2021/05/12 by Indaco Biazzo, Biazzo, Indaco, Mohsen Ghasemi Nezhadhaghighi +3 · 1 citation
Engineering · Social Sciences · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Traffic control and management #Transportation Planning and Optimization
paper · pdf · doi:10.48550/arxiv.2105.05839
openalex publication_date 2021/05/12 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Characterizing the efficiency of movements is important for a better\nmanagement of the cities. More specifically, the connection between the\nefficiency and uncertainty (entropy) production of a transport process is not\nestablished yet. In this study, we consider the movements of selfish drivers\nfrom their homes (origins) to work places (destinations) to see how\ninteractions and randomness in the movements affect a measure of efficiency and\nentropy production (uncertainty in the destination time intervals) in this\nprocess. We employ realistic models of population distributions and mobility\nlaws to simulate the movement process, where interactions are modelled by\ndependence of the local travel times on the local flows. We observe that some\nlevel of information (the travel times) sharing enhances a measure of\npredictability in the process without any coordination. Moreover, the larger\ncities display smaller efficiencies, for the same model parameters and\npopulation density, which limits the size of an efficient city. We find that\nentropy production is a good order parameter to distinguish the low- and\nhigh-congestion phases. In the former phase, the entropy production grows\nmonotonically with the probability of random moves, whereas it displays a\nminimum in the congested phase; that is randomness in the movements can reduce\nthe uncertainty in the destination time intervals. The findings highlight the\nrole of entropy production in the study of efficiency and predictability of\nsimilar processes in a complex system like the city.\n