Real-time Traffic Information Research Articles

FECO: An Efficient Deep Reinforcement Learning-Based Fuel-Economic Traffic Signal Control Scheme

Vehicle fuel efficiency (VFE) has a pivotal role in solving energy shortage issue due to the increasing global demand for energy. The high frequency of go-stop movements and long waiting times at intersections significantly reduce the VFE. Such negative impacts are particularly severe when the traffic flows are regulated by poorly designed traffic signal control. Existing works have successfully applied deep reinforcement learning (DRL) techniques to improve the efficiency of traffic signal control. However, to the best of our knowledge, few studies have explored traffic signal control for VFE through eco-driving techniques. To fill the gap, we propose a DRL-based fuel-economic traffic signal control for improving vehicle fuel efficiency. Briefly, we adopt the DRL-technique to develop an agent that can efficiently control traffic signals based on real-time traffic information at intersections, and adjust speed profiles for approaching vehicles to smooth traffic flows. We tested our method on both synthetic traffic dataset and real-world traffic dataset from surveillance cameras in Toronto. Through comprehensive experiments, we demonstrate that our method surpassed the performance of both pure eco-driving and pure traffic signal control techniques by significantly reducing vehicle fuel consumption and improving the efficiency of traffic signal control.

Communication platform concept for virtual testing of novel applications for railway traffic management systems

In recent years, the railway system has been facing the various challenges of the “digital age”. To increase its attractiveness, capacity, sustainability, and security, it needs to improve its’ everyday operational and planning process. This can be enabled using new generation digitised and automated Traffic Management Systems (TMS). Nowadays, railway dispatchers need a TMS that offers precise and real-time traffic information as fundamental condition for effective traffic management, and whose performance can be further improved by increasing availability and diversity of sources and data, for which an effective data management platform is required. The EU-funded OPTIMA project is designing and developing a communication platform to manage the connection between several services supporting TMS applications, also enabling their testing. It represents one of the steps required for the development and implementation of a new generation of TMS. This paper describes the concept for the OPTIMA platform which will link TMS applications used by railway dispatchers with infrastructure systems such as signalling and interlocking systems, maintenance, and energy management, as well as with data services (such a weather information) with the aim of providing standardised interfaces and common data structures as basis of a common and standardised communication.

On-Road Safety Prompt Framework for Platoon

In recent years, researchers are very interested in the development of autonomous vehicle platoons that can be commercialized on a large scale. The research progress in this field raises the new demand for real-time traffic flow information and safety prediction. Therefore, an On-road Safety Prompt Framework (OSPF) that combines the free space detection and lane change prediction has beed developed by this paper. This OSPF can predict the lane-change decision and lane-change trajectory around in a short period of time in the future according to the current state and historical trajectory of the surrounding vehicles, and combine these prediction information to provide the autonomous vehicle platoon with the predicted free space in the near future. The free space provided by this OSPF includes three types: continuing driving according to the current state or changing lanes to the left or right. At the same time, it also provides the required recommended driving trajectory. The evaluation test of this ADAS has been done through a simulation testbed of the NGSIM dataset built with Eclipse SUMO (Lopez et al. (2018)).

Security of communication 5G-V2X: A proposed approach based on securing 5G-V2X based on Blockchain

With the rapid evolution of wireless communication and autonomous vehicles, vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and real-time traffic information in-vehicle networks. V2X has evolved by integrating 5G cellular access technology and New Radio (NR) into V2X communications (i.e., 5G NR V2X). Since the security issue in 5G-V2X remains a crucial point, we conducted a literature review approach and identified the 5G-V2X communication levels in this paper. The main objective of this study was to develop an approach to secure 5G-V2X. The proposed methodology is based on Blockchain to ensure the 5G-V2X architecture levels.

A systematic approach for microscopic models based on cellular automata for road traffic

The transportation infrastructure is one of the most important resources for a country's economic and social well-being. The effectiveness of a country's street network will decide whether it develops further or stagnates. With the increasing number of vehicles on the road and the effects of urbanization, traffic roads are being subjected to a variety of requests and uses for which they were not designed, sized, or predicted. Because of the critical relevance of traffic roads, research must begin to lessen the effects of traffic jams in the streets, determine the appropriate number of traffic lanes, and integrate real-time traffic information into GPS systems. The goal of modeling a traffic-road system is to either build new traffic systems or gain a better knowledge of existing traffic systems so that they can be optimized. The accuracy, performance, stochastic and dynamic behavior of the model produced will be evaluated using a simulation of a genuine traffic system. This paper provides microscopic models based on cellular automation to replicate the behavior of various automobiles on a set of urban streets in Cluj Napoca city downtown. This model includes streets with multiple traffic lanes, various types of vehicles such as automobiles, buses, and trams, intersections with multiple possible upcoming streets controlled by traffic lights, bus stops inside and outside the traffic lane, tram stops inside the traffic lane, pedestrian crosswalks, and parking areas alongside and transversely with the right traffic lane of a street. TCA (Traffic Cellular Automata) is a proposed model that produces adequate findings in urban traffic theory. The results were obtained in both free-flow and traffic-jam conditions.

