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Federico Orsini
Mobility and Behavior Research Center—MoBe, University of Padua, 35131 Padua, Italy

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Journal article
Published: 13 August 2021 in Sustainability
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Real-time coaching programs are designed to give feedback on driving behavior to usage-based motor insurance users; they are often general purpose programs that aim to promote smooth driving. Here, we investigated the effect of different on-board real-time coaching programs on the driving behavior on highway deceleration lanes with a driving simulator experiment. The experiment was organized into two trials. The first was a baseline trial, in which participants drove without receiving any feedback; a cluster analysis was then performed to divide participants into two groups, based on their observed driving style. One month later, a second trial was carried out, with participants driving on the same path as the first trial, this time receiving contingent feedback related to their braking/acceleration behavior. Four feedback systems were tested; overall, there were eight experimental groups, depending on the clustered driving style (aggressive and defensive), feedback modality (visual and auditory), and feedback valence (positive and negative). Speed, deceleration, trajectory, and lateral control variables, collected before and onto the deceleration lane, were investigated with mixed ANOVAs, which showed that the real-time coaching programs significantly reduced speeds and maximum deceleration values, while improving lateral control. A change toward a safer exit strategy (i.e., entering the lane before starting to decelerate) was also observed in defensive drivers.

ACS Style

Federico Orsini; Mariaelena Tagliabue; Giulia De Cet; Massimiliano Gastaldi; Riccardo Rossi. Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior. Sustainability 2021, 13, 9089 .

AMA Style

Federico Orsini, Mariaelena Tagliabue, Giulia De Cet, Massimiliano Gastaldi, Riccardo Rossi. Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior. Sustainability. 2021; 13 (16):9089.

Chicago/Turabian Style

Federico Orsini; Mariaelena Tagliabue; Giulia De Cet; Massimiliano Gastaldi; Riccardo Rossi. 2021. "Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior." Sustainability 13, no. 16: 9089.

Journal article
Published: 03 February 2021 in Transportation Research Procedia
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Real-time conflict prediction models (RTConfPM) are an innovative approach to deal with real-time road safety analysis, even in absence of reliable crash data. This paper presents a RTConfPM to predict rear-end conflicts, using Time-To-Collision (TTC) values recorded with radar sensors on multiple motorway cross-sections to define unsafe situations, and traffic conditions recorded on the same sections as inputs to the model. Several classifiers were trained and compared: K-Nearest Neighbors (KNN), Naïve Bayes (NB), Discriminant Analysis (DA), Decision Trees (DT) and Support Vector Machine (SVM). All the proposed models provide better performance than most of crash-based real-time models found in the literature, with KNN and SVM significantly better than the others when considering Recall indicator.

ACS Style

Federico Orsini; Gregorio Gecchele; Massimiliano Gastaldi; Riccardo Rossi. Real-time conflict prediction: a comparative study of machine learning classifiers. Transportation Research Procedia 2021, 52, 292 -299.

AMA Style

Federico Orsini, Gregorio Gecchele, Massimiliano Gastaldi, Riccardo Rossi. Real-time conflict prediction: a comparative study of machine learning classifiers. Transportation Research Procedia. 2021; 52 ():292-299.

Chicago/Turabian Style

Federico Orsini; Gregorio Gecchele; Massimiliano Gastaldi; Riccardo Rossi. 2021. "Real-time conflict prediction: a comparative study of machine learning classifiers." Transportation Research Procedia 52, no. : 292-299.

