Fine-grained Traffic Information Research Articles

FMAA: A Flexible Signal Timing Method for An Isolated Intersection with Conflicting Traffic Flows

Optimizing traffic signal timing can effectively alleviate urban traffic congestion. However, most of the existing signal timing methods struggle to deal with conflicting traffic flows in heavy traffic; therefore, more effective methods are urgently required. In this paper, we propose a flexible signal timing method that combines all-red control and adaptive timing (FMAA) to deal with conflicting traffic flows at an isolated intersection. First, we consider a Vehicle-to-Infrastructure (V2I) communication-based vehicular network environment, in which fine-grained traffic information can be collected by Road Side Units (RSUs) and uploaded to a cloud server for designing signal timing methods. Second, the congestion degree of Conflict Area (CA) is defined and utilized to trigger all-red control in congested cases. Third, the tolerance degree of the Waiting Area (WA) is defined and utilized to perform adaptive timing in other cases. Finally, simulations were conducted using SUMO, and the proposed FMAA method performed better than the comparative methods in terms of average speed, waiting time, and congested vehicles, thus improving traffic efficiency at an isolated intersection with conflicting traffic flows.

PF‐ITS: Intelligent traffic service recommendation based on DeepAFM model

Due to the progressive complexity of traffic networks, the traffic pressure increases sharply and traffic accidents occur frequently, which is largely posed by imprecise traffic information service provided in existing Intelligent traffic service systems (ITSSs). To relieve traffic information trek encountered in complex traffic networks, the personalized traffic information recommendation based on click through rate (CTR) prediction has attracted extensive attention. However, the data sparsity and cold start problems in traditional recommendations hinder their real-time applications in ITSSs. In this paper, the multisource data with different types of context information are utilized to construct a personalized fine-grained recommendation method for intelligent traffic services (PF-ITS), which includes three components: a road condition optimization strategy (RCOS) to capture users' behavior preferences and traffic patterns, an encoder–decoder long short-term memory (LSTM) model to address the data sparsity and cold start problems with rich context information, and an improved DeepFM model based on attention mechanism (DeepAFM) to exert personalized fine-grained traffic information recommendation with embeddings of multisource data. More specifically, the RCOS is proposed to perform coarse-grained route recommendation based on path planning theory, in which the traffic data and drivers' preferences are fully utilized for comprehensive modeling. The encoder–decoder LSTM model is employed for representation learning, in which the traffic sequences and driving behaviors are mapped into dense distributed representations with rich semantic information. The DeepAFM is utilized to achieve effective driving safety guarantee according to an individual's requirements, in which the weighted low-order feature combinations and high-order feature interactions are incorporated for personalized fine-grained recommendation. We also conduct extensive experiments on public data sets and in real-world scenarios. The experimental results demonstrate that the PF-ITS method based on RCOS and DeepAFM outperforms the state-of-the-art baseline models in effectiveness and efficiency.

Fine-grained traffic information prediction at the turning-level based on low-frequency GNSS trajectory data

Fine-grained traffic information prediction at the turning-level based on low-frequency GNSS trajectory data

FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data

Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.

Intellevator: An Intelligent Elevator System Proactive in Traffic Control for Time-Efficiency Improvement

Elevator functioning as a vertical transport facility remains stand-alone and incapable of accessing the fine-grained traffic information. It significantly restricts the flexibility and efficiency of vertical transporting. In particular, while in peak-time, the limited elevator physical capacity with the momentary traffic increment gives rise to the vertical traffic bottleneck problem in large-scale buildings. The problem further results in the long waiting, dissatisfaction, frustration of passengers. In this paper, we present our proposal named Intellevator: an intelligent elevator system that enables the passively functioning elevator system be proactive and intelligent in traffic control for optimizing the transport efficiency. The proposal is an end-to-end architecture that composed of three aspects: Internet of things (IoT)-enabling technology on a conventional elevator; an agent server to enhance elevator computational capability and a novel user interface for delivering system intelligence to end-users. The proposal was experimented on a conventional elevator in a built smart-building environment. Numerical simulation results have demonstrated the system efficiency improvement. In addition, the system usability and user experience have been evaluated by a user study as well.

Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.

