Modelling, Simulation & Computing Laboratory (msclab) Faculty of Engineering, Universiti Malaysia Sabah, Malaysia

Transcription

1 1.0 Introduction Intelligent Transportation Systems (ITS) Long term congestion solutions Advanced technologies Facilitate complex transportation systems Dynamic Modelling of transportation (on-road traffic): NOT tap fullest potential Universal model is unavailable Information Updates, Analysis and Delays Data reliability and Performance o o Crowd-sourced Shortest route suggestion Delay? Data reliability? 1

2 1.0 Introduction Traffic flow modelling (3 categories): Macroscopic Microscopic Combination of both Macroscopic Traffic flow modelling: Vehicular flow as continuum flow (aggregated, bulk quantities) Relationships among traffic stream characteristics Average characteristics Microscopic Traffic flow modelling: Taking individual vehicles into account More precise, detailed, and more computations 2

3 2.0 Objective The purpose of this paper is to compute and adapt discrete-based hydrodynamic cell transmission model (CTM) to the changes on-road (disturbances) for adaptive traffic flow modelling. a. Derive a discrete-based model from hydrodynamic law of mass conservation (CTM) b. Formulate and compute CTM algorithm c. Simulate the road scenarios (disturbance) that cause congestion and recovery of traffic flow 3

4 3.0 Review Year The Chronology purpose of Hydrodynamic of this paper TFMis to explore Focus the implementation of clustering 1934Greenshields algorithm on Fundamental the extracted Diagram (FD) trajectory Linear data relation for of spacing-velocity behavioural plane pattern 1956Lighthill, learning. Whitham and Richards model (LWR) Vehicle conservations (PDE) 1959Godunov Discretization 1960sComputational Fluid Dynamics (CFD) 1973Lax-Friedwichs Discretization 1994Daganzo Cell Transmission Model (CTM) Year CTM Extensions 2011Stochastic cell transmission model (SCTM) 2013Maximum information CTM 2014Switching multimode CTM 2015Linear decreasing demand CTM 2016Multiple vehicle classes CTM Year CTM Applications 2013CTM-based traffic simulation 2014CTM in beacon transmission 2014Adaptive network signal control 2014Fault-adaptive traffic state correction Piecewise-constant approximation Dimensional fluid visualization Interpolation approximation using central data Bi-linear (triangular) density-flow plane Focus Stochasticities in FD, various speeds, capacity Velocity distribution in cells Flow pattern classifacation in FD More realistic queue discharging Share road flow adjustment, backward speeds Focus Matching real time traffic data Reduce wireless channel load (position, speed) Distributed network control Traffic state estimation and prediction 4

5 4.0 Methodology (Cell Transmission Model) No. of veh in cell i at time t+1 No. of veh in cell i at time t inflow outflow Sending flow Cell/flow capacity Available empty cell space 5

6 4.0 Methodology (Modelling Process Flow) Computation of Cell Transmission Model Step 1: Examine Road Conditions (Variables Input) Step 2: Determine Cell Conditions (Cell length, number of cells, Cell Capacity) Step 3: Iterative Cell Updates (Spatio- Temporal) and Tabulate Data Step 4: Simulation Time Stopped, Display Results and Data Checking Cell Conditions Cond 1: Checking Mass Balance (If hits cell capacity congested to the back) Cond 2: Propagation of flow (Once flow altered delays in flow alteration at cell behind) Cond 3: Recovery of flow (Acceleration to recover, until flow smooth again back to normal flow) 6

7 5.0 Results & Discussions The road condition and scenario: Total road length, L = 1.25 km, with average speed, v = 50 km/h, the jamming density, k j = 180 veh/km, and the maximum flow capacity, q max = 3000 veh/h. During normal flow, the vehicular flow achieved q = 2400 veh/h. The road disturbance happens at the 1/3 part from the end of the road, which reduces the traffic flow to q d = 600 veh/h. The disturbance lasted for 10 minutes until the road obstacle was removed. Sample Calculations 7

8 5.0 Results & Discussions Case 2 Detailed Pic - More cells (15), shorter sample time (6 s) Time (h) cell 1 cell 2 cell 3 8

9 5.0 Results & Discussions Case 2 Detailed Pic - More cells (15), shorter sample time (6 s) 9

10 6.0 Conclusions Discrete based hydrodynamic theory is more stable, flexible and easier to be implemented for dynamic traffic flow modelling (prompt to changes). Depends on the detail level of information, computation of CTM is adjustable for the trade-off between bulky computation and too general. Achievement: 1. Smaller cell length capture detailed changes (view inside big pic) 2. Included backward flow accumulations (traffic jam) upon encountering blockage and hitting road capacity 3. Included forward flow recovery propagations (relieve from hindrance) 1 0