Vehicle-ID sensor location for route flow recognition: Models and algorithms

Cerrone, Carmine; Cerulli, Raffaele; Gentili, Monica

doi:10.1016/j.ejor.2015.05.070

Cited by 43 publications

(23 citation statements)

References 23 publications

(20 reference statements)

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“…On each instance, we compare the results returned by Johnson, Vasco et.al (four different row knowledge function), Cerrone et al(2015)'s Tabu search and Meta-RAPS algorithms for each of our two problems. All algorithms were coded in MATLAB on a 2.5 GHz Intel i5 processor and 6.00 GB RAM.…”

Section: Resultsmentioning

confidence: 99%

“…And we chose Vasco (2016)'s greedy algorithm because in our problem number of rows is more than columns and it seemed that considering rows priority could help reaching better answers. Also reason of choosing Cerrone et al (2015)'s Tabu search was using it in solving some similar models by Cerrone et al (2015). We chose Meta-RAPS algorithm because of high randomness in choosing columns to see the effect of that in this type of models.…”

Section: Set Covering Problemmentioning

confidence: 99%

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Solving Location Problem For Vehicle Identification Sensors To Observe And Estimate Path Flows In Large-Scale Networks

Yazdi¹,

Shafahi²

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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Origin-Destination (OD) demand is one of the important requirements in transportation planning. Estimating OD demand could be an expensive and time consuming procedure. These days using vehicle identification sensors for OD estimation has become very common because of its low cost and high accuracy. In this paper, we focus on solving two location problems of these sensors: one to observe and one to estimate path flows. These problems have only been solved for small-scale networks until recently due to being computationally expensive. Therefore, we try to present a method to solve these models for large-scale networks. Due to resemblance of these models and set covering problem, we used heuristic and meta-heuristic methods based on set covering problem. For this purpose, we defined our new set covering matrix based on prime matrix. In order to determine which method is more appropriate, we chose a large-scale and six medium-scale networks. The results represent that through heuristic methods and meta-heuristic methods a greedy algorithm and a Tabu search are more appropriate respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Set Covering Problemmentioning

confidence: 99%

Solving Location Problem For Vehicle Identification Sensors To Observe And Estimate Path Flows In Large-Scale Networks

Yazdi¹,

Shafahi²

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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show abstract

“…However, there are limited studies that discuss the AVI sensor location problem for the purpose of path reconstruction. For example, the IMPL and P1 models presented by Zangui et al, (2015) and Cerrone et al, (2015) , respectively, are both extensions of the OBSV model. The use of some models other than the OBSV model will be discussed in Section 3.4 .…”

Section: Remarkmentioning

confidence: 99%

“…Furthermore, the method was extended to consider the effects of path-differentiated congestion pricing ( Zangui et al 2015 ). Cerrone et al, (2015) incorporated the effect of Vehicle-ID sensor order into a new sensor location model. In summary, most path reconstruction-oriented sensor location problems rely on the use of AVI sensors, which could be costly for large-scale network applications.…”

Section: Nomenclaturementioning

confidence: 99%

Heterogeneous sensor location model for path reconstruction

Zhu

Ling

et al. 2016

Transportation Research Part B: Methodological

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“…Locating traffic sensors in a network is therefore considered a problem of paramount importance in transportation engineering, in particular within estimation problems (e.g. real time traffic state estimation (Ahmed et al, 2014;Zhu et al, 2014), OD flows estimation (Hadavi and Shafahi, 2016;Hu and Liou, 2014;Zhou and List, 2010), link flow inference (Castillo et al, 2008c;Hu et al, 2009;Xu et al, 2016), travel time estimation (Viti et al, 2008;Xing et al, 2013) and path flow reconstruction (Cerrone et al, 2015;Fu et al, 2016Fu et al, , 2017Li and Ouyang, 2011).…”

Section: Introductionmentioning

confidence: 99%

Exact and approximate route set generation for resilient partial observability in sensor location problems

Rinaldi

Viti

2017

Transportation Research Part B: Methodological

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Sensor positioning is a fundamental problem in transportation networks, as the location of sensors strongly determines how traffic flows are observable and hence manageable. This paper aims to develop a methodology to determine sensor locations on a network such that an optimal trade-off solution is found between the amount of sensors installed and the resilience of the sensor set.In particular, we propose exact and heuristic solutions for identifying the optimal route sets such that no other route would include any additional information for finding optimal full and partial observability solutions. This is an important contribution to sensor location problems, as route-based link flow inference problems have non-unique solutions, strongly depending on the used link-route information.The properties of the new methodology are analysed and illustrated through different case studies, and the advantages of the algorithms are quantified both for full and for partial observability solutions. Thanks to the route sets found by our approach, we are able to find full observability solutions characterized by a small number of sensors, while yet being efficient also in terms of partial observability. We perform validation tests on both small and real-life sized network instances.

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Vehicle-ID sensor location for route flow recognition: Models and algorithms

Cited by 43 publications

References 23 publications

Solving Location Problem For Vehicle Identification Sensors To Observe And Estimate Path Flows In Large-Scale Networks

Solving Location Problem For Vehicle Identification Sensors To Observe And Estimate Path Flows In Large-Scale Networks

Heterogeneous sensor location model for path reconstruction

Exact and approximate route set generation for resilient partial observability in sensor location problems

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