Updating of a Travel Demand Model Based on Fuel Consumption Data

Abstract Traditional methods like Home Interview Survey (HIS) used for estimation of travel demand in an urban area are not only costly and time-consuming, but also suffer from problems like sample bias, significant no-response rate, and data coding errors. In addition, such data collection processes still need to be updated on a continuous basis. Due to the above issues, there is a need for the use of more accurate secondary big data sources for the estimation of travel demand, such as mobile call records, mobile/GPS navigation data, travel smart card, and fuel smart card data. Development of travel demand forecasting models (TDFM)s based on up-to-date variables can help decision-makers achieve efficient solutions to the sudden transport problems, highlighting the need for new tools and methods that are comparatively cheaper, yet more accurate and more efficient. Nevertheless, the validity assessment of combined revealed and stated preference (RP and SP) data for analysis purposes often becomes a complicated task. This study tries to offer an improved and efficient alternative to the conventional and tedious data instruments, like HIS, for the development of a TDFM, where the big data collected through the fuel smart cards (7.5 million records of refueling data), as well as water and electricity consumption data, are used for examining travel demand patterns in the city of Shiraz in Iran. Coupled with other information technologies, smart fuel card data can provide an alternative replacement to the conventional time-consuming transport planning surveys. The large dataset of all the 64 fuel stations, amounting to 7.5 million refueling records for 61 days, was analyzed in this study, and valuable information was obtained that correlated well with traffic parameters for the city.