Analysis of trip generation rates in residential commuting based on mobile phone signaling data
Keywords:Transport, Land Use
AbstractIn this paper, mobile phone signaling data are first processed to extract information such as the trip volume and spatial distribution from the starting point to the termination point. This information is then used to identify the residential and employment locations of users. Next, multiple Thiessen polygons based on cell towers are aggregated into Traffic Analysis Zones (TAZs) to minimize differences between the actual cell tower coverage and the theoretical coverage. Then, based on TAZ cluster analysis involving transport accessibility and commuting population density, multiple stepwise regression is applied to obtain the commuting trip production rates and attraction rates for overall residential land and each subdivided housing type during the peak morning hours. The obtained commuting trip generation rates can be directly applied to local transport analysis models. This paper suggests that as information and data sharing continue, mobile phone signaling data will become increasingly important for use in future trip rate research.
Agyemang-Duah, K., & Hall, F. L. (1997). Spatial transferability of an ordered response model of trip generation. Transportation Research Part A Policy & Practice, 31(5), 389–402.
Agyemang-Duah, K., Anderson, W. P., & Hall, F. L. (1995). Trip generation for shopping travel. Transportation Research Record, 1453, 12–20.
Arabani, M., & Amani, A. B. (2007). Evaluating the parameters affecting urban trip-generation. Iranian Journal of Science & Technology Transaction B Engineering, 31(5), 547–560.
Berki, Z., & Monigl, J. (2017). Trip generation and distribution modelling in Budapest. Transportation Research Procedia, 27, 172–179.
Bochner, B. S., Hooper, K., Sperry, B., & Dunphy, R. (2011). Enhancing internal capture estimation for mixed-use developments. NCHRP Report 684. Washington, DC: Transportation Research Board for the National Academies.
Bwamble, A., Choudhury, C. F. & Sanko, N. (2015). Modelling car trip generation in the developing world: The tale of two cities. Transportation Research Board 94th Annual Meeting, Washington, DC.
Bwambale, A., Choudhury, C., & Hess, S. (2017). Modelling trip generation using mobile phone data: A latent demographics approach. Journal of Transport Geography. https://doi.org/10.1016/j.jtrangeo.2017.08.020
Clifton, K. J., Currans, K. M., & Muhs, C. D. (2013). Evolving the Institute of Transportation Engineers’ trip generation handbook: A proposal for collecting multi-modal, multi-context, establishment-level data. Journal of the Transportation Research Board, 2344, 107–117.
Clifton, K. J., Currans, K. M., & Muhs, C. (2015). Adjusting ITE’s trip generation handbook for urban Context. Journal of Transport and Land Use, 8(1) 5–29.
Çolak, S., Alexander, L. P., Alvim, B. G., Mehndiratta, S. R., & González, M. C. (2015). Analyzing cell phone location data for Urban travel. Transportation Research Record, 2526(3), 126–135.
Currans, K. M. (2017). Issues in trip generation methods for transportation impact estimation of land-use development. Journal of Planning Literature, 32(4), 335–345.
Demissie, M. G., Antunes, F., Bento, C., Phithakkitnukoon, S., & Sukhvibul, T. (2016). Inferring origin-destination flows using mobile phone data: A case study of Senegal. 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. IEEE, June 28–July 1, Chiang Mai, Thailand.
Dey, S. S., & Fricker, J. D. (1994). Bayesian updating of trip generation data: Combining national trip generation rates with local data. Transportation, 21(4), 393–403.
Dock, S., Cohen, L. Rogers, J. D., Henson, J. Weinberger, R., Schrieber, J., & Ricks, K. (2015). Methodology to gather multimodal urban trip generation data. Paper presented at 94th Annual Meeting of the Transportation Research Board, Washington, DC.
Ellys, B. & Reid, F. (1982). Critique of ITE trip generation rates and alternative basis for estimating new area traffic. Transportation Research Record 1874, 1–2.
Everett, J. D. (2009). An investigation of the transferability of trip generation models and the utilization of a spatial context variable. Doctoral dissertation. Knoxville, TN: University of Tennessee.
Ewing, R., Deanna, M., & Li, S. C. (1996). Land-use impacts on trip generation rates. Transportation Research Record, 1518(1), 1–6.
Filitowish, N. (2011). Effectiveness of applying in-site trip rates for trip generation modeling in developing cities, Urban transportation research series. Cape Town: Cape Town University.
Hanson, S. (1982). The determinants of daily travel-activity patterns: Relative location and sociodemographic factors. Urban Geography, 3, 179–202.
Ko, J. (2013). Vehicle trip generation rates for office buildings under urban settings. ITE Journal, 8(2), 41–45.