Conference paper
Published: 31 August 2020 in Database Systems for Advanced Applications
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This work investigated the effect of mental models on the effectiveness of an advanced driver assistance system (ADAS). The system tested was a lateral control ADAS, which informed the drivers whether the vehicle was correctly positioned inside the lane or not, with the use of two visual and one auditory stimuli. Three driving simulator experiments were performed, involving three separate groups of subjects, who received different initial exposures to the technology. In Experiment 0 subjects were not exposed to ADAS in order to be able to indicate that no effect of learning affected the results. In Experiment A subjects were not instructed on the ADAS functionalities and they had to learn on their own; in Experiment B they were directly instructed on the functionalities by reading an information booklet. In all experiments drivers performed multiple driving sessions. The mean absolute lateral position (LP) and standard deviation of lateral position (SDLP) for each driver were considered as main dependent variables to measure the effectiveness of the ADAS. Findings from this work showed that the initial mental model had an impact on ADAS effectiveness, since it produced significantly different results in terms of ADAS effectiveness, with those reading the information booklet being able to improve more and faster their lateral control.

ACS Style

Riccardo Rossi; Massimiliano Gastaldi; Francesco Biondi; Federico Orsini; Giulia De Cet; Claudio Mulatti. A Driving Simulator Study Exploring the Effect of Different Mental Models on ADAS System Effectiveness. Database Systems for Advanced Applications 2020, 102 -113.

AMA Style

Riccardo Rossi, Massimiliano Gastaldi, Francesco Biondi, Federico Orsini, Giulia De Cet, Claudio Mulatti. A Driving Simulator Study Exploring the Effect of Different Mental Models on ADAS System Effectiveness. Database Systems for Advanced Applications. 2020; ():102-113.

Chicago/Turabian Style

Riccardo Rossi; Massimiliano Gastaldi; Francesco Biondi; Federico Orsini; Giulia De Cet; Claudio Mulatti. 2020. "A Driving Simulator Study Exploring the Effect of Different Mental Models on ADAS System Effectiveness." Database Systems for Advanced Applications , no. : 102-113.

Journal article
Published: 08 August 2020 in Transportation Research Part F: Traffic Psychology and Behaviour
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Self-evaluating methods are frequently used to identify driving styles. Among others, one of the most commonly used questionnaires is the Multidimensional Driving Style Inventory (MDSI), developed for the Israeli population. Because of the extensive use of the questionnaire, the present paper aims to validate an Italian version and to confirm the 8-factor structure of the original one, i.e, dissociative, anxious, risky, angry, high-velocity, distress-reduction, patient, and careful driving style. The Italian version of the MDSI was filled out by 561 Italian drivers, who had a driving license for at least 1 year. A confirmatory factorial analysis (CFA) was conducted on the 44-item of the translated questionnaire showing not so good values of the goodness of fit tests (SRMR = 0.085; RMSEA = 0.063). The total-item correlation of each scale indicated that 4 items had a low index of total-item correlation. A second CFA was conducted on the remaining 40 items: goodness fit parameters improved (SRMR = 0.0685, RMSEA = 0.0584). Previous validations of the original version of MDSI for different populations (Argentine, Romanian, Chinese, Malaysian, Butch and Belgian) showed several critical issues in confirming the original structure. In the Italian version of MDSI validated in the present paper, the original 8-factor structure was confirmed by removing the 4 items which did not properly contribute to the factors. The results not only confirmed the usefulness of the MDSI in assessing driving style but they also indicated that the concept of driving style is considered in the same way in Italy and Israel, even though traffic rules are different. The latter consideration raises interesting questions for future research concerning cross-cultural comparisons of driving behavior in different countries.

ACS Style

F. Freuli; G. De Cet; M. Gastaldi; F. Orsini; M. Tagliabue; R. Rossi; G. Vidotto. Cross-cultural perspective of driving style in young adults: Psychometric evaluation through the analysis of the Multidimensional Driving Style Inventory. Transportation Research Part F: Traffic Psychology and Behaviour 2020, 73, 425 -432.

AMA Style

F. Freuli, G. De Cet, M. Gastaldi, F. Orsini, M. Tagliabue, R. Rossi, G. Vidotto. Cross-cultural perspective of driving style in young adults: Psychometric evaluation through the analysis of the Multidimensional Driving Style Inventory. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 73 ():425-432.