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.

A low-power and low cost smart streetlight system based on Internet of Things technology

Street lighting, as the most essential and universal component of the urban lighting system, accounts for a large portion of public electricity usage. Therefore, improving street lamps working efficiency is vital for energy savings. This paper demonstrates the design of a smart street lighting system supported by the combination of NB-IoT and LoRa communication technology. By adopting an optimized street lamp control algorithm, the system can realize the automatic control of street lights according to the real-time traffic flow information. This system has been installed on Luyang Avenue, Lucheng City, Shanxi Province, China in May 2019. It managed to reduce the electricity consumption in this region significantly in June 2019, which was 18% lower than that in April 2019 and 19.7% lower than that in June 2018. By illustrating the unique advantages of this system in energy savings and cost reduction, this paper displays its potential for further application in the construction of smart cities on a large scale.

Reliability of variable message signs information: A field experiment study in Chongqing

A variable message sign (VMS), which is a key component of intelligent transportation systems, has been frequently used in the management of urban roads and motorways to provide drivers with real-time traffic condition information about a road section or area. Nevertheless, there is a lack of unified regulations for VMS design, which makes it difficult to accurately determine the understandability and legibility of VMS information. In practice, inappropriate designs of VMSs are common, such as overload of VMS information and excessive number of phases, particularly on the urban roads of China. Building on our earlier findings obtained by surveys, in this study, field tests were conducted to assess the reliability of VMS information in terms of the visual perception characteristics of drivers. For a full visual perception, the information displayed on a VMS must be reliable, i.e., it should be easy for drivers to detect, obtain, and understand the messages they require. Two basic theories—information theory and visual perception theory—were introduced to build a quantification model of VMS information, select evaluation indices, and design the experimental process. A total of 24 drivers participated in the field experiments and questionnaires. Five indices—information obtainment rate, periodic validity, driver subjective scoring and the corresponding UI (represents information intelligibility), and CE (represents information legibility)—were analyzed to evaluate their relationships to VMS information reliability. The results confirmed that the amount of information, and the scrolling period considering redundant distance, significantly affect the reliability of VMSs information. For static VMSs, the information obtainment rate and the subjective scoring decrease with increasing amount of information. The recollection accuracy of the drivers significantly declines when the amount of information shown increases to 90 bits, corresponding to an information obtainment rate of less than 0.8 and UI equal to 0. For dynamic VMSs, the information reliability deteriorates as the scrolling period shortens and the number of phases increases. Unreasonable reliability is found when the periodic validity is less than 0.9, i.e., the actual scrolling period is more than 10% less than the calculated one, corresponding to CE equal to 0. The reliability of information was evaluated by combining the subjective scoring of the drivers and a data-based statistical analysis and considering driving safety. Accordingly, it was recommended that 90 bit is the maximum amount of information to be shown on a VMS and that the preferred scrolling period of each phase of a dynamic VMS is 5 s. The results of this study support the objective of providing reliable information to drivers by addressing the problems related to the amount of information and presentation time for VMSs. These findings provide a basis for determining the thresholds for VMS information to promote practical and user-friendly designs of VMSs on urban roads.

Information System for Disaster Mitigation Using Google Data Traffic

The Head of the Data Information Center and Public Relations of the National Disaster Management Agency predicts the potential for forest fires in 2018 will increase. The areas that are burned are border areas, both provincial/district/city borders because border areas are poorly supervised. This study aims to (1) Develop an Information System that can be used for Disaster Mitigation Using Google Data Traffic (2) Ensure that the developed Information System is suitable for use for Disaster Mitigation Using Google Data Traffic to obtain accurate traffic data. Google Data Traffic is a giant database used by the Google company to provide real-time traffic information through the Google Maps application. Google Maps uses multiple iterations of satellite cameras and uses Google Earth to map traffic including congestion information and the shortest possible path alternatives. The development method chosen is the Rational Unified Process (RUP). This research uses several methods in data collection including observation, interviews, and questionnaires. Interviews were conducted with prospective users of the application, namely firefighter drivers and people in disaster-prone areas. Questionnaires are used to provide a set of questions to respondents. In the determination test, the recall and precision values are calculated from the data generated by the developed system. While the function test uses descriptive data analysis techniques with the percentage feasibility formula. This research produces an application that can assist firefighters in carrying out their daily duties to be more effective and efficient and the test results on this application show that it is declared feasible in terms of efficiency, performance and compatibility.

