Abstract

Two smartphone applications were developed with the aim of harvesting insights of vulnerable road user accidents. The apps require a backend database with software tools for generating and managing questionnaires, etc. Such a system was developed using a MySQL database where a user-friendly platform has been developed in CakePHP. The backend system has been successfully developed and been used to handle more than 11,000 participants. The original idea was to monitor VRU via apps to detect when they may have been involved in an accident as pedestrian or cyclist based on the motion patterns from the smartphone’s motion sensors (accelerometer, gyroscope) and collect as much information as possible regarding the accident automatically (e.g. time and location of the accident). If an accident was detected, the road user should receive a questionnaire to provide detailed information of the accident (e.g. road surface conditions, lighting conditions, other road users involved). This app was used by more than 400 participants resulting in a large amount of data.

Abstract

In the last decades the private vehicle has been the most commonly used transportation mode for daily journeys. This is a National and European trend and it is due to the economic growth and the investments focused on the road infrastructures. Because of that reason, cities have been often organized in terms of planning with especial attention to road vehicles and not well prepared for pedestrians and cyclists. There is a need to change this behavior to a society that privileges the active transportation modes. However, there is the need to ensure the safety of the most vulnerable users. This Dissertation was focused on analyzing the trends of road crashes involving cyclists and pedestrians and what are the main difficulties that people using active modes face in their daily journeys. In order to reach this objective, crashes registrations involving motor vehicles and vulnerable road users (pedestrians or cyclists) in the city of Aveiro were analyzed (given by the Public Safety Police “PSP” of Aveiro). In relation to accidents involving cyclists, 68% the victims are men; this can be possibly explained because there are more men cyclists than women as was verified in the sample of the survey, but there is a lack of statistics in Aveiro on the gender distribution of cyclists involved in accidents. In a second stage, a survey was prepared with the objective to understand the main difficulties of the university community who use the active modes in their daily journeys. From the survey it has been pointed out that the main problems found are the lack of drivers’ awareness and weather conditions. The third reason was the lack of dedicate cycle lanes. On the other hand, the main motivations for biking are the reduced cost and the easiness of mobility.

Abstract

Worldwide, it is estimated that every year 1.2 million road users lose their lives on road crashes. This situation has a huge impact in terms of health and economical development and costs to governments. Pedestrians and cyclists, often called vulnerable road users (VRUs), are more likely to be injured in road crashes as they are unprotected and more exposed to risk in the presence of motor vehicles. In 2015, 21% of the fatalities in European Union’s roads were pedestrians, while 8% were cyclists. The respective percentages in Portugal were 23% for pedestrians and 5% cyclists. The objective of the paper is to developed a comprehensive study between motor vehicle and pedestrians or cyclists collisions occurrences integrating spatio-temporal data analysis with crash prediction models. Spatial analysis is the first step to identify the patterns between blackspots in three cities from Portugal (Aveiro, Porto and Lisbon). Blackspots were identified using ArcGIS and the Kernel Density Estimation function taking into account the level of injury severity. The second step is a temporal analysis that involves a temporal distribution of the crashes or injured people. Lastly, a crash prediction model was developed for each city that calculates the likelihood of VRUs to be involved in a crash taking into account injury severity. The findings from the study highlighted target variables and specificities at a local level that may influence the number and severity of crashes between motor vehicle and VRUs. The identification of blackspots revealed that most injuries occurs surrounding high attraction places. Temporal analysis results showed a major percentage of crashes occurring during afternoon peak-hours (4-7p.m.). Comparison between the cities showed that Porto presents the worst scenario in terms of number of VRUs injuries per ten thousand inhabitants or kilometers squared. The developed Multinomial Logistic Regression models revealed that VRU gender and age, as well as weather conditions, are statistically significant for the prediction models. The spatio-temporal analysis of crashes taking into account the level of injury severity has the goal of establishing patterns between the different cities, which is important to understand risk factors. The outcomes of the study are valuable not only for VRUs awareness, but also for traffic planners and decision-makers.

