Showing papers on "Intelligent transportation system published in 2013"

Predicting Taxi–Passenger Demand Using Streaming Data

Abstract: Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.

Modular intelligent transportation system

Abstract: A modular intelligent transportation system, comprising an environmentally protected enclosure, a system communications bus, a processor module, communicating with said bus, having a image data input and an audio input, the processor module analyzing the image data and/or audio input for data patterns represented therein, having at least one available option slot, a power supply, and a communication link for external communications, in which at least one available option slot can be occupied by a wireless local area network access point, having a communications path between said communications link and said wireless access point, or other modular components.

Toward cloud-based vehicular networks with efficient resource management

Abstract: In the era of the Internet of Things, all components in intelligent transportation systems will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance the comfort of driving. The vision of all vehicles connected poses a significant challenge to the collection and storage of large amounts of traffic-related data. In this article, we propose to integrate cloud computing into vehicular networks such that the vehicles can share computation resources, storage resources, and bandwidth resources. The proposed architecture includes a vehicular cloud, a roadside cloud, and a central cloud. Then we study cloud resource allocation and virtual machine migration for effective resource management in this cloud-based vehicular network. A game-theoretical approach is presented to optimally allocate cloud resources. Virtual machine migration due to vehicle mobility is solved based on a resource reservation scheme.

Traffic Signal Control with Connected Vehicles

Abstract: The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a micro...

Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction

Abstract: Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.

Cooperative Positioning for Vehicular Networks: Facts and Future

Abstract: Intelligent transportation systems (ITSs) are increasingly being considered to mitigate the impacts of road transportation, including road injuries, energy waste, and environmental pollution. Vehicular positioning is a fundamental part of many ITS applications. Although global navigation satellite systems (GNSSs), e.g., Global Positioning System (GPS), are applicable for navigation and fleet management, the accuracy and availability of GNSSs do not meet the requirements for some applications, including collision avoidance or lane-level positioning. Cooperative positioning (CP) based on vehicular communications is an approach to tackle these shortcomings. The applicability of vehicular CP techniques proposed in the literature is questionable due to viability issues, including internode distance estimation, which is an important part of many CP techniques. Conventional CP systems such as differential GPS (DGPS) and other augmentation systems are also effectively incapable of addressing the given ITS applications. In this paper, modern and conventional CP systems are discussed, and the viability of radio ranging/range rating and constraints of vehicular communications as main pieces of modern CP systems are investigated. The general performance boundaries for modern CP systems are explained, as is the gap existing between the positioning accuracy required for crucial ITS applications and what modern CP can provide. This is followed by introduction of a novel trend for vehicular CP research, which is a potential reliable solution using a modified concept of real-time kinematic (RTK) GPSs for vehicular environments.

Review of Microscopic Lane-Changing Models and Future Research Opportunities

Abstract: Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.

Context-Aware Driver Behavior Detection System in Intelligent Transportation Systems

Abstract: Vehicular ad hoc networks (VANETs) have emerged as an application of mobile ad hoc networks (MANETs), which use dedicated short-range communication (DSRC) to allow vehicles in close proximity to communicate with each other or to communicate with roadside equipment. Applying wireless access technology in vehicular environments has led to the improvement of road safety and a reduction in the number of fatalities caused by road accidents through development of road safety applications and facilitation of information sharing between moving vehicles regarding the road. This paper focuses on developing a novel and nonintrusive driver behavior detection system using a context-aware system in VANETs to detect abnormal behaviors exhibited by drivers and to warn other vehicles on the road to prevent accidents from happening. A five-layer context-aware architecture is proposed, which is able to collect contextual information about the driving environment, to perform reasoning about certain and uncertain contextual information, and to react upon that information. A probabilistic model based on dynamic Bayesian networks (DBNs) in real time, inferring four types of driving behavior (normal, drunk, reckless, and fatigue) by combining contextual information about the driver, the vehicle, and the environment, is presented. The dynamic behavior model can capture the static and the temporal aspects related to the behavior of the driver, thus leading to robust and accurate behavior detection. The evaluation of behavior detection using synthetic data proves the validity of our model and the importance of including contextual information about the driver, the vehicle, and the environment.