A network sensor location problem for link flow observability and estimation

Effective traffic sensor systems can collect precise and reliable real-time traffic information to improve the performance of traffic guidance and traffic flow control. In particular, link flow data are among the most intuitive sources of information for reflecting road congestion and can be further used to estimate origin-destination (OD) and path demands. To efficiently obtain link flow information, much effort has been devoted to the network sensor location problem for link flow observability and estimation. The objective of the observability problem is to minimize the number of installed sensors while ensuring full link observability, whereas the objective of the link flow estimation problem is to maximize the information gain or minimize the estimation deviations given a limited number of sensors. To the best of our knowledge, our study is the first to integrate link flow observability, flow estimation and the impact of prior link flow uncertainty into the sensor location problem. Accordingly, we propose two new models. In detail, we first present a new mixed-integer optimization model to achieve full link flow observability. Since only topological information is used, this is a generalized version of the existing models as well as a basic representation of other models for the flow estimation problem in our study. The second model integrates linear regression into the traffic sensor location problem. The results of this model provide both location selection and estimation methods for the decision maker. Furthermore, to address the problems of data loss and low computational efficiency in large-scale networks, we exploit the concepts of ambiguity sets and linear decision rules in robust optimization to generate a tractable approximate counterpart to the above sample-based link estimation model. Sample information is further integrated into this tractable approximate model to enhance the simulation performance without incurring a loss in computational efficiency. Numerical experiments are conducted for networks of different sizes. The results show significant improvement in surveillance performance, with a significant reduction in the number of required sensors and good estimation performance. Our work also verifies that when the number of samples is small, compared with the sample-based model, our enhanced tractable approximate model has several advantages, including (i) better out-of-sample performance, (ii) a similar noninferred link distribution, (iii) less dependence on sample size, and (iv) lower computation time requirements for large-scale problems.

Data-Driven Multi-Agent Vehicle Routing in a Congested City

Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This paper studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. These data are made available to all users, such that they may be able to learn and predict the effects of congestion for building a route adaptively. This method is further enhanced by combining the traffic information system data with previous routing experiences to determine the fastest route with less exploration. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone.

A hybrid ACS-VTM algorithm for the vehicle routing problem with simultaneous delivery & pickup and real-time traffic condition

The vehicle routing problem with simultaneous delivery & pickup and real-time traffic information (VRPSDPTI) is a dynamic problem of combinatorial network optimisation in logistics and supply chain management. It is also a typical NP-hard problem that has plagued enterprises with reverse logistics operations for many years. The main objective of this research is to determine an optimal vehicle routing plan for the VRPSDPTI problem. To achieve this goal, a mixed integer programming (MIP) model is constructed, with the objective of minimising the total travel cycle for the VRPSDPTI problem. A hybrid algorithm of the ant colony system and virtual transformation method (ACS-VTM) is then designed to explore the vehicle routing plan for the VRPSDPTI problem; it adopts a flexible split of the time period for re-optimisation and an improved partial service policy for customer demand. Based on it, a practical case of the VRPSDPTI problem is used to verify the proposed model and algorithm. One major finding is proposed through the study of the practical case that adopting the hybrid ACS-VTM algorithm can effectively reduce the total travel cycle for the VRPSDPTI problem. The main contributions of this study are that it can provide an efficient decision tool to solve the VRPSDPTI, and can not only reduce the total travel cycle, but also improve the efficiency of vehicle utilisation.

An Improved Traffic Rerouting Strategy Using Real-Time Traffic Information and Decisive Weights

To alleviate possible traffic congestion during rush hours, an improved traffic rerouting strategy designed for (or interplayed with) the vehicular ad hoc networks (VANETs) <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> is proposed in this paper. The proposed strategy considers not only the real-time traffic information but also the maximization of road utilization to reach an optimized solution. Furthermore, an alternative path selection is incorporated so that an efficient rerouting path is determined according to the destination of the vehicle, the density and the capacity of the road, and the speed. Via simulations, we can successfully show that our proposed strategy significantly outperforms the closely related strategies in the literature in terms of the travel time, CO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> emission, and fuel consumption, in particular, when heavy traffic is involved. Specifically, our proposed strategy can gain up to 20% of reduction in the average travel time in a realistic scenario to be observed later. Additionally, our proposed strategy gains 19% at least of improvement in the case with different road networks and 28.6% at least of improvement in the case with different percentages of reduction of traffic lights as compared to the closely related strategies as shown in the Appendices given in the supplementary downloadable material.

