Vasileia Papathanasopoulou

Localization and Driving Behavior Classification with Smartphone Sensors in Direct Absence of Global Navigation Satellite Systems

Global navigation satellite systems have tremendous impact and potential in the development of intelligent transportation systems and mobility services and are expected to deliver significant benefits, including increased capacity, improved safety, and decreased pollution. However, there are situations in which there might not be direct location information about vehicles, for example, in tunnels and in indoor facilities such as parking garages and commercial vehicle depots. Various technologies can be used for vehicle localization in these cases, and other sensors that are currently available in most modern smartphones, such as accelerometers and gyroscopes, can be used to obtain information directly about the driving patterns of individual drivers. The objective of this research is to present a framework for vehicle localization and modeling of driving behavior in indoor facilities or, more generally, facilities in which global navigation satellite system information is not available. Localization technologies and needs are surveyed and the adopted methodology is described. The case studies, which use data from multiple types of sensors (including accelerometers and gyroscopes from two smartphone platforms as well as two reference platforms), provide evidence that the opportunistic smart-phone sensors can be useful in identifying obstacles (e.g., speed humps) and maneuvers (e.g., U-turns and sharp turns). These data, when cross-referenced with a digital map of the facility, can be useful in positioning the vehicles in indoor environments. At a more macroscopic level, a methodology is presented and applied to determine the optimal number of clusters for the drivers’ behavior with a mix of suitable indexes.

Towards distribution-based calibration for traffic simulation

Traffic simulation models have seen increasing use during the past decades. One of the biggest challenges related to their successful application is effective calibration and validation. Emerging data collection techniques provide richer data that can be used to improve this process. In this research, we explore the use of distributions of collected data (such as accelerations, using opportunistic sensors, such as smart-phone accelerometers) for calibration purposes. The performance of the considered ubiquitous sensors is benchmarked against reference equipment, to evaluate its accuracy under different conditions. A methodology is proposed for the integration of distributions of data in traffic simulation model calibration and validation.

Simulation-Based Analysis of Road-Pricing Prospects for Athens, Greece

The objective of this paper is to explore the potential future use of road pricing for Athens, Greece. Road-pricing schemes are surveyed, focusing primarily on the European experience, and are used to contribute to the discussion regarding the possible future adoption of a road-pricing scheme in Athens, Greece. The main features and problems of the transportation landscape in Athens are outlined, and results from a strengths-weaknesses-opportunities-threats (SWOT) analysis are presented. The key issues, required conditions, and options associated with a possible future implementation of an urban road-pricing scheme in the Athens metropolitan area are presented. The analysis is validated through a series of face-to-face interviews that were undertaken with a panel of key experts. The selected parameters of possible future road-pricing schemes in Athens are simulated and various measures of effectiveness are collected and analyzed. Sensitivity analysis of the demand levels that would result from the deployment of the system is also performed, while the elasticities of the demand in response to the system are also calculated. The results indicate that while currently there are more direct instruments to address traffic congestion, in the future, urban tolls may provide a useful complementary tool toward a sustainable transportation system for Athens.

Flexible car-following models incorporating information from adjacent lanes

In recent years, technological advances have significantly improved Driver Assistance Systems and there has been an increasing interest in autonomous vehicles. Aiming at safety, reliability and convenience, autonomous vehicles require detailed car-following models that could model driving behavior in an efficient way. In this research, an existing flexible car-following model is enriched by incorporating additional information about density of two adjacent lanes. This research aims to explore if the additional information on density of adjacent lanes could improve the accuracy of the car-following model. More realistic detailed models could provide a robust solution to autonomous driving. The updated model is applied to reconstructed NGSIM data using a flexible regression technique, loess method. For a more in depth analysis, a meta-model is developed to evaluate the magnitude of the effect of the considered predictor variables on the proposed model. Finally, conclusions are drawn and future prospects are suggested.

A demonstration of distribution-based calibration

Calibration plays a fundamental role in successful applications of traffic simulation models and Intelligent Transportation Systems. In this research, the use of distributions in calibration process is motivated. The optimization of model parameters is fulfilled using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The output of the optimization is a distribution of parameter values, capturing a wide range of various traffic conditions. As a proof of concept, a case study is also presented where the proposed framework is implemented for the distribution-based calibration of the car-following model used in the TransModeler microscopic traffic simulation model. The use of parameter distributions is preferred to using point parameter values, as it is more realistic, capturing the heterogeneity of driver behavior, and allows the simultaneous study of various driving behavior patterns. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.

Simulation Optimization of Car-Following Models Using Flexible Techniques

Car-following behavior is a key component of microscopic traffic simulation. Numerous models based on traffic flow theory have been developed for decades in order to represent the longitudinal interactions between vehicles as realistically as possible. Nowadays, there is a shift from conventional models to data-driven approaches. Data-driven methods are more flexible and allow the incorporation of additional information to the estimation of car-following models. On the other hand, conventional car-following models are founded on traffic flow theory, thus providing better insight into traffic behavior. The integration of data-driven methods in applications of intelligent transportation systems is an attractive perspective. Towards this direction, in this research an existing data-driven approach is further validated using another training dataset. Then, the methodology is enriched and an improved methodological framework is suggested for the optimization of car-following models. Machine learning techniques, such as classification, locally weighted regression (loess) and clustering, are innovatively integrated. In this chapter, validation of the proposed methods is demonstrated on data from two sources: (i) data collected from a sequence of instrumented vehicles in Naples, Italy, and (ii) data from the NGSIM project. In addition, a conventional car-following model, the Gipps’ model, is used as reference in order to monitor and evaluate the effectiveness of the proposed method. Based on the encouraging results, it is suggested that machine learning methods should be further investigated as they could ensure reliability and improvement in data driven estimation of car-following models.