Examination of Methods That Adjust Observed Traffic Volumes on a Network

Kikuchi, Shinya; Miljković, Dragana; Zuylen, Henk J van

doi:10.3141/1717-14

Cited by 19 publications

(10 citation statements)

References 5 publications

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“…These boardings, alightings and loads, do not satisfy the conditions defined by Eqs. (4) and (5), and are considered as the data that would be obtained during a conventional data collection, and they are the input for the model. 3.…”

Section: Examples For Validate and Generalize The Resultsmentioning

confidence: 99%

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Adjustment boarding and alighting passengers on a bus transit line using qualitative information

Oña

Gómez

Mérida-Casermeiro

2014

Applied Mathematical Modelling

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a b s t r a c tObtaining data to use in an urban public transport operation planning and analysis is problematic, particularly in urban bus transit lines. In an urban environment and for bus services, most ticketing methods can be used to record passengers getting on board but not getting off, and current methods are unable to make a proper adjustment of boardings and alightings based on the available data unless they do alighting counts. This paper presents a method whereby counts are made at fewer stops and qualitative information on alightings and/or vehicle loads between consecutive stops is used to make the boarding and alighting adjustment as a previous step to obtain the real origin and destination (O/ D) of passengers allowing the O/D matrix calibration by using the loads between stops. Qualitative information can be obtained by the vehicle's driver or an on board observer, avoiding the necessity of counting many stops in planning period. The method is applied to a real bus transit line in Malaga (Spain) and to a set of 50 different bus transit lines with number of stops ranging from 10 to 75. The results show that the proposed method reduces the adjustment errors with regard to traditional methods, such as Least Square Method, even in the situation where no qualitative information is used. When qualitative data is used on alightings and loadings, the reduction of the average error is over 50%.

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Section: Examples For Validate and Generalize The Resultsmentioning

confidence: 99%

“…Several methods have been developed to adjust data on a transit line when both boarding and alighting data are available [5]. In general, all methods seek to narrow the gap between observed values and adjusted values as much as possible, subject to contour conditions.…”

Section: Introductionmentioning

confidence: 99%

Adjustment boarding and alighting passengers on a bus transit line using qualitative information

Oña

Gómez

Mérida-Casermeiro

2014

Applied Mathematical Modelling

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“…Additionally, if information from different detection points and devices is inconsistent (e.g. on a node in the network the inflow and outflow are not in equilibrium), the inconsistencies can be eliminated using fuzzy reasoning [9] or a likelihood estimator [10]. The first approach assumes that each observation is a member of a fuzzy (uncertain) set.…”

Section: A Entropy Maximum Approachmentioning

confidence: 99%

“…The first approach assumes that each observation is a member of a fuzzy (uncertain) set. The volume that is consistent and fits as good as possible to the observed data (maximum membership to the set) is used as volume in Equ.2 [9]. The other approach assumes that traffic counts have a certain probability distribution and estimates the consistent traffic volumes as the most likely volumes [10].…”

Section: A Entropy Maximum Approachmentioning

confidence: 99%

Freight origin-destination estimation based on multiple data source

Zuylen

Kuik

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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² Freight origin-destination (OD) information is increasingly important for understanding the influence of transportation on network congestion. Traditional OD estimation methods based on a single data source, usually loop detectors, are not easily transferred to freight OD estimation. However, alternative data capture technologies are nowadays available to gather traffic information. Examples are automatic number plate recognition (ANPR), Bluetooth scanners, and Weigh-in-Motion systems. This paper aims to develop feasible approaches based on Entropy Maximization and Bayesian Networks to estimate freight OD matrix using multiple sources of captured data. In the case of the A15 motorway in the Netherlands, we illustrate how the captured data is informative about transport behavior in the area and how the proposed methods lead to an estimation of the freight OD matrix.

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“…The study derived the formula for constrained maximum likelihood estimates of the link flows. Kikuchi et al [13] examined and compared six different methods to adjust observed flow rate according to flow conservation. All of the methods have the same constraints but different objective functions.…”

Section: Introductionmentioning

confidence: 99%

ℓ1-minimization method for link flow correction

Yin¹,

Sun²,

Jin³

et al. 2017

Transportation Research Part B: Methodological

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A computational method, based on 1 -minimization, is proposed for the problem of link flow correction, when the available traffic flow data on many links in a road network are inconsistent with respect to the flow conservation law. Without extra information, the problem is generally ill-posed when a large portion of the link sensors are unhealthy. It is possible, however, to correct the corrupted link flows accurately with the proposed method under a recoverability condition if there are only a few bad sensors which are located at certain links. We analytically identify the links that are robust to miscounts and relate them to the geometric structure of the traffic network by introducing the recoverability concept and an algorithm for computing it. The recoverability condition for corrupted links is simply the associated recoverability being greater than 1. In a more realistic setting, besides the unhealthy link sensors, small measurement noises may be present at the other sensors. Under the same recoverability condition, our method guarantees to give an estimated traffic flow fairly close to the ground-truth data and leads to a bound for the correction error. Both synthetic and real-world examples are provided to demonstrate the effectiveness of the proposed method.

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Examination of Methods That Adjust Observed Traffic Volumes on a Network

Cited by 19 publications

References 5 publications

Adjustment boarding and alighting passengers on a bus transit line using qualitative information

Adjustment boarding and alighting passengers on a bus transit line using qualitative information

Freight origin-destination estimation based on multiple data source

ℓ1-minimization method for link flow correction

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