Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances
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Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances Jincheng Jiang1,2,4 · Nico Dellaert2 · Tom Van Woensel2 · Lixin Wu3
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origindestination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripplespreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and highorder continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation. Keywords Traffic flow · Disturbance · Time-varying OD · Route choice · Network disruption · Dynamic travel time * Jincheng Jiang [email protected] 1
Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning, Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China
2
School of Industrial Engineering and Innovation Sciences, Technical University of Eindhoven, P.O. Box 513, Eindhoven 5600 MB, Netherlands
3
School of Geoscience and Info‑Physics, Central South University, Changsha 410083, China
4
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Vol.:(0123456789)
Transportation
Introduction Traffic prediction (Matas et al. 2012) is important for the shortest path (Fu and Rilett 1998; Sever et al. 2013), traffic assignment (Peeta and Ziliaskopoulos 2001), disaster emergency (Jiang and Wu 2017; Jiang et al. 2017), and vehicle routing (Kim et al. 2005). However, traffic states are dynamic, whic
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