A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates

Abstract

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.

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

“…Yang & Zhou (1998) proposed a sensor deployment framework to maximize such utilities. This framework has been extended to accommodate turning traffic information (Bianco et al, 2001), existing installations and O-D information content (Ehlert et al, 2006), the screen line problem (Yang et al, 2006), time-varying network flows (Fei et al, 2007) and unobserved link flow estimation (Hu et al, 2009). The work of Bianco et al (2006) considered the problem of locating the minimum number of counting sensors on the network nodes in order to determine arc flow volumes of the entire network.…”

Identifying Achilles-heel roads in real-sized networks

“…The SLP has been found to be of the utmost difficult problems known as NP-hard [37][38][39]. 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].…”

“…Yang and Zhou [47] proposed four heuristic methods including maximal flow fraction, OD demand coverage. Some researchers consider geographical and/or topological disaggregation of link flows to place the sensors [38,48,49]. Zhang et al [50] proposed a genetic algorithm hybridized with simulated annealing to find appropriate traffic count posts to monitor network's traffic flow.…”

A heuristic for the bilevel origin–destination-matrix estimation problem

“…The models presented by van Zuylen (1978), van Zuylen and Willumsen (1980), Carey et al (1981), Willumsen (1981), van Zuylen andBranston (1982), Bell (1983), Willumsen (1984), McNeil and Hendrickson (1985), Spiess (1987), Brenninger-Göthe et al (1989), Bierlaire and Toint (1995) and Bianco et al (2001) 7 all belong to this first category. They differ basically in how the functions F 1 and F 2 are defined and motivated.…”