Showing papers on "Intelligent transportation system published in 2016"

A Review of Motion Planning Techniques for Automated Vehicles

Abstract: Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey

Abstract: Vehicle-to-anything (V2X) communications refer to information exchange between a vehicle and various elements of the intelligent transportation system (ITS), including other vehicles, pedestrians, Internet gateways, and transport infrastructure (such as traffic lights and signs). The technology has a great potential of enabling a variety of novel applications for road safety, passenger infotainment, car manufacturer services, and vehicle traffic optimization. Today, V2X communications is based on one of two main technologies: dedicated short-range communications (DSRC) and cellular networks. However, in the near future, it is not expected that a single technology can support such a variety of expected V2X applications for a large number of vehicles. Hence, interworking between DSRC and cellular network technologies for efficient V2X communications is proposed. This paper surveys potential DSRC and cellular interworking solutions for efficient V2X communications. First, we highlight the limitations of each technology in supporting V2X applications. Then, we review potential DSRC-cellular hybrid architectures, together with the main interworking challenges resulting from vehicle mobility, such as vertical handover and network selection issues. In addition, we provide an overview of the global DSRC standards, the existing V2X research and development platforms, and the V2X products already adopted and deployed in vehicles by car manufactures, as an attempt to align academic research with automotive industrial activities. Finally, we suggest some open research issues for future V2X communications based on the interworking of DSRC and cellular network technologies.

Cooperative Intersection Management: A Survey

Abstract: Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

Abstract: This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

Control of robotic mobility-on-demand systems

Abstract: In this paper we present queueing-theoretical methods for the modeling, analysis, and control of autonomous mobility-on-demand MOD systems wherein robotic, self-driving vehicles transport customers within an urban environment and rebalance themselves to ensure acceptable quality of service throughout the network. We first cast an autonomous MOD system within a closed Jackson network model with passenger loss. It is shown that an optimal rebalancing algorithm minimizing the number of autonomously rebalancing vehicles while keeping vehicle availabilities balanced throughout the network can be found by solving a linear program. The theoretical insights are used to design a robust, real-time rebalancing algorithm, which is applied to a case study of New York City and implemented on an eight-vehicle mobile robot testbed. The case study of New York shows that the current taxi demand in Manhattan can be met with about 8,000 robotic vehicles roughly 70% of the size of the current taxi fleet operating in Manhattan. Finally, we extend our queueing-theoretical setup to include congestion effects, and study the impact of autonomously rebalancing vehicles on overall congestion. Using a simple heuristic algorithm, we show that additional congestion due to autonomous rebalancing can be effectively avoided on a road network. Collectively, this paper provides a rigorous approach to the problem of system-wide coordination of autonomously driving vehicles, and provides one of the first characterizations of the sustainability benefits of robotic transportation networks.

An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control

Abstract: Urban traffic congestion has become a serious issue, and improving the flow of traffic through cities is critical for environmental, social and economic reasons. Improvements in Adaptive Traffic Signal Control (ATSC) have a pivotal role to play in the future development of Smart Cities and in the alleviation of traffic congestion. Here we describe an autonomic method for ATSC, namely, reinforcement learning (RL). This chapter presents a comprehensive review of the applications of RL to the traffic control problem to date, along with a case study that showcases our developing multi-agent traffic control architecture. Three different RL algorithms are presented and evaluated experimentally. We also look towards the future and discuss some important challenges that still need to be addressed in this field.

Platoon based Cooperative Driving Model with Consideration of Realistic Inter-vehicle Communication

Abstract: Recent developments of information and communication technologies (ICT) have enabled vehicles to timely communicate with each other through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Nevertheless, unreliable vehicular communications also introduce packet loss and transmission delay when vehicular kinetic information or control commands are disseminated among vehicles, which brings more challenges in the system modeling and optimization. Currently, no data has been yet available for the calibration and validation of a model for ITS, and most research has been only conducted for a theoretical point of view. Along this line, this paper focuses on the (theoretical) development of a more general (microscopic) traffic model which enables the cooperative driving behavior via a so-called inter-vehicle communication (IVC). To this end, the authors design a consensus-based controller for the cooperative driving system (CDS) considering (intelligent) traffic flow that consists of many platoons moving together. More specifically, the IEEE 802.11p, the de facto vehicular networking standard required to support ITS applications, is selected as the IVC protocols of the CDS, in order to investigate how the vehicular communications affect the features of intelligent traffic flow. This study essentially explores the relationship between IVC and cooperative driving, which can be exploited as the reference for the CDS optimization and design.

Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method

Abstract: Traffic speed prediction is a long-standing and critically important topic in the area of Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. Traffic speed prediction, however, is still in its initial stage without making full use of spatio-temporal traffic information. In light of this, in this paper, we propose a deep learning method with an Error-feedback Recurrent Convolutional Neural Network structure (eRCNN) for continuous traffic speed prediction. By integrating the spatio-temporal traffic speeds of contiguous road segments as an input matrix, eRCNN explicitly leverages the implicit correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback neurons to the recurrent layer, eRCNN learns from prediction errors so as to meet predictive challenges rising from abrupt traffic events such as morning peaks and traffic accidents. Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the strong predictive power of eRCNN in comparison to some state-of-the-art competitors. The necessity of weight pre-training using a transfer learning notion has also been testified. More interestingly, we design a novel influence function based on the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing.

Big Data for Social Transportation

Abstract: Big data for social transportation brings us unprecedented opportunities for resolving transportation problems for which traditional approaches are not competent and for building the next-generation intelligent transportation systems. Although social data have been applied for transportation analysis, there are still many challenges. First, social data evolve with time and contain abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions, and precision are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions in effective utilization of real-time traffic information. Third, social data are connected to cyber and physical spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, such as crowdsourcing, visual analysis, and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and we also suggest a few future research directions for this new social transportation field.

A Survey of Vision-Based Traffic Monitoring of Road Intersections

Abstract: Visual surveillance of dynamic objects, particularly vehicles on the road, has been, over the past decade, an active research topic in computer vision and intelligent transportation systems communities. In the context of traffic monitoring, important advances have been achieved in environment modeling, vehicle detection, tracking, and behavior analysis. This paper is a survey that addresses particularly the issues related to vehicle monitoring with cameras at road intersections. In fact, the latter has variable architectures and represents a critical area in traffic. Accidents at intersections are extremely dangerous, and most of them are caused by drivers' errors. Several projects have been carried out to enhance the safety of drivers in the special context of intersections. In this paper, we provide an overview of vehicle perception systems at road intersections and representative related data sets. The reader is then given an introductory overview of general vision-based vehicle monitoring approaches. Subsequently and above all, we present a review of studies related to vehicle detection and tracking in intersection-like scenarios. Regarding intersection monitoring, we distinguish and compare roadside (pole-mounted, stationary) and in-vehicle (mobile platforms) systems. Then, we focus on camera-based roadside monitoring systems, with special attention to omnidirectional setups. Finally, we present possible research directions that are likely to improve the performance of vehicle detection and tracking at intersections.

Revisiting Street Intersections Using Slot-Based Systems.

Abstract: Since their appearance at the end of the 19th century, traffic lights have been the primary mode of granting access to road intersections. Today, this centuries-old technology is challenged by advances in intelligent transportation, which are opening the way to new solutions built upon slot-based systems similar to those commonly used in aerial traffic: what we call Slot-based Intersections (SIs). Despite simulation-based evidence of the potential benefits of SIs, a comprehensive, analytical framework to compare their relative performance with traffic lights is still lacking. Here, we develop such a framework. We approach the problem in a novel way, by generalizing classical queuing theory. Having defined safety conditions, we characterize capacity and delay of SIs. In the 2-road crossing configuration, we provide a capacity-optimal SI management system. For arbitrary intersection configurations, near-optimal solutions are developed. Results theoretically show that transitioning from a traffic light system to SI has the potential of doubling capacity and significantly reducing delays. This suggests a reduction of non-linear dynamics induced by intersection bottlenecks, with positive impact on the road network. Such findings can provide transportation engineers and planners with crucial insights as they prepare to manage the transition towards a more intelligent transportation infrastructure in cities.

Short-Term Traffic Prediction Based on Dynamic Tensor Completion

Abstract: Short-term traffic prediction plays a critical role in many important applications of intelligent transportation systems such as traffic congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traffic data. In this paper, we present a novel short-term traffic flow prediction approach based on dynamic tensor completion (DTC), in which the traffic data are represented as a dynamic tensor pattern, which is able capture more information of traffic flow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traffic flow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the efficacy of the proposed approach is validated on the experiments of traffic flow prediction, particularly when dealing with incomplete traffic data.

Latent Space Model for Road Networks to Predict Time-Varying Traffic

Abstract: Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamism associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges holistically. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learns the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the superiority of our framework for real-time traffic prediction on large road networks over competitors as well as baseline graph-based LSM's.

Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

Abstract: Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

Intelligent Transport Systems (ITS)

Abstract: The term Intelligent Transport Systems (ITS) refers to any technology applied to transport and infrastructure to transfer information between systems, and to transport users, for improved safety, efficiency and environmental outcomes. This is a fast evolving field that includes stand-alone applications such as traffic management systems, information and warning systems installed in individual vehicles, as well as applications involving vehicle to infrastructure and vehicle to vehicle communications. Many ITS applications combine some or all of the above with Smartphone applications and GPS devices to enable transport users to make informed decisions.

Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication

Abstract: Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are emerging components of intelligent transport systems (ITS) based on which vehicles can drive in a cooperative way and, hence, significantly improve traffic flow efficiency. However, due to the high vehicle mobility, the unreliable vehicular communications such as packet loss and transmission delay can impair the performance of the cooperative driving system (CDS). In addition, the downstream traffic information collected by roadside sensors in the V2I communication may introduce measurement errors, which also affect the performance of the CDS. The goal of this paper is to bridge the gap between traffic flow modelling and communication approaches in order to build up better cooperative traffic systems. To this end, we aim to develop an enhanced cooperative microscopic (car-following) traffic model considering V2V and V2I communication (or V2X for short), and investigate how vehicular communications affect the vehicle cooperative driving, especially in traffic disturbance scenarios. For these purposes, we design a novel consensus-based vehicle control algorithm for the CDS, in which not only the local traffic flow stability is guaranteed, but also the shock waves are supposed to be smoothed. The IEEE 802.11p, the defacto vehicular networking standard, is selected as the communication protocols, and the roadside sensors are deployed to collect the average speed in the targeted area as the downstream traffic reference. Specifically, the imperfections of vehicular communication as well as the measured information noise are taken into account. Numerical results show the efficiency of the proposed scheme. This paper attempts to theoretically investigate the relationship between vehicular communications and cooperative driving, which is needed for the future deployment of both connected vehicles and infrastructure (i.e. V2X).

Comprehensive survey on security services in vehicular ad-hoc networks

Abstract: Vehicular ad-hoc networks (VANETs) are the most hopeful approach to provide safety information and other infotainment applications to both drivers and passengers. VANETs are formed by intelligent vehicles equipped with On Board Units and wireless communication devices. Hence, VANETs become a key component of the intelligent transport system. Even though VANETs are used in enormous number of applications, there are many security challenges and issues that need to be overcome to make VANETs usable in practice. A great deal of study has been done towards it, but security mechanisms in VANETs are not effective. This study provides a summary about the VANET, characteristics and security challenges. This study also provides a summary of some major security attacks on security services such as availability, confidentiality, authentication, integrity and non-repudiation and the corresponding countermeasures to make VANET communications more secure.

How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Abstract: Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.

Advances in vehicular ad-hoc networks (VANETs): Challenges and road-map for future development

Abstract: Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of vehicular ad-hoc networks (VANETs) over the past few years. A vehicular network consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of intelligent transportation system (ITS). Hence, governments, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interesting field of mobile wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well as the ways forward to achieving the long awaited ITS.

Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads

Abstract: This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper for every HEV, where the higher level and the lower level controller share information with each other and solve two different problems that aim at improving its fuel efficiency. The higher level controller of each HEV is considered to utilize traffic light information, through vehicle to infrastructure (V2I) communication, and state information of the vehicles in its near neighborhood, via vehicle to vehicle (V2V) communication. Apart from that, the higher level controller of each HEV uses the recuperation information from the lower level controller and provides it the optimal velocity profile by solving its problem in a model predictive control framework. Each lower level controller uses adaptive equivalent consumption minimization strategy (ECMS) for following their velocity profiles, obtained from the higher level controller, in a fuel efficient manner. In this paper, the vehicles are modeled in Autonomie software and the simulation results are provided in the paper that shows the effectiveness of the proposed control architecture.

SimMobility: A Multi-scale Integrated Agent-Based Simulation Platform

Abstract: Developments in integrated agent-based platform has shown progress, however, most of efforts are based on integrating activity-based demand models with dynamic traffic assignment model. Integration beyond this level is limited and mostly based on loosely coupled mechanism (i.e. manual exchange of data). SimMoblity is a simulation platform that integrates various mobility-sensitive behavioral models within a multi-scale simulation platform that considers land-use, transportation and communication interactions. It particularly focuses on impacts on transportation networks, intelligent transportation services and vehicular emissions, thereby enabling the simulation of a portfolio of technology, policy and investment options under alternative future scenarios. In short, SimMobility encompasses the modeling of millions of agents, from pedestrians to drivers, from phones, traffic lights to GPS probes, from cars to buses and trains, from second-by-second to year-by-year simulations. Simmobility is designed to support the activity-based modeling paradigm. All choices are ultimately tied to the agent?s goal of performing activities on a time scale that can vary from seconds to years. Agents can be grouped in broad ways, from households to firms, and can have varying roles including operators, bus drivers or real-estate agents. Thus, the range of possible decisions is also broad, from travel (e.g. Mode or route choice, driving behaviour) to land-use (e.g. household or firm location choice). This paper describes the SimMobility framework, its key features such as event-based implementation, parallel and distributed architecture and flow of data across three integrated levels. Additionally, application of the whole platform in Singapore context with some details on application of autonomous mobility on demand study is also presented.

Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation

Abstract: While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and under specific conditions, and later on all public roads at all times, a phase transition will occur. Once a sufficient number of autonomous vehicles is deployed, the opportunity for explicit coordination appears. This article treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades.