A privacy-preserving route planning scheme for the Internet of Vehicles

Internet of the Things (IoT) is being integrated into applications that are continuing to reshape many elements of our daily life. One of the major application areas is the Internet of Vehicles which can enhance existing capabilities, such as efficient vehicle route planning. Such systems usually rely on real-time traffic information that includes the temporal location feed of a vehicle. Despite offering clear advantages (such as overcoming congestion, saving fuel/energy/time, and reducing CO2 emission), privacy concerns emerge due to the use of location data. Motivated by this, a privacy-preserving vehicular location (e.g. positioning) sharing scheme is developed for edge cloud-assisted vehicles. In addition, data utility bounds are theoretically analysed, and vehicle routing efficacy is empirically analysed to evaluate the impact of the proposed scheme. Rather than sharing perturbed location on two-dimensional space, we propose a graph-based differential privacy solution for sharing location. The novelty of this work relies on translating the vehicular geospatial data to the graph-structured data for its higher applicability on the road network, designing a real-time application, and empirical analysis of privacy-efficacy optimality.

A customized deep learning approach to integrate network-scale online traffic data imputation and prediction

Online data imputation and traffic prediction based on real-time data streams are essential for the intelligent transportation systems, particularly online navigation applications based on the real-time traffic information. However, the inevitable data missing problem caused by various disturbances undermines the information contained in such real-time data, thereby threatening the reliability of data acquisition as well as the prediction results. Such scenarios raise a strong need for integrating the tasks of network-scale online data imputation and traffic prediction, because the existing two-step approaches that separate the above procedures cannot be implemented in an online manner. In this paper, we propose a customized spatiotemporal deep learning architecture, named the graph convolutional bidirectional recurrent neural network (GCBRNN), to combine network-scale online data imputation and traffic prediction into an integrated task. The imputation mechanism and bidirectional framework are developed to cooperatively estimate missing entries and infer future values. We further design a network-scale graph convolutional gated recurrent unit (NGC-GRU) within the GCBRNN, which applies the graph convolution operation and 1×1 convolution module to capture the spatiotemporal dependencies in the traffic data. Experiments are carried out on two real-world traffic networks, including traffic speed and flow datasets. The comparison results demonstrate that our approach significantly outperforms several classical benchmark models with respect to both the imputation and prediction tasks on two datasets under various missing data rates.

엣지카메라기반 실시간 도로위험 검지 및 정보 제공 시스템

현재 C-ITS 서비스는 레이다 혹 카메라 기반으로 돌발상황을 검지하고 도로위험 정보를 운전자에게 제공한다. 레이다기반의 돌발상황검지기는 가격이 비싸고 교통 이상 상황을 확인하기 위해서는 별도의 카메라가 필요하다. 또한, 레이다는 차량과 보행자 검출이 가능하나 그 외 오토바이, 자전거, 낙하물과 같은 객체는 검출이 어렵다. 기존 C-ITS 시스템에서 카메라기반의 돌방상황검지의 경우, C-ITS 센터의 영상분석 서버에서 돌발상황을 검지하고 위험정보를 운전자에게 제공하기 때문에 실시간 서비스를 제공하기 어렵다. 이러한 문제를 해결하기 위해 본 논문은 엣지카메라기반 실시간 도로위험인식 및 정보제공시스템을 제안한다. 엣지카메라는 로컬에서 영상처리를 통해 객체를 인식하고 위험상황을 판단하여 RSU를 통해 운전자에게 위험정보를 제공한다. 제안 시스템은 실시간 처리를 위해 경량 딥러닝 네트워크를 사용하고, 정확도 향상을 위해 지역최적화를 수행한다. 그리고 기존 C-ITS 응용 표준 프로토콜을 수정 없이 사용할 수 있도록 엣지카메라와 RSU의 통신 인터페이스를 설계하였다. 실험을 통해 교통객체를 정확하게 인식하고 100ms 이내에 실시간으로 도로위험상황을 운전자에게 정보를 제공함을 확인할 수 있다.

