Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach

Xing, Tao; Zhou, Xuesong; Taylor, Jeffrey

doi:10.1016/j.trb.2013.09.007

Cited by 50 publications

(16 citation statements)

References 35 publications

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“…According to the review conducted by Zhu, Fu, and Ma (), studies on network sensor location can be categorized into studies on flow‐observability‐oriented sensor location, studies on flow‐estimation‐oriented sensor location, and those on travel‐time‐estimation‐oriented sensor location. Here, the network sensor location problem belongs to the facility location problem, such as the location problem of freight facility (Hajibabai, Bai, & Ouyang, ; Xie & Ouyang, ), railroad wayside defect detection (Ouyang, Li, Barkan, Kawprasert, & Lai, ), and recharge facility for electric vehicles (Zhang, Rey, & Waller, ), in operations research area. The flow‐observability‐oriented sensor location aims to determine how many sensors are needed as well as where they should be located for the unique determination of the unobserved link flows.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To reduce the other type of uncertainty, traffic sensors should be located to maximize information gains for the OD estimation problem (Zhou & List, ) or minimize the second type of uncertainty associated with estimated OD matrix (Simonelli et al., ). The travel‐time‐estimation‐oriented sensor location problem is to determine the optimal sensor location, which minimizes the uncertainty associated with travel time estimation (Xing, Zhou, & Taylor, ; Zhu et al, ; Zhu, Ma, & Zheng, ). In addition to the category of sensor location problem summarized by Zhu et al.…”

Section: Literature Reviewmentioning

confidence: 99%

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Road side unit location optimization for optimum link flow determination

Liang

2019

Computer aided Civil Eng

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This study addresses the problem of road side unit (RSU) location optimization for optimum link flow determination. The error of link flow determination using RSU information comes from two sources: measurement error and inference error. The inference error is caused by the propagation and accumulation of measurement error during the link flow inference process. The direct problem formulation aiming to minimize the total error is impossible because the minimization of inference error has to be indirectly formulated as the minimization of the cumulative number of unobserved links. Further, for a given number of RSU, the decrease in the cumulative number of unobserved links results in the increase of the number of observed links, and thus results in the increase of measurement error due to data packet queueing delay. Therefore, for a given RSU installation budget, the balance between the two types of error needs to be optimized in order to achieve the optimum link flow determination accuracy. To fulfill this goal, the RSU location optimization problem is formulated as a bi‐objective nonlinear binary integer programming. This programming is constrained by complete link flow determination requirements. In order to accelerate computation, an efficient ε‐constraint method is designed to generate Pareto optimal frontier. The subproblem solved at each iteration is linearized using piecewise linear approximation and solved using a constraint generation method. The proposed model and the solution algorithm are evaluated through numerical examples. The results reveal that the Pareto optimal solutions achieved by the proposed model are at least not inferior to a non‐Pareto solution obtained in the baseline scenario. Further, both the measurement error and inference error associated with some of the Pareto optimal points are lower than those associated with the non‐Pareto optimal solution.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Road side unit location optimization for optimum link flow determination

Liang

2019

Computer aided Civil Eng

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“…A bi-objective optimization was used to simultaneously maximize the total vehiclemiles and minimize the variance of predicted travel times ( Mirchandani et al, 2009 ). Xing et al, (2013) developed an information-theoretic framework to minimize travel time uncertainty. More recently, a stochastic programming model was developed to address uncertainty of traffic conditions by using both traditional point sensors and point-to-point sensors ( Park and Haghani, 2015 ).…”

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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Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach

Cited by 50 publications

References 35 publications

Road side unit location optimization for optimum link flow determination

Road side unit location optimization for optimum link flow determination

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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