Bicycle Traffic Volume Estimation Based on GPS Data

Pogodzińska, Sylwia; Kieć, Mariusz; D’Agostino, Carmelo

doi:10.1016/j.trpro.2020.02.081

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…Being aimed exclusively at route profiles, GPS data are sampled from three up to five seconds in order to minimize their dimension for the storage requirements. In addition, the point data from Bike Share Programmes (BSP) stations are becoming input data for the study of cyclist safety [ 24 , 25 , 26 ], and the correlation with weather conditions [ 27 ] and land use [ 28 , 29 ].…”

Section: Gps Data and Cyclingmentioning

confidence: 99%

A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment

Murgano

Caponetto

Pappalardo

et al. 2021

Sensors

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With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety of urban cycling, identification of high-risk location and routes are major obstacles for safety countermeasures. Risk assessment is performed by crash data analysis, but the lack of data makes that approach less effective when applied to cyclist safety. Furthermore, the availability of data collected with the modern technologies opens the way to different approaches. This research aim is to analyse data needs and capability to identify critical cycling safety events for urban context where bicyclist behaviour can be recorded with different equipment and bicycle used as a probe vehicle to collect data. More specifically, three different sampling frequencies have been investigated to define the minimum one able to detect and recognize hard breaking. In details, a novel signal processing procedure has been proposed to correctly deal with speed and acceleration signals. Besides common signal filtering approaches, wavelet transformation and Dynamic Time Warping (DTW) techniques have been applied to remove more efficiently the instrument noise and align the signals with respect to the reference. The Euclidean distance of the DTW has been introduced as index to get the best filter parameters configuration. Obtained results, both during the calibration and the investigated real scenario, confirm that at least a GPS signal with a sampling frequency of 1Hz is needed to track the rider’s behaviour to detect events. In conclusion, with a very cheap hardware setup is possible to monitor riders’ speed and acceleration.

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Section: Gps Data and Cyclingmentioning

confidence: 99%

A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment

Murgano

Caponetto

Pappalardo

et al. 2021

Sensors

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“…Bicycle traffic models allow the resolution of problems related, among other things, to demand and supply [116,122], route choice [123], lane change, and queueing behaviour [124][125][126]. The demand for cycling, the choice of bicycle routes, and the choice of cycling modes were found to be influenced by many factors, including the built environment [127][128][129][130]; socioeconomic [131][132][133][134], psychological (habits, attitudes, norms, stress), and physical characteristics [135][136][137][138][139]; policies that promote cycling [46,59,84,138,[140][141][142]; infrastructure for cyclists [39,[143][144][145]; cost; effort; distance travelled; travel time; road safety; climate and weather; and travel motivation [146][147][148][149][150]. The frequency of commute to work, the time to cycle, and the length of the journey are important features of active commute behaviour.…”

Section: Modelling Of Bicycle Trafficmentioning

confidence: 99%

“…Traffic participants do not have complete knowledge of the transport network, which means that they do not always choose the route rationally according to their preferences [179,180] • Cyclists have different levels of tolerance to weather conditions [89,149,150].…”

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confidence: 99%

Bicycle Traffic Model for Sustainable Urban Mobility Planning

2021

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Modelling tools and transport models are required to assess the impact of measures for the effective planning of cycling routes in cities. This paper presents the methodology for developing a four-stage macroscopic model of bicycle traffic for the city of Gdynia, and its use in planning new bicycle routes, considering a modal shift. The model presented in this paper allows for the evaluation of the influence of the characteristics of the cycling infrastructure, along with the development of the cycling network based on the choice of cycling as an alternative to other modes of transport, by taking into account the modal shift. The model takes into account the influence of the longitudinal gradient, link, and surface type of cycling routes on the distribution and demand for bicycle traffic. The results of our research allow us to assess the impact of planned cycling routes on the reduction in the volume of car traffic, which is crucial for reducing energy consumption and negative environmental impacts. Experiences from the application of the model in Gdynia suggest that the model provides a strong basis to support mobility planning and monitoring processes in cities worldwide. Cities should take into account the methods proposed in this paper when planning the development of their transport systems.

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“…Linear regression models enable the estimation of bicycle speed based on GPS data were presented. The paper is a supplement of research on using GPS data in bicycle traffic parameters estimation, which first part related to bicycle traffic volumes was presented in (Pogodzinska, Kiec and D'Agostino, 2020) and (Pazdan, Kiec and D'Agostino, 2021).…”

Section: Introductionmentioning

confidence: 99%

Bicycle free-flow speed estimation based on GPS data – comparison of bikesharing system and Strava data

Pazdan,

Kiec

2023

AoT

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The increasing number of cyclists in cities around the world results in a greater focus on bicycle traffic. Next to traffic volume, the main characteristic of traffic used in road safety analysis, infrastructure planning, design, etc. is its speed. Bicycle speed is strongly affected by the type of bicycle facility, motor vehicle traffic parameters (volume, speed, share of heavy vehicles), trip motivation, weather conditions, etc., and therefore it is difficult to estimate. Traditionally, bicycle speed is determined directly using speed radar or indirectly, as a quotient of measurement base length and travel time calculated using a stopwatch or video technique. There are also researches where bicycle speed was estimated based on GPS sources, mainly mobile apps. However, depending on the GPS source and the group of cyclists, bicycle speed gained from GPS data can be different from the speed of regular cyclists (due to different levels of experience or types of bicycle). In the paper, the relationships between bicycle speed obtained from empirical measurements and two different GPS sources, which were bikesharing system (Wavelo) and Strava app, were analysed. In total 18 research sites were selected different in terms of bicycle facility (bicycle path, shared pedestrian/bicycle path, contraflow lane) and element of road network (road segment, bicycle crossing with or without traffic signals). Two-tailed test for two means was conducted to analyse the statistical significance of differences in bicycle speed estimated based on GPS data and empirical measurements using video technique. It showed that Wavelo and Strava speeds are by 17.4% lower are by 23.1% higher than the speeds of regular cyclists respectively. Two linear regression models describing relationships between bicycle speeds from empirical measurements and GPS data were developed. The results show that the variance of bicycle speed is almost 80% described by the variance of Wavelo speed and 60% described by the variance of Strava speed, which suggests that bicycle free-flow speed can be estimated based on GPS data either from bikeshare system or dedicated app.

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Bicycle Traffic Volume Estimation Based on GPS Data

Cited by 15 publications

References 11 publications

A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment

A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment

Bicycle Traffic Model for Sustainable Urban Mobility Planning

Bicycle free-flow speed estimation based on GPS data – comparison of bikesharing system and Strava data

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