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Identification of Link Congestion Dependence Patterns for Dynamic Route Guidance

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Identification of Link Congestion Dependence Patterns for Dynamic Route Guidance

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Presentation Transcript

    1. Identification of Link Congestion Dependence Patterns for Dynamic Route Guidance 14th ITS world Congress Beijing, 2007

    2. Outline Here is my presentation outline, itll include five parts. First Id like to give you an introduction on the background of this study. Second, Ill present you to the problem and our approach to solve the problem. Third, Ill show you the method of analysis. Its followed the results. Finally, Ill recap the main findings and future work in the conclusion. So first, let me give you an introduction of the background of this study. Here is my presentation outline, itll include five parts. First Id like to give you an introduction on the background of this study. Second, Ill present you to the problem and our approach to solve the problem. Third, Ill show you the method of analysis. Its followed the results. Finally, Ill recap the main findings and future work in the conclusion. So first, let me give you an introduction of the background of this study.

    3. Introduction

    4. Introduction

    5. Introduction Here is an example of in-vehicle display of a BMW. These triangles are representing the current congestion on the network. Now, Im moving on to our next section, problem formulation and approach Here is an example of in-vehicle display of a BMW. These triangles are representing the current congestion on the network. Now, Im moving on to our next section, problem formulation and approach

    6. Problem formulation

    7. Problem formulation Here, first we look at the phenomenon of congestion propagation and dispersion. I present a result of traffic simulation from a city network model. The first diagram shows the traffic condition at the initial link failure. The green represents the normal traffic condition on the link and red means the traffic on that link is highly congested. Traffic is flowing anti-clockwise on the ring road. The second diagram shows the traffic condition after 30 minutes, we can notice that on the congestion almost propagates most of right side network. After 30 minutes, we unblock the initial failed link. And last diagram is the traffic situation after release of the congestion. Here, first we look at the phenomenon of congestion propagation and dispersion. I present a result of traffic simulation from a city network model. The first diagram shows the traffic condition at the initial link failure. The green represents the normal traffic condition on the link and red means the traffic on that link is highly congested. Traffic is flowing anti-clockwise on the ring road. The second diagram shows the traffic condition after 30 minutes, we can notice that on the congestion almost propagates most of right side network. After 30 minutes, we unblock the initial failed link. And last diagram is the traffic situation after release of the congestion.

    8. Method of analysis

    9. Test network The link travel time data is available from the Coventry city (West Midland, England) microsimulation model for this study. The method used in exploring the failure dependent relationship among the links is to hold one link failed and observe the impact on the adjacent links over a period of time. below shows the method of analysis. The failed link is red colored on the left. The impact of this link failure is observed through other adjacent links, especially the subsequent links. The travel time data on each links is generated from microsimulation modelling and decoded into binary variable data to determine the state of the link which is only normal and abnormal state. The congestion propagation patterns could be illustrated by color coded these binary data. The link travel time data is available from the Coventry city (West Midland, England) microsimulation model for this study. The method used in exploring the failure dependent relationship among the links is to hold one link failed and observe the impact on the adjacent links over a period of time. below shows the method of analysis. The failed link is red colored on the left. The impact of this link failure is observed through other adjacent links, especially the subsequent links. The travel time data on each links is generated from microsimulation modelling and decoded into binary variable data to determine the state of the link which is only normal and abnormal state. The congestion propagation patterns could be illustrated by color coded these binary data.

    10. Method of analysis

    11. Method of analysis

    12. Method of analysis

    13. Results

    14. Results

    15. Results

    16. Conclusions

    17. Q & A ?

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