Building a TDM Impact Analysis System for the Introduction of a Short-Term Congestion Management Program in Seoul

1. Building a TDM Impact Analysis System for the Introduction of a Short-Term Congestion Management Program in Seoul Jin-Ki Eom, Kee-Yeon Hwang, Ikki Kim Researcher of Seoul Development Institute (SDI)San4-5, Yejang-dong, Jung-ku, Seoul 100-250, KoreaE-mail : eom@sdi.re.kr

2. Outline • SCMP(Sort-term Congestion Management Program) • SECOMM(Seoul Congestion Management Model) • SECOMM Case Study • Conclusion

3. Why Seoul needs SCMP ? Short-term Congestion Management Program • Seoul has been known for the notoriety of its severe traffic congestion. In order to mitigate the congestion problems, the transportation policy of Seoul Metropolitan Government had been mainly focused on the supply of transportation systems until the early 1990’s. • The sharp decrease of investment on transportation infrastructures followed by recent economic recession. • The massive implementation of subway system does not reduce auto rider-ship as much as we expected.

4. 2. SCMP (Short-term Congestion Management Program) 1. Setting up Short-term Target of Traffic Management 2. Selecting TDM Programs to Reduce the Excessive Auto Demand 3. Building a Methodology for Forecasting the Expected Impacts of Programs(ex. SECOMM) 4. Monitoring Traffic Conditions Regularly

5. 3. SECOMM(SEoul COngestion Management Model) • Assumption • Structure of the SECOMM • Mode Split Model • Assignment Model • Link Travel Speed Adjustment Function

6. Assumption The assumptions of SECOMM are as follows • Mode split and route choice are variable while trip generation and trip distribution are not in short-run • Investment is fixed in the short-run

7. Structure of the SECOMM

8. Data Requirements

9. Process of Building Mode Split Model

10. Logit Model (1) Nested Tree for Each Alternative

11. Logit Model (2) The Parameter Values and T-Values of Nested-Logit Models

12. Assignment Process

13. Process of Predicting Link Travel Speed Using the adjustment factor, We can predict link travel speed

14. 4. SECOMM Case Study Study Title : Impact Analysis of Gasoline Tax Increase • Study Process • Structure of Emme/2 Macro • Study Results

15. Study Process

16. Structure of Emme/2 Macro

17. Case Study Results (1) The Speed Changes by Gasoline Tax Increase

18. Case Study Results (2) Auto-Mode Split Ratio Changes Resulting from Gasoline Tax Increase

19. Monitoring Data Response to Oil Price Increased <Response of Car to OIL_P> <Response of SPEED to OIL_P>

20. Case Study Results (3) • Peak Hour Auto Volume Changes Resulted by Gasoline Tax Increase

21. 5. Conclusions • SECOMM is a TDM impacts analysis system integrating mode choice model and trip assignment model in a module and iterating the interactions between them until the stop conditions are accomplished. • Using SECOMM, we can quickly forecast the impacts of TDM therefore, we can implement SCMP in Seoul. • To enhance the usefulness of SECOMM, there are several things to be done: • checking the estimated results of SECOMM through continuous monitoring on traffic situation in Seoul • updating the O-D data at least every 5 years • updating the network and travel behavior data