Probabilistic online POMDP decision making for lane changes in fully automated driving

Abstract: The Stadtpilot project aims at fully automated driving on Braunschweig's inner city ring road. The TU Braunschweig's research vehicle “Leonie” is one of the first vehicles having the ability of fully automated driving in real urban traffic scenarios. This paper shows our decision making approach for performing lane changes while driving fully automated in urban environments. We apply an online Partially Observable Markov Decision Process (POMDP) to accommodate inevitable sensor noise to be faced in urban traffic scenarios. In this paper we propose a two step algorithm to keep the complexity of the POMDP low enough for real-time decision making while driving. The presented approach has been integrated in our vehicle and was evaluated in real urban traffic.

A Survey on Intelligent Transportation Systems

Abstract: Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. The idea of virtual technologies integration is a novel in transportation field and it plays a vital part to overcome the issues in global world. This paper tackles the great variety of Intelligent Transport System applications, technologies and its different areas. The objective of this literature review is to integrate and synthesize some areas and applications, technologies discuss with all prospects. Furthermore, this research focuses on a wide field named Intelligent Transport Systems, discussed its wide applications, used technologies and its usage in different areas respectively.

Hardware and embedded security in the context of internet of things

Abstract: Internet of Things (IoT) is the interconnection of a large number of resource-constrained devices such as sensors, actuators, and nodes that generate large volumes of data which are then processed into useful actions in areas such as home and building automation, intelligent transportation and connected vehicles, industrial automation, smart healthcare, smart cities, and others. Important challenges remain to fulfill the IoT vision including data provenance and integrity, trust management, identity management, and privacy. We describe how embedded and hardware security approaches can be the basis to address these security challenges.

Mapping and localization using GPS, lane markings and proprioceptive sensors

Abstract: Estimating the pose in real-time is a primary function for intelligent vehicle navigation. Whilst different solutions exist, most of them rely on the use of high-end sensors. This paper proposes a solution that exploits an automotive type L1-GPS receiver, features extracted by low-cost perception sensors and vehicle proprioceptive information. A key idea is to use the lane detection function of a video camera to retrieve accurate lateral and orientation information with respect to road lane markings. To this end, lane markings are mobile-mapped by the vehicle itself during a first stage by using an accurate localizer. Then, the resulting map allows for the exploitation of camera-detected features for autonomous real-time localization. The results are then combined with GPS estimates and dead-reckoning sensors in order to provide localization information with high availability. As L1-GPS errors can be large and are time correlated, we study in the paper several GPS error models that are experimentally tested with shaping filters. The approach demonstrates that the use of low-cost sensors with adequate data-fusion algorithms should lead to computer-controlled guidance functions in complex road networks.

Routing systems to extend the driving range of electric vehicles

Abstract: This study develops a more accurate range prediction for electric vehicles (EVs) resulting in a routing system that could extend the driving range of EVs through calculating the minimum energy route to a destination, based on topography and traffic conditions of the road network. Energy expenditure of EVs under different conditions is derived using high-resolution real-world data from the SwitchEV trial. The SwitchEV trial has recorded the second-by-second driving events of 44 all-electric vehicles covering a distance of over 400 000 miles across the North East of England, between March 2010 and May 2013. Linear models are used to determine the energy expenditure equations and Dijkstra's graph search algorithm is used to find the route minimising energy consumption. The results from this study are being used to better inform the decisions of the smart navigation and eco-driving assist systems in EVs, thus improving the intelligent transport systems provisions for EV drivers. The outputs of the research are twofold: providing more accurate estimations of available range and supporting drivers' optimisation of energy consumption and as a result extending their driving range. Both outputs could help mitigate range anxiety and make EVs a more attractive proposition to potential customers.

