Effect of Electronically Enhanced Driver Behavior on Freeway Traffic Flow

Alain L. KornhauserProfessor, Operations Research & Financial Engineering

Director, Transportation Research Program

Princeton University

Presented at 53rd Annual Meeting Transportation Research ForumTampa, FlMarch, 2012

Scott H. ChaconAnalyst, Wells Fargo Investment Banking

and

Overview• Well known that congestion is made worse by variations in how drivers react to vehicles

ahead– lane changing and as well

• Thus, augmenting Driver Behavior will improve Traffic Flow• Most near-term realizable Driver Behavior augmentation is via intelligent cruise

control• Paper investigates, through detailed micro-simulation, extent to which

intelligent cruise control-type augmentation of driver behavior, will reduce the tendency of bottle neck formation in a realistically simulated freeway– Compared are minimum time-headway and minimum separation intelligent cruise

control based augmentations of driver behavior.• Paper shows that if intelligent cruise control constrains vehicles from getting too

close together, then vehicles are constrained from getting too close together, even is stop and go traffic, the frequency of the formation of traffic shock waves is significantly reduced resulting in higher traffic flow and shorter travel times.

• Paper also finds the optimal minimum following distance

Micro Simulation of Traffic Flow• Used is extended version of: http://www.traffic-simulation.de/

– Longitudinal behavior: Triber et al. (2000) Intelligent Driver Model (IDM)

– Lane change behavior: Kesting et al. (2007) Minimizing Overall Braking decelerations Induced by Lane-changes (MOBIL)

• Implemented over– 4-lane, 3,000 meter stretch of freeway in Pasadena, Ca.– Segment chosen because of the ready availability of

loop detector data from the Performance Measurement System (PeMS)

• Vehicle flow, occupancy and speed by lane in 4 locations plus entrance and off ramps

The Freeway Segment

• Eastbound I-210 Btwn Lake Blvd & N. Sierra Madre Blvd.– Loop Detectors in each of the 4 lanes before on-ramps @

• Lake Blvd., N. Hill Ave., N. Allen Ave & Sierra Madre Blvd.– On-ramp Loop Detectors @

• Lake, Hill and Sierra Madre– Off-ramp Loop Detectors @

• Hill, S. Altadena and Sierra Madre

Modification of Micro Simulation

Modification of Micro Simulation• Expanded to include

– 4 lanes (substantial addition)– Multiple on and off ramps (major addition)– Minimum following distance driver behavior augmentation– UI changes to allow:

• Switch between two simulations:– Use Observed Ramp Data

» Used for calibrating driver behavior so as to have simulation replicate the observed PeMS observations

– Set All Variable Manually» Ability to select values for 11 parameters

• Upstream in-flow, in-flow & out-flow at each ramp, lane-change politeness, speed of simulation and minimum allowable distance.

• Output plots of absolute and relative-to-observed flows in each lane at each station

Calibration of Micro Simulation

• Done over a 24 hour period to match data from Sept 22, 2008

– Each new vehicle appropriately assigned a “destination” (off-ramp or continue on I-210)

– A right-bias incentive to change lanes behavior was added to exiting vehicles.

• Proportional to the number of lanes that need to be changed and nversely proportional to Distance-to-Exit

Calibrated Parameters of Micro Simulation

Parameter Car nominal value Truck nominal value

Desired Velocity (v0) 140 km/h 100 km/h

Time headway (T) 1.5 Sec 1.7 Sec

Min Gap (s0) 2.0 m 2.0 m

Acceleration (amax) 0.3 m/s2 0.3 m/s2

Deceleration (dmax) 3.0m/s2 2.0m/s2

Input:Flow @ 717635 & On-Ramps w destinations matching off-ramps and station 717646

Parameters Calibrated to match:Flow @ 717638, 717640, 717646 & Off-Ramps

Observed Flows

I-210

Hour of day Hour of day

Hour of day

Hour of day

Off-Ramp

On-Ramp

% Error in Flow

717638

Since Midnight

717640

Since Midnight

717646

Flow Implications of Cruise Control Augmentation of Driver Behavior

• Previous studies by Ionnou & Chien (1993) & Arem (2006) – Constant minimum time headway (min separation proportional to

distance ensuring safety) smoothes traffic flow and increases flow rate (in single lane)

• What about: Reliance on “normal” Driver Behavior; except, as it might encroach on some Minimum Separation constraint – Deceleration Behavior of closing vehicles (Vtrailing > Vleading; S > Smin)

Controlled by intelligent cruise controller so as to achieve a feedback-controlled minimum separation, S = Smin at Vtrailing = Vleading

– Smin = 0 is equivalent to zero augmentation of Driver Behavior, e.g. existing condition

Average Flow Improvement v Minimum Separation Distance

• Average Flow Improvement at peak times over pure Driver Behavior (no augmentation)

• Peak @ 10 meters

Improvement in Travel Time v Min Separation

• Cumulative Distributions of Travel time for all vehicles traveling the whole length (from 717635 to 717646)

• While simulation was done for only one freeway segment and only one day’s demand– Does indicate that augmenting driver behavior

using minimum separation does reduce the formation of shocks in the traffic flow.

Conclusions