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.
@cite_30: Accurately classifying roadway surface disruptions (RSDs) plays a crucial role to enhance quality transportation and road safety. To this end, smartphones are becoming an ad hoc tool to collect road data, while the user is at the steering wheel. In this paper, for the first time, sensed data are represented with a novel technique inspired in the bag of words representation. New results suggest that segments of accelerometer readings play a key role to characterize different classes of events, boosting classification performance. A novel data collection process was conducted in real-life environments, where the smartphones were freely placed at five user-surveyed locations, within a fleet of cars and trucks. To the best of our knowledge, this is the largest and most heterogenous data set for RSDs, and we make it publicly available. We approach the problem of identifying RSDs as one of supervised learning, where we contrast representative classifiers, most of them not previously reported. We exhaustively evaluated the performance of all classifiers in six data sets, most of them resembling actual data sets used in similar projects. We found that in all cases, the best classifier outperforms the best results reported so far. The proposed methodology was extensively evaluated through a sensitivity analysis to determine the relevance of the parameters. Experimental results reveal that the representation technique boosts considerably the classification performance when compared with the state of the art solutions, reducing in one order of magnitude the false-positives negatives rate and surpassing the classification accuracy for about 10 in a multiclass data set.
@cite_22: Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.
@cite_32: 
@cite_17: 
@cite_24: This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70 of the swerves and 95 of the turns on the road.
@cite_15: 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.
@cite_34: In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.
@cite_12: 
@cite_11: 
In the past few years, sensing human activity has become ubiquitous and traffic has been no exception. In this vein, several studies have focused on collecting traffic data to monitor road conditions @cite_30 @cite_24 , roadway operation @cite_11 @cite_12 , and assessing driving experience @cite_22 @cite_32 @cite_34 . However, the large majority of these works @cite_30 @cite_32 @cite_22 @cite_34 @cite_24 target motorized vehicles as these are still dominant in today's traffic volume. Nevertheless, bicycle usage has recently grown mainly in urban areas @cite_15 ; and perhaps the marginal decrease of cyclists' fatality in comparison to all other road groups @cite_17 is a by-product of this trend. These facts have raised awareness to cyclists' safety, and the research community is starting to give more and more attention to this issue (see, for example, the February 2017 Safety Science special issue on Cycling Safety).