Abstract

When driven at low speeds, cars operating in electric mode have been found to be quieter than conventional cars. As a result, the auditory cues which pedestrians and cyclists use to assess the presence, proximity and location oncoming traffic may be reduced, posing a safety hazard. This laboratory study examined auditory localisation of conventional and electric cars including vehicle motion paths relevant for cycling activity. Participants (N = 65) in three age groups (16–18, 30–40 and 65–70 year old) indicated the location and movement direction (approaching versus receding) of cars driven at 15, 30 and 50 km/h in two ambient sound conditions (low and moderate). Results show that low speeds, higher ambient sound level and older age were associated with worse performance on the location and motion direction tasks. In addition, participants were less accurate at determining the location of electric and conventional car sounds emanating from directly behind the participant. Implications for cycling safety and proposals for adding extra artificial noise or warning sounds to quiet (electric) cars are discussed.

Abstract

most half of all traffic fatalities worldwide are non-motorised road users (NMRUs). In Denmark, the number has increased with about 30%. NMRUs consist of about 63% of the injured in the Danish traffic. Much has been done to reduce the number of injured NMRUs with counterparts, while little effort is put into the reduction of the vast majority of the accidents, NMRU single accidents, which are about 90% of all injured NMRUs. There are no efficient tools available to reduce this number. A significantly better designed, maintained, and illuminated road network would most likely help. However, that is expensive and not possible for most road authorities. Despite this, the challenges with NMRUs in single accidents need more attention, if road safety is to be improved. The situation in Denmark is more than likely the case in many other countries as well; although the documentation is scarce. Almost half of all traffic fatalities worldwide are non-motorised road users (NMRUs). In Denmark, the number has increased with about 30%. NMRUs consist of about 63% of the injured in the Danish traffic. Much has been done to reduce the number of injured NMRUs with counterparts, while little effort is put into the reduction of the vast majority of the accidents, NMRU single accidents, which are about 90% of all injured NMRUs. There are no efficient tools available to reduce this number. A significantly better designed, maintained, and illuminated road network would most likely help. However, that is expensive and not possible for most road authorities. Despite this, the challenges with NMRUs in single accidents need more attention, if road safety is to be improved. The situation in Denmark is more than likely the case in many other countries as well; although the documentation is scarce.

Abstract

The involvement of cyclists in road crashes has not been decreasing with the same magnitude as the involvement of other road users. In particular, the interactions between cyclists and motorized traffic can lead to high-severity crashes. To improve the safety of these interactions, a thorough understanding of road user behaviour is first needed. In this study, we focused on drivers overtaking cyclists on rural roads. The two main objectives of this study were to develop models that predicted: (a) drivers’ decisions to perform either a flying or an accelerative overtaking manoeuvre in the presence of oncoming traffic, and (b) the lateral comfort distance that drivers maintain from cyclists during the overtaking. A driving simulator study was designed to assess driver decision-making during the overtaking. The 37 drivers who participated in the study each performed seven overtaking manoeuvres with oncoming traffic. Out of the 259 overtaking manoeuvres, 168 were flying and 91 were accelerative. Binary logistic-regression models with mixed effects predicted the type of overtaking strategy (flying or accelerative). Driving speeds were found to significantly affect the strategy. The overall performance of the models predicting the strategy was 85–90%. Models were also developed for predicting the lateral comfort distance. The results show that the lateral comfort distance is mostly affected by the longitudinal distance between the subject vehicle and the oncoming vehicle, the longitudinal distance between the subject vehicle and the cyclist, and the presence of an oncoming vehicle—as well as by the drivers’ characteristics (sensation seeking in flying overtaking manoeuvres and ordinary violations in accelerative manoeuvres). The root mean square error, which was used to assess the performance of the models, ranged from 0.56 to 0.62. In conclusion, the models predicting the overtaking strategy performed reasonably well, while the models predicting lateral distance did not provide accurate predictions. The models predicting overtaking strategy may support (1) the development and evaluation of active safety systems, (2) the design of automated driving, and (3) policy making.