Capacidad de asignación de recursos

Mixed-traffic flow is composed of standard vehicle types such as passenger cars, buses, and trucks, as well as non-standard vehicles such as motorcycles and bicycles.

Normally, the behaviour of standard and non-standard vehicle types are different, therefore it becomes difficult to model mixed-traffic flow. In the past, mixed-traffic flow phenomena have been described by statistics and simulation of the mixed-traffic flow using some microscopic behaviour principles. Faghri (1999) created an equation of vehicle following, which was applicable to the cases of car-bicycle, bicycle-car and bicycle-bicycle following. Oketch (2000) incorporated car-following rules and lateral movement to model mixed-traffic flow, where lateral movement was governed by fuzzy logic rules. Chaos theory and artificial neural network theory were also used to develop a mixed-traffic flow mode (Li, 2004).

The movement of motorcycles is a two dimensional movement which includes longitudinal and lateral movement. The longitudinal movement makes motorcycles go forward and the lateral movement makes motorcycles take up appropriate positions for overtaking. A motorcyclist gets longitudinal gaps, while accelerating its speed and managing to move laterally in less gap distance during the lateral movement. The motorcycle traffic flow is influenced by driver characteristics, vehicle interactions and the external environment. Cho and Wu (2004) modelled the motorcycle traffic flow taking account of all these parameters for urban area. They proposed a model, which includes:

vehicle (b) external environment: the width of the motorcycle lane and markings (c) the vehicle interactions include: longitudinal and lateral movement models. A link was chosen for motorcycles, which was not influenced, by signals or intersections.

5.4.1 Motorcycle driving behaviour

Driver behaviour affects the traffic flow and its dynamics, for example, motorcycle has higher power to mass ratio, which causes more acceleration rate in less time. Driving behaviour models describe vehicles‘ movements under different traffic conditions. These models include speed/acceleration models and lane changing models.

These models are an important component of microscopic traffic simulators. They are also important to several other application areas, such as safety studies and capacity analysis, in which aggregate traffic flow characteristics may be deduced from the behaviour of individual drivers (Hidas, 2005).

Typically, in the literature, these models have been developed independently and used parallel to microscopic simulation models. Toledo (2007) developed a framework for driving behaviour modelling that integrates the various decisions, such as acceleration, lane changing and gap acceptance. Driver decision on acceleration, lane changing, and gap acceptance change from vehicle to vehicle. Normally, at intersections such decisions are critical for motorcycles. The time needed for a vehicle to proceed through an intersection varies from one vehicle to another. For example, a bus takes longer time to drive through an intersection than a car and hence impedes other users of the intersection more than a car. In order to account for this variability in impedance on other users, traffic volumes are described in passenger car units (pcus) rather than vehicles.

Based on data from Bangkok, May and Montgomery (1986) reported that motorcycles crossing the stop line in the first 6 seconds of effective green time impose little impedance on other traffic, since they wait at the front of the queue and accelerate faster than other vehicles at the start of the green period. They suggested that a pcu value of 0 should be used for motorcycles of this type (other authors have even suggested that a

later in the cycle had a pcu value that varied from 0.53 to 0.65, depending on the lateral positioning of the motorcycle and its eventual turning movement.

Powell (2000) created a model to represent motorcycle behaviour at signalised intersections. The model was tested against video data of motorcycles collected at intersections in Indonesia, Malaysia and Thailand, where the proportion of motorcycles is high (up to 70%), and predicted the number of motorcycle passing per cycle with a high degree of accuracy. The motorcycle behaviour factor was important in predicting the number of motorcycle passing the signal at the cross line. Factors were both temporal (inputs for the amended first order macroscopic model) and spatial (average lane width, number of lanes and number of buses and trucks per lane per cycle). The importance of the storage space at the front of the queue was noted and included within the model.

Lee (2007) developed an agent-based model to simulate motorcycle behaviour in mixed traffic flow on a section of the road in London. The mathematical models were developed for describing the motorcycle behaviour implemented in this simulator.

Applications of those simulators showed that it was possible to carry out policy tests using the simulator and was a powerful tool for conducting a study on mixed traffic flow containing motorcycles. However, this study was limited to mid block section and was unable to carry out simulation for longer sections of multiple intersections.

Chakroborty et al., (2004) developed a comprehensive microscopic model of driver behaviour in uninterrupted traffic flow. Rongviriyapanich and Suppattrakul (2005) analysed the effects of motorcycles on traffic operations at signalised intersections and a mid-block section of an urban road. Data collection was done at two intersections, one with motorcycle queue storage and the other without. At mid-block, Passenger Car Equivalent (pcu) of motorcycles at different traffic volume and proportion of motorcycles in traffic stream were determined. It was found that the pcu of motorcycles consistently decreases with the share of motorcycles in total traffic.

Many studies have been carried out on different aspects of motorcycles such as pcu, motorcycle traffic flow, motorcycle behaviour at junction (e.g. motorcycle crossing the stop line) and at road sections. However, not much research to date has attempted to

simulate motorcycles driving cycles in micro simulation models taking into account local driving conditions.