Showing papers on "Intelligent transportation system published in 2017"

LSTM network: a deep learning approach for short-term traffic forecast

Abstract: Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges

Abstract: There could be no smart city without a reliable and efficient transportation system. This necessity makes the ITS a key component of any smart city concept. While legacy ITS technologies are deployed worldwide in smart cities, enabling the next generation of ITS relies on effective integration of connected and autonomous vehicles, the two technologies that are under wide field testing in many cities around the world. Even though these two emerging technologies are crucial in enabling fully automated transportation systems, there is still a significant need to automate other road and transportation components. To this end, due to their mobility, autonomous operation, and communication/processing capabilities, UAVs are envisaged in many ITS application domains. This article describes the possible ITS applications that can use UAVs, and highlights the potential and challenges for UAV-enabled ITS for next-generation smart cities.

LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications

Abstract: This article provides an overview of the long-term evolution-vehicle (LTE-V) standard supporting sidelink or vehicle-to-vehicle (V2V) communications using LTE's direct interface named PC5 in LTE. We review the physical layer changes introduced under Release 14 for LTE-V, its communication modes 3 and 4, and the LTE-V evolutions under discussion in Release 15 to support fifth-generation (5G) vehicle-to-everything (V2X) communications and autonomous vehicles' applications. Modes 3 and 4 support direct V2V communications but differ on how they allocate the radio resources. Resources are allocated by the cellular network under mode 3. Mode 4 does not require cellular coverage, and vehicles autonomously select their radio resources using a distributed scheduling scheme supported by congestion control mechanisms. Mode 4 is considered the baseline mode and represents an alternative to 802.11p or dedicated shortrange communications (DSRC). In this context, this article also presents a detailed analysis of the performance of LTE-V sidelink mode 4, and proposes a modification to its distributed scheduling.

Blockchain-Based Dynamic Key Management for Heterogeneous Intelligent Transportation Systems

Abstract: As modern vehicle and communication technologies advanced apace, people begin to believe that the Intelligent Transportation System (ITS) would be achievable in one decade. ITS introduces information technology to the transportation infrastructures and aims to improve road safety and traffic efficiency. However, security is still a main concern in vehicular communication systems (VCSs). This can be addressed through secured group broadcast. Therefore, secure key management schemes are considered as a critical technique for network security. In this paper, we propose a framework for providing secure key management within the heterogeneous network. The security managers (SMs) play a key role in the framework by capturing the vehicle departure information, encapsulating block to transport keys and then executing rekeying to vehicles within the same security domain. The first part of this framework is a novel network topology based on a decentralized blockchain structure. The blockchain concept is proposed to simplify the distributed key management in heterogeneous VCS domains. The second part of the framework uses the dynamic transaction collection period to further reduce the key transfer time during vehicles handover. Extensive simulations and analysis show the effectiveness and efficiency of the proposed framework, in which the blockchain structure performs better in term of key transfer time than the structure with a central manager, while the dynamic scheme allows SMs to flexibly fit various traffic levels.

Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City

Abstract: In recent decades, the ad hoc network for vehicles has been a core network technology to provide comfort and security to drivers in vehicle environments. However, emerging applications and services require major changes in underlying network models and computing that require new road network planning. Meanwhile, blockchain widely known as one of the disruptive technologies has emerged in recent years, is experiencing rapid development and has the potential to revolutionize intelligent transport systems. Blockchain can be used to build an intelligent, secure, distributed and autonomous transport system. It allows better utilization of the infrastructure and resources of intelligent transport systems, particularly effective for crowdsourcing technology. In this paper, we proposes a vehicle network architecture based on blockchain in the smart city (Block-VN). Block-VN is a reliable and secure architecture that operates in a distributed way to build the new distributed transport management system. We are considering a new network system of vehicles, Block-VN, above them. In addition, we examine how the network of vehicles evolves with paradigms focused on networking and vehicular information. Finally, we discuss service scenarios and design principles for Block-VN.

Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals

Abstract: Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish. Incorporating additional spatio-temporal information is vital for solving the challenging re-identification task. Existing vehicle re-identification methods ignored or used oversimplified models for the spatio-temporal relations between vehicle images. In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results. Given a pair of vehicle images with their spatiotemporal information, a candidate visual-spatio-temporal path is first generated by a chain MRF model with a deeply learned potential function, where each visual-spatiotemporal state corresponds to an actual vehicle image with its spatio-temporal information. A Siamese-CNN+Path- LSTM model takes the candidate path as well as the pairwise queries to generate their similarity score. Extensive experiments and analysis show the effectiveness of our proposed method and individual components.