Chicago/Turabian Style

F. Freuli; G. De Cet; M. Gastaldi; F. Orsini; M. Tagliabue; R. Rossi; G. Vidotto. 2020. "Cross-cultural perspective of driving style in young adults: Psychometric evaluation through the analysis of the Multidimensional Driving Style Inventory." Transportation Research Part F: Traffic Psychology and Behaviour 73, no. : 425-432.

Journal article
Published: 15 May 2020 in Case Studies on Transport Policy
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The maritime containerised transport continues to grow; thus, an efficient seaport inland access becomes crucial for the performance of the entire intermodal transport chain. Dry ports are inland intermodal terminals directly connected to seaports via rail, which operate as trans-shipment hubs and provide services, such as: storage, consolidation, depot and custom clearance. In addition to offering benefits in terms of operational costs and efficiency, dry port concept implementation can produce positive effects also under an environmental point of view, promoting the modal shift from road to rail. This paper presents a simulation-based method to estimate the environmental benefits, in terms of emissions reduction, of a dry port implementation. This method is organized into three parts: (i) traffic demand analysis, (ii) traffic supply model, (iii) assignment and emission model. The methodology is applied to a real-world case study in Northern Italy, where the current scenario is compared to a future scenario in which a new railway connection is established between a seaport (Port of Venice) and an existing intermodal freight hub (Interporto of Padua). According to our findings, in the future scenario emissions of the main pollutants are reduced by 17%. In terms of CO2 this corresponds to about 8000 tons per year.

ACS Style

Angela Carboni; Federico Orsini. Dry ports and related environmental benefits: a case study in Italy. Case Studies on Transport Policy 2020, 8, 416 -428.

AMA Style

Angela Carboni, Federico Orsini. Dry ports and related environmental benefits: a case study in Italy. Case Studies on Transport Policy. 2020; 8 (2):416-428.

Chicago/Turabian Style

Angela Carboni; Federico Orsini. 2020. "Dry ports and related environmental benefits: a case study in Italy." Case Studies on Transport Policy 8, no. 2: 416-428.

Journal article
Published: 25 April 2020 in Transportation Research Procedia
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A general procedure for the validation of a driving simulation environment for the analysis of gap-acceptance behavior was developed in this study. It allows to test whether a synthetic indicator of gap-acceptance behavior (the mean critical gap) shows significant differences when computed on the basis of field observations versus observations collected in the simulated environment. If such differences are not significant, driver behavior can be considered similar in the two contexts, thus supporting validation of the driving simulation environment. In order to demonstrate its effectiveness, the proposed procedure is applied to the case of a three-leg roundabout located in the Veneto region (Italy). The results show that the mean critical gap estimated in the field and the mean critical gap estimated in the virtual environment are not significantly different. The proposed procedure can be applied in various contexts in which gap-acceptance behavior is a central element in terms of safety and operational performance of the traffic system under analysis.

ACS Style

Riccardo Rossi; Claudio Meneguzzer; Federico Orsini; Massimiliano Gastaldi. Gap-acceptance behavior at roundabouts: validation of a driving simulator environment using field observations. Transportation Research Procedia 2020, 47, 27 -34.

AMA Style

Riccardo Rossi, Claudio Meneguzzer, Federico Orsini, Massimiliano Gastaldi. Gap-acceptance behavior at roundabouts: validation of a driving simulator environment using field observations. Transportation Research Procedia. 2020; 47 ():27-34.

Chicago/Turabian Style

Riccardo Rossi; Claudio Meneguzzer; Federico Orsini; Massimiliano Gastaldi. 2020. "Gap-acceptance behavior at roundabouts: validation of a driving simulator environment using field observations." Transportation Research Procedia 47, no. : 27-34.

