Volume List  / Volume 9 (4)

Article

EXPLORING THE POTENTIAL OF DETAILED LAND USE VARIABLES IN MODELLING EXTERNAL - INTERNAL TRIPS

DOI: 10.7708/ijtte.2019.9(4).04


9 / 4 / 387 - 396 Pages

Author(s)

Niloufar Shirani-bidabadi - Civil and Environmental Engineering Department, University of Alabama in Huntsville, Alabama, 35899, USA -

Michael Anderson - Civil and Environmental Engineering Department, University of Alabama in Huntsville, Alabama, 35899, USA -


Abstract

The common methodology for distributing External-Internal (EI) trips is to collect traffic counts at the cordon line of a study area and to use total employment of the internal zone as the factor upon which zone attractiveness can be based. This methodology assumes that trips originating from outside the study area have a destination within the study area which is towards areas that are suitable for employment. However, depending on the type of employment at the destination, the attraction may vary. This paper examines different employment-based which makes the zones attractive for external trips. The paper concludes that the three scenarios in which the land use variables are utilized, indicate more improvement than those without considering the land use mix. A combination of retail employment and the number of different land-uses at the destination was the best possible methodology to determine external trip attraction within a study area. Furthermore, simply switching from total employment to retail employment at the destination location can also improve the accuracy of the travel model.


Download Article

Number of downloads: 511


References:

Abedin, M.; Farhangdoust, S.; Mehrabi, A. B. 2019. Fracture detection in steel girder bridges using self-powered wireless sensors. In Proceedings of the In Risk-Based Bridge Engineering: Proceedings of the 10th New York City Bridge Conference, August 26-27, New York City, USA. 13 p.

 

Abedin, M. Mehrabi, A. B. 2019. Novel approaches for fracture detection in steel girder bridges, Infrastructures 4(3): 42.

 

Anderson, M.D. 2005. Spatial economic model for forecasting the percentage splits of external trips on highways approaching small communities, Transportation Research Record 1931(1): 68-73.

 

Anderson, M.D.; Abdullah, Y.M.; Gholston, S.E.; Jones, S.L. 2006. Development of a methodology to predict through-trip rates for small communities, Journal of Urban Planning and Development 132(2): 112-114.

 

Baqueri, S.F.; Adnan, M.; Bellemans, T. 2018. Modeling external trips: Review of past studies and directions for way forward, Journal of Transportation Engineering, Part A: Systems 144(9): 04018051.

 

Baqueri, S.F.; Adnan, M.; Knapen, L.; Bellemans, T.; Janssens, D. 2019. Modelling distribution of external–internal trips and its intra-region and inter-region transferability, Arabian Journal for Science and Engineering 44(5): 4517-4532.

 

Cao, X. 2010. Exploring causal effects of neighborhood type on walking behavior using stratification on the propensity score, Environment and Planning A 42(2): 487-504.

 

Cascetta, E.; Cartenì, A.; Montanino, M. 2013. A new measure of accessibility based on perceived opportunities, Procedia-Social and Behavioral Sciences 87: 117-132.

 

Cervero, R.; Duncan, M. 2006. Which reduces vehicle travel more: jobs-housing balance or retail-housing mixing?, Journal of the American Planning Association 72(4): 475-490.

 

Cordera, R.; Sañudo, R.; dell’Olio, L.; Ibeas, Á. 2018. Trip distribution model for regional railway services considering spatial effects between stations, Transport Policy 67: 77-84.

 

Doustmohammadi, M.; Anderson, M.D.; Swain, J.J. 2016. Evaluation of trip generation at a free standing discount superstore, International Journal for Traffic and Transport Engineering 6(4): 495 - 502.

 

Gehrke, S.R.; Clifton, K.J. 2016. Toward a spatial-temporal measure of land-use mix, Journal of Transport and Land Use 9(1): 171-186.

 

Gehrke, S.R.; Clifton, K.J. 2019. An activity-related land use mix construct and its connection to pedestrian travel, Environment and Planning B: Urban Analytics and City Science 46(1): 9-26.

 

Gim, T.H.T. 2013. The relationships between land use measures and travel behavior: A meta-analytic approach, Transportation Planning and Technology 36(5): 413-434.

 

Han, Y.; Stone, J. R. 2008. Synthesized through-trip models for small and medium urban areas, Transportation Research Record 2077(1): 148-155.

 

Huntsinger, L.F.; Ward, K. 2015. Using mobile phone location data to develop external trip models, Transportation Research Record 2499: 25-32.

 

Khan, T.; Anderson, M. 2014. Estimation of through trips using existing traffic counts, International Journal for Traffic and Transport Engineering 4(4): 415-424.

 

Mishra, S.; Wang, Y.; Zhu, X.; Moeckel, R.; Mahapatra, S. 2013. Comparison between gravity and destination choice models for trip distribution in Maryland. In Proceedings of the Transportation Research Board 92nd Annual Meeting, Washington, DC, USA, 13–17.

 

Manaugh, K.; Kreider, T. 2013. What is mixed use? Presenting an interaction method for measuring land use mix, Journal of Transport and Land Use 6(1): 63-72.

 

Mavoa, S.; Boulangé, C.; Eagleson, S.; Stewart, J.; Badland, H.M.; Giles-Corti, B.; Gunn, L. 2018. Identifying appropriate land-use mix measures for use in a national walkability index, Journal of Transport and Land Use 11(1): 681-700.

 

Mokhtarian, P.L.; Cao, X. 2008. Examining the impacts of residential self-selection on travel behavior: A focus on methodologies, Transportation Research Part B: Methodological 42(3): 204-228.

 

Mokhtarian, P. L.; Van Herick, D. 2016. Quantifying residential self-selection effects: A review of methods and findings from applications of propensity score and sample selection approaches, Journal of Transport and Land Use 9(1): 9-28.

 

Munshi, T. 2016. Built environment and mode choice relationship for commute travel in the city of Rajkot, India, Transportation research part D: transport and environment 44: 239-253.

 

Qian, Z.; Han, Y.; Stone, J. R. 2012. Forecasting external trips in small and medium cities based on local economic context, Procedia-Social and Behavioral Sciences 43: 284-293.

 

Talbot, E.S.; Burris, M.W.; Farnsworth, S. 2011. Estimating through-trip travel without external surveys, Transportation Research Record 2254(1): 104-111.

 

Van Wee, B. 2009. Self-selection: A Key to a Better Understanding of Location Choices, Travel Behaviour and Transport Externalities?, Transport Reviews 29(3): 279-292.

 

Zhang, M.; Zhao, P. 2017. The impact of land-use mix on residents' travel energy consumption: New evidence from Beijing, Transportation Research Part D: Transport and Environment 57(2017): 224-236.