Traffic Engineering

1. TFT
Lec 2: Macro and Micro parameters

2. Learning Objectives
• Identify major traffic factors affecting traffic
performance
• Define and compute microscopic and
macroscopic traffic parameters
• Differentiate between time mean speed and
space mean speed

3. MODEL
Approximate representation of real-world
phenomenon or process of interest
Often representation is made consistent with
the application driving the model.
Different Models of same process may be
suitable for different applications
Assumptions need to be checked in relation
to application of interest
Model performance compared to other
models available for practical applications

4. Desirable Features of Models
• ACTIONABLE (CONTROL/POLICY)
• SUPPORT DECISION MAKING APPLICATION
• GOOD PREDICTIVE ABILITY
• ROBUST
• TRANSFERABLE
• REST ON MINIMAL ASSUMPTIONS
• COST-EFFECTIVE

5. Applications of Traffic Flow Theory
• LOS Analysis
• Queue length and Delay Analysis
• Identification of problem spots and causes
• Safety and stability of traffic flow

6. • Choice of control actions
• Safety implications vehicular/crowd/event
management
• Planning for disasters - Incident Management
• Number of toll booths
• Reliability and Predictability

7. Sources of difficulty
• Traffic flow is a time-space phenomenon
• Highly non-linear system response
• Complex Interactions
• Conflicting Objectives

8. Why Traffic Flow Modeling is Difficult
• Human behaviour
• Traffic demand – stochastic and time
dependent
• Transportation system infrastructure and
performance – varied site by site
• Traffic control – flow interrupted

9. Traffic Problems and Methods
• Two types of problems: Modeling and Operation
Problems
• Two levels of analyses: Component (Micro)
Level and Network (Macro) Level
• Two time-scale of analyses: static vs. dynamic
• Two type of approaches/tools:
• Analytical (pen, paper and calculator) and
• Simulation-based (generation of random traffic
process and interaction modeling)

10. Simulation Models
• Functionality and level of detail
– Network representation
– Flow representation
– Traffic dynamics
– Support of control strategies
– Surveillance
– Travel behavior/demand
• Overall structure
– Event-based
– Time-based
• Output
– Measures of effectiveness (MOE’s)

11. Level of Detail
• Based on their flow and traffic dynamics representation
traffic simulation models are characterized as:
– Macroscopic
• Fluid representation of flow
• Time and space discretization
– Mesoscopic
• Individual vehicle representation
• Continuous space
• Usually discrete time
– Microscopic
• Individual vehicle representation
• Traffic dynamics through vehicle interactions and movements

12. Factors affecting traffic performance
• Driver
– Vision
– Age
– Psychological Condition
• Experience
• Reaction time
• Judgement
• Compliance
• Familiarity
• Aggressiveness

13. Vehicle Characteristics
• Geometric dimensions
• Condition of vehicles
• Operating characteristics
– Speed
– Power
– Acceleration

14. Roadway Characteristics
• Type of facility
– Mobility and accessibility
• Pavement condition
• Geometric design
• Control devices
– Signs, signals, marking, islands, information etc.

15. Environmental Factors
• Area (urban, rural)
• Day/night
• Weather
• Trip purpose
• Adjoining land use

16. Microscopic and Macroscopic Traffic
Parameters
• Microscopic:
• Focuses on elemental unit – individual vehicles
• And Interactions with neighbours
• Discrete particle model
• Decisions of interest:
– Overtaking
– Lane-changing
– Vehicle following
– Gap acceptance
– Placement

17. Macroscopic Characteristics
• Focus on stream characteristics
• Aggregation of individual vehicle properties
• Applied to model overall stream features such as
congestion, delays approximately.
• Computationally easier