Intelligent Traffic Research Articles

How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm.

Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

Vehicular Communications Utility in Road Safety Applications: A Step toward Self-Aware Intelligent Traffic Systems

The potential of wireless technologies is significant in the area of the safety and efficiency of road transport and communications systems. The challenges and requirements imposed by end users and competent institutions demonstrate the need for viable solutions. A common protocol by which there could be vehicle-to-vehicle and vehicle-to-road communications is ideal for avoiding collisions and road accidents, all in a vehicular ad hoc network (VANET). Ways of transmitting warning messages simultaneously by vehicle-to-vehicle and vehicle-to-infrastructure communications by various multi-hop routings are set out. Approaches to how to improve communication reliability by achieving low latency are addressed through the multi-channel (MC) technique based on two non-overlaps for vehicle-to-vehicle (V2V) and vehicle-to-road (V2R) or road-to-vehicle (R2V) communications. The contributions of this paper offer an opportunity to use common communication adaptable protocols, depending on the context of the situation, coding techniques, scenarios, analysis of transfer rates, and reception of messages according to the type of protocol used. Communications between the road infrastructure and users through a relative communication protocol are highlighted and simulated in this manuscript. The results obtained by the proposed and simulated scenarios demonstrate that it is complementary and that the common node of V2V/V2R (R2V) communication protocols substantially improves the process of transmitting messages in low-latency conditions and is ideal for the development of road safety systems.

Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

EPNS: Efficient Privacy Preserving Intelligent Traffic Navigation from Multiparty Delegated Computation in Cloud-Assisted VANETs

Real-time navigation is a fundamental service with the emerging techniques of intelligent transportation and crowdsensing. Unfortunately, the breach of location privacy significantly impedes its wide adoption. Most existing state-of-the-art exploit either pseudonyms or public key fully homomorphic encryption (FHE) to protect location privacy, which requires an online certificate authority (CA) or loads intolerably heavy computational/communication overhead on resource-constrained vehicular users. In this paper, a new cryptographic primitive named efficient multiparty delegated computation (MPDC) is firstly proposed, where any one-way trapdoor permutation is required to perform constant times (i.e. twice) on each resource-constrained data provider, to encrypt a batch of messages and enable types of secure evaluations over ciphertexts encrypted under multiple keys of multiple parties. Based on MPDC, we devise a lightweight privacy-preserving real-time intelligent traffic navigation scheme (EPNS) in cloud-assisted VANETs. The proposed approach predicts an optimal driving route of shortest time without disclosing either vehicular users' private location or the navigation result to the collusion between the semi-trusted cloud/cryptography service provider (CSP) and unauthorized users, by securely evaluating auto-regression moving average (ARMA) model with spatiotemporal correlations of high accuracy and efficiency. Finally, formal security proofs and the extensive evaluations demonstrate the utility and the practicability of our approaches.

The Role of Collaborative E-Government in Surabaya Intelligent Traffic System

The Role of Collaborative E-Government in Surabaya Intelligent Traffic System

The impact and potential of cooperative and automated driving for intelligent traffic signal corridors: A field-operational-test and simulation experiment

A Field-Operational-Test (FOT) with CACC vehicles and wireless communication with intelligent intersections on arterial roads was performed in autumn 2018, initiated by the Province of Noord-Holland in The Netherlands. The goal of the pilot was to demonstrate the feasibility of CACC platooning and V2I/I2V (infrastructure-to-vehicle) communication and investigate the potential effects on traffic flow and safety in such an urban environment. During the pilot, seven CACC-enabled vehicles traversed a provincial road corridor, crossing five intelligent intersections, sending Cooperative Awareness Message (CAM) and receiving time-to-green information from Intelligent Traffic Signals (iTS). A simulation model was calibrated using the data from the pilot to give indications of the potential future effects of CACC and I2V communication. The results showed that full CACC penetration can lead to 5% lower travel times on average with an enhanced improvement when intelligent intersections with V2I/I2V technology is applied (11% lower). If scaled up to the provincial road network, this could lead to a reduction of 12% in the total experienced delay for road users. As for previous tests on motorways, a reduction in effectiveness was also found for lower CACC- penetration rates with penetration rates of 10% no longer showing any substantial gains.