PB-MII: replacing static RSUs with public buses-based mobile intermediary infrastructure in urban VANET-based clouds

The success of vehicular ad hoc network (VANET) among vehicle consumers is subject to the quality of comfort and realism of safety promised by this technology. Recently VANET evolved to a rather more application and services-rich paradigm referred to as VANET-based clouds. However, the initial deployment stage of VANET and its successor VANET-based cloud is going to be a daunting challenge due to less market penetration rate of the technology-enabled vehicles, and the deployment and cost of road-side infrastructure. To fill the gaps, in this paper, after arguing on the predictability of spatiotemporal characteristics of the public transport buses in urban areas, we propose a mechanism where these buses are used as mobile gateways (MGs) among vehicles on the road, VANET authorities, and the cloud infrastructure. MGs work as functional entities of the mobile intermediary infrastructure (MII) in VANET-based clouds. Our proposed scheme can serve as a feasible, cost-effective, and pre-established MII for standalone VANET and VANET-based clouds. We furthermore, carry out feasibility analysis through a communication scheme in VANET-based clouds. More precisely we consider the traffic information aggregation and dissemination in VANET-based clouds. In order to argue on the feasibility of buses as MGs, we consider real-time road network dynamics in Seoul, South Korea where the public buses provide perfect connectivity to other vehicular nodes in the neighborhood. In VANET-based clouds application, vehicles share coarse-grained information with clouds through MGs and receive fine-grained traffic information from cloud infrastructure through MGs in real-time. Our simulation results show that MGs provide almost 100% coverage in average traffic scenarios and about 98% coverage in worst traffic scenarios. These MGs also provide the vehicles with about 84% traffic information in worst case and over 90% traffic information in average traffic scenarios. Our proposed infrastructure can be a strong rationale for the initial deployment of these technologies and can possibly be a reasonable partial or full replacement for static RSUs in the urban scenarios.

PTFA: a secure and privacy-preserving traffic flow analysis scheme for intelligent transportation system

With the pervasiveness of vehicle information systems (VIS) and the advance of vehicular ad hoc networks (VANET), intelligent transportation systems (ITS), which can improve road traffic and reduce traffic accidents, have attracted considerable interest recently. However, the development of ITS still faces many challenges including privacy of individual data and locations. In this paper, we propose a secure and privacy-preserving traffic flow analysis scheme, called PTFA, for ITS. With PTFA, user privacy can be guaranteed when the manager acquires the fine-grained traffic information. We first map the information of roads to a super increasing sequence; then, construct an improved multi-dimensional aggregation scheme based on the homomorphic Paillier cryptosystem. For data communications from user to traffic regional-centre (TRC), data aggregation is performed directly on ciphertext at road side units (RSUs) without decryption, and the aggregation result of the original data can be obtained at the TRC. Detailed security analysis shows that the proposed PTFA scheme can efficiently achieve user privacy-preserving in ITS. In addition, performance evaluations via extensive simulations demonstrate the PTFA's effectiveness in terms of providing fine-grained traffic information processing and minimising communication cost.

