Heterogeneous sensor location model for path reconstruction

Fu, Chenyi; Zhu, Ning; Ling, Shuai; Ma, Shoufeng; Huang, Yongxi

doi:10.1016/j.trb.2016.04.013

Cited by 59 publications

(16 citation statements)

References 41 publications

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“…The first kind of uncertainty is due to the nature of non‐unique OD matrices to a given set of correctly observed link flows. Another kind of uncertainty, such as priori OD demand variability (Simonelli, Marzano, Papola, & Vitiello, ), priori route flow variability (Fu, Zhu, Ling, Ma, & Huang, ; Fu, Zhu, & Ma, ), and measurement error associated with traffic counts (Zhou & List, ; Zhu, Liu, Ma, & He, ), exists in the collected data. The two types of uncertainty correspond to two types of OD matrix estimation‐oriented network sensor location problem: OD matrix estimation‐oriented network sensor location problem without uncertainty and with uncertainty.…”

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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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%

Road side unit location optimization for optimum link flow determination

Liang

2019

Computer aided Civil Eng

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“…This reformulation bears a very attractive property: an equivalent solution for (7) can be determined by solving a constrained form of the maximum clique problem on the graph HG , a well-known problem in operations research and graph theory literature for which several formulations and algorithms are readily available (Alidaee et al, 2007;Macambira and de Souza, 2000). Recently, a similar solution scheme was proposed by (Fu et al, 2016) to locate a combination of active and passive sensors in the network.…”

Section: We Represent the Individual Hypergraph Verticesmentioning

confidence: 99%

“…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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“…The SLP has been of great interest to electrical engineering as well as computer science for which a number of different methods are presented [40][41][42][43][44][45]. Given the fact that SLP is NP-hard, the heuristic methods are deemed valid [46,47].…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Achilles-heel roads in real-sized networks

et al. 2017

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Ensuring a minimum operational level of road networks in the presence of unexpected incidents is becoming a hot subject in academic circles as well as industry. To this end, it is important to understand the degree to which each single element of the network contributes to the operation and performance of a network. In other words, a road can become an ''Achilles-heel'' for the entire network if it is closed due to a simple incident. Such insight of the detrimental loss of the closure of the roads would help us to be more vigilant and prepared. In this study, we develop an index dubbed as Achilles-heel index to quantify detrimental loss of the closure of the respective roads. More precisely, the Achilles-heel index indicates how many drivers are affected by the closure of the respective roads (the number of affected drivers is also called travel demand coverage). To this end, roads with maximum travel demand coverage are sorted as the most critical ones, for which a method-known as ''link analysis''-is adopted. In an iterative process, first, a road with highest traffic volume is first labeled as ''target link,'' and second, a portion of travel demand which is captured by the target link is excluded from travel demand. For the next iteration, the trimmed travel demand is then assigned to the network where all links including the target links run on the initial travel times. The process carries on until all links are labeled. The proposed methodology is applied to a largesized network of Winnipeg, Canada. The results shed light on also bottleneck points of the network which may warrant provision of additional capacity or parallel roads.

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Heterogeneous sensor location model for path reconstruction

Cited by 59 publications

References 41 publications

Road side unit location optimization for optimum link flow determination

Road side unit location optimization for optimum link flow determination

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

Identifying Achilles-heel roads in real-sized networks

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