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Traffic Flow Characteristics. Dr. Attaullah Shah

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Title: Traffic Flow Characteristics. Dr. Attaullah Shah


1
Transportation Engineering - I
Traffic Flow Characteristics. Dr. Attaullah Shah

2
Traffic Flow
  • Traffic flow No of vehicle passing per unit time
    at a road section
  • q n/t n No of vehicles t duration of
    time, usually an hour
  • Time head way The time between passing of
    successive vehicles i.e. bumpers
  • t S hi t duration of time interval hi
    time head way of ith vehicle.
  • q n/ S hi Average mean time headway
  • The average speed is defined in two ways
  • Arithmetic mean of total speeds at a particular
    point also called time mean speed Mean Ut
    Sui/n
  • Space Mean Speed Assumed that the average speed
    for all vehicles is measured at the same length
    of roadway.
  • Space mean speed in unit distance per unit time
    length of strip/mean time

3
Example
  • The speed of five vehicles is measured with radar
    at the midpoint of 0.8Km strip (0.5 mi) The
    speeds are measured as 44,42,51,49 and 46 mi/h.
    Assuming the vehicles were travelling constant
    speed, determine the time mean speed.
  • Tim Mean speed Ut Sui/n (4442514946)/5
    46.4 mi/h
  • The space mean
    5/ 1/441/421/511/491/46
  • 46.17 mi/h
  • Space mean speed is always smaller than time mean
    speed.

4
Traffic density
  • K n/l The No of vehicles per unit length.
  • The density is also related to individual
    spacing of the vehicles
  • The total length of roadway l S si
  • k 1/( Mean space)
  • Traffic flow q uk
  • q flow of traffic No of vehicles/hour
  • Speed ( Space mean speed)
  • k density units of vehicle/mile.
  • Example 5.2

5
Traffic Flow
  • Complex between vehicles and drivers, among
    vehicles
  • Stochastic variability in outcome, cannot
    predict with certainty
  • Theories and models
  • Macroscopic aggregate, steady state
  • Microscopic disaggregate, dynamics
  • Human factor driver behavior

6
Speed (v)
  • Rate of motion
  • Individual speed
  • Average speed
  • Time mean speed
  • Arithmetic mean
  • Space mean speed
  • Harmonic mean

7
Individual Speed
(1)
Spot Speed
8
Time Mean Speed
Mile post
Observation Period
9
Space Mean Speed
Observation Distance
Observation Period
10
Volume (q)
  • Number of vehicles passing a point during a given
    time interval
  • Typically quantified by Rate of Flow (vehicles
    per hour)

11
Volume (q)
12
Density (k)
  • Number of vehicles occupying a given length of
    roadway
  • Typically measured as vehicles per mile (vpm),
  • or vehicles per mile per lane (vpmpl)

13
Density (k)
14
Density (k)
15
Spacing (s)
  • Front bumper to front bumper distance between
    successive vehicles

S1-2
S2-3
16
Headway (h)
  • Time between successive vehicles passing a fixed
    point

T3sec
T0 sec
h1-23sec
17
Spacing and Headway
spacing
headway
18
Spacing and Headway
What are the individual headways and the average
headway measured at location A during the 25 sec
period?
A
19
Spacing and Headway
What are the individual headways and the average
headway measured at location A during the 25 sec
period?
A
h1-2
h2-3
20
Lane Occupancy
  • Ratio of roadway occupied by vehicles

L1
L2
L3
D
21
Clearance (c) and Gap (g)
  • Front bumper to back bumper distance and time

Clearance (ft) or Gap (sec)
Spacing (ft) or headway (sec)
22
Basic Traffic Stream models
  • 1. Speed density Model Relationship between
    speed and density
  • Initially when there is only one vehicle, the
    density is very low and driver will move freely
    at the speed closely to the design speed of the
    highway. This is called free flow speed.
  • When more and more vehicles would come, the
    density of the road will increase and the speed
    of vehicles would reduce. The speed of the
    vehicle may ultimately reduce to u0
  • This high traffic density is referred as Jam
    Density
  • This model can be expressed as U Uf ( 1- k/kj)
  • U Space mean speed in mi/h (km/h)
  • Uf Free flow speed in mi/h
  • k density in veh/mi ( veh/km)
  • This gives relationship between traffic flow
    speed and density
  • The speed density relationship tends to be non
    linear at low density and very high density.
  • Non linear relationship at low densities that has
    speed slowly declining from free flow to value
  • Linear relationship over the medium density
    region ( speed declining linearly with the
    density).
  • Non linear relationship near the traffic jam
    density relationship

