2012

Computer aided Civil Eng

View full text Add to dashboard Cite

Lane‐changing behavior has received increasing attention during the recent years in traffic flow modeling. Researchers have developed various algorithms to model the maneuvers on both highways and urban streets. However, the majority of these models was derived and validated using data such as vehicle trajectories, without many considerations of driver characteristics. In this study, an instrumented vehicle‐based experiment was carefully designed to observe the drivers’ action under various urban lane‐changing scenarios. The personal background data, and “in‐vehicle” driver behavior and trajectory data were obtained from the experiment, and used to classify 40 drivers into four general groups according to the lane‐changing maneuvers performed in an urban street environment. Additional comparisons and analysis were conducted to confirm the categorization results. The article concludes by providing recommendations related to the implementation of study findings into micro‐simulators, such as using only four driver types in CORSIM instead of the existing 10 types, to better replicate driver behavior in urban street networks.

show abstract

“…(), Duret et al. (), and Geroliminis and Ramezani (). The quantity of data produced by such activities could be enormous.…”

Section: Introductionmentioning

confidence: 99%

Lossless Compression of All Vehicle Trajectories in a Common Roadway Segment

Lovell

2018

Computer aided Civil Eng

View full text Add to dashboard Cite

This article describes a method for compressing position and identification data for files containing comprehensive trajectory records of all vehicles traversing the same roadway segment over an arbitrary time period, in such a way that no loss of information content occurs. Such complete trajectory records are important for the study of traffic flow theory and could become increasingly relevant as test data against which to study the behavior of autonomous vehicles in a mixed traffic environment. Compression steps include differential encoding, motion prediction, and parsimonious binary storage, plus steps that are unique to the context of vehicle trajectories, including vehicle ID numbers and lane occupancy. The effectiveness of the compression is due, in part, to the strong correlations between positions (and speeds) of vehicles traveling near each other, as well as recognition that certain trajectory artifacts change with spatial and temporal frequencies much lower than the sampling rates. The algorithm is demonstrated on two sets of publicly available complete trajectory records, and compression performance statistics are given. Compression ratios in the range 10:1 to more than 20:1 are achieved for the sample files.

show abstract

Passing Rates to Measure Relaxation and Impact of Lane-Changing in Congestion

Cited by 56 publications

References 44 publications

Number of Lane Changes Determined by Splashover Effects in Loop Detector Counts

Number of Lane Changes Determined by Splashover Effects in Loop Detector Counts

Lane‐Changing Behavior on Urban Streets: An “In‐Vehicle” Field Experiment‐Based Study

Lossless Compression of All Vehicle Trajectories in a Common Roadway Segment

Contact Info

Product

Resources

About