Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Bayesian Networks for the Driver Overtaking Assistance System on Two-lane Roads

Unsuccessful overtaking maneuvers on two-lane rural roads are one of the major causes of road accidents in the 21st century. The complexity of this maneuver merits the adoption of a thorough method for developing a proposed assistance system to prevent accidents and consequently reduce the high number of fatalities and the associated economic costs. This study aims to introduce an intelligent Driver Overtaking Assistance System (DOAS) to assist drivers in performing overtaking maneuvers safely. The study also will introduce a method to assess the impact of all the influential variables related to the driver, vehicle, traffic, road, and the surrounding environment. In momentary driving situations, the DOAS uses the communicated information via Hello beacon messages (HBM) and a set of input sensors to measure the possibility of overtaking the preceding vehicle(s) proactively by considering whether the distance gap to the oncoming vehicle is sufficient for overtaking. Besides, the proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity to acquire all relevant information regarding the ambient driving environment and the vehicles involved in the overtaking. To do this, DOAS uses a Bayesian Network (BN) to model overtaking maneuvers. The work presented shows high accuracy and promising results in aiding safe overtaking, with significant improvements to overtaking maneuvers on two-lane rural roads

Bayesian analysis of factors affecting crash frequency and severity during winter seasons in Iowa

Traffic safety during winter seasons has been a serious concern in Iowa as hundreds of people are injured on Iowa\u27s highways each winter. As the goal of the state transportation agency is to ensure the mobility of road users without compromising the safety during winter periods, it is important to understand the factors affecting winter-weather crash frequency and occupant injury risk through quantitative prediction models. It is of utmost importance to identify locations prone to winter-weather crashes to utilize the limited resources efficiently for improving safety during winter conditions. This research intended to develop a systematic prioritization technique to identify winter-weather crash hotspots by using Empirical Bayes technique that addresses the serious limitations of the traditional methods to screen road networks for identifying high crash locations. This research also addresses the issue of hierarchical structure in the crash data by developing quantitative models to predict occupant injury risk for crashes occurring during winter seasons to obtain unbiased and accurate estimation of the parameters for better management of road safety during winter seasons. Along with developing site prioritization techniques for identifying roadway segments with potential for safety improvement through traditional statistical methods using raw crash data, Empirical Bayes technique is used to screen roadway segment through developing safety performance functions for winter-weather crashes. A novel approach is adopted to extract weather data from information reported by winter maintenance crew members to incorporate weather related factors in developing safety performance functions at network level for three roadway types in Iowa. Weather factors such as visibility, wind velocity, air temperature are found to have statistically significant effects on winter-weather crash frequency. The ranking of roadway segments based on Potential for Safety Improvement (PSI) by employing Empirical Bayes technique differs from the ranking produced by simple crash frequency. Safety Performance Functions developed in this research can be used to produce ranking based on PSI by using crash observations made over a specific number of years for winter-weather crashes. Models predicting occupant injury risk with binomial logit formulation are developed considering the hierarchical structure of the crash data in a Bayesian framework in this research for weather-related crashes, non-weather related crashes, and all crashes occurring during the four winter seasons (2008/09 to 2011/12) in Iowa. These models are developed using disaggregate crash data with occupants nested within crashes. High values of between-crash variance for the three models underscore the justification of considering the hierarchical nature of the crash data due to the natural crash data collection process. Factors related to occupants (gender, seating position, trap status, ejection status, airbag deployment, safety equipment used) had statistically significant effects on occupant injury risk for all the models. Weather-related variables such as visibility and air temperature were found significant predictors of all crashes and weather-related crashes during the winter seasons. The variable representing road surface condition is also found to be a significant factor in all three models developed to predict occupant injury risk during the winter seasons

Short-term crash risk prediction considering proactive, reactive, and driver behavior factors

