Emerging data for pedestrian and bicycle monitoring: Sources and applications

Abstract

Growing attention on the benefits of non-motorized travel has increased the demand for accurate and timely pedestrian and bicycle travel data. Advancements in technologies and the proliferation of smartphones have created new data sources that can help eliminate limitations related to small sample size and infrequent updates due to limited resources. This study reviews the emerging data sources and their current use, focusing on non-motorized travel monitoring. In this study, the emerging data are categorized into mode-unspecified and mode-specified data based on whether the mode used can be detected with no or little effort. While mode-unspecified data are collected without sorting out non-motorized travelers, mode-specified data at least know who (which mode) is being monitored. So far, commercial vendors provide a vast volume of mode-unspecified data, but their products have been mainly used for motorized trips or are in initial stages of development. Meanwhile, readily available data sources and their applications are more concentrated on mode-specified data, which have enabled varying non-motorized travel studies—including travel pattern identification, route-choice modeling, crash/air pollution exposure estimation, and new facility provision evaluation—but are mostly focused on bicycling. Despite the potential of emerging data, their use also has several challenges, such as limited mode inference, sample bias, and lack of detailed trip/traveler information due to privacy issues. More efforts are needed, such as improving data accuracy and developing robust data fusion techniques, to be able to fully utilize the emerging data sources.

Keywords

• Emerging data
• Pedestrian
• Bicycle
• Smartphone
• Data collection
• Crowdsource