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Microscopic
Traffic Modeling
Page 2


Microscopic
Traffic Modeling
Traffic model classification Traffic model classification ( (1 1) )
nn Static Static
– – Models average steady Models average steady- -state traffic situation state traffic situation
nn Dynamic Dynamic
– – Models Models changes over time changes over time of the traffic situation of the traffic situation
7:00 10:00 15:00 18:00
Dynamic
Static
Page 3


Microscopic
Traffic Modeling
Traffic model classification Traffic model classification ( (1 1) )
nn Static Static
– – Models average steady Models average steady- -state traffic situation state traffic situation
nn Dynamic Dynamic
– – Models Models changes over time changes over time of the traffic situation of the traffic situation
7:00 10:00 15:00 18:00
Dynamic
Static
raffic model classification ( Traffic model classification (2 2) )
nn Different levels of detail in Different levels of detail in simulation simulation
models: models:
–– Macroscopic: Macroscopic:
» » Like water flowing through a pipe Like water flowing through a pipe
–– Mesoscopic Mesoscopic
» » Individual vehicles with aggregate behaviour Individual vehicles with aggregate behaviour
–– Microscopic Microscopic
» » Individual vehicles with detailed behaviour Individual vehicles with detailed behaviour
Page 4


Microscopic
Traffic Modeling
Traffic model classification Traffic model classification ( (1 1) )
nn Static Static
– – Models average steady Models average steady- -state traffic situation state traffic situation
nn Dynamic Dynamic
– – Models Models changes over time changes over time of the traffic situation of the traffic situation
7:00 10:00 15:00 18:00
Dynamic
Static
raffic model classification ( Traffic model classification (2 2) )
nn Different levels of detail in Different levels of detail in simulation simulation
models: models:
–– Macroscopic: Macroscopic:
» » Like water flowing through a pipe Like water flowing through a pipe
–– Mesoscopic Mesoscopic
» » Individual vehicles with aggregate behaviour Individual vehicles with aggregate behaviour
–– Microscopic Microscopic
» » Individual vehicles with detailed behaviour Individual vehicles with detailed behaviour
raffic model classification ( Traffic model classification (3 3) )
nn Other dimensions: Other dimensions:
–– Stochastic or Deterministic Stochastic or Deterministic::
»» stochastic stochastic modelling captures variation in e.g. modelling captures variation in e.g.
reaction time, arrival processes, route choice. reaction time, arrival processes, route choice. But But
every simulation run results in different outcome, so every simulation run results in different outcome, so
you need to you need to replicate simulation runs replicate simulation runs
–– Time Time- -stepped or event stepped or event- -based: based:
»» Time stepped Time stepped: the model calculates the changes in : the model calculates the changes in
the system for finite steps (e.g. the system for finite steps (e.g. 1 1 second) second)
»» event based: event based: the model calculates changes in the the model calculates changes in the
system when something ’happens’ (events) system when something ’happens’ (events)
Page 5


Microscopic
Traffic Modeling
Traffic model classification Traffic model classification ( (1 1) )
nn Static Static
– – Models average steady Models average steady- -state traffic situation state traffic situation
nn Dynamic Dynamic
– – Models Models changes over time changes over time of the traffic situation of the traffic situation
7:00 10:00 15:00 18:00
Dynamic
Static
raffic model classification ( Traffic model classification (2 2) )
nn Different levels of detail in Different levels of detail in simulation simulation
models: models:
–– Macroscopic: Macroscopic:
» » Like water flowing through a pipe Like water flowing through a pipe
–– Mesoscopic Mesoscopic
» » Individual vehicles with aggregate behaviour Individual vehicles with aggregate behaviour
–– Microscopic Microscopic
» » Individual vehicles with detailed behaviour Individual vehicles with detailed behaviour
raffic model classification ( Traffic model classification (3 3) )
nn Other dimensions: Other dimensions:
–– Stochastic or Deterministic Stochastic or Deterministic::
»» stochastic stochastic modelling captures variation in e.g. modelling captures variation in e.g.
reaction time, arrival processes, route choice. reaction time, arrival processes, route choice. But But
every simulation run results in different outcome, so every simulation run results in different outcome, so
you need to you need to replicate simulation runs replicate simulation runs
–– Time Time- -stepped or event stepped or event- -based: based:
»» Time stepped Time stepped: the model calculates the changes in : the model calculates the changes in
the system for finite steps (e.g. the system for finite steps (e.g. 1 1 second) second)
»» event based: event based: the model calculates changes in the the model calculates changes in the
system when something ’happens’ (events) system when something ’happens’ (events)
Introduction to micro Introduction to micro- -simulation simulation
nn ““A numerical technique for A numerical technique for
conducting experiments on a digital conducting experiments on a digital
computer, which may computer, which may…… involve involve
mathematical models that describe mathematical models that describe
the behaviour of a transportation the behaviour of a transportation
system over extended periods of time. system over extended periods of time.””
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FAQs on PPT: Microscopic Traffic Modeling - Transportation Engineering - Civil Engineering (CE)

1. What is microscopic traffic modeling in civil engineering?
Ans. Microscopic traffic modeling in civil engineering is a technique used to simulate and analyze the movement of individual vehicles within a transportation network. It takes into account various factors such as vehicle characteristics, driver behavior, traffic signals, and road geometry to predict traffic flow patterns and evaluate the impact of different scenarios on congestion, safety, and efficiency.
2. How is microscopic traffic modeling different from macroscopic modeling?
Ans. Microscopic traffic modeling, unlike macroscopic modeling, focuses on the individual behavior of vehicles and drivers within a transportation network. It considers detailed characteristics such as vehicle acceleration, deceleration, lane changing, and interaction with other vehicles. On the other hand, macroscopic modeling deals with aggregate traffic flow characteristics and does not capture individual vehicle movements.
3. What are the benefits of using microscopic traffic modeling in civil engineering projects?
Ans. Microscopic traffic modeling provides several benefits in civil engineering projects. It allows engineers to accurately assess the impact of proposed changes, such as road expansions or signal timings, on traffic flow. It helps identify potential bottlenecks, congestion points, and safety concerns. Additionally, it can aid in optimizing transportation system design, evaluating alternative scenarios, and improving overall network performance.
4. What data is required for microscopic traffic modeling?
Ans. Microscopic traffic modeling requires various types of data to accurately represent real-world conditions. This includes information on road geometry, traffic control devices, vehicle characteristics, driver behavior, and origin-destination patterns. Data collection methods may involve manual surveys, video recordings, GPS tracking, or automated traffic counters. Accurate and comprehensive data is crucial for the reliability and validity of the model's predictions.
5. What software tools are commonly used for microscopic traffic modeling in civil engineering?
Ans. There are several software tools available for microscopic traffic modeling in civil engineering. Some commonly used ones include VISSIM, PARAMICS, AIMSUN, and TRANSIMS. These tools offer a range of features to simulate and analyze traffic flow, including vehicle movement, traffic signal control, lane changing, and pedestrian interactions. The choice of software depends on the specific project requirements, complexity, and available resources.
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