Cooperative Intelligent Transport Systems in Europe: Current Deployment Status and Outlook

Abstract: The cooperative intelligent transport system (C-ITS) (also known as connected vehicle technology in the United States) is an application using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, at a carrier frequency of 5.9 GHz, to increase road traffic safety and efficiency in Europe. In this article, we hope to shed light on the current status of the C-ITS in Europe and the activities that must be accomplished before deployment can commence in 2019, the date announced by the CAR-2-CAR Communication Consortium (C2C-CC). There is immense activity regarding the launch of the C-ITS in Europe, and the automotive industry is also currently planning for the future.

Efficient Privacy-Preserving Dual Authentication and Key Agreement Scheme for Secure V2V Communications in an IoV Paradigm

Abstract: The Internet of Vehicles (IoV) aims to provide a new convenient, comfortable, and safe driving way, and in turn enables intelligent transportation through wireless communications among road-side units, on-board units (OBUs), phones, and other devices inside a vehicle. However, significantly increasing reliance on wireless communication, control, and computing technology makes IoV more vulnerable to potential attacks, such as remote intrusion, control, and trajectory tracking. Therefore, efficient authentication solutions preventing unauthorized visitors need to be addressed to cope with these issues. Hence, in this paper we focus on the security and privacy-preserving by developing a dual authentication scheme for IoV according to its different scenarios. First, the OBU self-generates an anonymous identity and temporary encryption key to open an authentication session. Second, the legitimacy of the vehicle’s real and anonymous identity can be verified by trust authority (TA). After that, the vehicle’s reputation is evaluated according to its history interactive behavior and the session key for V2V can be finally established. There are three major advantages, including privacy-preserving and security enhancement without a burden of key management in the condition of acceptable time delay range, introducing trust evaluation into authentication protocol, as well as considering the vehicle behavior attributes in the new reputation evaluation method. In addition, we also prove the correctness of this scheme using the Burrows–Abadi–Needham (BAN) logic, and the performance comparison against the existing schemes is given as well.

A Survey on Reinforcement Learning Models and Algorithms for Traffic Signal Control

Abstract: Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.

Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things

Abstract: In recent years, there has been a paradigm shift in Internet of Things (IoT) from centralized cloud computing to edge computing (or fog computing). Developments in ICT have resulted in the significant increment of communication and computation capabilities of embedded devices and this will continue to increase in coming years. However, existing paradigms do not utilize low-level devices for any decision-making process. In fact, gateway devices are also utilized mostly for communication interoperability and some low-level processing. In this paper, we have proposed a new computing paradigm, named Edge Mesh, which distributes the decision-making tasks among edge devices within the network instead of sending all the data to a centralized server. All the computation tasks and data are shared using a mesh network of edge devices and routers. Edge Mesh provides many benefits, including distributed processing, low latency, fault tolerance, better scalability, better security, and privacy. These benefits are useful for critical applications, which require higher reliability, real-time processing, mobility support, and context awareness. We first give an overview of existing computing paradigms to establish the motivation behind Edge Mesh. Then, we describe in detail about the Edge Mesh computing paradigm, including the proposed software framework, research challenges, and benefits of Edge Mesh. We have also described the task management framework and done a preliminary study on task allocation problem in Edge Mesh. Different application scenarios, including smart home, intelligent transportation system, and healthcare, are presented to illustrate the significance of Edge Mesh computing paradigm.

5G Software Defined Vehicular Networks

Abstract: With the emergence of 5G mobile communication systems and software defined networks, not only could the performance of vehicular networks be improved, but also new applications of vehicular networks are required by future vehicles (e.g., pilotless vehicles). To meet requirements of intelligent transportation systems, a new vehicular network architecture integrated with 5G mobile communication technologies and software defined networking is proposed in this article. Moreover, fog cells have been proposed to flexibly cover vehicles and avoid frequent handover between vehicles and roadside units. Based on the proposed 5G software defined vehicular networks, the transmission delay and throughput are analyzed and compared. Simulation results indicate that there is a minimum transmission delay of 5G software defined vehicular networks considering different vehicle densities. Moreover, the throughput of fog cells in 5G software defined vehicular networks is better than the throughput of traditional transportation management systems.