Assessment of Major Air Pollution Sources in Efforts of Long Term Air Quality Improvement in İstanbul

Air pollution affected quality of life and public health due to high concentration levels of air pollutants in Istanbul, especially in 1990s. Major air pollution sources in Istanbul caused elevation of the air pollutants in ambient air of the megacity. To protect human health, the levels of PM10 and SO2 were reduced by taking effective actions such as the reduction of utilization of coal, fuel oil, wood combustion for residential heating, expending natural gas network and improving the quality of diesel and gasoline. Intelligent Traffic Systems (ITS) were applied to reduce the air pollutant emission from transportation by reducing travelling time. Overall, this study evaluates air pollution sources in Istanbul based on previous source apportionment studies that guide the emission reduction strategies. The improvement on PM10 and SO2 demonstrated as 50% and 98% reduction respectively since 1990s to 2014.

A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics.

Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

Toward Driver Face Recognition in the Intelligent Traffic Monitoring Systems

This paper models the driver face recognition problem under the intelligent traffic monitoring systems as severe illumination variation face recognition with single sample problem. Firstly, in the point of view of numerical value sign, the current illumination invariant unit is derived from the subtraction of two pixels in the face local region, which may be positive or negative, we propose a generalized illumination robust (GIR) model based on positive and negative illumination invariant units to tackle severe illumination variations. Then, the GIR model can be used to generate several GIR images based on the local edge-region or the local block-region, which results in the edge-region based GIR (EGIR) image or the block-region based GIR (BGIR) image. For single GIR image based classification, the GIR image utilizes the saturation function and the nearest neighbor classifier, which can develop EGIR-face and BGIR-face. For multi GIR images based classification, the GIR images employ the extended sparse representation classification (ESRC) as the classifier that can form the EGIR image based classification (GIRC) and the BGIR image based classification (BGIRC). Further, the GIR model is integrated with the pre-trained deep learning (PDL) model to construct the GIR-PDL model. Finally, the performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, self-built Driver and VGGFace2 face databases. The experimental results indicate that the proposed methods are efficient to tackle severe illumination variations.

Improved Novel Clustering Technique for Diverse and Self-motivated Traffic Data Stream for IoT Scenario

Technologies are changing day by day and IoT is worldwide data and may of great business important to various users. sTo create such reasonable data, majority adaptive and K-mediod clustering techniques are employed in data mining. In research work, it focus on comparing adaptive, K-medisod and novel clustering technique to internet-of-things data collection in ITSs (Intelligence Traffic System). In traffic DataStream is composed form online site, it challenges of 30,000 instances with 9 attributes, clusters formed after evaluation and number of clusters is identified after the evaluation. Proposed techniques are significant too easy than some other clustering techniques with respect to all computation recall and precision parameters. In traffic databases depends on the data separation and cluster enhancement that is quality of clusters. To resolve the major issues that over load the system or Centre’s in IoT which consequences the huge kind of data on internet. It evaluated a set of consequences experiments using token and manufacture data from traffic use case view where the traffic considerations from the city monitor. Comparison of clustering methods that helps in determining suitable clustering approach for the offer internet of things database which results in optimal performance metrics.

Improvement of traffic flux with introduction of a new lane-change protocol supported by Intelligent Traffic System

Abstract A new Cellular Automata traffic model based on Revised S-NFS model was established, which considers traffic density ahead of a car in next 50 [m] and also accounts for a decision making process of whether a lane change should be tried or not so as to diminish the frequency of meaningless lane-changes. It intends to be applied as one of the protocols to improve traffic efficiency in premise with Intelligence Traffic System (ITS) that is able to provide information on traffic density next hundred meters in front of a focal vehicle. A series of systematic simulations reveals that the presented lane changing protocol enhances traffic flux vis-a-vis the conventional lane change rule based on the traditional incentive criterion and safe criterion. Social dilemma analysis suggests our new protocol mitigates a strong social dilemma encouraged by a competition between a cooperator; not intending any lane-changes and a defector; trying to lane-changes to minimize his own travel time.