Journal article
Published: 27 March 2020 in IET Intelligent Transport Systems
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Emerging international transportation system (ITS) and sensing technologies allow collecting of large amounts of high-quality traffic data in highways, which can be used for road safety analysis. With this kind of data, it is possible to apply extreme value theory (EVT), which is gaining interest in the field of road safety, thanks to its ability to produce quick and reliable safety evaluations. EVT can estimate the probability of extreme events (i.e. road crashes) from relatively short observation periods, using surrogate measures of safety in place of crash data. In this work, EVT is applied for a large-scale case study in two motorways, located in north-eastern Italy. Vehicle-by-vehicle information was collected using microwave radars in 19 motorways cross-sections for one year, and time-to-collision was calculated for each pair of consecutive vehicles. A six-year crash database of the toll road was used to validate model results. For each cross-section, two traditional approaches, block-maxima and peak-over-threshold, were applied to estimate EVT parameters. Both approaches produced reliable predictions of annual rear-end collisions; in particular, in about 90% of the cross-sections, the observed number of crashes fell within the 95% confidence interval of the predicted number of crashes.

ACS Style

Federico Orsini; Gregorio Gecchele; Massimiliano Gastaldi; Riccardo Rossi. Large‐scale road safety evaluation using extreme value theory. IET Intelligent Transport Systems 2020, 14, 1004 -1012.

AMA Style

Federico Orsini, Gregorio Gecchele, Massimiliano Gastaldi, Riccardo Rossi. Large‐scale road safety evaluation using extreme value theory. IET Intelligent Transport Systems. 2020; 14 (9):1004-1012.

Chicago/Turabian Style

Federico Orsini; Gregorio Gecchele; Massimiliano Gastaldi; Riccardo Rossi. 2020. "Large‐scale road safety evaluation using extreme value theory." IET Intelligent Transport Systems 14, no. 9: 1004-1012.

Journal article
Published: 31 January 2020 in Journal of Safety Research
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Introduction: This study investigates the effect of precision teaching signals on lane maintenance. Methods: In experiment 1, the control group drove a simulator with no signals. In experiment 2, drivers were presented with auditory signals depending on their position within or outside the lane. In experiment 3, visual signals were presented in addition to auditory signals to examine the effect of redundancy on drivers’ lane maintenance. Results: Results showed an improvement in lane maintenance in experiment 2. Cross-experiment analysis indicated this effect not to be the result of learning. Data from experiment 3 also showed that presenting redundant signals did not further reduce lane variability or help drivers maintain a more central position within the lane. Conclusions: Taken together, data suggest precision teaching be effective as an educational tool to improve lane maintenance.

ACS Style

Francesco N. Biondi; Riccardo Rossi; Massimiliano Gastaldi; Federico Orsini; Claudio Mulatti. Precision teaching to improve drivers’ lane maintenance. Journal of Safety Research 2020, 72, 225 -229.

AMA Style

Francesco N. Biondi, Riccardo Rossi, Massimiliano Gastaldi, Federico Orsini, Claudio Mulatti. Precision teaching to improve drivers’ lane maintenance. Journal of Safety Research. 2020; 72 ():225-229.

Chicago/Turabian Style

Francesco N. Biondi; Riccardo Rossi; Massimiliano Gastaldi; Federico Orsini; Claudio Mulatti. 2020. "Precision teaching to improve drivers’ lane maintenance." Journal of Safety Research 72, no. : 225-229.

Journal article
Published: 24 January 2019 in Transportation Research Procedia
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The current practice in crash-based safety analysis is hindered by some weaknesses: rarity of crashes, lack of timeliness, mistakes in crash reporting. Researchers are testing alternative approaches to safety estimation without the need of crash data. This paper presents an application of Extreme Value Theory in road safety analysis, using Time-To-Collision as a surrogate safety measure to estimate the risk to be involved in a freeway rear-end collision. The method was tested using data from an Italian toll-road with good results.

ACS Style

Gregorio Gecchele; Federico Orsini; Massimiliano Gastaldi; Riccardo Rossi. Freeway rear-end collision risk estimation with extreme value theory approach. A case study. Transportation Research Procedia 2019, 37, 195 -202.

AMA Style

Gregorio Gecchele, Federico Orsini, Massimiliano Gastaldi, Riccardo Rossi. Freeway rear-end collision risk estimation with extreme value theory approach. A case study. Transportation Research Procedia. 2019; 37 ():195-202.

