IEEE 2015 Intelligent Transportation Abstract Title Topics List

optimization software CPLEX.

video surveillance at level crossing (LC) environments. In this paper we explore the possibility of implementing a smart video surveillance security system that is tuned toward detecting and evaluating abnormal situations induced by users (pedestrians, vehicle drivers, and unattended objects) in LCs. This intelligent security system starts by detecting, separating, and tracking moving objects shot in the LC. Then, a hiddenMarkov model is developed to estimate ideal trajectories, allowing the detected targets to discard dangerous situations. After that, the level of risk of each target is instantly estimated by using the Dempster–Shafer data fusion technique. The proposed analysis allows for also recognizing hazard scenarios. The video surveillance system is connected to a communication system (the Wireless Access for Vehicular Environment), which takes the information on the dynamic status of the LC (safe or presence of a dangerous situation) and sends it to users approaching the LC. Four hazard scenarios are tested and evaluated with different real video image sequences: presence of the obstacle in the LC, presence of the stopped vehicles line, vehicle zigzagging between two closed half barriers, and pedestrian crossing the LC area.

connected to a resonance converter. The study carries out the system operation, electromagnetic radiation, and testing. It is shown that the presence of the chassis leads to a double resonance and has a strong influence on the radiated fields.

brain activity measured by electroencephalographic (EEG) signals.

Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects.

During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver’s needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

and improve their passenger service. Particularly in the case of disruptions, when traditional route choice models may not be valid, this is an advantage. This paper proposes a method for deducing the chosen route of passengers based on smart card data and validates this method on a real-life data set. The method deduces the correct route for about 95% of the journeys per day in our validation sample, and also in the case of disruptions. Moreover, it is shown how this method can be used to analyze and evaluate passenger service by a case study based on a real-life data

set of Netherlands Railways, which is the largest passenger railway operator in the Netherlands.

to exceptional road conditions, such as highway workzones, because such unusual events can alter previously known traffic rules and road geometry. In this paper, we present a set of computer vision methods that recognize, through identification of workzone signs, the bounds of a highway workzone and temporary changes in highway driving environments. Through testing using video data about highway workzones recorded under various weather conditions, our approach was able to perfectly identify the boundaries of workzones and robustly detect a majority of driving condition changes. In addition to these tests, we evaluated, using a mock workzone setup, the usefulness of our workzone recognition systems’ outputs for safe-guarding a self-driving car.

when it is finished with its current request. An urban environment approximated by Singapore is then analyzed through simulation. Charging requests are simulated through a trip generation model based on Singapore. In such a realistic environment, an updated practical NJN service strategy is proposed. For an MP system in an urban environment such as Singapore, there exists an optimal battery capacity with a threshold battery charge rate. Similarly, the battery swap capacity of an MS system does not need to be large for the system to perform.

the interdependency between traffic and infrastructure, significant advantages can be expected if the monitoring system for traffic can be integrated with that for infrastructure. In October 2011, a

wireless sensing network (WSN) was installed on Virginia State Route 114. Since then, Virginia Polytechnic Institute and State University (Blacksburg, VA, USA) has been developing an integrated monitoring system of pavements and traffic. This paper presents the achievement of this study: a prototype system that can monitor pavement conditions and collect traffic information simultaneously using the same embedded sensing network. In the sensing network, the sensors are ranged in longitudinal and transverse groups, and the compositions, characteristics, and functionalities of the two arrangements are introduced. The two monitoring patterns of the system, i.e., continuous monitoring and periodic testing, are explained, and the data processing methods in the two patterns are illustrated. The whole system is presented combining hardware and software, and a flowchart is used to

clarify its component and function modules. This system has the potential to achieve comprehensive monitoring functions for both traffic and infrastructure based on the embedded sensing network. This system is still under study, and needs to be consummated with various loading and environmental conditions taken into consideration. Once finished, this system will be valuable for the Intelligent Transportation System in the future.

such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.

Fleet of Electric Vehicles

to address pollution problems and fuel shortages. Transportation agencies in various countries, along with several standardization organizations, have proposed different types of energy sources (such as hydrogen, biodiesel, electric, and hybrid technologies) as alternatives to fossil fuel to achieve a more ecofriendly and sustainable environment. However, to achieve this goal, there are significant challenges that still need to be addressed. We present a survey on sustainable transportation systems that aim to reduce pollution and greenhouse gas emissions. We describe the architectural components of a future sustainable means of transportation, and we review current solutions, projects, and standardization fforts related to green transportation with particular focus on electric vehicles.We also highlight the main issues that still need to be addressed to achieve a green transportation management system. To address these issues, we present an integrated architecture for sustainable transportation management systems.