Microscopic Traffic Simulation • Key Logic – Car-following – Lane Change • Other modeling issues

Car Following Models Follow-the-leader model started in 1950s Currently 3 categories of models: safe-distance models stimulus-response models psycho-spacing models Discussed in Chapter 6

Lane Change Types • Mandatory lane change • Discretionary lane change • Random lane change

Discretionary Lane Change Increase speed • Bypass a slow or heavy vehicle • Avoid the lane connected to a ramp

Discretionary Lane Change Factors Driver Characteristics – Driver aggressiveness. – Desired free flow speed. – Normal/Maximum Acceleration/Deceleration. – Impatience

Discretionary Lane Change Factors External Stimuli 1) In the current lane: – Relative distance between the subject vehicle and its leader. – Relative speed of the subject vehicle and its leader. – Current leader is making/will make a lane change. – Current leader is making/will make a left/right turn. – Heavy vehicles (e.g., truck, bus) downstream. 2) In the adjacent lanes: – Relative longitudinal distance (Lead Gap) between the subject vehicle and the candidate leader (if available). – Relative speed between the subject vehicle and the candidate leader – Candidate leader is making/will make a left/right turn. – Candidate leader is making/will make a lane change. – Candidate leader is a heavy vehicle (e.g. truck, bus).

Mandatory Lane Change Vehicle make mandatory lane change in order to position itself to reach its destination.

Mandatory Lane Change Connect to the next link to maintain their path: – changing to the proper lane before making the turning movement at an intersection – merging to the freeway mainline – diverging to the deceleration lanes before exiting through offramp • Bypass a lane blockage downstream (typically caused by incidents or special events) • Avoid entering a restricted lane (e.g., a HOV lane, or a lane in which no truck is allowed) • Respond to message signs (e.g., lane drop warning sings)

Lane Change and Critical Gap • Gap Acceptance studies typically used to assess capacity for merging, crossing, turning, and for lane change • Available gaps and their acceptance by drivers are the two main components for such studies

Example: Gaps for Ramp Merging

HCM Critical Gap Min gap size that would be acceptable. Drivers would reject smaller gaps and accept larger gaps.

Critical Gaps for Ramp Merging

Lane Change and Gap Acceptance

Lane Change and Gap Acceptance

CORSIM Car-Following Model FRESIM: Pitt model Acknowledge Hasham

AIMSUN Car-Following Based on Gipps model

VISSIM Car-Following Modified from two models developed by Wiedemann (Weidemann74 and 99 models) Psychophysical or action-point models. This family of models uses thresholds or action-points where the driver changes his/her driving behavior. Drivers react to changes in spacing or relative speed only when these thresholds are crossed. The thresholds and the regimes they define are presented in the relative speed/spacing diagram for a pair of lead and follower vehicles.

VISSIM Car-Following Steady-state criteria (sn ≈ sdesired, Δun ≈ 0). The desired vehicle spacing is an interval (ABX ≤ s ≤ SDX) instead of a single value.

PARAMICS Car-Following Psychophysical car-following model Developed by Fritzsche Fritzsche’s model uses the same modeling concept as the Weidemann74 car-following model. The difference between these two models is the way thresholds are defined and calculated.

PARAMICS Car-Following Where A0 is the vehicle spacing at jam density, Tr is the risky time gap (usually 0.5 s), TD is the desired time gap (with a recommended value of 1.8 s).

INTEGRATION Car-Following Developed by Van Aerde

Car-Following Models in Micro Packages

Multi-Regime Approach* • Car-following regimes • Lane change regimes * Zhang et. al, 1998

Car-Following Normal car-following Regime – 5th generation GM Model

Car-Following Uncomfortable regime – too close When the calculated acceleration value from the GM Model is greater than or equal to zero, and the “comfort” spacing requirement is not satisfied, the vehicle is in the uncomfortable regime and needs to decelerate to extend the headway to a comfortable and safe range. In this regime, the acceleration is determined by the following equation

Car-Following Free-flow Regime If the distance/time headway between a leader and its follower is greater than a pre-defined threshold, then the vehicle is in the free-flow regime. If the vehicle’s current speed is less than its desired free flow speed , then the vehicle will accelerate until it reaches its desired free flow speed. The vehicle maintains the speed until it falls into another regime. The acceleration is Computed as follows:

Car-Following Emergency Regime If a vehicle has a headway smaller than its pre-determined minimum distance, then it is in the emergency regime. Emergency braking is activated to avoid collision. The deceleration is calculated from the following Emergency Collision Model.