Introduction to Intelligent Transportation Systems

Abstract: Transportation systems are very important in modern life; therefore, massive research efforts has been devoted to this field of study in the recent past. Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, alleviate the impact of congestion; devising the so-called Intelligent Transportation Systems (ITS) experience. This chapter aims to provide basic concepts and background that is useful for the understanding of this book. An overview of intelligent transportation systems and their applications is presented, followed by a brief discussion of vehicular communications. The chapter also overviews the concepts related to dependability on distributed real-time systems in the scope if ITS.

Multifunctional Transceiver for Future Radar Sensing and Radio Communicating Data-Fusion Platform

Abstract: A unified and reconfigurable multifunctional transceiver for future integrated data-fusion services of radar sensing and radio communication (RadCom) is studied and developed in this paper. This proposed alternative of the state-of-the-art architectures presents an unprecedented integration of all radar sensing and RadCom functions together in a time-division platform. Furthermore, it is capable of offering a positioning function of both moving and static objects with an enhanced resolution in ranging in addition to providing a greater capability of data communication. The design and the performance incompatibilities between radar and radio systems are explored and investigated. A systematic top–bottom approach is presented, which involves the step-by-step methodology, building block design considerations, and the system level simulation. With the purpose of validating the proposed scheme, a low-frequency prototype around the FCC-commissioned dedicated short range communication (DSRC) band is developed, and its performance is evaluated. Since such a unified transceiver can find applications in intelligent transportation infrastructures, the system demonstrator is designed and examined according to the desired specifications of future automotive radar networks. Through various system level measurements, the proposed scheme has demonstrated attractive features in connection with both radar and radio functions. With the radar mode, the added ability of angle detection and the improved range resolution against the previously demonstrated version make the system suitable for driving assistance applications. With the radio mode, the system demonstrator has proved a great capability of communication at a data rate of 25 Mb/s.

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Abstract: Summary Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short-term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.

Time-Optimal Maneuver Planning in Automatic Parallel Parking Using a Simultaneous Dynamic Optimization Approach

Abstract: Autonomous parking has been a widely developed branch of intelligent transportation systems. In autonomous parking, maneuver planning is a crucial procedure that determines how intelligent the entire parking system is. This paper concerns planning time-optimal parallel parking maneuvers in a straightforward, accurate, and purely objective way. A unified dynamic optimization framework is established, which includes the vehicle kinematics, physical restrictions, collision-avoidance constraints, and an optimization objective. Interior-point method (IPM)-based simultaneous dynamic optimization methodology is adopted to solve the formulated dynamic optimization problem numerically. Given that near-feasible solutions have been widely acknowledged to ease optimizing nonlinear programs (NLPs), a critical region-based initialization strategy is proposed to facilitate the offline NLP-solving process, a lookup table-based strategy is proposed to guarantee the on-site planning performance, and a receding-horizon optimization framework is proposed for online maneuver planning. A series of parallel parking cases is tested, and simulation results demonstrate that our proposal is efficient even when the slot length is merely 10.19% larger than the car length. As a unified maneuver planner, our adopted IPM-based simultaneous dynamic optimization method can deal with any user-specified demand provided that it can be explicitly described.

Designing CPS/IoT applications for smart buildings and cities

Abstract: Internet of Things (IoT) and cyber-physical systems (CPS) technologies can be applied to many application domains. Examples include intelligent green house, intelligent transportation system, power distribution grid, smart home, smart building, and smart city. Among these application domains, some of them have been extensively studied, e.g., smart home and intelligent transportation systems. In the meantime, smart buildings and smart cities attract researchers and industries to investigate these two use scenarios. Well-designed IoT/CPS can reduce energy consumption, enhance safety in buildings and cities, or can increase the comfortability in the building. In the last few years, the research communities and industrial partners started to study and investigate these two use scenarios to develop prototype or commercial services for these two scenarios. Although many works have been conducted on these two scenarios, many challenges remain open. In this study, the authors study the development and challenges in five topics. They are middleware, computation model, fault tolerance, quality of data, and virtual run-time environment.

Parallel Transportation Management and Control System and Its Applications in Building Smart Cities

Abstract: Advancements in complexity, complex systems, and the intelligence sciences, particularly smart city technologies, have shown great potential in aiding to ease traffic congestion. The overall approach and the main ideas in building smart transportation for smart cities, particularly ACP (artificial system, computational experiment, and parallel execution)-based parallel transportation management and control systems (PTMS), are presented. PTMS can be expanded to the new generation of intelligent transportation systems. The main components of the proposed architecture include social signal and social traffic, ITS clouds and services, agent-based traffic control, and transportation knowledge automation. Some technical details of these components are discussed. Finally, one case study is introduced, and the effectiveness is analyzed.