REPLACE: A Reliable Trust-Based Platoon Service Recommendation Scheme in VANET

Abstract: The fast development of intelligent transportation has paved the way for innovative techniques for highways, and an entirely new driving pattern of highway vehicular platooning might offer a solution to the persistent problem of road congestion, travel comfort, and road safety. In this vehicular platooning system, a platoon head vehicle provides platoon service to its user vehicles. However, some badly behaved platoon head vehicles may put the platoon in danger, which makes it crucial for user vehicles to distinguish and avoid them. In this paper, we propose a reliable trust-based platoon service recommendation scheme, which is called REPLACE, to help the user vehicles avoid choosing badly behaved platoon head vehicles. Specifically, at the core of REPLACE, a reputation system is designed for the platoon head vehicles by collecting and modeling their user vehicle's feedback. Then, an iterative filtering algorithm is designed to deal with the untruthful feedback from user vehicles. A detailed security analysis is given to show that our proposed REPLACE scheme is secure and robust against badmouth, ballot-stuffing, newcomers, and on–off attacks that exist in vehicular ad hoc networks (VANETs). In addition, we conduct extensive experiments to demonstrate the correctness, accuracy, and robustness of our proposed scheme.

An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition

Abstract: Video-based traffic sign detection, tracking, and recognition is one of the important components for the intelligent transport systems. Extensive research has shown that pretty good performance can be obtained on public data sets by various state-of-the-art approaches, especially the deep learning methods. However, deep learning methods require extensive computing resources. In addition, these approaches mostly concentrate on single image detection and recognition task, which is not applicable in real-world applications. Different from previous research, we introduce a unified incremental computational framework for traffic sign detection, tracking, and recognition task using the mono-camera mounted on a moving vehicle under non-stationary environments. The main contributions of this paper are threefold: 1) to enhance detection performance by utilizing the contextual information, this paper innovatively utilizes the spatial distribution prior of the traffic signs; 2) to improve the tracking performance and localization accuracy under non-stationary environments, a new efficient incremental framework containing off-line detector, online detector, and motion model predictor together is designed for traffic sign detection and tracking simultaneously; and 3) to get a more stable classification output, a scale-based intra-frame fusion method is proposed. We evaluate our method on two public data sets and the performance has shown that the proposed system can obtain results comparable with the deep learning method with less computing resource in a near-real-time manner.

Effects of Heterogeneous Mobility on D2D- and Drone-Assisted Mission-Critical MTC in 5G

Abstract: mcMTC is starting to play a central role in the industrial Internet of Things ecosystem and have the potential to create high-revenue businesses, including intelligent transportation systems, energy/ smart grid control, public safety services, and high-end wearable applications. Consequently, in the 5G of wireless networks, mcMTC have imposed a wide range of requirements on the enabling technology, such as low power, high reliability, and low latency connectivity. Recognizing these challenges, the recent and ongoing releases of LTE systems incorporate support for lowcost and enhanced coverage, reduced latency, and high reliability for devices at varying levels of mobility. In this article, we examine the effects of heterogeneous user and device mobility -- produced by a mixture of various mobility patterns -- on the performance of mcMTC across three representative scenarios within a multi-connectivity 5G network. We establish that the availability of alternative connectivity options, such as D2D links and drone-assisted access, helps meet the requirements of mcMTC applications in a wide range of scenarios, including industrial automation, vehicular connectivity, and urban communications. In particular, we confirm improvements of up to 40 percent in link availability and reliability with the use of proximate connections on top of the cellular-only baseline.

Modeling and Design of Millimeter-Wave Networks for Highway Vehicular Communication

Abstract: Connected and autonomous vehicles will play a pivotal role in future intelligent transportation systems and smart cities, in general. High-speed and low-latency wireless communication links will allow municipalities to warn vehicles against safety hazards, as well as support cloud-driving solutions to drastically reduce traffic jams and air pollution. To achieve these goals, vehicles need to be equipped with a wide range of sensors generating and exchanging high rate data streams. Recently, millimeter wave (mmWave) techniques have been introduced as a means of fulfilling such high data rate requirements. In this paper, we model a highway communication network and characterize its fundamental link budget metrics. In particular, we specifically consider a network where vehicles are served by mmWave base stations (BSs) deployed alongside the road. To evaluate our highway network, we develop a new theoretical model that accounts for a typical scenario where heavy vehicles (such as buses and lorries) in slow lanes obstruct line-of-sight (LOS) paths of vehicles in fast lanes and, hence, act as blockages. Using tools from stochastic geometry, we derive approximations for the signal-to-interference-plus-noise Ratio (SINR) outage probability, as well as the probability that a user achieves a target communication rate (rate coverage probability). Our analysis provides new design insights for mmWave highway communication networks. In considered highway scenarios, we show that reducing the horizontal beamwidth from $90^\circ$ to $30^\circ$ determines a minimal reduction in the SINR outage probability (namely $4 \cdot 10^{-2}$ at maximum). Also, unlike bidimensional mmWave cellular networks, for small BS densities (namely one BS every $500\,\text{m}$ ) it is still possible to achieve an SINR outage probability smaller than 0.2.

