Citation

Poliziani C, Rupi F, Schweizer J, Saracco M, Capuano D. Transp. Res. Proc. 2022; 62: 325-332.

Copyright

(Copyright © 2022, Elsevier Publications)

DOI

10.1016/j.trpro.2022.02.041

PMID

unavailable

Abstract

Waiting time plays an important role in the cyclists' route choice, most likely because cyclists, after a stop, need to pedal harder to regain their previous speed. Literature review highlights that cyclists generally overestimate waiting time approximately three to five times higher than their actual waiting time. The aim of this paper is to quantify cyclists' waiting time in function of specific intersection characteristics and person attributes. This aim is achieved in two steps: (1) a recent algorithm that estimates cyclists' waiting time from GPS traces is validated, using data from a manual survey, (2) a second manual survey has been conducted to test the representativeness of a big data set of 270,000 GPS traces recorded in the city of Bologna, Italy; the same survey also showed how many cyclists pass with the red signal for different maneuvers; and finally (3) the mentioned algorithm is applied to the big data set in order to estimate the waiting time for different intersection types and cyclist attributes. Such estimations have not been addressed in literature due to the difficulty of associating the cyclists' waiting times with infrastructure elements based using GPS traces.

RESULTS show that waiting time represents a not-negligible share of the bike trip (11% of total trip duration). On average, particularly large waiting times have been found (1) at complex intersections by (2) for cyclists younger than 25 years old, (3) for infrequent cyclists and (4) for women. During rush hour, cyclists have recorded waiting times only 6% above the daily average, demonstrating that traffic congestion has a limited effect on waiting times. Furthermore, approximately 14% of all cyclists have crossed the red traffic light, especially when the opposite traffic volume is not high and there is good visibility. The study contributes to provide a novel and validated tool to evaluate waiting times of cyclists from GPS traces, which can support the calibration of cyclists route choice models.

Language: en

Keywords

big data; cyclist; GPS trace; manual survey; map matching; waiting time