Chicago/Turabian Style

Gregorio Gecchele; Federico Orsini; Massimiliano Gastaldi; Riccardo Rossi. 2019. "Freeway rear-end collision risk estimation with extreme value theory approach. A case study." Transportation Research Procedia 37, no. : 195-202.

Articles
Published: 05 September 2018 in Transportmetrica A: Transport Science
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This work contributes to study the application of extreme value theory (EVT) in road safety analysis, estimating the risk of being involved in an entering–circulating collision in single-lane roundabouts. Detailed trajectory data of the vehicles were derived from a driving simulator experiment, and the time-to-collision (TTC) was used as a surrogate measure of safety. Three EVT approaches were applied, tested and compared: (1) the Generalized Extreme Value distribution used in the block maxima (BM) approach, (2) the Generalized Pareto Distribution used in the peak-over-threshold approach (POT), with negated-TTC (nTTC), and (3) shifted-reciprocal-TTC (srTTC). Case-study results analysis showed that BM and POT with shifted-reciprocal-TTC confidence intervals included the number of observed crashes, while POT with negated-TTC did not include it. According to these findings, both BM and POT-with-shifted-reciprocal-TTC appear promising and deserve further attention in order to develop effective ready-to-practice crash prediction models, useful in intersection design and operational analysis.

ACS Style

Federico Orsini; Gregorio Gecchele; Massimiliano Gastaldi; Riccardo Rossi. Collision prediction in roundabouts: a comparative study of extreme value theory approaches. Transportmetrica A: Transport Science 2018, 15, 556 -572.

AMA Style

Federico Orsini, Gregorio Gecchele, Massimiliano Gastaldi, Riccardo Rossi. Collision prediction in roundabouts: a comparative study of extreme value theory approaches. Transportmetrica A: Transport Science. 2018; 15 (2):556-572.

Chicago/Turabian Style

Federico Orsini; Gregorio Gecchele; Massimiliano Gastaldi; Riccardo Rossi. 2018. "Collision prediction in roundabouts: a comparative study of extreme value theory approaches." Transportmetrica A: Transport Science 15, no. 2: 556-572.

Journal article
Published: 12 February 2018 in IET Intelligent Transport Systems
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This work presents a decision support system for providing information and suggestions to airport users. The aim of the study is to design a system both to improve passengers' experience by reducing time-spent queuing and waiting and to raise airport revenues by increasing the time passengers spend in discretionary activities. Passengers' behaviour is modelled with an activity-choice model to be calibrated with their mobile phone traces. The model allows predicting activity sequences for passengers with given socio-demographic characteristics. To predict queue length at check-in desks and security control and congestion inside commercial areas, passengers' movements are simulated with a microscopic simulation tool. A system to generate suggestion has been designed: passengers are advised to perform mandatory activities when the predicted queue length is reasonable and specific discretionary activities according to time available, user profiles, location distance, location congestion and airport management preferences. A proof-of-concept case study has been developed: passengers' behaviour in both cases of receiving and not receiving suggestion has been simulated. In the first case, passengers experienced less queuing and waiting time; the time saved was spent in discretionary activities, improving passengers' airport experience and increasing airport revenues.

ACS Style

Riccardo Rossi; Massimiliano Gastaldi; Federico Orsini. How to drive passenger airport experience: a decision support system based on user profile. IET Intelligent Transport Systems 2018, 12, 301 -308.

AMA Style

Riccardo Rossi, Massimiliano Gastaldi, Federico Orsini. How to drive passenger airport experience: a decision support system based on user profile. IET Intelligent Transport Systems. 2018; 12 (4):301-308.

Chicago/Turabian Style

Riccardo Rossi; Massimiliano Gastaldi; Federico Orsini. 2018. "How to drive passenger airport experience: a decision support system based on user profile." IET Intelligent Transport Systems 12, no. 4: 301-308.