JARINGAN AD-HOC VEHICULAR (VANET) : TINJAUAN TENTANG ARSITEKTUR, KARAKTERISTIK, APLIKASI, DAN PROTOKOL MEDIUM ACCESS CONTROL (MAC)

VANET (Vehicular Ad-hoc Network) is an ad-hoc wireless network which nodes are composed by mostly moving vehicles and infrastructure communications or access points along the road. It integrates adhoc networking, cellular technology, wireless LAN and other technologies, which include GPS(Global Positioning System), digital mapping, sensing technologies and advanced information processing tools to support Intelligent Traffic Systems (ITS). VANET is developed mainly to improve road safety and to increase the traffic efficiency. It is designed to support the exchange of warning messages among the vehicles to increase the passenger safety and other information related to VANET applications and services . To achieve these goals, there are several issues that should be addressed. One of these challenges is designing reliable and efficient Medium Access Control (MAC) protocol for VANET. Due to VANET’s distinguishing characteristics, such as high mobility of the nodes, dynamic network topology, enough battery power, high processing power, etc, the MAC protocols designed for MANET (Mobile Ad-hoc Network) are not suitable for VANET. The research community has been working to develop MAC protocols appropriate for this ad-hoc network by proposing new design or modifying the existing one.In this paper, the VANET’s architecture, characteristics, applications and standard are discussed and then a comprehensive study of several MAC protocols available in the literature is presented.

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN.

Urban Traffic Optimization with Real Time Intelligence Intersection Traffic Light System

Traffic system is a complex system where a lot of smart components which include signals, vehicles, sensor and pedestrian have communication skills with together on local level and act in a particular manner on high level. Insufficient traffic light control system on intersections brings about unnecessary delays and waste of time, extremely oil firing of engine which run idle mode on lights and increasing greenhouse gas emission. Various systems have been developed in order to overcome these traffic problems. In this paper we proposed an Intelligence Traffic Light System. In this system, the traffic flow at intersections is optimized using instant traffic information. These are the primary developed methods for the traffic optimization systems: fixed time period systems of lighting where time is pre-determined, green wave lighting system and real time optimization system of traffic light. Real data of traffic has been gathered on Karabuk-Safranbolu route to test above systems for different data density. The results of these tests on data show that real time traffic light optimization systems get better results than fixed time period and green wave lighting systems.

Application of Wireless Sensor Network in Urban Intelligent Traffic Information Acquisition

With the rapid development of economic and scientific levels, urban traffic in China has been further improved. Moreover, the number of private cars is increasing because of the improvement of living standard. However, various traffic problems such as traffic jam, traffic accidents, disordered traffic order and unreasonable travel structure come along. Therefore, the utilization and development of urban intelligent traffic is an inevitable choice for the improvement of Chinese traffic. This study mainly investigated the application of wireless sensor network in urban intelligent traffic information acquisition. Differential Time of Arrival (DTOA) technology, least square method and Kalman filter algorithm were used to improve the location accuracy of vehicles. Moreover, Matlab simulation experiment was performed. The method proposed in this study can improve the acquisition speed of information and positioning accuracy of vehicles. This work is beneficial to the solution of traffic disorder.

Modeling Cyber Attacks at Intelligent Traffic Signals

Transportation networks are considered one of the critical physical infrastructures for resilient cities (cyber-physical systems). In efforts to minimize adverse effects that come with the advancement of vehicular technologies, various governmental agencies, such as the U.S. Department of Homeland Security and the National Highway Traffic Safety Administration (NHTSA), work together. This paper develops belief-network-based attack modeling at signalized traffic networks under connected vehicle and intelligent signals frameworks. For different types of cyber attacks, defined in the literature, risk areas and impacts of attacks are evaluated. Vulnerability scores, technically based on the selected metrics, are calculated for signal controllers. In addition, the effect of having redundant traffic sensing systems on intersection performance measures is demonstrated in terms of average queue length differences.

Model and Algorithm Research on High-speed Railway Intelligent Traffic Dispatching

According to the characteristics of high-speed railway traffic dispatching in holiday, analyses the strategy of traffic dispatching and sets up the intelligent traffic dispatching model of high-speed railway in holiday. Multi-objective particle swarm optimization algorithm that based on exterior archive is adopted to solve the intelligent traffic dispatching model. At last, the actual data of Chengdu, Mianyang, Leshan intercity high-speed railway in Chengdu railway bureau during the May Day is used as experimental data to do the simulation. The result shows that the intelligent traffic dispatching model of high-speed railway and the algorithm adopted are feasibility and effectiveness.

基于大数据背景下内蒙古智慧交通的建设的问题和对策研究

基于大数据背景下内蒙古智慧交通的建设的问题和对策研究