23
Speed vs. Density
ufFree Flow Speed
Speed (mph)
kj Jam Density
Density (veh/mile)
24
Basic Traffic Stream models
  • Flow Density Model
  • As we know that q uk and U Uf ( 1- k/kj)
  • Parabolic density model q Uf ( k k2/Kj )
  • The max flow rate qcap The highest flow rate
  • The traffic density that corresponds to this
    capacity flow rate is kcap and the corresponding
    values qcap ,Kcap ,Ucap
  • dq/dk Uf (1-2k/kf ) 0 Since the free flow
    speed cannot be zero.
  • Kcap Kj / 2
  • Substituting the value Ucap ( k Kj /2kj )
    Uf /2

25
Flow vs. Density
Congested Flow
Optimal flow, capacity, qm
FLow (veh/hr)
kj Jam Density
kmOptimal density
Uncongested Flow
Density (veh/mile)
26
Speed Flow Model
  • This is parabolic relationship
  • Two speeds are possible as it is quadratic
    equation, up to the highway capacity
  • Similarly two densities are possible for given
    flow

27
Speed vs. Flow
ufFree Flow Speed
Uncongested Flow
um
Speed (mph)
Optimal flow, capacity, qm
Congested Flow
Flow (veh/hr)
28
Example
At a section of highway in Islamabad the free
flow speed is 90km/h and capacity of 5000 veh/h.
During traffic census at a particular section
2500 vehicles were counted. Determine the space
mean speed of the vehicle. Solution The
traffic flow is related to the space mean speed
and jam density as The jam To determine the
jam density From above equation of traffic flow
we can rearrange the equation as
Both the speeds are feasible as
shown on previous slide
29
Traffic Flow Models-Poisson Model
  • The Poisson distribution
  • Is a discrete (as opposed to continuous)
    distribution
  • Is commonly referred to as a counting
    distribution
  • Represents the count distribution of random events

30
Poisson Distribution
P(n) probability of having n vehicles arrive in
time t ? average vehicle arrival rate in
vehicles per unit time t duration of time
interval over which vehicles are counted e base
of the natural logarithm (e2.718)
31
Example
  • An observer counts 360veh/h at a specific highway
    location. Assuming the arrival of vehicles at
    this point follows Poissons distribution.
    Estimate the probabilities of 0,1,2,3,4 and 5
    vehicles arriving in 20 sec time interval.
  • Solution
  • The average arrival rate ? 360 veh/h
    360/60x600.1 veh/sec
  • t20 sec
  • For probability of no vehicle
  • P(1) 0.271 P(2) 0.271 P(3) 0.180 P(4)
    0.090
  • For 5 or more vehicles
  • P(ngt 5) 1-P(nlt5) 1-(0.1350.2710.2710.180.090
    0.053
  • Draw the histogram of the distribution

32
Poisson Example
  • Example
  • Consider a 1-hour traffic volume of 120 vehicles,
    during which the analyst is interested in
    obtaining the distribution of 1-minute volume
    counts

33
Poisson Example
  • What is the probability of more than 6 cars
    arriving (in 1-min interval)?

34
Poisson Example
  • What is the probability of between 1 and 3 cars
    arriving (in 1-min interval)?

35
Poisson distribution
  • The assumption of Poisson distributed vehicle
    arrivals also implies a distribution of the time
    intervals between the arrivals of successive
    vehicles (i.e., time headway)

36
Negative Exponential
  • To demonstrate this, let the average arrival
    rate, ?, be in units of vehicles per second, so
    that
  • Substituting into Poisson equation yields

(Eq. 5.25)
37
Negative Exponential
  • Note that the probability of having no vehicles
    arrive in a time interval of length t i.e., P
    (0) is equivalent to the probability of a
    vehicle headway, h, being greater than or equal
    to the time interval t.

38
Negative Exponential
  • So from Eq. 5.25,

(Eq. 5.26)
Note
This distribution of vehicle headways is known as
the negative exponential distribution.
39
Negative Exponential Example
  • Assume vehicle arrivals are Poisson distributed
    with an hourly traffic flow of 360
    veh/h.Determine the probability that the
    headway between successive vehicles will be less
    than 8 seconds.Determine the probability that
    the headway between successive vehicles will be
    between 8 and 11 seconds.

40
Negative Exponential Example
  • By definition,

41
Negative Exponential Example
42
Negative Exponential
For q 360 veh/hr
43
Negative Exponential
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