SimRa: Using crowdsourcing to identify near miss hotspots in bicycle traffic

Karakaya, Ahmet-Serdar; Hasenburg, Jonathan; Bermbach, David

doi:10.1016/j.pmcj.2020.101197

Cited by 19 publications

(15 citation statements)

References 12 publications

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“…The development of mobile technology in recent years made it easier to collect data from users in all case. Moreover, according to Karakaya et al (2020), crowdsourcing systems have been developed and frequently used with mobile devices in recent years. Further, the success of gamification in crowdsourcing systems is undeniable.…”

Section: Introductionmentioning

confidence: 99%

Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market

Macakoğlu

Çınar

Peker

2022

IJWIS

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Purpose In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets. Design/methodology/approach Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score. Findings The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability. Practical implications The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices. Originality/value This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.

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Section: Introductionmentioning

confidence: 99%

Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market

Macakoğlu

Çınar

Peker

2022

IJWIS

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“…Aside from actual accidents, an often overlooked aspect of cycling safety are near miss incidents 1 such as close passes or near dooring [1,2]. Information on these have previously not been available or have not been easily accessible as they are distributed over private video recordings, social media posts, public CCTV footage, and other sources.…”

Section: Introductionmentioning

confidence: 99%

“…Information on these have previously not been available or have not been easily accessible as they are distributed over private video recordings, social media posts, public CCTV footage, and other sources. In 2019, we therefore launched the SimRa 2 project in which cyclists record their rides and annotate them with incident information via a smartphone app [2].…”

Section: Introductionmentioning

confidence: 99%

“…While our previous approach [2] already used a rudimentary heuristic for automatically detecting incidents based on acceleration sensors, it is inherently limited resulting in significant manual annotation efforts which makes it unattractive for some groups of cyclists -in particular, elderly cyclists who have problems using smartphone apps and cyclists with significant daily mileage for whom the labeling approach is too much effort. To increase participation, it is hence crucial to decrease manual efforts through an improved pre-detection of incidents.…”

Section: Introductionmentioning

confidence: 99%

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CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors

Karakaya¹,

Ritter²,

Bießmann³

et al. 2022

Preprint

Self Cite

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In cities worldwide, cars cause health and traffic problems which could be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to a lack of perceived safety. For city planners, addressing this is hard as they lack insights into where cyclists feel safe and where they do not. To gain such insights, we have in previous work proposed the crowdsourcing platform SimRa, which allows cyclists to record their rides and report near miss incidents via a smartphone app.In this paper, we present CycleSense, a combination of signal processing and Machine Learning techniques, which partially automates the detection of near miss incidents. Using the SimRa data set, we evaluate CycleSense by comparing it to a baseline method used by SimRa and show that it significantly improves incident detection.

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“…The SimRa and RideVibrations projects go one step further, taking into account other sensors such as acceleration sensors in addition to the smartphone positioning service. In SimRa by (Karakaya et al, 2020) from Berlin, data is collected with the objective of detecting sudden maneuvers due to (near-) accidents. The aim is to identify dangerous spots.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Joint Estimation of Road Roughness From Crowd-Sourced Bicycle Acceleration Measurements

Wage

Sester

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In contrast to cars, route choices for cycling are barely influenced by the respective traffic situation, but to a large extent by the routes’ comfort. Especially in urban settings with several alternatives, segments with many or long stops at traffic lights and badly maintained roads are avoided due to a low comfort and cyclists vary from the shortest route. This fact is only indirectly considered in common navigation applications.This work aims to integrate surface roughness measurements collected from diverse bicycles to a joint scale via a least-squares adjustment. Data was collected using smartphones, which were mounted to bike hand bars and measured positions and vertical accelerations on user’s trips. As this way sensed roughness also depends on the bike setting and type, the resulting values would be different for different users. Thus, this paper presents a novel approach to harmonize observations from differing sensitive setups. The basic concept idea of bundle block adjustment is adapted to calibrate a basic scale model and in parallel adjust the observations of surface roughness to a common scale.This way a crowd-sourced roughness map can be generated. Such a map can be used to enrich bike focused routing services and thus encourage cycling in daily live. In addition, it can also be used to derive hints for infrastructure servicing.

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SimRa: Using crowdsourcing to identify near miss hotspots in bicycle traffic

Cited by 19 publications

References 12 publications

Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market

Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market

CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors

Joint Estimation of Road Roughness From Crowd-Sourced Bicycle Acceleration Measurements

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