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Abstract: Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and the use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines’ computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to the different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions, including social transportation, recommender systems, and policy implications.

Intelligent Transportation System(ITS): Concept, Challenge and Opportunity

Abstract: Over the past decades, Intelligent Transportation Systems (ITS) have developed and deployed in order to improve trans-portation safety and mobility, reduces environmental impact, promotes sustainable transportation development and enhances productivity. ITS combines high technology and improvements in information systems, communication, sensors, controllers and advanced mathematical methods with the conventional world of transportation infrastructure. As an inter-disciplinary research field, it is difficult to have a clear picture about the whole system for a novice researcher. This paper 1) proposes an ITS architecture, which is based on the requirement and technology at the present stage, 2) sort out the bottleneck issue that lie behind intelligent transportation research, 3) explores the future prospect of the ITS and research focus as new technologies become available.

A Real-Time En-Route Route Guidance Decision Scheme for Transportation-Based Cyberphysical Systems

Abstract: In transportation-based cyberphysical systems (TCPS), also known as intelligent transportation systems (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining optimal routes for their travels. To determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information collected by vehicular networks to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and to reduce travel time and fuel consumption. DEDR considers real-time traffic information generation and transmission by vehicular networks. Based on the shared traffic information, DEDR introduces the trust probability to predict traffic conditions and to dynamically, en route, determine alternative optimal routes. DEDR also considers multiple metrics to comprehensively assess traffic conditions so that drivers can determine the optimal route with a preference to these metrics during travel. DEDR considers effects of external factors (bad weather, incidents, etc.) on traffic conditions as well. Through a combination of extensive theoretical analysis and simulation experiments, our data show that DEDR can greatly increase traffic efficiency in terms of time efficiency, balancing efficiency, and fuel efficiency, in comparison with existing schemes.

Short-term traffic flow prediction with LSTM recurrent neural network

Abstract: Accurate and timely short-term traffic flow prediction plays an important role in intelligent transportation management and control. Traffic flow prediction has a long history and is still a difficult problem due to intrinsically highly nonlinear and stochastic characteristics of complex transportation systems. In this paper, we employ the long short-term memory (LSTM) recurrent neural network to analyze the effects of various input settings on the LSTM prediction performances. Flow, speed, and occupancy at the same detector station are used as inputs to predict traffic flow. The results show that the inclusion of occupancy/speed information may help to enhance the performance of the model overall. Further, we include as inputs traffic variables from the upstream and/or downstream detector stations for traffic flow prediction. The evaluation of such spatial-temporal input interactions show that the inclusion of both downstream and upstream traffic information is useful in improving prediction accuracy.

Resource Allocation for Cellular-based Inter-Vehicle Communications in Autonomous Multiplatoons

Abstract: With the significant population growth in megacities everywhere, traffic congestion is becoming a severe impediment, leading to long travel delays and large economic loss on a global scale. Platooning is a promising intelligent transportation framework that can improve road capacity, on-road safety, and fuel efficiency. Furthermore, enabling inter-vehicle communications within a platoon and among platoons (in a multiplatoon) can potentially enhance platoon control by keeping constant inter-vehicle and inter-platoon distances. However, an efficient resource allocation (RA) approach is required for the timely and successful delivery of inter-vehicle information within multiplatoons. In this paper, subchannel allocation scheme and power control mechanism are proposed for LTE-based inter-vehicle communications in a multiplatooning scenario. We jointly consider the evolved multimedia broadcast multicast services and device-to-device (D2D) multicast communications to enable intra- and inter-platoon communications such that a desired tradeoff between the required cellular resources and the imposed communication delay can be achieved. Simulation results are given to demonstrate that the proposed approaches can reduce the communication delay comparing to D2D-unicast based RA scheme, especially in a multiplatoon scenario with a large number of vehicles.

