On this page we are collected our research on road safety and microsimulation
Tritone: a microsimulator for road safety
Since 2009, the University of Calabria has developed Tritone, a microsimulatore of road networks capable of providing not only the evolution of traffic, possible areas of a road network at greater risk of accidents. Tritone is able to represent in a timely, accurate and specific traffic, its instantaneous evolution and the situations of risk, taking into account the geometrical aspects of retail infrastructure and the actual behavior of drivers by examining in detail the ‘ coupling of the characteristics of the vehicle and the driver. Tritone was founded with the aim of becoming a leading tool in the area of road safety, because not only is efficient functionally and from a usability point of view, re-mirroring full new trends of a user-friendly user interface, but also it allows you to automatically evaluate and dynamically many road safety parameters such as DRAC indicators, TTC, etc., which allow you to pre-see situations of danger and aim to the prevention already in the planning stage or verification.
Calibration of a New Microsimulation Package for the Evaluation of Traffic Safety Performances
Recently, some studies have confirmed that the reproduction by simulation of user behaviour under different flow and geometric conditions, can identify a potential incident hazard and allow to take appropriate countermeasures at specific points of the road network (Cunto and Saccomanno, 2007; Cunto and Saccomanno, 2008; Saccomanno et al. 2008). In this paper a calibration and validation technique of a microsimulation model for short-term road safety analysis is presented. The microscopic model developed allows the estimation of road safety performance through a series of indicators (Crash Potential Index, Deceleration Rate to Avoid Crash, Maximum Available Deceleration Rate, Time to Collision, etc.), representing interactions in real time between different pairs of vehicles belonging to the traffic stream. When these indicators take a certain critical value, a possible accident scenario is identified. The calibration procedure was applied using an optimization algorithm to systematically modify the 5 parameters of the core behavior model (the General Motors car following model that is a module of the main traffic microsimulation model) in order to fit travel times obtained from simulations to the measured travel times. Experimental measures where obtained in one measurement site from a survey on a two lane undivided rural highway and were provided using a specifically developed video digital processing algorithm. In order to assess the capability of the microsimulation model to reflect reality and, consequently, to make further predictions, a validation technique was carried out. In the validation process the optimization algorithm shows the ability to increase the goodness of fit of estimated travel times to measured values. The estimated car following model parameters are used in estimating the goodness of fit with a set of observed data which have not previously been used in the calibration process. This procedure has brought very good results that show how the travel times estimation errors can be greatly reduced, even in different traffic conditions relatively to the calibration scenario, by using the set of parameters obtained in the optimization procedure.
A new microsimulation model for the evaluation of traffic safety performances
Some papers have been recently presented (Cunto and Saccomanno 2007, Cunto and Saccomanno 2008, Saccomanno et al. 2008) on the potential of traffic microsimulation for the analysis of road safety. In particular, studies have confirmed that the reproduction by simulation of user behaviour under different flow and geometry conditions, can identify a potential incident hazard and allow to take appropriate countermeasures at specific points of the road network. The objective of this paper is to assess the validity of this approach; for this reason a microsimulation model and an automatic video detection system have been developed. The microscopic model allows the estimation of road safety performance through a series of indicators (Deceleration Rate to Avoid Crash, Time to Collision, Proportion of Stopping Distance), representing interactions in real time, between different pairs of vehicles belonging to the traffic stream. When these indicators take a certain critical value, a possible accident scenario is identified. The microscopic simulation model is used combined with a new video image traffic detection algorithm to calculate vehicle trajectories. Microscopic traffic flow parameters obtained by video detection are used to calibrate the microsimulation model, and the safety performance indicators obtained by the real vehicles trajectories can be compared with simulated scenarios where safety performance indicators are obtained on the simulated trajectories. Results indicate that the methodology can be useful in the estimation of safety performance indicators and in evaluating traffic control measures.
Safety performance measures: A comparison between microsimulation and observational data
Safety performance measures can be obtained either through simulation (based on well specified or calibrated traffic models) or experimentally through observational vehicle tracking data. Accurate calibration of traffic models ensures that simulated measures of safety performance are reflective of “real world” traffic conditions. The microscopic model, for a case study, allows the estimation of road safety performance through a series of indicators, representing interactions in real time between different pairs of vehicles belonging to the traffic stream. When these indicators reach a certain critical value, a possible accident scenario is identified. For the same case study, safety performance indicators are obtained through a video image processing algorithm for vehicle detection and tracking. The accuracy of the algorithm is evaluated with respect to GPS tracking measurements. The algorithm adopts a background subtraction-based approach for vehicle detection in 0.1 second increments. Since this approach is sensitive to background changes (or noise), a median filter technique has been introduced. Individual vehicles are detected and tracked using a region-based approach, whereby a connected zone (or blob) is assigned to each image, which is then tracked over time. In case of overlapping, where the designated blob may correspond to several vehicles, a real time sub-routine is accessed that manually discriminates each constituent vehicle’s specific position within the blob. Output from the algorithm application is expressed in terms of several trajectory descriptors over time, such as position and speed. The focus of this paper is on the analysis of road safety from two different perspectives: microsimulation and observational data. In this way it is possible to determine how microsimulation reflects “real” driver behavior and traffic conditions for a given case study.