Car-Following Other regimes – Intersection arrival regime • Reacts to signal, not only the leader • Decelerates to stop at the stop-bar – Queue discharge regime • 1st vehicle to achieve FFS • Followers using 1 generation GM model

Lane Change

Lane Change

Lane Change Mandatory lane change regimes

Lane Change Frequently, drivers have to adjust their accelerations to execute the lane changing maneuvers (mandatory). Lane Changing without Acceleration Change The subject Vehicle Needs to Accelerate The subject Vehicle Needs to Decelerate Vehicles in the Target Lane Need to Change Speeds (cooperative drivers) The subject vehicle stops

Other Modeling issues • Drivers reaction to changing traffic, geometric and control conditions critical to the success of simulation. • Special car-following, lane change scenarios arise from those conditions

Vehicle generation Headway distribution: – Normal distribution – Negative exponential distribution – Erlang distribution – Uniform distribution – Composite Distribution

Vehicle generation

Headway Generation: Composite Models

Vehicle generation Select proper distribution based on traffic volume • More important for low traffic situation – For high volume conditions, vehicle generation mainly only affects entry links. As soon as vehicles enter the network, car-following and lane change logic take control

Lane distribution • Distribution at generation – Depending on volume For low volume, most vehicles generated in right lane • As volume increases, distribution across lane tends to be more equal among lanes Trucks typically use right lane(s) • Lane distribution on internal links should be determined by car-following and lane change logic • Improper lane distribution at generation (especially on short entry links may produce undesired results

Lane distribution

Sources/sink • Modeling mid-block activities – Parking lots – Driveways – Abutting properties • Does not have to create more links/intersections

Incident/on-street parking Modeling disturbances to traffic – Blocking – Rubber-necking – Interferences – Reduced speed – Special lane change behavior – More “cooperative” drivers

Lane add/lane drop Reaction distance critical for lane drop – “Warning” sign placement to let drivers “see” the situation – Modeling similar to blockage

Driver behavior modeling • Aggressiveness • Familiarity • Cooperation • Anticipation

Driver Aggressiveness Aggressive drivers Follow leader closer Change speed more rapidly (higher max. acc. dec. rates) Shorter reaction time Take smaller gaps for lane change Need to map them consistently: an aggressive driver most likely will do all of the above consistently. Random mapping is not appropriate

Driver Familiarity with Network • Driver familiarity with route very critical for urban streets with multiple lanes and short link • Driver familiarity with the network help vehicles position themselves for lane changes • Familiarity: know the next turns and lane configurations ahead and prepare in advance

Cooperative Drivers • A change in the percentage of cooperative driver can change flow conditions in many scenarios: – Lane blockage – Merging – Moving into turning pockets

Driver Anticipation Related to driver familiarity with network Related to cooperative drivers

Vehicle performance • Acceleration performance • Deceleration performance • Desired maximum speed wrt free-flow speed/speed limit • Modeling trucks properly is very important

Driver Reaction Time Driver reaction time is a critical aspect of car-following • Driver reaction time should be consistent with driver aggressiveness • Reaction times should be different for different car-following scenarios – Normal car-following – Emergency braking – Reaction to signal change/intersection arrival/departure

Intersection Modeling • Driver response to signal state changes – When signal in amber/red, vehicle slow down to stop and does not follow the leader even if there is one. – First vehicle discharge from the intersection will gain speed to the FFS with a normal acc. rate.

O-D • Some Programs are O-D based • Some programs are turning movements based • Inputs of full-OD may not be a good idea for a large network as the data is typically not available. • Traffic assignment may be employed • For turning movement based program, OD may be entered to eliminate unreasonable u-turns

Curves/Turning Speed • Many programs do not model turning paths at intersections or on ramps. What you see in animation may not be the result of simulation. • Truck turning on sharp curves may worth modeling. • Turning speeds (LT, RT) may or may be modeled in many programs.

Signalization • Vehicles respond to signal states • Signal timing scheme: – Pre-timed – Actuated – Lane assignments very important

Accident generation? Most programs assume vehicles run safely ensured by car-following and lane change logic • Can simulate accidents by user inputs • Some programs are looking at automated accident generation based on flow conditions, such as speed variance, etc.