Architecture and key technologies for Internet of Vehicles:a survey

Abstract: In recent years, IoV (Internet of Vehicles) has become one of the most active research fields in network and intelligent transportation system. As an open converged network, IoV plays an important role in solving various driving and traffic problems by advanced information and communications technology. We review the existing notions of IoV from different perspectives. Then, we provide our notion from a network point of view and propose a novel IoV architecture with four layers. Particularly, a novel layer named coordinative computing control layer is separated from the application layer. The novel layer is used for solving the coordinative computing and control problems for human-vehicle-environment. After summarizing the key technologies in IoV architecture, we construct a VV (Virtual Vehicle), which is an integrated image of driver and vehicle in networks. VVs can interact with each other in cyber space by providing traffic service and sharing sensing data coordinately, which can solve the communication bottleneck in physical space. Finally, an extended IoV architecture based on VVs is proposed.

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling☆

Abstract: Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protege web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.

Sensor Fusion Methodology for Vehicle Detection

Abstract: A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriageways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems.

Big data analytics architecture for real-time traffic control

Abstract: The advent of Big Data has triggered disruptive changes in many fields including Intelligent Transportation Systems (ITS). The emerging connected technologies created around ubiquitous digital devices have opened unique opportunities to enhance the performance of the ITS. However, magnitude and heterogeneity of the Big Data are beyond the capabilities of the existing approaches in ITS. Therefore, there is a crucial need to develop new tools and systems to keep pace with the Big Data proliferation. In this paper, we propose a comprehensive and flexible architecture based on distributed computing platform for real-time traffic control. The architecture is based on systematic analysis of the requirements of the existing traffic control systems. In it, the Big Data analytics engine informs the control logic. We have partly realized the architecture in a prototype platform that employs Kafka, a state-of-the-art Big Data tool for building data pipelines and stream processing. We demonstrate our approach on a case study of controlling the opening and closing of a freeway hard shoulder lane in microscopic traffic simulation.

Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach

Abstract: Traffic flow prediction is an essential component of the intelligent transportation management system (ITS). This paper combines recurrent neural network and gated recurrent unit (GRU) to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, our method improves predictive accuracy and also decreases the prediction error rate. To our best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under Gated Recurrent Unit (GRU) based deep learning framework.

Smart Parking Guidance, Monitoring and Reservations: A Review

Abstract: As the urban population is increasing, more and more cars are circulating in the city to search for parking spaces which contributes to the global problem of traffic congestion. To alleviate the parking problems, smart parking systems must be implemented. In this paper, the background on parking problems is introduced and relevant algorithms, systems, and techniques behind the smart parking are reviewed and discussed. This paper provides a good insight into the guidance, monitoring and reservations components of the smart car parking and directions to the future development.

SA-EAST: Security-Aware Efficient Data Transmission for ITS in Mobile Heterogeneous Cloud Computing

Abstract: The expected advanced network explorations and the growing demand for mobile data sharing and transferring have driven numerous novel applications in Cyber-Physical Systems (CPSs), such as Intelligent Transportation Systems (ITSs). However, current ITS implementations are restricted by the conflicts between security and communication efficiency. Focusing on this issue, this article proposes a Security-Aware Efficient Data Sharing and Transferring (SA-EAST) model, which is designed for securing cloud-based ITS implementations. In applying this approach, we aim to obtain secure real-time multimedia data sharing and transferring. Our experimental evaluation has shown that our proposed model provides an effective performance in securing communications for ITS.

Design of Lightweight Authentication and Key Agreement Protocol for Vehicular Ad Hoc Networks

Abstract: Due to the widespread popularity in both academia and industry, vehicular ad hoc networks (VANETs) have been used in a wide range of applications starting from intelligent transportation to e-health and itinerary planning. This paper proposes a new decentralized lightweight authentication and key agreement scheme for VANETs. In the proposed scheme, there are three types of mutual authentications: 1) between vehicles; 2) between vehicles and their respective cluster heads; and 3) between cluster heads and their respective roadside units. Apart from these authentications, the proposed scheme also maintains secret keys between roadside units for their secure communications. The rigorous formal and informal security analysis shows that the proposed scheme is capable to defend various malicious attacks. Moreover, the ns-2 simulation demonstrates the practicability of the proposed scheme in VANET environment.