Though the microscopic modelling is a better approach for describing mixed traffic streams, it requires more intensive and detailed data to understand the behaviour exhibited by individual drivers under different traffic conditions and to develop efficient behavioural models. The performance of a microscopic model highly depends on the accuracy of data obtained from the field traffic. The peculiar driving behaviour of 2D traffic (no-lane based mixed traffic i.e.
lateral/longitudinal movement) can be modelled by collecting empirical data from real traffic streams. The factors that influence drivers’ perception and decision making are mainly related to traffic characteristics and in their immediate neighbourhood.
2.5.1 Parameters used in Existing Modified 1D Models (CF/Lane Changing)
In the above sections a brief review on the driver behavioural models developed for both lane- based and non-lane based traffic conditions are discussed briefly. After understanding the advantages and disadvantages of microscopic approach for 1D traffic, the data/parameters required for developing them are identified. Table 2.3 describes the parameters used in the existing microscopic models developed for 1D traffic and later modified for 2D traffic by different authors.
Table 2.3 Microscopic Parameters used in Different Models
Δv Δd A/D Tr Dr Tv Dv TTC Latd Gapcr Vh Micro-Simulation Models
Stimulus-
Response
* * * *
Collision-
Avoidance
* * * * * *
AIMSUN, CORSIM,SIMTRAFFIC, Optimal-
Velocity
* * * * * * * * *
Psychophysical
* * * * * *
PARAMICS, VISSIMLane Changing
* * * * * *
Δv-Relative speed, Δd-Relative Spacing, A/D-Longitudinal Acceleration/Deceleration of each vehicles, Tr-Reaction Time, Dv-Distance travelled during reaction time, TTC-Time To Collision, Latd-Lateral Spacing, Gapcr-Critical Gap between two vehicles for lane change, Vh-Vehicle Heterogeniety
The parameters mentioned in Table 2.3, describes the car-following and lane changing behaviour of the vehicles on the traffic stream. The relative spacing (Δv), Relative spacing (Δd), Acceleration/Deceleration, Reaction time (Tr), Distance travelled during reaction time (Dr) and Time To Collision (TTC), etc. mainly used to describe the vehicle-following or the longitudinal manoeuvre of the vehicles on a traffic stream in micro level. Several modifications in the existing vehicle-following models have been made by different researchers by incorporating vehicle heterogeneity and lateral separation effects like the Veering Distance (Dv), Time taken to veer (Tv), lateral distance between vehicles, etc. to describe the overtaking or passing manoeuvre of vehicles. Also the lane changing manoeuvre of vehicles are incorporated by the researchers in the basic car-following models to describe the lateral behaviour of vehicles. Most of the existing lane changing models are rule-based models. In case of mixed traffic, drivers exploit their manoeuvring capability depending on the type of vehicle and take advantage of weak lane discipline. This peculiar behaviour of vehicles cannot be modelled by the existing models. Hence, to model such traffic condition, a detailed micro-level knowledge of the existing traffic stream and the behaviour of vehicles while manoeuvring is needed.
2.5.2 Parameters Used in 2D Models Developed by different Authors
Keeping in view of the Indian heterogeneous and no lane disciplined traffic behaviour.
Different authors have tried to develop the driver behaviour model to describe such traffic stream. Table 2.4 describes the different criteria or parameters used by various authors to describe such complex traffic behaviour other than the parameters used for 1D traffic.
Table 2.4 Different traffic stream criteria implemented by different authors to represent no-lane based heterogeneous traffic
Authors Road
Type Shoulder
type Median
Type Vehicle
Type Over Taking Criteria
Lateral Clearance / Vehicles Placement
Passing
speed Steering Angle
Palanswamy et
al., (1985)
* * * * *
Ramanayya,
(1988)
* * * * * * *
Chakroborty et
al., (2004)
* * * * *
Arasan and
Koshy, (2005)
* * * *
Dey et al., (2008)
* * * * *
Maurya, (2007)
* * * * * * * *
Mathew et al.,
(2013)
* * * *
From the Table 2.4, it can be observed that the overtaking, lane changing and the vehicle heterogeneity are also introduced to the basic models. However, the real world traffic swerving or weaving manoeuvre of the vehicles or steering angle are not examined on field (Maurya, 2007). Mathew et al., (2013), used very fine strips to identify the lateral movement of the vehicles. It does not include the behaviour of 2Ws from real traffic. The congested traffic behaviour are not studied by various authors. A large scale validation of these models based on the field data is needed. Though the variability in road geometry as well as the effect or road edge is considered by Chakroborty et al. (2004), the impact of variability in edge conditions on the driving behaviour are not included in the model.
2.5.3 Parameters required to Model Indian Mixed Traffic
In case of Indian traffic conditions, the roads are having different types of geometric features with various types of edge conditions. The heterogeneity of vehicles in Indian traffic leads to a complex traffic behaviour. The impact of road features such as shoulder, median, road width, horizontal and vertical alignment etc. on driver behaviour need to be studied. A large scale validation with field data is required. The driving behaviour of mixed no-lane based traffic is divided in three categories.
Mixed Traffic (Vehicle heterogeneity)
The mixed traffic included multiple type of vehicles in the same traffic stream with different manoeuvring or driving capabilities and different size (length or width) of the vehicles.
Road Characteristics
The roads include different width of roads with variability in horizontal and vertical alignment and different median and shoulder types.
Weak Lane disciplined traffic
Weak lane discipline leads lateral movements with in lanes and integrated movements by interacting with all surrounding vehicles at once. Occupying any lateral space within the road width leads multiple leader following, weaving and filtering manoeuvre.
Hence to study the driving behaviour in no-lane based mixed traffic and to incorporate the above traffic behaviours the following manoeuvring/behaviour are important to study from field traffic;
a) Longitudinal manoeuvre: Longitudinal acceleration/deceleration with their operating speed
b) Lateral manoeuvre: Lateral acceleration as a measure of lateral manoeuvre
c) Road Characteristics: Impact of road characteristics (road width, median condition, etc.) on vehicle manoeuvre.
Therefore, longitudinal acceleration/deceleration, lateral acceleration and the impact of road characteristics are important parameters to study such traffic stream and their accurate measurement and analysis at micro level is necessary to understand the driver behaviour. The following sections describe the brief literature review conducted on these parameters in mixed traffic.