Landrock, J. N. (1981). Spatial stability of average daily travel times and trip rates within Great Britain. Transportation Research A, 15A, 55–62.
Lee, J. (2016). Impact of neighborhood walkability on trip generation and trip chaining: Case of Los Angeles. Journal of Urban Planning & Development, 142(3), 05015013.
Li, Z.-F., Yu, L., Gao, Y., et. al., (2016). Extraction method of temporal and spatial characteristics of residents’ trips based on cellular signaling data, Transportation Research, 2(1), 51–57. (In Chinese)
Lim, K. K., & Inivasan, S. (2011). Comparative analysis of alternate econometric structures for trip generation models. Transportation Research Record, 2254, 68–78.
Macababbad, R. J. R., & Regidor, J. R. (2009). A study on the local trip generation characteristics of government office land use. Proceedings of the Eastern Asia Society for Transportation Studies, 7. https://www.jstage.jst.go.jp/browse/eastpro/2009/0/_contents/-char/ja/
Meyer, M. D., & Miller, E. J. (2001). Urban transportation planning: A decision-oriented approach (2nd edition). New York: Mc-Graw-Hill Companies.
Moreno-Monroy, A. I., Lovelace, R., & Ramos, F. R. (2017). Public transport and school location impacts on educational inequalities: Insights from São Paulo. Journal of Transport Geography, 67, 110–118.
Ohlms, P. B. (2016). Trip generation at Virginia agritourism land uses. Charlottesville, VA: Virginia Transportation Research Council.
Pan, T. (2008). Assignment of estimate average annual daily traffic on all roads in Florida. Graduate Theses and Dissertations. Retrieved from https://scholarcommons.usf.edu/etd/442/
Pitsiava-Latinopoulou, M., Tsohos, G., Basbas, S. (2001). Trip generation rates and land use—Transport planning in urban environment. Paper presented at the 7th International Conference on Urban Transport and the Environment in the 21st Century, Lemnos, Greece.
Quintero, P. A., Diaz, G. M., & Moreno, E. G. (2016). Trip generation by transportation mode of private school, semi-private and public. Case study in Merida, Venezuela. Transportation Research Procedia, 18, 73–79.
Roorda, M. J., Páez, A., Morency, C., Mercado, R., & Farber, S. (2010). Trip generation of vulnerable populations in three Canadian cities: A spatial ordered probit approach. Transportation, 37(3), 525–548.
Safwat, K. N. A., & Magnanti, T. L. (1988). A combined trip generation, trip distribution, modal split, and trip assignment model. Transportation Science, 22(1), 14–30.
Schmocker, J. D., Quddus, M. A., Noland, R. B., & Bell, M. G. H. (2005). Estimating trip generation of elderly and disabled people: Analysis of London data. Transportation Research Record, 1924, 31–60.
Schneider, R. J., Shafizadeh, K., Sperry, B. R, & Handy, S. L. (2013). Methodology to gather multimodal trip generation data in smart-growth areas. Transportation Research Record, 2354, 68–85.
Shoup, D. C. (2003). Truth in transportation planning. University of California Transportation Center Working Papers, 2003, 6(1).
Singleton, A. A. (2014). GIS approach to modelling CO2 emissions associated with the pupil-school commute. International Journal of Geographical Information Science, 28(2), 256–273.
Soltani, A., Saghapoor, T., Izadi, H., & Pakshir, A. (2012). Trip generation and its relationship with land-use diversity: Case studies of four urban districts in Shiraz metropolitan area. Journal of Urban–Regional Studies and Research, 3(12).
Tian, G., & Ewing, R. A. (2017). A walk trip generation model for Portland, OR. Transportation Research Part D Transport & Environment, 52, 340–353. (In Chinese)
Tilsdor, M. (2012). Provision and effective use of alternative modes of transport in the developing cities. Kampala, Uganda: Makerere University, Department of Architectural and Physical Planning.
Wilfred, G., Bwire, H., Mattsson, L., & Jonsson, D. (2015). Effects of land use on trip generation in urban areas: Comparison between estimated trip generation rates and planning practices. Paper presented at the 34th Annual Southern African Transport Conference, Dar es Salaam, Tanzania.
Wilmot, C. G., & Mei B. (2004). Comparison of alternative trip generation models for hurricane evacuation. Natural Hazards Review, 5(4), 170–178.
Wu, D., Sall, E., & Newhouse, S. (2012). Trip rates and accessibility: Gleaning basic planning information from activity-based travel demand model. Transportation Research Record, 2303(1), 81–88.
Zhou, T., Zhao, B., & Yu, B. (2017). Transportation big data analysis methodology based on CRISP-DM: An example of cellular signaling and RFID data in Chongqing. Urban Transport of China, 2017(5), 15–20. (In Chinese)
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