Secure and privacy-aware traffic information as a service in VANET-based clouds

The multitude of applications and services in Vehicular Ad hoc NETwork (VANET) and cloud computing technology that are dreamed of in the near future, are visible today. VANET applications are set to explode in the next couple of years as a result of the advancements in the wireless communication technologies and automobile industry. Nevertheless, it is speculated that future high-end vehicles will potentially under-utilize their on-board storage, computation, and communication resources. This phenomenon sets the ground for the evolution of traditional VANET to a rather more applications-rich paradigm referred to as VANET-based clouds. In this paper, we aim at a framework of VANET-based clouds namely VANET using Clouds (VuC) and propose a novel secure and privacy-aware service referred to as Traffic Information as a Service (TIaaS) atop the cloud computing services stack. TIaaS provides vehicles (more precisely subscribers) with fine-grained traffic information from the cloud as a result of subscribers’ cooperation with the cloud in a privacy-preserving way. Legitimate VANET users share their frequent whereabouts information referred to as Mobility Vectors (MV) with the cloud infrastructure through gateways (static Road Side Units—RSUs and mobile vehicles with 3/4 G Internet). The gateways forward coarse-grained traffic information (MVs) from vehicles to the cloud whereas after processing, cloud modules construct and re-forward the fine-grained traffic information along with location-based warnings to the subscribers based on their physical locations and moving directions. The communication among vehicles, gateways, and the cloud infrastructure is carried out in a privacy-preserving way. More precisely vehicles share their MVs with cloud infrastructure anonymously. The MVs are hard to link to the sender, until and unless necessary, otherwise. Similarly every vehicle receives fine-grained traffic information in a privacy-preserving manner. The proposed TIaaS keeps the adversaries at bay from abusing users’ privacy and/or constructing profiles against targeted users. Moreover for location confidentiality and privacy, we also propose a novel location-based encryption technique that keeps the insider and outsider adversaries at bay from manipulating the contents of the message. Furthermore, the proposed TIaaS preserves conditional privacy and with the help of an efficient revocation mechanism, revocation authorities can revoke any node in case of a dispute. The proposed TIaaS also introduces the thin-client concept for vehicles where most of the time-consuming processing is offloaded to the cloud and the processing resources of the vehicles can be used elsewhere, for instance for the critical safety related applications. More precisely the cloud processes the big traffic data (BTD) and produces timely, decisive, and meaningful results.

Cooperation-Aware VANET Clouds: Providing Secure Cloud Services to Vehicular Ad Hoc Networks

Over the last couple of years, traditional VANET (Vehicular Ad Hoc NETwork) evolved into VANET-based clouds. From the VANET standpoint, applications became richer by virtue of the boom in automotive telematics and infotainment technologies. Nevertheless, the research community and industries are concerned about the under-utilization of rich computation, communication, and storage resources in middle and high-end vehicles. This phenomenon became the driving force for the birth of VANET-based clouds. In this paper, we envision a novel application layer of VANET-based clouds based on the cooperation of the moving cars on the road, called CaaS (Cooperation as a Service). CaaS is divided into TIaaS (Traffic Information as a Service), WaaS (Warning as a Service), and IfaaS (Infotainment as a Service). Note, however, that this work focuses only on TIaaS and WaaS. TIaaS provides vehicular nodes, more precisely subscribers, with the fine-grained traffic information constructed by CDM (Cloud Decision Module) as a result of the cooperation of the vehicles on the roads in the form of mobility vectors. On the other hand, WaaS provides subscribers with potential warning messages in case of hazard situations on the road. Communication between the cloud infrastructure and the vehicles is done through GTs (Gateway Terminals), whereas GTs are physically realized through RSUs (Road-Side Units) and vehicles with 4G Internet access. These GTs forward the coarse-grained cooperation from vehicles to cloud and fine-grained traffic information and warnings from cloud to vehicles (subscribers) in a secure, privacy-aware fashion. In our proposed scheme, privacy is conditionally preserved wherein the location and the identity of the cooperators are preserved by leveraging the modified location-based encryption and, in case of any dispute, the node is subject to revocation. To the best of our knowledge, our proposed scheme is the first effort to offshore the extended traffic view construction function and warning messages dissemination function to the cloud.

BiCo: Network operator-friendly P2P traffic control through bilateral cooperation with peers

The volume of P2P application traffic has increased so much that network operator-friendly traffic control techniques are recently proposed to localize the P2P traffic. In the existing approaches, the network operator provides network information as a guidance to peers so that the P2P traffic will flow as they intend, thus, realizing unilateral interaction from the network operator to the peers. In this paper, we propose bilateral cooperation between the network operator and the peers, in short BiCo. In BiCo, both parties participate in the P2P traffic control actively to improve a network efficiency while solving the identified limitations of existing work. In a nutshell, the peers provide peer-level information to the network operator, which would otherwise require the network operator to perform a flow-level measurement and analysis. We divide measurement work into two parts, letting the peers collect fine-grained traffic information and enabling the network operator to grasp macroscopic information in order to issue useful guidances (including allowable traffic volume missing in the existing work) to the peers. Our simulation results show that BiCo improves the network efficiency by distributing the traffic evenly over intra-domain links and by trying to fully utilize inter-domain links with given constraints while showing similar download completion time compared to the existing unilateral interaction.