A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling

Abstract: Real-time traffic speed estimation is a fundamental task for urban traffic management centers and is often a critical element of Intelligent Transportation Systems (ITS). For this purpose, various sensors are used to collect traffic information. For many applications, the information provided by individual sensors is incomplete, inaccurate and/or unreliable. Therefore, a fusion based estimate provides a more effective approach towards traffic speed estimation. In this paper, seven multi-sensor data fusion-based estimation techniques are investigated. All methods are implemented and compared in terms of their ability to fuse data from loop detectors and probe vehicles to accurately estimate freeway traffic speed. For the purposes of a rigorous comparison, data are generated from a microsimulation model of a major freeway in the Greater Toronto Area (GTA). The microsimulation model includes loop detectors and a newly implemented traffic monitoring system that detects Bluetooth-enabled devices traveling past roadside Bluetooth receivers, allowing for an automated method of probe vehicle data collection. To establish the true traffic speed that each fusion method attempts to estimate, all vehicles in the microsimulation model are equipped with GPS devices. Results show that most data fusion techniques improve accuracy over single sensor approaches. Furthermore, the analysis shows that the improvement by data fusion depends on the technique, the number of probe vehicles, and the traffic conditions.

Adaptive Kalman filtering for multi-step ahead traffic flow prediction

Abstract: Given the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring.

A Study of Wireless Sensor Networks for Urban Traffic Monitoring: Applications and Architectures

Abstract: With the constant increasing of vehicular traffic around the world, especially in urban areas, existing traffic management solutions become inefficient. This can be clearly seen in our life through persistent traffic jam and rising number of accidents. Wireless sensor networks (WSN) based intelligent transportation systems (ITS) have emerged as a cost effective technology that bear a pivotal potential to overcome these difficulties. This technology enables a new broad range of smart city applications around urban sensing including traffic safety, traffic congestion control, road state monitoring, vehicular warning services, and parking management. This manuscript gives a comprehensive review on WSN based ITS solutions. The main contribution of this paper is to classify current WSNs based ITS projects from the application perspective, with discussions on the fulfillment of the application requirements.

Building Accurate 3D Spatial Networks to Enable Next Generation Intelligent Transportation Systems

Abstract: The use of accurate 3D spatial network models can enable substantial improvements in vehicle routing. Notably, such models enable eco-routing, which reduces the environmental impact of transportation. We propose a novel filtering and lifting framework that augments a standard 2D spatial network model with elevation information extracted from massive aerial laser scan data and thus yields an accurate 3D model. We present a filtering technique that is capable of pruning irrelevant laser scan points in a single pass, but assumes that the 2D network fits in internal memory and that the points are appropriately sorted. We also provide an external-memory filtering technique that makes no such assumptions. During lifting, a triangulated irregular network (TIN) surface is constructed from the remaining points. The 2D network is projected onto the TIN, and a 3D network is constructed by means of interpolation. We report on a large-scale empirical study that offers insight into the accuracy, efficiency, and scalability properties of the framework.

Urban and social sensing for sustainable mobility in smart cities

Abstract: In this contribution, we describe the SMARTY project The project is funded by the Tuscany Region and aims to develop innovative services for sustainable transport and mobility in smart cities These services are based on data collected by environmental and social sensors: such data are pre-processed and analysed by data mining techniques for determining useful information such as state of the traffic flow and parking lots, special events, demonstrations and accidents All this information is used by SMARTY to suggest optimal routes to the users, taking also the multi-modality into account

A Graph-Based Cooperative Scheduling Scheme for Vehicular Networks

Abstract: Vehicle-to-vehicle (V2V) communications are considered to be a significant step forward toward a highly secure and efficient intelligent transportation system. In this paper, we propose the use of graph theory to formulate the problem of cooperative communications scheduling in vehicular networks. In lieu of exhaustive search with intractable complexity for the maximum sum rate (MSR), we propose a bipartite-graph-based (BG) scheduling scheme to allocate the vehicle-to-infrastructure (V2I) and V2V links for both single-hop and dual-hop communications. The Kuhn–Munkres (KM) algorithm is adopted to solve the problem of maximum weighted matching (MWM) of the constructed BG. Simulation results indicate that the proposed scheme performs extremely close to the optimal scheme and results in better fairness among vehicle users with considerably lower computational complexity. Moreover, cooperative communications can improve both the throughput and spectral efficiency (SE) of vehicular networks.

