The solution to this problem requires the use of a professional traffic simulation software, in our case we used Aimsun 8.1. We will discuss the features of Aimsun in the next chapter. Here we describe the role of the software in better understanding and analyzing the problem and the parameters in different scenarios.
4.2.1 Step 1: Data collection
The number of vehicles that enter the system (input flow) through various entry points (Origin), flow along each section of the system and the vehicle counts for exit data (Destination) – all these data are crucial for accurate modeling of the real-world situation in Aimsun. The model is only as good as the data that we feed the software. So, this data accumulation process was accomplished by site visits over multiple days. 15-minute intervals were chosen to facilitate better imitation of the real world by accounting for varying traffic flow during each interval.
All data have been collected by site visits and observing the traffic flows at different points in the network on different days of the week during different 15-minute intervals within the peak periods.
First, we describe the OD pair names:
N: The northernmost OD pair.
NW: To the left of N, a bit towards the west (North-West). It has 2 input and output flow sections.
SW1, SW2 and SW3: Are the three OD pairs in the South-West.
S: Southernmost OD pair with 4 input and output flow sections.
C: Center of the network and has 4 input and output flow sections.
SE1: In the South and towards the East.
Having collected the input flow, the next challenge was to determine the OD Matrix for the model. As discussed in the literature review, there are several mathematical models to estimate the OD matrix with different sets of data. Due to lack of data about the census and the lack of smart technologies (vehicles fitted with trackers and RFID readers), the OD matrix estimation was achieved by using weighted ratios.
Following is an illustration of the method of estimating the OD matrix.
Assume three OD pairs X, Y and Z. Say x vehicles come into the system from X, y from Y and Z from Z.
So,
Total number of vehicles incoming = x+y+z
Similarly, assuming the outgoing vehicles from each of these centroids is a, b and c, Total number of vehicles outgoing = a+b+c
Weight of each centroid in terms of outgoing traffic, For X, a/ (a+b+c)
For Y, b/ (a+b+c) For Z, c/ (a+b+c)
Now we use these weights to determine what portion of the incoming traffic (x, y & z) the three destinations attract.
Number of vehicles going from X to Y = x (a/ (a+b+c)) Similarly, the values for each OD pair is calculated.
Here is an example of the data collected during the time interval 17h30 to 17h45:
Table 1: Traffic counts with their weights for the time period 1730 to 1745
1730-1745
Centroid Vehicles coming IN Weights Vehicles going OUT Weights
5571: SW1 856 0,27003 547 0,206259427
In the table above, “Vehicles coming IN” holds the counts for number of vehicles entering the system through the corresponding centroid. Similarly, “Vehicles going OUT” holds the count for vehicles exiting
The Aimsun 8.1 version does not allow simulation of pedestrians so this data was used just for analysis purposes. It could not be simulated in the model. Lane width, driver reaction time (response in case of stopping a car or starting from a stopped state), average queue lengths – these data were also collected by site visit and have been summarized below.
Tabela 2: Summary of data during site visits
Characteristic Specifications
Lane width: Highways 3 meters
Lane width: Local Roads 2 meters
Pedestrian counts (every 15 minutes) 50 (peak during school hours)
Driver reaction time 1 to 2 seconds
Average queue length 30 vehicles/100 meters
4.2.2 Step 2: Modelling the Network in Aimsun
Area selection: To obtain best results, appropriate area selection with respect to incoming and outgoing (Origin and Destination, OD) selection is important. In our situation, we decided to include 9 O-D pairs with 15 incoming and outgoing sections related to them.
Figure 10: Indication of centroids, multi-junction and the Infias roundabout in the system model.
Road Lane Width: On the National Highways, the lane width is 3 meters and in the local streets/roads it is around 2 meters.
Pedestrian Crossings: In Aimsun 8.1 we don’t have the access to model pedestrian data.
Section Parameters: Modelling the sections (roads) involves three main inputs: section length, section type and slope. The length is inputted in meters, and the slope requires the initial and final altitudes. In the case of varying altitudes in between, we input the intermediate altitudes too. This information is available for public use via Google Earth. The section type is either
“reserved for public transport” or “for all vehicles”. In our area selection, we have 3 lanes (in separate sections) in the category “reserved for public transport”.
Public Transport: Public transport in Braga is operated by TUB (Transportes Urbanos de Braga).
In the area selected for our model, we have 6 public bus stops, with two of them having reserved lanes for this purpose.
Data Inputs: Firstly, and most importantly, the traffic demand data. The data collected in Step 1 feeds the Traffic Demands via OD matrices. We have divided the two-hour time intervals in the morning and evening are divided into four 15-minute intervals to best imitate the real-world scenario. Second are the vehicle specifications: vehicle type, dimensions and emission model. Vehicle type include cars, trucks, buses etc. Vehicle dimensions can be changed according to different mix of vehicles in Braga.
These data parameters are set for the real world and the future scenarios as suggested by the Municipality of Braga and some other scenarios with control plans.
4.2.3 Step 3: Running the Simulation
After data input, Aimsun can simulate the model displaying the 2D and 3D views as per our requirement during the simulation. And the output data is recorded according to the analyses to be done.
4.2.4 Step 4: Simulation, Recording the Data and its Analyses
With Aimsun providing detailed statistical and map/graph-based outputs of all the major parameters, proper interpretation and conclusions are drawn using correlation between different KPIs.