Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios
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Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios Author: Flurin Weber Supervisor: Alexander Genser Co-Supervisor: Dr. Mehdi Keyvan-Ekbatani (University of Canterbury, New Zealand) Professor: Dr. Anastasios Kouvelas Master Thesis January 2020 Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios January 2020 Acknowledgements I would like to extend my deepest gratitude to my supervisors Alexander Genser and Dr. Anastasios Kouvelas from ETH Zürich. Without your knowledge, support and advice, be it via e-mail, Skype, or in personal conversation, I would not have been able to complete this thesis. Furthermore, my thanks go to my co-supervisor Mehdi Keyvan-Ekbatani from University of Canterbury (New Zealand), who took the time for regular skype meetings and supported me via e-mail. I could absolutely benefit from your knowledge of perimeter control. I also would like to thank Richard de Witt and Franziska Baumgartner from the Departement für Bau, Verkehr und Umwelt (BVU) of Kanton Aargau for the meetings and interesting inputs from a more practical point of view, as well as for providing the access to the traffic management computers. Also, my thanks are extended to Manuel Kalt, who gave me valuable inputs about different kinds of signal control logic. i Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios January 2020 Master Thesis, handed in on 27th of January 2020 Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios Flurin Weber IVT ETH Zurich Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland Phone: +41 79 684 48 24 E-Mail: [email protected] Abstract Nowadays, congestion in urban networks is abundant. Especially in the peak hours, this can lead to large delays for individual and public transport. The Swiss city of Baden is no exception, with lots of congestion focused on a few arterials during the peak hours. Perimeter control is a common traffic management tool which aims at maximising traffic flow within a pre-defined perimeter (here the city centre) and reducing overall delays. It creates queues at gating traffic lights that hold back vehicles such as not to exceed a certain vehicle accumulation inside the perimeter. Therefore, the goal of this thesis was to simulate MFD- based perimeter control in Baden and to compare the performance with the no-control case. In theory, this leads to an overall average travel time reduction. For this purpose, a simplified microsimulation model of the city of Baden in Vissim has been developed, together with a MATLAB script that controls Vissim during the simulation over the COM interface. Two control schemes (bang-bang control and PI control) were implemented, together with several scenarios regarding the controller’s occupancy set point, supplemented by a queue management strategy. The analyses have shown that single-region perimeter control using the demand of 2015 improves the traffic situation inside the perimeter, but fails to do so for the whole network, as the amount of congestion in the city centre is not enough. Therefore, several scenarios with increased demand have been implemented and tested, delivering the same results. This is due to an inhomogeneous spatial distribution of congestion inside the perimeter. A clustering approach with homogeneously congested subclusters might perform better overall. The results should be interpreted with care, however, because several important details are missing in the Vissim model. Keywords Perimeter Control; Gating; MFD; PI controller; Vissim; Bang-bang control; Microsimulation; Queue management Preferred citation style Weber, F. (2020) Perimeter Control in the Swiss City of Baden: Evaluation of different Scenarios, Master Thesis, IVT, ETH Zurich, Zurich. ii Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios January 2020 Table of contents 1 Introduction and motivation .............................................................................................. 12 2 Theoretical Background .................................................................................................... 13 2.1 Macroscopic fundamental diagram ........................................................................... 13 2.2 Perimeter Control ...................................................................................................... 16 2.3 Control Strategies ...................................................................................................... 18 2.3.1 Bang-bang control .............................................................................................. 18 2.3.2 PI and PID controllers ........................................................................................ 19 2.4 Traffic Simulation Tools ........................................................................................... 22 2.4.1 Macroscopic, mesoscopic and microscopic simulation ..................................... 22 2.4.2 PTV Vissim ........................................................................................................ 23 2.4.3 Car-following model .......................................................................................... 23 2.4.4 Lane-changing model ......................................................................................... 24 2.4.5 Stochastic variations in microsimulation ........................................................... 26 3 Case study and research questions .................................................................................... 27 4 Existing traffic control strategy implementation .............................................................. 29 4.1 Description of existing traffic responsive strategy .................................................... 29 4.2 Implementation in Vissim ......................................................................................... 35 4.2.1 Conversion of the macroscopic to a microscopic model ........................................... 35 4.2.2 Adaptive signal control of gating traffic lights with vehicle-actuated programming 37 4.2.3 Modelling intersections ............................................................................................. 39 4.2.4 Modelling the demand ............................................................................................... 44 4.2.5 Combination of perimeter control with today’s traffic-responsive strategy .............. 46 4.2.6 Unrealistic vehicle behaviour in Vissim .................................................................... 47 4.3 Traffic assignment strategies ..................................................................................... 49 5 Implementation of Perimeter Control ............................................................................... 51 5.1 Conceptual Framework .............................................................................................. 51 5.2 Estimation of the macroscopic fundamental diagram ............................................... 53 5.3 Control strategies ....................................................................................................... 57 iii Perimeter Control in the Swiss City of Baden: Evaluation of Different Scenarios January 2020 5.3.1 General considerations ....................................................................................... 57 5.3.2 Bang-bang control .............................................................................................. 58 5.3.3 PI controller ........................................................................................................ 59 5.3.4 Queue management strategy .............................................................................. 69 5.4 Evaluation measures .................................................................................................. 72 5.4.1 VHT .................................................................................................................... 73 5.4.2 VKT .................................................................................................................... 74 5.4.3 Average speed .................................................................................................... 75 5.4.4 Total vehicle delay ............................................................................................. 76 5.4.5 Delays at gates .................................................................................................... 76 5.4.6 Number of stops ................................................................................................. 77 5.4.7 Percentage of vehicles in a queue ...................................................................... 77 5.4.8 Maximum relative queue length ......................................................................... 78 5.4.9 Excess green time due to QM ............................................................................ 78 5.4.10 Number of vehicles served ................................................................................. 78 5.5 Structure of code ........................................................................................................ 78 5.6 Analysed scenarios .................................................................................................... 81 6 Results and Discussion ..................................................................................................... 83 6.1 No control / baseline (demand = 100%)