Model calibration and validation

The credibility of a simulation model depends on its ability to replicate field conditions accurately. A large number of parameters in a simulation model may influence the performance of the model. Hence, calibration and validation procedures are required to simulate the real-world traffic networks by adjusting model parameters through trial-and-error. In a data-rich situation, two independent datasets are generally used in this procedure. The first dataset is used to calibrate the model parameters to represent local traffic conditions. The second is for validating the model by comparing the outputs generated from the calibrated model and the field observed data. Many publications have discussed the general requirement for a calibration procedure with the goodness-of-fit tests for model validation, for example Hourdakis

et al. (2003); Jha et al. (2004) and Toledo & Koutsopoulos (2004).

The simulation parameters to be calibrated in AIMSUN can be classified into three main categories: global parameters, local parameters and vehicle attributes (TSS, 2004). Global parameters, including a driver‟s reaction time, response time at stop, queuing-up and queuing-leaving speeds, are used for all vehicles and affect the

Page | 137 performance of the entire simulation network. Local section parameters, such as section speed limit, lane speed limit, turning speed and visibility distance at junctions, affect only a specific section of the network regardless of vehicle types. Vehicle parameters, such as maximum desired speed, maximum acceleration, normal deceleration and maximum deceleration, influence all vehicles of a determinate type in the simulation network.

As summarised by Hourdakis et al. (2003), an ideal method for model calibration has three stages. The first stage is volume-based calibration, the objective of which is to obtain simulated traffic volumes which are as close as possible to the real measured volumes. The global parameters and vehicle characteristics are modified in this stage. In the second stage, speed-based calibration, most local parameters and global parameters need further modification to accurately simulate real-world traffic networks. The third stage, objective-based calibration (e.g. queue lengths), is an optional stage that depends on the specific objective of the simulation model.

The main purpose of model calibration and validation is to ensure that the simulated network replicates the real traffic network as closely as possible by comparing the simulated outputs with measured data. There are various approaches to simulation validation available in the literature. These include statistics (such as correlation efficient, root mean squared percentage error, Theil‟s inequality coefficient, error mean relative positive and error mean relative negative, as summarised in Vilarinho & Tavares (2012)), other statistical analyses (Student‟s-t test and hypothesis test, e.g. Barcelo & Casas (2004)) and graphical representation (band comparison and scatter-grams, e.g. Haas (2001); Barcelo & Casas (2004)).

Page | 138 The values of some important parameters used in AIMSUN are provided in Table 4.4.

Table 4.4: Important parameters in AIMSUN

Parameter Name Value Unit

Driver’s reaction time 0.75 sec

Reaction time at stop 1.35 sec

Reaction time at traffic light 1.35 sec

Car

Length 2.5-5.16 metre

Width 1.4-2.08 metre

Maximum desired speed 95-160 km/h

Maximum acceleration 2.8 m/s2

Normal deceleration 4-6 m/s2

Maximum deceleration 8-11 m/s2

Speed acceptance2 1-1.4

Minimum distance between vehicles 1-2 metre

Give way time 10-50 sec

Guidance acceptance 100 %

HGV

Length 12 metre

Width 2.3 metre

Maximum desired speed 80-100 km/h

Maximum acceleration 1.4-1.6 m/s2

Normal deceleration 3.5 m/s2

Maximum deceleration 8 m/s2

Speed acceptance3 0.9-1.2

Minimum distance between vehicles 1 metre

Give way time 5-60 sec

Guidance acceptance4 100 %

Section 338

Distance on ramp5 5 sec

Visibility distance 25 metre

Yellow box speed 10 km/h

Maximum speed 50 km/h

2 _{A parameter measures the driver‟s degree of accomplishment of the speed limits on the section.} 3 _{This parameter can be interpreted as the „level of goodness‟ of the drivers or the degree of acceptance} of speed limits.

4 _{This parameter gives the level of compliance of this vehicle type with the guidance indications, such} as information given through Variable Messages Signs or particular Vehicle Guidance Systems. 5 _{The distance on ramp in AIMSUN were set as a time and internally converted to a distance using the} desired speed of each vehicle.

Page | 139 The main purpose of model calibration and validation is described above. There are some „features‟ of the simulation model, however, such as vehicle crashes, disappearing vehicles and vehicles stopped in appropriately on the link which also . need to be examined during the procedure of model calibration and validation. In this simulation experiment, the following actions were taken to check the „features‟ mentioned above:

 To visually monitor links and junctions when a model is running on the frontend;

 To monitor the dialogue of Simulating Replication written by AIMSUN that records the number of „lost‟ vehicles when a model is running; and  To check the output file that records the number of input and output

vehicles into the road network.

These non-standard „features‟ should be checked during every single run of the model; in practice, however, this process is time consuming. Hence, two simulated days were randomly selected. One was under normal traffic conditions; the other was under heavily congested condition. Under normal traffic condition, the „features‟ of vehicle crashes and inappropriately stopped vehicles did not happen when monitoring the running simulation model on the frontend. In this simulation experiment, 68,287 vehicles entered the road network; 15 vehicles stopped inside the network when the simulation was finished and 68,098 vehicles exited the network, resulting in 174 „lost‟ vehicles. The percentage of „lost‟ vehicle is therefore 0.25%. Under heavily congested conditions, the problems of vehicle crashes and inappropriately stopped vehicles did not happen when monitoring the running model. During the simulation, 68,428 vehicles entered the network; 16 vehicles stopped inside the network when the

Page | 140 simulation was finished and 68,254 vehicles exited the network, resulting in 158 „lost‟ vehicles. The percentage of „lost‟ vehicles is therefore 0.23%.