Measuring Delays for Bicycles at Signalized Intersections Using Smartphone GPS Tracking Data

Abstract

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values.

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data

“…The novelty of these studies lies in a preprocessed map-matching operation. Gillis et al (2020) measured bike delays at signalized intersections by interpolation between GPS locations before and after the intersections [ 5 ], relying on data from the Bike Count Week in Belgium [ 14 ]. A similar approach to deriving delay times can be found in studies in Canada [ 28 ] and Sweden [ 29 ].…”

“…The geometry of the design, layout, pavement, and visibility will influence the extent to which the cyclist interacts with other road users as well as with the environment [ 20 , 28 , 29 ]. The controller scheme (e.g., signals, logic, priority rules) contributes the most to the stop delay and control delay at signalized intersections [ 5 , 30 , 31 ]. Additionally, the local traffic conditions need to be considered, such as the demand and queue length of various transport modes, the state of being saturated or oversaturated, because the control signals may assign more green times to the mode with greater priorities.…”

“…Weather can be considered one of the most significant external factors in cycling trip generation, comfort, and efficiency [ 28 , 33 , 34 ]. Additionally, various approaches and algorithms play a role in computing the magnitude of delays [ 5 , 20 , 28 , 29 ].…”

“…This surge in popularity is partly due to the advent of e-bikes and the lifestyle changes brought about by the COVID era, leading to an unprecedented increase in cycling [ 3 , 4 ]. From a policy perspective, bicycles are increasingly considered a sustainable substitute for short- to medium-distance car journeys (up to 10 km for regular bikes up to 20 km for electric bikes) [ 5 ]. A growing number of cities (such as New York [ 6 ], Paris [ 7 ], Oslo [ 8 ], and Barcelona [ 9 ]) are prohibiting car traffic from their city centers to lower CO 2 emissions and create more space for pedestrians and cyclists.…”

“…Note that preprocessing data operations (e.g., map-matching [ 20 ] or interpolation [ 5 ]) may be used to improve the measurement accuracy of travel times and cycling distances. This remains to be refined further in future applications.…”