You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as a keystone to foster urban cyclists' safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk criteria based on the cyclist's motion and object detection. Moreover, since it is entirely based on images, our method provides context on the situation and is independent from the expertise level of the cyclist. Additionally, we build on an existing platform and introduce several improvements on its mobile app to acquire smartphone sensor data, including video. From the inertial sensor data, we automatically detect the route segments performed by bicycle, applying behavior analysis techniques. We test our methods on real data, attaining very promising results in terms of risk classification, according to two different criteria, and behavior analysis accuracy.

Cited Abstract(s):
[1]: Abstract Urban areas in North American cities with positive trends in bicycle usage also witness a high number of cyclist injuries every year. Previous cyclist safety studies based on the traditional approach, which relies on historical crash data, are known to have some limitations such as the fact that crashes need to happen (a reactive approach). This paper explores the use of GPS deceleration events as a surrogate-proactive measure and investigates the relationship between reported cyclist road injuries and deceleration events. The surrogate safety measure is defined based on deceleration values representing hard breaking situations. This work uses a large sample of GPS cyclist trip data from a smartphone application to extract deceleration rates at intersections and along segments and to explore its relationship with the number of observed injuries and validate deceleration rate (DR) as a surrogate safety measure. Using Spearman’s rank correlation coefficient, we compared the ranking of sites based on the expected number of injuries and based on DR. The ranks of expected injuries and dangerous decelerations were found to have a correlation of 0.60 at signalized intersections, 0.53 at non-signalized intersections and 0.57 at segments. Despite the promising results of this study, more granular data and validation work needs to be done to improve the reliability of the measures. The technological limitations and future work are discussed at the end of the paper.

[2]: Objective: To examine the relationship between the numbers of people walking or bicycling and the frequency of collisions between motorists and walkers or bicyclists. The common wisdom holds that the number of collisions varies directly with the amount of walking and bicycling. However, three published analyses of collision rates at specific intersections found a non-linear relationship, such that collisions rates declined with increases in the numbers of people walking or bicycling. Data: This paper uses five additional data sets (three population level and two time series) to compare the amount of walking or bicycling and the injuries incurring in collisions with motor vehicles. Results: The likelihood that a given person walking or bicycling will be struck by a motorist varies inversely with the amount of walking or bicycling. This pattern is consistent across communities of varying size, from specific intersections to cities and countries, and across time periods. Discussion: This result is unexpected. Since it is unlikely that the people walking and bicycling become more cautious if their numbers are larger, it indicates that the behavior of motorists controls the likelihood of collisions with people walking and bicycling. It appears that motorists adjust their behavior in the presence of people walking and bicycling. There is an urgent need for further exploration of the human factors controlling motorist behavior in the presence of people walking and bicycling. Conclusion: A motorist is less likely to collide with a person walking and bicycling if more people walk or bicycle. Policies that increase the numbers of people walking and bicycling appear to be an effective route to improving the safety of people walking and bicycling.

[3]: In recent years, the modal share of cycling has been growing in North American cities. With the increase of cycling, the need of bicycle infrastructure and road safety concerns have also raised. Bicycle flows are an essential component in safety analysis. The main objective of this work is to propose a methodology to estimate and map bicycle volumes and cyclist injury risk throughout the entire network of road segments and intersections on the island of Montreal, achieved by combining smartphone GPS traces and count data. In recent years, methods have been proposed to estimate average annual daily bicycle (AADB) volume and injury risk estimates at both the intersection and segment levels using bicycle counts. However, these works have been limited to small samples of locations for which count data is available. In this work, a methodology is proposed to combine short- and long-term bicycle counts with GPS data to estimate AADB volumes along segments and intersections in the entire network. As part of the validation process, correlation is observed between AADB values obtained from GPS data and AADB values from count data, with R-squared values of 0.7 for signalized intersections, 0.58 for non-signalized intersections and between 0.48 and 0.76 for segments with and without bicycle infrastructure. The methodology is also validated through the calibration of safety performance functions using both sources of AADB estimates, from counts and from GPS data. Using the validated AADB estimates, the factors associated with injury risk were identified using data from the entire population of intersections and segments throughout Montreal. Bayesian injury risk maps are then generated and the concentrations of expected injuries and risk at signalized intersections are identified. Signalized intersections, which are often located at the intersection of major arterials, witness 4 times more injuries and 2.5 times greater risk than non-signalized intersections. A similar observation can be made for arterials which not only have a higher concentration of injuries but also injury rates (risk). On average, streets with cycle tracks have a greater concentration of injuries due to greater bicycle volumes, however, and in accordance with recent works, the individual risk per cyclist is lower, justifying the benefits of cycle tracks.
Concerning cyclists' safety, [3] and [2] study the relation between the number of cyclists going through a given lane or intersection and the risk of crash with other cyclist and motorist, respectively. More recently, [1] use a large sample of GPS cyclists' trip data acquired via a smartphone application in order to validate deceleration rate as a surrogate safety measure. Particularly, the authors explore the correlation of deceleration with accidents at intersections as a potential proactive measure to prevent cyclist injuries.