Traffic Modeling. Name: Tan Nguyen Mentor: Scott Sanner

Transcription

1 Traffic Modeling Name: Tan Nguyen Mentor: Scott Sanner

2 Outline Motivation Project Background Extension Empirical Evaluation of Policy Demo Video

3 Motivation

4 Motivation

5 Motivation

6 Motivation Traffic directly affects: People, economy, environment

7 Motivation Traffic directly affects: People, economy, environment Better traffic solution would save billions $

8 Motivation How to build better traffic solution?

9 Motivation How to build better traffic solution? (A) Extend infrastructure: expensive, slow, social/environmental impacts.

10 Motivation How to build better traffic solution? (A) Extend infrastructure: expensive, slow, social/environmental impacts. (B) Better traffic control: cheaper, faster, more efficient, no impacts.

11 Motivation How to build better traffic solution? (A) Extend infrastructure: expensive, slow, social/environmental impacts. (B) Better traffic control: cheaper, faster, more efficient, no impacts. This is where my project starts.

12 My Project How to make a better traffic control system? More intelligent control software Needs efficient computer model of traffic

13 My project objectives: My Project 1. A compact (but realistic) macroscopic model in RDDL for the traffic dynamics based on difference equation models in the literature.

14 My project objectives: My Project 1. A compact (but realistic) macroscopic model in RDDL for the traffic dynamics based on difference equation models in the literature. 2. A visualization of that model (in JAVA).

15 My project objectives: My Project 1. A compact (but realistic) macroscopic model in RDDL for the traffic dynamics based on difference equation models in the literature. 2. A visualization of that model (in JAVA). 3. Using the model to empirically evaluate some common traffic control policies.

16 Background

17 Background Daganzo C.F (The cell-transmission model): Road is divided into homogeneous sections (cells) with lengths equal the distance traveled by free-flowing traffic in one clock interval.

18 Cell equation: Background Shockwave speed

19 Background From Cell to Network: Strict network topology, allow only: Normal link, Merge, or Diverge.

20 Background From Cell to Network: Strict network topology, allow only: Normal link, Merge, or Diverge. Bk NORMAL Ek

21 Background From Cell to Network: These are simple for calculation, yet they can represent any traffic network. Bk NORMAL Ek

22 Background Not going to details, but each type of cell link has a difference equation to calculate flow between cells.

23 Background And if all flows are known The network dynamics is easy:

24 Extended CTM Daganzo C.F : Road divided into homogeneous sections (cells) with lengths equal the distance traveled by free-flowing traffic in one clock interval.

25 Extended CTM Daganzo C.F : Road divided into homogeneous sections (cells) with lengths equal the distance traveled by free-flowing traffic in one clock interval. Problem for practical implementation. Need different cell length for road s section with different characteristics.

26 Extended CTM Daganzo C.F : Road divided into homogeneous sections (cells) with lengths equal the distance traveled by free-flowing traffic in one clock interval. Problem for practical implementation. Need different cell length for road s section with different characteristics. Solution = incorporate cell length, reformulate all equations of CTM.

27 Extended CTM The result is a compact & realistic model

28 Policy Evaluation Use the built model to test efficiency of traffic control policies in different traffic scenario.

29 Policy Evaluation Use the built model to test efficiency of traffic control policies in different traffic scenario. Control policies: Random, Fixed Time with Offset, Local Adaptive Control.

30 Policy Evaluation Use the built model to test efficiency of traffic control policies in different traffic scenario. Control policies: Random, Fixed Time with Offset, Local Adaptive Control. Goal: which traffic control policy is suitable for which traffic situation?

31 Policy Evaluation Test Setup

32 Policy Evaluation Test results: Fixed Time Policy Result WE / NS High Med Low High Medium Low Random Policy Result WE / NS High Med Low High Medium Low Local Adaptive Policy Result WE / NS High Med Low High Medium Low

33 Test conclusion: Policy Evaluation Consolidated Result WE / NS High Med Low High F F A Medium F F A Low A A RFA Local adaptive control outperform fixed time with offset by about 20% in H-L or M-L scenarios. putting local adaptive control in right places can boost current traffic control efficiency significantly.

34 An interesting finding: Policy Evaluation Local adaptive control outperforms fixed time with offset policy by about 20% in H-L or M-L scenarios.

35 An interesting finding: Policy Evaluation Local adaptive control outperforms fixed time with offset policy by about 20% in H-L or M-L scenarios. Putting local adaptive control in right places can boost traffic control efficiency significantly.

36 Run Demo Video Questions? Thank you!