Empirical Validation of Bimodal MFD Models

This work attempts to validate the dynamic outputs of bi-modal MFD-based models, also referred to as 3D-MFD models, using empirical data. A previous study (Loder et al., 2017) gathered cars and public transport vehicles data in two different regions of Zurich city and showed that a well-defined 3D-MFD exists by proposing a functional form relating overall travel production to the accumulations of cars and public transport vehicles. This work aims to go one step further with the same data set to investigate if 3D-MFD embedded in dynamic conservation laws can predict the time evolution of the network traffic states. Two different approaches to estimate the inflow demand using outflow and mean speed evolutions are presented. The mean trip lengths are estimated using a network exploration technique. Accumulation-based, trip-based and accumulation-based with outflow delay models are considered in the validation study. It is concluded that a single bi-linear 3D-MFD fit is insufficient to predict traffic state evolution accurately. The current work proposes multi bi-linear 3D-MFD fits segregated depending on the time of the day. The proposed approach significantly improved the simulation results, where good correspondence with empirical data is obtained. Finally, it is shown that in multi-modal networks like Zurich, it is essential to consider the effect of public transport vehicles, when considering aggregated simulations. It is also shown that using a 2D-MFD by treating public transport vehicles and private cars alike, result in poor accordance with the field observations.

[1]  Meead Saberi,et al.  H∞ robust perimeter flow control in urban networks with partial information feedback , 2020 .

[2]  Nikolas Geroliminis,et al.  The Morning Commute in Urban Areas: Insights from Theory and Simulation , 2016 .

[3]  Markos Papageorgiou,et al.  Exploiting the fundamental diagram of urban networks for feedback-based gating , 2012 .

[4]  Anastasios Kouvelas,et al.  Optimum route guidance in multi-region networks: A linear approach , 2020 .

[5]  Jorge A. Laval,et al.  Macroscopic urban dynamics: Analytical and numerical comparisons of existing models , 2017 .

[6]  Ludovic Leclercq,et al.  Bi-modal macroscopic traffic dynamics in a single region , 2020 .

[7]  Ludovic Leclercq,et al.  Macroscopic Traffic Dynamics Under Fast-Varying Demand , 2019, Transp. Sci..

[8]  Kay W. Axhausen,et al.  Capturing network properties with a functional form for the multi-modal macroscopic fundamental diagram , 2019, Transportation Research Part B: Methodological.

[9]  N. Geroliminis,et al.  A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks , 2014 .

[10]  Hani S. Mahmassani,et al.  INVESTIGATION OF NETWORK-LEVEL TRAFFIC FLOW RELATIONSHIPS: SOME SIMULATION RESULTS , 1984 .

[11]  Marta C. González,et al.  Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data , 2020 .

[12]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[13]  Ludovic Leclercq,et al.  Dynamic macroscopic simulation of on-street parking search: A trip-based approach , 2017 .

[14]  Nikolas Geroliminis,et al.  Approximating Dynamic Equilibrium Conditions with Macroscopic Fundamental Diagrams , 2014 .

[15]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[16]  Jun Zhang,et al.  Investigation of Bimodal Macroscopic Fundamental Diagrams in Large-Scale Urban Networks: Empirical Study with GPS Data for Shenzhen City , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[17]  Nikolaos Geroliminis,et al.  Estimation of the network capacity for multimodal urban systems , 2011 .

[18]  Nikolas Geroliminis,et al.  Analysis of the 3D-vMFDs of the Urban Networks of Zurich and San Francisco , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[19]  Carlos F. Daganzo,et al.  Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .

[20]  Nikolas Geroliminis,et al.  Macroscopic modelling and robust control of bi-modal multi-region urban road networks , 2017 .

[21]  Monica Menendez,et al.  Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram , 2017 .

[22]  Agachai Sumalee,et al.  An optimal control framework for multi-region macroscopic fundamental diagram systems with time delay, considering route choice and departure time choice , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[23]  Zhiyuan Liu,et al.  Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram , 2018, Transportation Research Part C: Emerging Technologies.

[24]  Carlos F. Daganzo,et al.  Distance-dependent congestion pricing for downtown zones , 2015 .

[25]  Christine Buisson,et al.  Exploring the Impact of Homogeneity of Traffic Measurements on the Existence of Macroscopic Fundamental Diagrams , 2009 .

[26]  Kay W. Axhausen,et al.  A case study of Zurich’s two-layered perimeter control , 2017 .

[27]  Jack Haddad,et al.  Coordinated distributed adaptive perimeter control for large-scale urban road networks , 2017 .

[28]  Ludovic Leclercq,et al.  Validation of Macroscopic Fundamental Diagrams-Based Models with Microscopic Simulations on Real Networks: Importance of Production Hysteresis and Trip Lengths Estimation , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[29]  Terry L. Friesz,et al.  Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control Problem , 1989, Oper. Res..

[30]  Ludovic Leclercq,et al.  Flow exchanges in multi-reservoir systems with spillbacks , 2019, Transportation Research Part B: Methodological.

[31]  J. Gerring.  A case study , 2011, Technology and Society.

[32]  Meead Saberi,et al.  Traffic State Estimation in Heterogeneous Networks with Stochastic Demand and Supply: Mixed Lagrangian–Eulerian Approach , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[33]  Emmanouil N. Barmpounakis,et al.  On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment , 2020 .

[34]  Nikolas Geroliminis,et al.  Equilibrium analysis and route guidance in large-scale networks with MFD dynamics , 2015 .

[35]  Ludovic Leclercq,et al.  Performance analysis for different designs of a multimodal urban arterial , 2014 .

[36]  Nikolas Geroliminis,et al.  Estimation of regional trip length distributions for the calibration of the aggregated network traffic models , 2019, Transportation Research Part B: Methodological.

[37]  Bin Ran,et al.  A New Class of Instantaneous Dynamic User-Optimal Traffic Assignment Models , 1993, Oper. Res..

[38]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[39]  Jin Cao,et al.  System dynamics of urban traffic based on its parking-related-states , 2015 .

[40]  R. G. D. ALLEN.  Equilibrium Analysis , 1973, Nature.

[41]  Zhengfei Zheng,et al.  Adaptive perimeter control for multi-region accumulation-based models with state delays , 2020 .

[42]  N. Geroliminis.  Dynamics of Peak Hour and Effect of Parking for Congested Cities , 2009 .

[43]  Richard Arnott,et al.  A Bathtub Model of Downtown Traffic Congestion , 2013 .