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Indirect observation of rerouting phenomena in traffic networks – case study of Warsaw bridges

Kucharski R., Gentile G.

Archives of Transport

2014

Vol. 32, iss. 4

29--41

In this paper we propose estimation procedure in which traffic flows resulting from rerouting model are matched with traffic flows observed during unexpected events. We show practical value of observing a entire cut-set of the transportation network and propose theoretical closed-form formulation of estimation problem for the rerouting model. We apply proposed framework on field-data from Warsaw bridges to observe rerouting phenomena. Most importantly we observed that: a) around 20% of affected traffic flow reroutes, b) rerouting flows are increasing in time, c) drivers show strategic capabilities, d) and maximize their utility while rerouting. All of the which were hypothesized in Information Comply Model (Kucharski et. al., 2014) and are now supported with field observations.

Direct observation of rerouting phenomena in traffic networks

Kucharski R., Gentile G.

Archives of Transport

2014

Vol. 30, iss. 2

57--66

In this paper we propose how available dataset can be used to estimate rerouting phenomena in traffic networks. We show how to look at set of paths observed during unexpected events to understand the rerouting phenomena. We use the information comply model [1] and propose its estimation method. We propose the likelihood formula and show how the theoretical and observed rerouting probabilities can be obtained. We conclude with illustrative example showing how a single observed path can be processes and what information it provides. Contrary to parallel paper [2] where rerouting phenomena is estimated using real traffic flow measures from Warsaw, here we use only synthetic data. The paper is organized as follows. First we elaborate on rerouting phenomena and define the traffic network, then we summarize the literature behind rerouting phenomena. We follow with a synthetic definition of dynamic traffic assignment needed to introduce ICM model in subsequent section. Based on that introduction we define the observations and propose estimation method based on them followed by illustrative example. Paper is summarized with conclusions and pointing of future directions.