The literature review clearly demonstrates that emerging technologies have the main advantage of providing much broader and more diverse samples of the active transportation population with fewer resource constraints, while traditional monitoring methods have heavily relied on the massive efforts from data collectors, which often lead to limited data collection locations and under-sampled data. Nonetheless, the conventional counting methods have the strength with widespread application and thus more historical data available for various assessment purposes. Most transportation agencies have standard practices or guidance for collecting non-motorized data using the typical methods. In addition, they have developed “rules-of-thumb” to adjust the data collected to enhance the accuracy based on a large multitude of evaluation/validation studies which cover various counting technologies , expansion of short-term counts to longer-period ones, estimate of average daily volumes, development of weather and other adjustment factors, calibration of nonmotorized volume modeling, etc. In contrast, there is little research dedicated to the evaluation of emerging counting methods even though there are growing applications of such technologies. As of now, most of these evaluation studies have been centered on the crowdsourced active data. As for passively crowdsourced data, little research has been conducted based on small-scale GPS and MPP data. To the best knowledge of authors, there is no evaluation performed based on the LBS for the walking and cycling count data accuracy. To fill the research gap, the proposed study is dedicated to the comprehensive assessment of LBS data accuracy for non-motorized metrics recently made available by StreetLight. Moreover, development of system, temporal, weather, land use and facility type adjustment factors will be carried out using the deep learning technology.
The proposed study dedicated to the evaluation the counting performance of emerging technology is expected to benefit Californians in many ways:
Cal Poly Pomona