Day-to-day dynamic models for intelligent transportation systems design and appraisal

Abstract: Transportation Supply Design (TSD) with demand assignment provides a powerful framework to support project appraisal, since modifications of existing and/or introduction of new transport facilities and/or services may greatly affect traveller behaviour, concerning path choice at least. TSD also includes the Design of Intelligent Transportation Systems (ITSs), such as Advanced Traveller Information Systems (ATISs) or Advanced Driver Assistance Systems (ADASs). Solution approaches available for all the above problems are based on user equilibrium (UE) assignment, with either Wardrop or (less frequently) probabilistic path choice models. Still, optimization of transportation supply under equilibrium assumption may not guarantee that an effective solution is obtained; indeed the system may not evolve towards the equilibrium state, if this state is not stable. Thus results of project appraisals based on equilibrium assignment only may be misleading. On the other hand, day-to-day dynamic models provide a more general approach to demand assignment, including as special cases equilibrium state. This paper aims at supporting this conclusion, by describing an extension of a deterministic process model to include the evolution over time of the total user surplus. According to this approach a project appraisal should be based both on the value of user surplus and its stability over time. A simple but effective application shows that the proposed approach can be applied to model the effect of ITS. Results for a small network show that an accurate design of ITS based on the effects on total user surplus requires a day-to-day dynamic analysis.

The 4 th International Conference on Ambient Systems, Networks and Technologies (ANT 2013) A Study of Wireless Sensor Networks for Urban Traffic Monitoring: Applications and Architectures

Abstract: With the constant increasing of vehicular traffic around the world, especially in urban areas, existing traffic management solutions become inefficient. This can be clearly seen in our life through persistent traffic jam and rising number of accidents. Wireless sensor networks (WSN) based intelligent transportation systems (ITS) have emerged as a cost effective technology that bear a pivotal potential to overcome these difficulties. This technology enables a new broad range of smart city applications around urban sensing including traffic safety, traffic congestion control, road state monitoring, vehicular warning services, and parking management. This manuscript gives a comprehensive review on WSN based ITS solutions. The main contribution of this paper is to classify current WSNs based ITS projects from the application perspective, with discussions on the fulfillment of the application requirements. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]

The Evolution of Connected Vehicle Technology: From Smart Drivers to Smart Cars to....Self-Driving Cars.

Abstract: In this article, the author discusses the evolution of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technology, which promise to impact the travel scenes in the United States and Europe. The information technology that underlies intelligent transportation systems (ITS), global positioning system (GPS), and in-vehicle technologies now provides the framework for these connected-vehicle (CV) technologies. V2V technologies will allow roadway vehicles to communicate with each other, creating active safety systems that can prevent crashes and reduce traffic injuries and fatalities. The article describes the Connected Vehicle Safety Pilot Model Deployment Program, the world's largest real-world test of dedicated short-range communication (DSRC)-based connected vehicle communication technology. Other topics highlighted in this article include the National Highway Traffic Safety Administration (NHTSA)'s anticipated regulatory involvement in connected vehicle technology, the Maricopa County Department of Transportation's SMARTDrive Program, the international deployment of cooperative intelligent transportation systems between Europe and the United States, and connected vehicle technology developments in the European Union.

Potentials and limitations of Green Light Optimal Speed Advisory systems

Abstract: The reduction of CO2 emissions is one of the most anticipated features of future transportation systems. Smart traffic lights are believed to contribute to achieving this by either adapting their signal program or by informing approaching drivers. In this paper we investigate the potentials and limitations of the latter, that is, Green Light Optimal Speed Advisory (GLOSA) systems in a realistic, large scale simulation study. We examine the impact of different equipment rates of both traffic lights and vehicles on environmental related metrics but also study how these systems can increase the comfort for drivers by reducing waiting times and the number of stops. We find that at low traffic densities these systems can meet all their goals and lower CO2 emissions by up to 11.5% whereas in dense traffic several side-effects could be observed, including overall longer waiting times and even higher CO2 emissions for unequipped vehicles.

A Survey on Security in Vehicular Ad Hoc Networks

Abstract: Vehicular Ad-hoc Networks (VANETs) are the most prominent enabling network technology for Intelligent Transportation Systems. VANETs provide many new exciting applications and opportunities albeit transportation safety and facilitation applications are their core drivers. Security of vehicular networks remains the most significant concern in VANETs deployment – because it is mandatory to assure public and transportation safety. In this paper, we review the various dimensions of VANETs security including security threats, challenges in providing security in vehicular networks environment, requirements and attributes of security solutions. We also provide taxonomy and critically review of the notable security solutions – available for VANETs in literature.