Types of micro-simulation model

5.2.1 Introduction

The development of car-following and lane-changing rules spawned the development of many early micro-simulation models Gipps (1981, 1986). In recent years, micro-simulation models have been increasingly used to aid studies of a wide range of transportation systems (e.g. Urban Traffic Management and Control System Development [Fox et al., 1995; Scemama et al., 1996], Motorway Management System Development, Micro-simulation validation [Druitt, 1998], Adaptive Signal Control System Evaluation, [Fox et al., 1998], LRT Priority System Development). Alger et al., (1997) reviewed several micro-simulations models — for example, AIMSUN-2, CORISIM, DRACULA, VISSIM etc. — that are listed in Table 5.1, which provides an overview of the most widely used micro-simulation models. Models such as DRACULA, PADSIM, and PARAMICS were developed in UK, while other simulation models such as VISSIM and CORISIM were developed outside the UK, although they are widely used in the UK.

Table 5.1 Summary of the most widely used micro-simulation models

Model Organisation Country Purpose Location Emission

Model

France Fixed-time algorithm and three adaptive control

Italy Standardisation of the interface between the

Australia Evaluate various traffic scenarios and alternative

Germany Signal control, transit operators, city planners

From the review of the micro simulation software (Table 5.1 and Appendix 5.1), it can be concluded that VISSIM software is the most appropriate available option to carry out this current study. This is mainly because of the availability of emission module into VISSIM as well as the availability of two wheels vehicle-type, which is needed to create a motorcycle vehicle-type and model its lateral and longitudinal movement. This is crucial in order to be able to replicate motorcycle driving behaviour and its driving cycle. The detail of VISSIM is presented below.

5.2.2 VISSIM

VISIM is a macroscopic transportation model developed by PTV AG, Germany, for transportation planning, travel demand modelling and network management. It can be used to model both public and private transportation modes. Internally VISIM is comprised of several modules.

The results obtained from VISSIM are used to define optimal vehicle actuated signal control strategies, test various layouts and lane allocations of complex intersections, test the location of bus bays, test the feasibility of complex transit stops, test the feasibility of toll plazas, and find appropriate lane allocations of weaving sections on motorways. VISSIM is coupled with micro-scale decentralized controllers of various signal control manufacturers to test their control strategies in detail before they are implemented. VISSIM is a multipurpose simulator used by technical staff at cities responsible for signal control, transit operators, city planners, and researchers to evaluate the influence of new control and vehicle technologies. Vehicles follow each other in an oscillating process. As a faster vehicle approaches a slower vehicle on a single lane, it has to decelerate. The action point of conscious reaction depends on the speed difference, distance, and driver-dependent behaviour. On multi-lane links, vehicles check whether they improve their speed by changing lanes. If so, they seek acceptable gaps on neighbouring lanes. Car following and lane changing together form the traffic flow model that is the engine of VISSIM (VISSIM User Manual, 2008).

(a) Behaviour Modelling in VISSIM

VISSIM uses the psychophysical driver behaviour model developed by Wiedemann (1974). The basic concept of this model is that the driver of a faster moving vehicle starts to decelerate as he reaches his individual perception threshold to a slower moving vehicle. Since he cannot exactly determine the speed of that vehicle, his speed will fall below that vehicle‘s speed until he starts to slightly accelerate again after reaching another perception threshold. This results in an iterative process of acceleration and deceleration. Stochastic distributions of speed and spacing thresholds replicate individual driver behaviour characteristics. The model has been calibrated through multiple field measurements at the Technical University of Karlsruhe. Periodical field measurements and their resulting updates of model parameters ensure that changes in driver behaviour and vehicle improvements are accounted for (VISSIM, 2008).

(b) The ‗Wiedemann‘ Approach

The traffic flow model in VISSIM is a discrete, stochastic, time-step based microscopic model with driver-vehicle-units as single entities. The model contains a psychophysical car following model for longitudinal vehicle movement and a rule-based algorithm for lateral movements. The model is based on the continued work of Wiedemann (Wiedemann, 1974). The basic idea of the Wiedemann model is the assumption that a driver can be in one of the four driving modes as follows:

 Free driving: No influences of preceding vehicles are observable in this mode.

The driver seeks to reach and maintain a certain speed, his individually desired speed. In reality, the speed in free driving cannot be kept constant, but oscillates around the desired speed due to imperfect throttle control.

 Approaching: The process of adapting the driver‘s own speed to the lower speed of a preceding vehicle. While approaching, a driver applies a deceleration so that the speed difference of the two vehicles is zero in the moment he reaches his desired safety distance.

 Following: The driver follows the preceding car without any conscious acceleration or deceleration. He keeps the safety distance more or less constant, but again due to imperfect throttle control and imperfect estimation the speed difference oscillates around zero.

 Braking: The application of medium to high deceleration rates if the distance falls below the desired safety distance. This can happen if the preceding car changes speed abruptly, of if a third car changes lanes in front of the observed driver.

For each mode, the acceleration is described in terms of speed, speed difference, distance and the individual characteristics of driver and vehicle. The driver switches from one mode to another as soon as he reaches a certain threshold that can be expressed as a combination of speed difference and distance. For example, a small speed difference can only be realized in small distances, whereas large speed differences force approaching drivers to react much earlier. The ability to perceive speed differences and to estimate distances varies among the driver population, as well as the desired speeds and safety distances. Because of the combination of psychological aspects and physiological restrictions of the driver‘s perception, the model is called a psycho-physical car-following model. The following sections describe the various behaviour parameters in VISSIM.

(c) Wiedemann 74 Model Parameters

This model is an improved version of Wiedemann‘s 1974 car-following model. The following parameters are available:

 Average standstill distance (ax): It defines the average desired distance between stopped cars. It has a fixed variation of ±1m.

 Additive part of desired safety distance (bx_add)

 Multiplicative part of desired safety distance (bx_mult) — affects the computation of the safety distance.

The distance d between two vehicles is computed using this formula:

bx ax d 

Where ax is the standstill distance.

Where v is the vehicle speed (ms^-1). Z is a value of range [0, 1], which is normally distributed around 0.5 with a standard deviation of 0.15. These are the main parameters that affect the capacity flow. In addition, Wiedemann‘s 1999 car following model for driver behaviour in VISSIM is suitable for freeway traffic.