On this page we are collected our research on road safety through information provided by smartphone
Co-operative ITS: ESD a Smartphone Based System for Sustainability and Transportation Safety
Co-operative Intelligent Transportation Systems (C-ITS) are emerging rapidly due to recent development in Global Navigation Satellite System GNSS systems and mobile internet. The main goal of these systems is to improve traffic conditions and safety level on the road networks. With the rapid growth of smartphone technologies and mobile internet, C-ITS based on smartphone may contribute increasingly in vehicle data collection and in traffic safety and sustainability issues.
A co-operative methodology to estimate car fuel consumption by using smartphone sensors
The European Commission has recently promoted research programs aimed at finding solutions to the ever more compelling problem of air pollution from road vehicles and has also indicated a better sustainability among the possible impacts of co-operative Intelligent Transportation Systems. In fact many practical solutions can be developed that allow drivers and management to optimise resources and to contain costs and the emissions of pollutants by applying communication systems between vehicles (V2V) and between vehicles and infrastructure (V2I). Along this mainstream this paper present a co-operative system which offer drivers the ability to manage their consumption and driving style, suggesting corrections to the usually adopted behaviour. The new contribution of this paper is both the co-operative approach between drivers to achieve a common goal of a better common energy consumption strategy and a methodology to estimate fuel consumption just by using Satellite data obtained from a simple smartphone. Since the fuel consumption has to be evaluated with regards to the specific vehicle type the system is based also on crowdsourcing of the specific vehicle consumption performances. The paper describes a system that gathers data on fuel consumption from the co-operating drivers that can build together the data set necessary to the system itself once they accept this paper paradigm: crowd sourced co-operation for a smarter and more sustainable transport system.
Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones
Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable.
Co-operative ITS: Smartphone based Measurement Systems for Road Safety Assessment
Co-operative Intelligent Transportation Systems (C-ITS) are attracting a lot of attention, and many resources are devoted to the development of new platforms integrating vehicle-to-vehicle (V2 V) and vehicle-to-infrastructure (V2I) communications. The main goal of these systems is to improve safety level on the road networks through a new intelligent services available on-board supporting smarter driving. Due to the rapid growth of smartphone technology and smartphone worldwide sales, C-ITS may have a great contribution from its applications in vehicles data collection. Worldwide sales of smartphones totaled 968 million units in 2013, according to Gartner Inc., exceeding annual sales of feature phones for the first time. This study presents a new co-operative systems based on a client/server platform in which client units are GPS-enabled smartphones capable to acquire individual vehicle’s kinematics to be shared on a web server for road operators and users analysis. The cooperative system allows drivers to watch detailed information about their individual driving style and global statistics on their trips. On the other hand, road operators can analyze the whole database to highlight critical points on the network (where unsafe behaviors occur more frequently) and to reward users with safer driving style. This study underscores the usefulness of the smartphone technology for improving C-ITS and assessing potential safety problems.
Estimation of Safety Performance Measures from Smartphone Sensors
Safety performance measures represent an useful tool for evaluating road safety conditions on the basis of objective parameters deducible from the vehicle kinematics. In this context, safety performances are expressed in terms of indicators representing interactions between different pairs of vehicles belonging to the traffic stream. Safety performance is expressed from the perspective of rear-end vehicle interactions. Differences in safety performance are discussed with respect to type of indicator and traffic conditions. When these indicators reach a certain critical value (threshold), a possible accident scenario is identified. Most common approaches used to acquire vehicle tracking data are based on video image processing algorithms and satellite navigation systems. However, many studies are increasingly interested in the emerging smartphone technologies for tracking people, and hence vehicles. Due to the fact that smartphones are becoming a valid alternative to Tablets, PDAs and laptops, offering phone features coupled with multiple mobile internet applications, smartphone sales will more than triple to 491.9 million units by 2012 from 139.3 million in 2008 (Gartner Inc. forecasts). The main goal of this study is to present a procedure for extracting vehicle tracking data from smartphone sensors and to use them in the estimation of safety performance indicators. The accuracy of tracking data from smartphone sensors is evaluated with respect to GPS tracking measurements. The results of this analysis identify interactions potentially dangerous and highlight high risk zones that reflect locations characterized by high vehicular interactions. This study underscores the usefulness of the smartphones for providing meaningful experimental data to assess potential safety problems.
Localization issues in the use of ITS
The problem to rectify and optimize distance measures from satellites in GNSS devices has been thoroughly explored in many researches. Instead there is not much information on available procedures to replicate the observed errors. The replication of GNSS errors can, in fact, be useful in many traffic simulation scenarios to test for ITS performances. The purpose of this article is to present cases where such a methodology can be useful and then introduce a methodology for the explicit simulation of errors in GNSS systems. The proposed methodology is based on the experimental analysis of some statistical distributions. Such distributions, arising from multiple observations in the field, are able to reproduce the behavior of the error in time as a function of the factors that influence it. The analyzed data were extracted by the GPS/GLONASS sensors of common smartphones and compared with a high-precision GPS equipment. These data were evaluated in different signal coverage conditions, in an open field where the signal quality is expected to be better, suburban and urban areas, where the signal is expected to be worse. The analysis considers many aspects such as the signal reflection problems and the sudden loss of the signal because of a change of the received satellite constellation. The research was conducted by following various steps: a field survey through smartphones and high-precision instrumentation in different conditions, the creation of reference distributions for each parameter that can have an influence on the error, the analysis of correlation functions between the variables, and a final implementation of the proposed algorithm coupled with microsimulation. The paper intends to shed some light on this problem allowing scientist and developers of new ITS system methodologies to reproduce in a simulated environment not only the movement of single vehicles (as usually carried on with microsimulation) but also the data that could be obtained from on-vechicle GNSS instrumentation. The reproduction of this GNSS tracks can be useful to assess the overall response of some new ITS systems before implementing them in the field. The proposed simulation methodology could become a standard tool to help in making better decisions in ITS implementation and to develop better ITS systems.