Jam density and stopbar location estimation with trajectory data at signalized intersections

Roger Lloret-Batlle and Jianfeng Zheng

Transportation Research Part B: Methodological, 2023, vol. 173, issue C, 162-175

Abstract: Jam density, or its reciprocal jam spacing, is a parameter difficult to estimate. In fact, most traffic signal control and traffic state estimation studies published until date generally assume a given value, with no estimation from data whatsoever. Nevertheless, estimating jam density is crucial, since any deviation from its true value will propagate to queue lengths and volume estimates. In the possession of only trajectory data, its estimation is even harder since not all vehicles are observed. In this paper, we first define the data generating process of the jam spacing parameter using sparse trajectory data. Then, we propose several estimators to estimate jam spacing and its variance. We use as measurements the distances between successive vehicle stops. The first estimator is a Maximum-Likelihood estimator (MLE) of a Geometrically Skewed Normal (GSN) distribution, to be used whenever there is lane information. The second estimator is a MLE of a shifted GSN with Normal Stopbar (SGSN-NSB) to be used when observed distances are measured from the stopbar estimate. In addition, we propose two least-squares counterparts, LSE and LSE-SB, based on least absolute remainders. Finally, we assess and compare the bias and efficiency of the estimators in both synthetic and real-world data, obtaining satisfactory results. MLE estimators are shown to outperform their LSE counterparts in both situations.

Keywords: Jam density; Jam spacing; Trajectory data; Connected vehicles; Least squares; Mixture model; Maximum likelihood (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0191261523000206
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:173:y:2023:i:c:p:162-175

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.trb.2023.02.007

Access Statistics for this article

Transportation Research Part B: Methodological is currently edited by Fred Mannering

More articles in Transportation Research Part B: Methodological from Elsevier
Bibliographic data for series maintained by Catherine Liu ().