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Understanding the Complexity of Urban Road Congestion. Ed Manley Department of Civil, Environmental and Geomatic Engineering Supervisors: Dr Tao Cheng (UCL) and Mr Andy Emmonds ( TfL ). - PowerPoint PPT Presentation
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Understanding the Complexity of Urban Road Congestion
Ed ManleyDepartment of Civil, Environmental and Geomatic Engineering
Supervisors: Dr Tao Cheng (UCL) and Mr Andy Emmonds (TfL)
The problem is well known. Urban road congestion is the bane of many commuters lives. It’s almost certain that anyone reading this text has spent far too much of their time sitting in traffic. It’s one of those urban phenomena that people accept as ‘inevitable’ or as ‘one of those things you have to put up with if you want to live in the city’.
While this is somewhat true – too many people trying to drive along the same road is bound to cause some slow down in traffic – the way in which we manage congestion can undoubtedly be improved.
What is needed is a more in-depth understanding of the drivers of congestion: people. How do human interactions exacerbate congestion? What do people do when they reach congested roads? Do they alter their route? Or keep on going?
This project, working in conjunction with Transport for London (TfL), seeks to better understand and model the complex behaviours taking place on the roads, particularly those that contribute to congestion. In doing so, we can understand how to manage people on the roads, in normal and abnormal situations.
This poster provides an idea of the approaches being taken, as part of this project, towards tackling this important problem.
Technical Approach
Agent-based modelling is a relatively new approach in computing, one that seeks to simulate the behaviour of an individual in a given environment. Through the interactions of well-modelled individuals one is able to replicate the conditions that lead to macroscopic phenomena. The approach has been used in a wide number of disciplines, although the best known example is probably the replication of bird flocking behaviour by Craig Reynolds in 1986. Prior to his simulation, flocking was widely believed to be driven by one ‘lead bird’.
Agent-based modelling has not been widely used in transport. Instead, there has been focus on flow-based aggregate models, and disaggregate models that only replicate the physical behaviours of a vehicle. While these models effectively demonstrate the behaviours of drivers in a normal conditions, they fail when it comes to predicting how one reacts to congestion and how it might spread throughout the road network.
Behavioural Model
Any attempt to model the interactions of individuals must be based on a strong behavioural model. The model must encompass both the local and higher-level decisions an individual makes. In this case, although a driver is a very complex being, the decision-making process can be divided into two key scales: local interactions and route-choice decisions.
Local interactions: This part of the model will seek to replicate the variation in driving style and trip purpose that there is on the road. Is the driver cooperative? Or aggressive? Is the vehicle a bus? Or a lorry? Or just a car? The massive range of interactions occurring here must be well modelled to ensure a complete understanding of the situation.
Route-choice: Here we will attempt to model the higher-level decision making powers of the individual. What do drivers do when there is a problem on the network? Do they risk taking another route? Do they know the network? Or are they constrained in their actions?
The work will not only model people’s behaviour in busy situations, but also how they respond to congestion and react to others. The result is a two-tier scaled model of behaviour (below), and a complex network of driver interactions (right).
Low experience of networkHigh perception of congestionLow cooperationHigh frustrationWilling to take risks
Experienced taxi driverLow perception of Congestion
High cooperationNo frustration
Waiting to turnHigh frustrationRestricted choices
High-level planning
Perception and knowledge of network
Origin and Destination Current conditionsand perception
Local behaviours
Behaviour of those around them
Driver psychology
Function of vehicle
Implementing the two-tiered behavioural model described above, within an agent-based model, the emergence of road congestion from the interactions of intelligent agents will be demonstrated.
This simulation will help reveal how congestion forms, how it spreads and the measures that might be taken to tackle it.
For more information please email [email protected]
Network of driver interactions
Two-tiered model of driver behaviour
Understanding the Complexity of Urban Road Congestion
Ed ManleyDepartment of Civil, Environmental and Geomatic Engineering
Supervisors: Dr Tao Cheng (UCL) and Mr Andy Emmonds (TfL)
The problem is well known. Urban road congestion is the bane of many commuters lives. It’s almost certain that anyone reading this text has spent far too much of their time sitting in traffic. It’s one of those urban phenomena that people accept as ‘inevitable’ or as ‘one of those things you have to put up with if you want to live in the city’.
While this is somewhat true – too many people trying to drive along the same road is bound to cause some slow down in traffic – the way in which we manage congestion can undoubtedly be improved.
What is needed is a more in-depth understanding of the drivers of congestion: people. How do human interactions exacerbate congestion? What do people do when they reach congested roads? Do they alter their route? Or keep on going?
This project, working in conjunction with Transport for London (TfL), seeks to better understand and model the complex behaviours taking place on the roads, particularly those that contribute to congestion. In doing so, we can understand how to manage people on the roads, in normal and abnormal situations.
This poster provides an idea of the approaches being taken, as part of this project, towards tackling this important problem.
For more information please email [email protected]
CONGESTED NETWORK
Route PlanFeedback on
network situation
Network conditions
Driving behaviour
Experience
Time
Driver Personality
Vehicle function
THINKING LAYER
ACTION LAYER
INTERACTION LAYER
Condense network
Local interactions have big impact
Network
Origin-Destination
DRIVER STRATEGYReal-time Route Selection
NETWORKInteractions of Drivers
Perception of Congestion
DRIVER ACTIONSLocal driving behaviour
Understanding the Complexity of Urban Road Congestion
Ed ManleyDepartment of Civil, Environmental and Geomatic Engineering
Supervisors: Dr Tao Cheng (UCL) and Mr Andy Emmonds (TfL)
The problem is well known. Urban road congestion is the bane of many commuters lives. It’s almost certain that anyone reading this text has spent far too much of their time sitting in traffic. It’s one of those urban phenomena that people accept as ‘inevitable’ or as ‘one of those things you have to put up with if you want to live in the city’.
While this is somewhat true – too many people trying to drive along the same road is bound to cause some slow down in traffic – the way in which we manage congestion can undoubtedly be improved.
What is needed is a more in-depth understanding of the drivers of congestion: people. How do human interactions exacerbate congestion? What do people do when they reach congested roads? Do they alter their route? Or keep on going?
This project, working in conjunction with Transport for London (TfL), seeks to better understand and model the complex behaviours taking place on the roads, particularly those that contribute to congestion. In doing so, we can understand how to manage people on the roads, in normal and abnormal situations.
This poster provides an idea of the approaches being taken, as part of this project, towards tackling this important problem.
For more information please email [email protected]
Route Plan
Feedback on network situation
Network conditions
Driving behaviour
Experience Time
Driver Personality
Vehicle function
THINKING LAYER
ACTION LAYER
Origin-Destination
NETWORKInteractions of Drivers
Perception of Congestion
DRIVER BEHAVIOUR
DRIVER ACTIONSLocal driving behaviour
DRIVER STRATEGYReal-time Route Selection
