Modeling HOV lane choice behavior for microscopic simulation models and its application to evaluation of HOV lane operation strategies

Jun-Seok OhWestern Michigan UniversityLianyu ChuUniversity of California, Irvine

Investigation of HOV Modeling Capability in Microscopic simulation Models

Jun-Seok OhWestern Michigan UniversityLianyu ChuUniversity of California, Irvine

Content

Motivation and Objectives Classification and Operation of HOV

System Analytical Model for HOV Lane Traffic

Estimation HOV Modeling in Microsimulation Models Experiment and Performance Comparison New Modeling Approach Concluding Remarks

Motivation

FHWA encourages the installation of HOV lanes as an important part of an area-wide approach

There are still questions on the effectiveness of HOV systems their impacts on air quality

The benefits of HOV systems have not been well quantified

Microsimulation might be a good way, but still involves some limitations

Objectives

Compare HOV modeling capability and performance in Paramics AIMSUN

Identify limitations and investigate methods to enhance HOV behavior modeling in microsimulation

Develop an improved HOV simulation analysis tool using API capability

Classification of HOV System

Infrastructure Mainline HOV lane Freeway-to-freeway direct connectors Direct local access ramps Freeway ramp meter bypass lanes Toll plaza bypass lanes

Designed Access Open system Closed system (Limits access with barrier)

Use Restriction 2 people minimum occupancy 3 people minimum occupancy Buses Vehicles paying toll (High Occupancy Toll)

Operational Period

Full time operation Part time operation

HOV Operations

Analytical Model for HOV Lane Traffic Estimation

User Equilibrium between HOV/GP HOV lane is faster than GP lanes tHL ≤ tGL

fHOV(VHOV - VHG) ≤ fGP(VSOV + VHG)

If fHOV(VHOV) ≤ fGP(VSOV), VHG = 0

If fHOV(VHOV) > fGP(VSOV), VHG > 0 VHG can be found by solving

fHOV(VHOV - VHG) = fGP(VSOV + VHG)

HOV Modeling in Microsimulation Models

Vehicle Types SOV & HOV

Defining HOV Lane (Open HOV System) Allow HOV only on HOV lane

Lane barrier (Closed HOV System) Closed HOV available in AIMSUN Closed HOV via plug-in in Paramics

HOV Behavior Modeling

Optional By allowing HOV only on HOV lane May underestimate HOV on HOV lane

Compulsory By forcing all HOV to use HOV lane 100% HOV on HOV lane Unrealistic

Separate links for HOV lane Route choice with dynamic feedback Not applicable to Open HOV

Paramics provides HOV plug-in for more HOVs on HOV lanes

Experiment Scenarios

Scenario 1: Closed HOV Using given capability

Scenario 2: Separate Links for Closed HOV Treating closed HOV lanes as separated links

Scenario 3: Open HOV No barrier between HOVL & GPL

Assumption: HOV demand - 15% of total traffic MOEs

Traffic volume split between HOVL & GPL HOV demand split b/w HOVL & GPL HOV demand split w.r.t speed of GPL

Study Network I-405, Irvine, California

Study Network I-405, Irvine, California

HOV: open HOV: closed HOV: closedHOV: open

Northbound I-405

6 km freeway stretch

Scenario 1: Closed HOV Paramics: Plug-in provided by vendor

add additional layers of detail to the HOV modeling influence lane changing behavior and lane discipline model both open/closed HOV lanes

AIMSUN: Default function Restrict lane-changing with solid-line

Dotted-line: open areaSolid-line: barrier

S1: Volume Comparison

GP lane volume

HOV lane traffic is underestimated Paramics HOV lane

traffic: constant during simulation period

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6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Observed

Paramics

AIMSUN

0

20

40

60

80

100

120

140

160

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Observed

Paramics

AIMSUN

S1: HOV traffic

% of HOV lane traffic

% of HOVs on HOVL

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Observed

Paramics

AIMSUN

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Paramics

AIMSUN

S1: HOVs on HOVL w.r.t GPL Speed

Paramics Not sensitive to the

traffic condition on GPL

AIMSUN Slower speed on

GPL leads to more HOVs on HOVL

0%

10%

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90%

40 50 60 70 80 90 100 110

0%

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90%

40 50 60 70 80 90 100 110

Scenario 2: Separate links for closed HOV lanes Separate links for closed HOV lanes Use route choice model in HOV lane choice Dynamic link costs update HOVs are treated as guided drivers

change route (lane) while driving

Dotted-line: open area

Separate link for HOV lane

S2: Volume Comparison

% of HOV lane traffic Close to observed

HOVL volume

% of HOVs on HOVL 70 – 80% during

congested period

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Observed

Paramics

AIMSUN

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Paramics

AIMSUN

S2: HOVs on HOVL w.r.t GPL Speed

Paramics

AIMSUN

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

40 50 60 70 80 90 100 110

0%

10%

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40 50 60 70 80 90 100 110

Scenario 3: Open HOV Lane

HOV can access anywhere HOV lanes are restricted only for HOVs Rely only on lane-changing & restriction model

Dotted-line: all open area

S3: Volume Comparison

% of HOV lane traffic Underestimates

HOVL volume

% of HOVs on HOVL Low HOLV utilization

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Observed

Paramics

AIMSUN

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

6:35 7:00 7:25 7:50 8:15 8:40 9:05 9:30 9:55

Paramics

AIMSUN

S3: HOVs on HOVL w.r.t GPL Speed

Paramics

AIMSUN

0%

10%

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30%

40%

50%

60%

70%

80%

90%

40 50 60 70 80 90 100 110

0%

10%

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30%

40%

50%

60%

70%

80%

90%

40 50 60 70 80 90 100 110

Findings

Closed HOV Lanes Underestimates HOVL traffic

Paramics 65%, AIMSUN 85% of observed Paramics Plug-in need improvement

Better when incorporating route choice behavior with dynamic cost update

Performance varies by route choice model Open HOV Lanes

Current HOV modeling NOT satisfactory Paramics 60%, AIMSUN 78% of observed

Underestimates due to the lack of capability to measure lane-by-lane traffic condition

Other Scenarios

Compulsory HOV Lane AIMSUN has an option for compulsory HOV

Almost 100% HOVs use HOVL Not realistic for HOV lane analysis Useful tool for exclusive bus-lane

Paramics Can implement by defining HOV only lane and

SOV only lane But need to define area where both types can use

for exiting and entering

No HOV Lane

250

260

270

280

290

300

310

320

Closed HOV SeparateHOV

Open HOV CompulsoryHOV

No HOV

Avg Vehicle-Travel Time

Avg Person-Travel Time

Overall Travel Time Comparison

Limited analyses Compulsory and No HOV lane case outperformed

Elasticity of HOV demand NOT considered

New HOV Modeling Approach

Using API (Applications Programming Interface) capability

Consider HOV driver’s visual perception on traffic condition

Visual perception-based instant HOV lane choice model

Concluding Remark

Microsimulation needs to be enhanced for HOV analysis Closed HOV can be analyzed by incorporating route

choice model with separate HOV links Open HOV analysis needs enhanced model

Need to develop improved HOV behavior model considering driver’s visual perception on traffic condition

Need to calibrate model using real-world data HOV demand and elasticity survey

Microsimulation has potential for HOV evaluation, but only with enhanced behavior model

Thank you!