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing the number and severity of crashes. More recently, the advancements have been made in developing proactive crash risk models to assess short-term crash risks in near-real time. Such models could be applied as part of traffic management strategies to prevent and mitigate the crashes. The driver behavior is found to be the leading cause of highway crashes. Nevertheless, due to data unavailability, limited studies have explored and quantified the role of driver behavior in crashes. The Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) offers an unprecedented opportunity to perform an in-depth analysis of the impacts of driver behavior on crashes events. The research presented in this dissertation is divided into three parts, corresponding to the research objectives. The first part investigates the application of advanced data modeling methods for proactive crash risk analysis. Several proactive models for segment level crash risk and severity assessment are developed and tested, considering the proactive data available to most transportation agencies in real time at a regional network scale. The data include roadway geometry characteristics, traffic flow characteristics, and weather condition data. The analysis methods include Random-effect Bayesian Logistics Regression, Random Forest, Gradient Boosting Machine, K-Nearest Neighbor, Gaussian Naive Bayes (GNB), and Multi-layer Feedforward Deep Neural Network (MLFDNN). The random oversampling technique is applied to deal with the problem of data imbalance associated with the injury severity analysis. The model training and testing are completed using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over a period of two years. The second part of the study analyzes the potential improvement in the prediction abilities of the proposed models by adding reactive data (such as vehicle characteristics and driver characteristics) to the analysis. Commonly, the reactive data is only available (known) after the crash occurs. In the proposed research, the crash analysis is performed by classifying crashes in multiple groupings (instead of a single group), constructed based on the age of drivers and vehicles to account for the impact of reactive data on driver injury severity outcomes. The results of the second part of the study show that while the simultaneous use of reactive and proactive data can improve the prediction performance of the models, the absolute crash probability values must be further improved for operational crash risk prediction. To this end, in the third part of the study, the Naturalistic Driving Study data is used to calibrate the crash risk models, including the driver behavior risk factors. The findings show significant improvement in crash prediction accuracy with the inclusion of driver behavior risk factors, which confirms the driver behavior to be the most critical risk factor affecting the crash likelihood and the associated injury severity

Effects of life events and attitudes on vehicle transactions: A dynamic Bayesian network approach

Individual and household life events are interdependent and influence mobility-related decisions at different levels over time. This paper developed an integrated dynamic model to capture the interdependences among life events, with a special focus on vehicle transactions. Particular attention was paid to the inclusion of vehicles’ characteristics such as the age, fuel type, and size of cars, which are pertinent to emission forecast. A dynamic Bayesian network (DBN), containing individual and household characteristics and latent attitudes toward car ownership and use alongside life events, was employed to study the interdependences. The temporal relationships among life events and lead-lag effects were also captured in the DBN. The longitudinal survey data “the Netherlands Mobility Panel (MPN)” from 2013 to 2018 was used to train and test the DBN. The analysis results confirm the dynamic interdependences between vehicle transactions and other life events and reveal noticeable associations between attitudes and purchase decisions. It is found that several life events (e.g., “Birth of a baby”, “Marital status change”) have concurrent or varied lag-effects on vehicle transaction decisions. The validation indicates that the proposed DBN approach has a high predictive accuracy of vehicle transaction decisions and other life events

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE

Traffic crashes have resulted in significant cost to society in terms of life and economic losses, and comprehensive examination of crash injury outcome patterns is of practical importance. By inferring the parameters of interest from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically examine the applicability of Bayesian methods in traffic crash driver injury severity prediction in traffic crashes. In this study, three types of Bayesian models are defined: hierarchical Bayesian regression model, Bayesian non-regression model and knowledge-based Bayesian non-parametric model, and a conceptual framework is developed for selecting the appropriate Bayesian model based on discrete research purposes. Five Bayesian models are applied accordingly to test their effectiveness in traffic crash driver injury severity prediction and variable impact estimation: hierarchical Bayesian binary logit model, hierarchical Bayesian ordered logit model, hierarchical Bayesian random intercept model with cross-level interactions, multinomial logit (MNL)-Bayesian Network (BN) model, and decision table/na\xefve Bayes (DTNB) model. A complete dataset containing all crashes occurring on New Mexico roadways in 2010 and 2011 is used for model analyses. The studied dataset is composed of three major sub-datasets: crash dataset, vehicle dataset and driver dataset, and all included variables are therefore divided into two hierarchical levels accordingly: crash-level variables and vehicle/driver variables. From all these five models, the model performance and analysis results have shown promising performance on injury severity prediction and variable influence analysis, and these results underscore the heterogeneous impacts of these significant variables on driver injury severity outcomes. The performances of these models are also compared among these methods or with traditional traffic safety models. With the analyzed results, tentative suggestions regarding countermeasures and further research efforts to reduce crash injury severity are proposed. The research results enhance the understandings of the applicability of Bayesian methods in traffic safety analysis and the mechanisms of crash injury severity outcomes, and provide beneficial inference to improve safety performance of the transportation system

Please, talk about it! When hotel popularity boosts preferences

Many consumers post on-line reviews, affecting the average evaluation of products and services. Yet, little is known about the importance of the number of reviews for consumer decision making. We conducted an on-line experiment (n= 168) to assess the joint impact of the average evaluation, a measure of quality, and the number of reviews, a measure of popularity, on hotel preference. The results show that consumers' preference increases with the number of reviews, independently of the average evaluation being high or low. This is not what one would expect from an informational point of view, and review websites fail to take this pattern into account. This novel result is mediated by demographics: young people, and in particular young males, are less affected by popularity, relying more on quality. We suggest the adoption of appropriate ranking mechanisms to fit consumer preferences. © 2014 Elsevier Ltd