1. Joint Development of Land Use and Light Rail Stations The Case of Tel Aviv Regional Science Association International -The Israeli Section Daniel Shefer, Shlomo Bekhor, Avigail Ferdman Centre for Urban and Regional Studies Transportation Research Institute Technion – Israel Institute of Technology Beer Sheva University 6/6/04 “New Direction in Urban and Regional Development”

2. Structure of the presentation Introduction Literature Review Purpose of the Study Hypotheses Methodology Findings Conclusions

3. Introduction The land-use transportation interaction Empirical evidence of the built environment’s impact on travel demand Land-use intensification and mixed land uses – their impact on transit use The role and function of Light Rail Transit - LRT

4. What can light rail transit do? at the micro level Enhancing accessibility “Getting people out of their cars” at the macro level Relieving traffic congestion Reducing emissions Rejuvenating urban centres Stimulating economic growth LRT is perceived as a powerful mode for transferring ridership from private to public transportation Literature Review

5. Tel Aviv as a case study 1 st light rail line in Tel Aviv, due to open in 2010. Connects 4 major cities The stations location was based on travel demand Purpose of the study exploring various scenarios of the built environment around planned LRT stations Purpose of the Study

6. The built environment and travel demand Impacts on daily travel demand: Intensified areas attract trips Mixed land uses around LRT stations induce transit use and walking/cycling Trip generation is not affected by land use Hypotheses

7. Methodology Land uses and travel demand population densities, commercial densities, degree of land use mix workforce-population ratio Parameters of land use scenarios:

8. Land Use Scenarios Methodology workforce-population ratio

9. High densities - Scenario 1 Methodology

10. High densities - Scenario 2 Methodology

11. High densities - Scenario 3 Methodology workforce-population ratio

12. Mixed uses - Scenario 4 Methodology

13. Models calibrated Trip generation Trip attraction Methodology 2.

14. 2. Calibration of the NTA Model  Focus on the first 2 steps of the trip forecast models – generation & attraction  Retrofitting the models with land use variables  Job density, Job-population balance, workforce- population (variables found significant and positive)  Population density, workforce density (variables found insignificant and/or negative) The original NTA model Retrofitting the model:

15. Findings – trip generation No difference between NTA models & retrofitted models, save for scenario 4 Alternative scenarios produce differential trip generation demand Trip Generation

16. Findings – Scenarios Trip Generation Higher workforce ratios generate more trips per household

17. Findings – Model calibration Measurement of job-population & workforce-population balances produces higher home-base-work attraction trip rates per workerjob-population Trip attraction base scenario Intensified employment areas attract more trips per worker

18. HBW model coefficients VARIABLESub Model NTA Refrofitted Sub Model total employees1.461.34 t value37.3915.91 job-population balance 222.58 t value 2.93 workforce-population balance 810.01 t value 2.19 Observations158287 R square0.900.88

19. Findings- Model Calibration Trip attraction base scenario

20. VARIABLESub Model NTASub Model 9 employees in services0.630.55 t value8.747.33 employees in commerce1.481.27 t value6.035.06 square of total households0.001 t value7.36 workforce-population balance 4329.75 t value 7.48 mixed use (dummy) 1222.60 t value 2.20 observations158156.00 R square0.87 NHB model coefficients

21. Mixed and intensified land uses attract more trips Findings Trip attraction

22. Job–population ratios affect home-base-work trips Higher job–population rates – attract more motorized home-base-work trips Scenarios 1-2-3 have a greater concentration of jobs Home-base-work trip attraction Findings – trip attraction Job-population ratio

23. More trip attraction at the outer stationsLess trip attraction Intensified commercial areas attract less motorized home-base commuting trips Findings – trip attraction

24. Findings Mixed land uses attract more trip chaining Non-home-base trip attraction per workplace

25. Lower job densities attract higher rates of home base shopping trips Lower job densities attract higher rates of home base shopping trips (rerofitted models). Possibility of trip- chaining in intense employment environments

26. Home Base Shopping model coefficients variablemodel 1model 8 commerce employees1.470.94 t value12.135.92 log of job density per km² 332.97 t value 4.97 observations 158 155 R square 0.48 0.55

27. findings Scenario 4’s high results in retrofitted model – due to the dummy variable for mixed uses variable model1a (not chosen in nta models) model 7 commerce employees0.70 t value1.51 service employees1.000.92 t value7.229.27 total households0.900.67 t value10.385.25 mixed use (dummy) 2558.29 t value 2.72 workforce- population balance 3985.92 t value 2.44 observations 156 R square0.820.85

28. Main findings Intensified land uses tend to generate more motorized trips per household than mixed land uses or the base scenario Intensified and mixed land uses attract more trips per worker Findings

29. Conclusions Alternative land use scenarios generate and attract differential trip rates Mixed land uses are different from intensified land uses, in terms of travel demand High density & mixed land uses can serve as strategic decision variables in locating transit stations Conclusions

30. Thank you Joint Development of Land Use and Light Rail Stations

31. Further research Metropolitan level forecasts Exploring the most conducive land use mix for lrt ridership - »Before and after lrt introduction »Comparison to other lrt systems »Trip mode share Exploring station location by trip demand and land use characteristics

32. Trip generation with same workforce ratio for all the scenarios

33. Planned LRT red line in Metropolitan Tel Aviv

34. Differences in population densities

35. Differences in job densities