Vehicle Infrastructure Integration in VISSIM

1.8 miles segment along 3500 South in Salt Lake County with 5 signalized intersections Experiments performed for three 4-legged intersections (4000W, 4400W & 4800W) (4000W Loaded with traffic data for 4:00 – 5:00 PM peak period, period calibrated and validated
Analysis: A l i
Parameter: number of stopped vehicles Aggregate i t A t interval: 120 s l
Verification:
VISSIM queue counters Visual verification
14
VII vs. Queue Counters (100%) Q ( )
9
Vehicle Identification Algorithm g
For each active vehicle in micro simulation
No
Vehicle is in TM zone
Yes
Add vehicles to the TM zone
For each vehicle in TM zone
4000W – 20 2% 20.2% 4400W – 29.4% 4800W – 34 3% 34.3%
VII vs. Queue counters error for 1 hour, 60% VII:
4000W – 71 0% 71.0% 4400W – 58.5% 4800W – 69 0% 69.0%
60
Numb of Stopped Veh ber hicles
50
40 VII 30 VISSIM
20
10
0
Aggregate Intervals (s)
ቤተ መጻሕፍቲ ባይዱ
16
VII vs. Queue Counters (100%) Q ( )
120
4800W R2=0.94
100
Numb of Stopped Veh ber hicles
100 90
4000W R2=0.92
80
70
Number of Stopped Veh hicles
60
50 VII 40 VISSIM
30
20
10
0
Aggregate Intervals (s)
15
VII vs. Queue Counters (100%) Q ( )
80 70
4400W R2=0.82
7
VII Input p
8
Vehicle Identification Algorithm g
Identifies all vehicles within the TM zone Records the speed of each vehicle If the actual speed is lower than the critical, marks the vehicle as “stopped” stopped If the actual speed is greater than the critical, and the vehicle is positioned behind another stopped vehicle(s), marks the vehicle as “arriving”
Contains data for each time step on a second-bysecond basis:
Number of stopped vehicles pp Number of arriving vehicles Number of departing vehicles
12
VISSIM Test-bed Network
22
Findings g
The algorithm does the work it was designed for With certain modifications it can provide a great variety of traffic data (future researches) Our O goal: provide an algorithm which will enable l id l ih hi h ill bl adaptive traffic control via VII A problem: VISSIM – P th i t bl Python integration slows down ti l d the simulation significantly, as the number of monitored intersection and the number of vehicles increases (modify the algorithm?)
Add vehicle to the MOVING
No
Vehicle speed <= Vstopped
Yes
Add vehicle to the STOPPED
For each vehicle in MOVING
For each vehicle in STOPPED
VEH_MOV in front of VEH_STOP
13
VII Experiments p
Inputs:
Traffic Management zone: ~400 ft for N–S & S–N movements g and ~1000 ft for E–W and W–E movements Critical speed: 10 mph Critical stopped time: 200 s VII vehicles penetration rates: 100%, 80%, 60%
Outputs:
A database of stopped vehicles for each movement A “Python Shell” record of each time step containing the Python Shell step, number of stopped, arriving and departing vehicles
5
VII in VISSIM
6
VII Data
Inputs:
Coordinates for monitored sections of each intersection and each movement VII vehicles penetration rate (through traffic compositions) Critical speed – the speed below which a vehicle is considered as stopped Critical stopped time – the stopped time of a vehicle after which the algorithm will report a problem with the vehicle
Vehicle Infrastructure Integration (VII) in VISSIM Applied to Signalized Intersection Operations
Milan Zlatkovic Aleksandar Stevanovic, PhD Peter T. Martin, PhD Civil and Environmental Engineering University of Utah PTV Users Group Meeting, Portland, Oregon, May 1, 2009
18
Visual Verification
19
Visual Verification
20
Visual Verification
Out of TM zone
21
VII vs. Queue Counters – Lower VII Vehicle Penetration Rates
The error in reported number of stopped vehicles increases as the penetration rate decreases p VII vs. Queue counters error for 1 hour, 80% VII:
3
VII Concept p
Vehicle-vehicle & vehicle-roadside communications via DSRC Main objective: increase safety Big potential for many other purposes
4
VII in VISSIM
If the actual speed is greater than the critical, and the vehicle is positioned in front of another stopped vehicle(s), marks the vehicle as “departing”
No
Yes
Add vehicle to DEPARTING
Add vehicle to ARRIVING
10
VII Output - Database p
Raw output p Decoding