Solving in real-time the dynamic and stochastic shortest path problem for electric vehicles by a prognostic decision making strategy

The adoption of Electric Vehicles (EVs) has substantially increased during the last decade, creating the need for customized EV-oriented routing strategies capable of using the enormous amount of historical, and real-time, traffic data that is collected through Intelligent Transport Systems (ITSs). Existing EV routing algorithms, however, concentrate mostly on the usage historical data to compute offline optimal paths, whereas the use of real-time traffic information to compute en-route path updates is still an almost unexplored topic; mainly due to its inherent computational challenges. This research effort proposes a Prognostic Decision Making (PDM) strategy to solve in real-time the Electric Vehicle Dynamic Stochastic Shortest Path Problem (EV-DSSPP); aiming at the simultaneous utilization of historical and real-time traffic data. Factors such as recurring and non-recurring traffic congestion, elevation, velocity and EV’s parameters are incorporated into the decision-making process. The proposed strategy has two hierarchical functional layers. The lower layer consists of a fast-computing routing algorithm that, by construction, guarantees a real-time execution. The higher layer organizes the periodic en-route execution of the first layer to compute en-route path updates during a trip. This strategy can hence serve as an expert router that works jointly with an ITS to assist EV drivers on route. The proposal is validated through a simulation study based on real-world traffic data collected in Santiago, Chile. The results show that periodic en-route path updates can generate a reduction in both travel time and energy consumption, which evidences the benefits of incorporating real-time traffic information into the EV-routing problems.

CHRT: Clustering-based hybrid re-routing system for traffic congestion avoidance

Re-routing system has become an important technology to improve traffic efficiency. The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic, making the planned routes unable to cope with the changing traffic conditions. Based on real-time traffic information, it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion. Moreover, how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system. To deal with these challenges, this paper proposes CHRT, a clustering-based hybrid re-routing system for traffic congestion avoidance. CHRT develops a multi-layer hybrid architecture. The central server accesses the global view of traffic, and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead. Then, a clustering-based priority mechanism is proposed, which sets priorities for clusters based on real-time traffic information to avoid secondary congestion. Furthermore, to plan the optimal routes for vehicles while alleviating global traffic congestion, this paper presents a multi-metric re-routing algorithm. Through extensive simulations based on the SUMO traffic simulator, CHRT reduces vehicle traveling time, fuel consumption, and CO2 emissions compared to other systems. In addition, CHRT globally alleviates traffic congestion and improves traffic efficiency.

AutoCoin: Secure Content Sharing Based on Blockchain for Vehicular Cloud

A future smart car will be more than a means of transportation, as it will not only move people to a destination without requiring them to drive but will enable people to work or enjoy a trip with entertainment. For this, smart vehicles need to deal with various types of data for safety and infotainment, such as real-time traffic, multi-media contents, documents and weather information. Recently, a fleet of vehicles connected to other vehicles and infrastructure (i.e., road side units) using a legacy or 5G mmWave spectrum has been considered as a platform to cooperate for those new tasks, known as the vehicular cloud or fog. Within the vehicular cloud, data management should consider security, high availability and interoperability between vehicles. However, these are not easily achievable without a centralized service provider; it is difficult for an autonomous P2P system to guarantee data integrity, and it cannot compensate drivers that actively participate in the vehicular cloud. Fortunately, the many successes achieved in the field of crypto-currency raise the possibility of defining incentives that are necessary for a sustainable digital economy. In this paper, we propose autoCoin—an approach that aims to encourage smart vehicles to cooperate to create and exchange infotainment data securely under the assumption of rationality. We introduce a scalable blockchain architecture for autoCoin and a smart contract to exchange contents without third-parties using an off-chain technique.

Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference

In this paper, an energy management strategy (EMS) based on model predictive control (MPC) is proposed to minimize fuel cost, electricity usage and battery ageing. To fulfil the MPC framework, a novel speed predictor with a variable horizon based on a K-means algorithm and a radius basis function neural network, which contains various predictive submodels, is designed to cope with different input drive states. In addition, a Q-learning algorithm is applied to construct an adaptive multimode state-of-charge (SOC) reference generator, which takes advantage of velocity forecasts for each prediction horizon. The algorithm fully considers the model nonlinearities and physical constraints and requires less computational effort. Based on the SOC reference and predictive velocity, the MPC problem is formulated to coordinate fuel consumption and battery degradation. Moreover, considering the influence of real-time traffic information, a traffic model that simulates actual road conditions is constructed in VISSIM to evaluate the performance of the proposed EMS. The simulation results show that the proposed speed predictor can effectively improve the predictive accuracy, and the multimode control laws based on drive condition classification present superior adaptability in SOC reference generation compared to single-mode law. With the aforementioned two improvements, the proposed EMS achieves desirable performance in fuel economy and battery lifetime extension.