Solving Optimal Location of Traffic Counting Points at Urban Intersections in CLP(FD)
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Experimental evaluation of approximation and heuristic algorithms for the dominating paths problem
Computers and Operations Research
Models and algorithms for the screen line-based traffic-counting location problems
Computers and Operations Research
A real time data streaming approach to best route planning
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
Models and algorithms for the screen line-based traffic-counting location problems
Computers and Operations Research
Optimal use of plate-scanning resources for route flow estimation in traffic networks
IEEE Transactions on Intelligent Transportation Systems
Matrix tools for general observability analysis in traffic networks
IEEE Transactions on Intelligent Transportation Systems
Locating sensors to observe network arc flows: Exact and heuristic approaches
Computers and Operations Research
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In this paper, we define and solve the sensor location problem (SLP), that is, we look for the minimum number and location of counting points in order to infer all traffic flows in a transport network. We set up a couple of greedy heuristics that find lower and upper bounds on the number of sensors for a set of randomly generated networks. We prove that solving the SLP implies that the Origin/Destination (O/D) matrix estimation error be always bounded. With respect to alternative sensor location strategies, simulation experiments show that: (i) measurement costs being equal, the O/D estimation error is lower, and (ii) conversely, O/D estimation error being equal, the number of sensors is smaller.