Investigating road safety issues through a microsimulation model
Some papers have been recently presented (Cunto and Saccomanno, 2007; Cunto and Saccomanno, 2008; Saccomanno et al., 2008) on the potential of traffic microsimulation for the analysis of road safety. In particular, studies have confirmed that the reproduction by simulation of user behaviour under different flow and geometric conditions, can identify a potential incident hazard and allow to take appropriate countermeasures at specific points of the road network. The objective of this paper is to assess the validity of this approach and present a new methodology for investigating road safety issues. A calibrated microsimulation model has been developed to analyze vehicle trajectories, and hence vehicle interactions, in some different scenarios and verify traffic safety levels. The microscopic model allows the estimation of road safety performance through a series of indicators (Crash Potential Index, Deceleration Rate to Avoid Crash, Maximum Available Deceleration Rate, Time to Collision, etc.), representing interactions in real time between different pairs of vehicles belonging to the traffic stream. When these indicators take a certain critical value, a possible accident scenario is identified. The validation of the proposed methodology can be done by comparing the value assumed by safety performance indicators in simulated and real scenarios. The microscopic simulation model is also combined with a new video image traffic detection algorithm to detect vehicle trajectories. Microscopic traffic flow parameters obtained by video detection are, in fact, used to calibrate the microsimulation model. The above described methodology has been applied in the analysis of overtaking maneuvers on single lane for direction fast rural roads. Results indicate that the methodology can be useful in the estimation of safety performance indicators and in evaluating traffic control measures.
Influence of Travel Behavior on Road Safety Performance Measures in Rural Highways
Safety performance measures represent an increasingly more useful means for the evaluation of road safety conditions and allow the planners and the researchers to evaluate strategies apt to solve safety related problems. The main goal of this paper is to present a procedure for extracting vehicle tracking data and to use them in the estimation of safety performance in terms of a series of indicators representing interactions in real time between different pairs of vehicles belonging to the traffic stream. The context for this experiment is the measurement of safety performance at rural highways with respect to the potential for rear-end and head-on vehicle interactions. Individual vehicles are detected and tracked using a video image processing algorithm. The results of this study provide meaningful experimental indicators of potential safety problems at different rural highway locations subject to behavioral driver responses to different traffic conditions.
A new microsimulation model for the evaluation of traffic safety performances
Some papers have been recently presented (Cunto and Saccomanno 2007,Cunto and Saccomanno 2008, Saccomanno et al.2008) on the potential of traffic microsimulation for the analysis of road safety. In particular, studies have con?rmed that the reproduction by simulation of user behaviour under different ?ow and geometry conditions can identify a potential incident hazard and allow to take appropriate countermeasures at specific points of the road network.. The objective of this paper is to asses the validity of this approach, for this reason a microsimulation model and an automatic video detection system have been developed. The microscopic model allows the estimation of road safety performance through a series of indicators (Crash Potential Index, Rate Deceleration to Avoid Crash, Available Maximum Deceleration Rate, Time to Collision,etc..), representing interactions in real time between different pairs of vehicles belonging to the traffic stream. When these indicators take a certain critical value, a possible accident scenario is identified. The validation of the proposed methodology is done by comparing the value assumed by the indicators of safety performance in simulated and real scenarios. The microscopic simulation model is used combined with a new video image traffic detection algorithm to calculate vehicle trajectories. Microscopic traffic ?ow parameters obtained by video detection are used to calibrate the microsimulation model and the safety performance indicators obtained by the real vehicles trajectories can be compared with simulated scenarios where safety performance indicators are obtained on the simulated trajectories. The above described procedure has been applied in the analysis of overtaking manoeuvres on single lane for direction fast rural roads. Results indicate that the methodology can be useful in the estimation of safety performance indicators and in evaluating traffic control measures.
Investigating safety issues in two-way rural highways
In the last years the growing need for a better mobility has coincided with an increase of congestion levels on transportation infrastructures and a consequent repercussion on safety aspects. For this reason researchers and technicians have focused on the study of safety performances on road networks identifying and applying all kinds of countermeasures useful to decrease accident risks. One of the most common methodologies to estimate safety makes use of inferential statistics applied to crash databases in what can be considered as a reactive approach to the problem. Although this method seems to intuitively link causes to effects, a good knowledge of the dynamics of the events preceding the crash may provide a more useful support to the implementation of appropriate countermeasures. Moreover, the problems of consistency and availability of crash data as well as the methodological challenges posed by the extremely random nature and the uniqueness of accidents have led to the development of complementary ap-proaches to improve road safety assessment, such as the observation of traffic conflicts and the use of microscopic traffic simulation. The potential of micro-scopic simulation in traffic safety has gained a growing interest due to recent de-velopment in human behavior modeling and real time vehicle data acquisition (Cunto and Saccomanno 2007, Cunto and Saccomanno 2008, Saccomanno et al. 2008, Yang et al. 2010, Cheol and Taejin 2010). A recent FHWA review of these models has concluded that: “when properly calibrated, traffic simulation models can provide useful and reliable information on individual driver responses to changing traffic and geometric road conditions” (FHWA, 2003). Since traffic con-ditions are inputs into safety performance, these models can also provide a solid causal platform for the study of safety.
New features of Tritone for the evaluation of traffic safety performances
Recent research papers have confirmed that traffic simulation can identify near crashes events and establish a good base for the estimation of real crashes risk. The objective of this paper is to present the new features of a microsimulation model originally developed to estimate road safety performance.
The presented microsimulator has many new features that can be useful to engineers and researchers such as:
- Dynamic calculation of traffic and road safety indicators.
- Simulation of satellite location data obtained by GPS and smartphones.
- Simulation of adaptive traffic lights activated by FCD data.
- 20 different acoustic emission models.
- Possibility of taking into account “instrumented” vehicles to assess new Intelligent Transportation System performances.
The paper describes the above listed new features of TRITONE that combined with the calculation of road safety indicators evaluations allow planners to benefit from the availability of a useful and innovative tool for traffic simulation.