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Extracting Pedestrian Behavior Using Low Cost GNSS Receiver Data

This study presents the design and implementation phases of a pedestrian – positioning field data collection experiment by performing Post Process Kinematic positioning using highsensitivity GNSS receivers. The aim of this research is the investigation of the impact that various parameters, such as pedestrians’ characteristics, mobile phone use and walking pace may have on the characteristics of the pedestrian’s behaviour using low cost GNSS receiver data. Pedestrians’ characteristics include age, gender, body mass index, cellphone use. For this purpose, a controlled experiment was designed involving participants walking along a 220m track in a pedestrian area under different types of distraction (no distraction, hand-held conversation and texting) and different walking pace (normal-fast). The study sample size was 36 participants (20-73 years old), 15 male and 21 female who walked during the morning and afternoon hours. The equipment utilized involved a u-blox EVK-M8/NEO-M8T GNSS receiver in a small bag and a GNSS antenna located on the pedestrian's cap. An initial evaluation in terms of accuracy, availability and sampling rate was performed against a reference trajectory ensuring the suitability of the system for the designed approach. Pedestrian’s behaviour was captured through six behaviour indicators: average and maximum speed, average and maximum acceleration and average and maximum deceleration. Within the framework of the statistical analysis, discrete choice models were developed to investigate the impact of mobile phone use as well as various other parameters on the aforementioned indicators. In particular, the results showed that walking speed, acceleration and deceleration are reduced with mobile phone use, regardless of the manner in which the cellphone is used (hand-held conversation, texting). At the same time, it was shown that older pedestrians move at lower speeds than younger, while gender does not appear to have a statistically significant impact on any of the explored variables.