Methods for Estimating Daily Bicycle Volumes on Cycling Networks

In light of the international and the national direction toward a sustainable development in every aspect of modern life, sustainable transportation comes as one of the fundamental foundations of such development. Nowadays, motorized travel represents the major share of trips in most cities, both in developed and developing countries. A modal shift can be achieved if appropriate polices are set in place to encourage the use of sustainable modes of transportation such as bicycles and walking. The first part of the research presented in this application focused on promoting the use of bicycles as one of the most sustainable modes of travel. The aim is to improve bicycle facility and network designs through better modeling of bicycle demand. The research made use of a huge dataset of cycling count data collected over a period of three years in the City of Vancouver, Canada. Modern statistical parametric and non-parametric models, as well as other types of models such as geostatistical techniques were used to explain the relationship between cycling demand and other factors such as weather conditions and time factors. The spatial and temporal volume variations of bicycle counts were also analyzed. As well, the impact of data gaps on the accuracy of annual and monthly average daily bicycle volume calculation at permanent count stations was studied. Different types of multiple imputation models were applied in order to fill-in data gaps when the data collection counters were down due to vandalism or malfunction. Despite the fact that this research was based on data collected from Canada; it is still applicable to cities in other countries including Egypt once a cycling road network becomes available.

Research Team

  1. Mohamed Elfaramawy El Esawey

Related Journal Publication List

  1. El Esawey, M. Using Spatio-temporal Data for Estimating Missing Cycling Counts: A Multiple Imputation Approach. In Transportmetrica A: Transport Science. Volume (16), Issue (1): Spatiotemporal Big Data Analytics for Transportation Applications, pp 5-22, 2020.
  2. El Esawey, M. Impact of Data Gaps on the Accuracy of Annual and Monthly Average Daily Bicycle Volume Calculation at Permanent Count Stations. In Computers, Environment, and Urban Systems, Volume (70), pp. 125-137, July 2018.
  3. El Esawey, M. Daily Bicycle Traffic Volume Estimation: Comparison of Historical Average and Count Models. In Journal of Urban Planning and Development, ASCE, Volume (144), Issue (2), June 2018.
  4. El Esawey, M. Estimation of Daily Bicycle Traffic Volumes using Spatiotemporal Relationships. In Journal of Transportation Engineering, Part A: Systems, ASCE, Volume (143), Issue (11), 2017.
  5. El Esawey, M. Directional Distribution Factors for Bicycle Traffic: Development and Applications. In Journal of Transportation Engineering, ASCE, Volume (142), Issue (10), 2016.
  6. El Esawey, M. Toward a Better Estimation of the Annual Average Daily Bicycle Traffic: A Comparison of Different Methods for Calculating Daily Adjustment Factors. In Transportation Research Record, No. 2593, pp. 28-36, 2016.
  7. El Esawey, M., Lim, C., and Sayed, T. Development of a Cycling Data Model: City of Vancouver Case Study. In the Canadian Journal of Civil Engineering, Volume (42), pp. 1000-1010, October 2015.
  8. El Esawey, M., Mosa, A., Nasr, K. Estimation of Daily Bicycle Traffic Volumes Using Sparse Data. In Computers, Environment, and Urban Systems, Volume (54), pp. 195-203, 2015.
  9. El Esawey, M., and Mosa, A. Determination and Application of Standard K Factors for Bicycle Traffic. In Transportation Research Record, No. 2527, pp. 58-68, 2015.
  10. El Esawey, M. Estimation of Annual Average Daily Bicycle Traffic with Adjustment Factors. In Transportation Research Record, No. 2443, pp. 106-114, 2014.