Real-time Traffic Conditions Research Articles

Investigations of the Dynamic Travel Time Information Impact on Drivers’ Route Choice in an Urban Area—A Case Study Based on the City of Bialystok

Increasing traffic volumes in cities leads to common traffic congestions building up, especially during peak hours. To protect city dwellers from excessive fuel exhaust and traffic noise and to prevent drivers from time loss due to overloaded routes, it is important to inform them about real-time traffic conditions and possible delays in advance. Effectively influencing drivers’ decisions to divert from an original route choice in case of traffic hinderance is essential, and application of dynamic travel information in the form of variable message signs (VMSs) is believed to be effective in these terms. The paper examines drivers’ willingness to divert from an initial route choice due to the information provided on VMS boards. Their behavior was analyzed in terms of their response to everyday and artificially elongated travel times displayed on the VMSs. Maximum simulated elongation reached 200% and 300% of the initial state, depending on the characteristics of the pre-peak conditions. To assess the effectiveness of VMSs, the changes in traffic intensities were statistically analyzed. In general, apart from few significant differences, the results revealed drivers’ ignorance of the travel time information provided on the VMS, regardless of the extension of the original times.

Temporal and spatial heterogeneity research of urban anthropogenic heat emissions based on multi-source spatial big data fusion for Xi’an, China

Anthropogenic heat emission (AHE) influences the local energy balance and intensify the urban heat island (UHI) effect. An accurate calculation of the AHE can improve the precision of UHI predictions. However, reliable AHE calculations with high temporal and spatial resolution in domestic research is still lacking. Therefore, this study proposes an approach to estimate the dynamic AHE by integrating multi-source Internet big data and high-precision urban spatial data. First, we quantified the dynamic distribution of residents’ trajectories by tracking multi-stage Internet geographic location data, real-time traffic conditions of Xi’an city, supplemented by on-site drone monitoring. Then the parameters of cooling and the thermal load coefficient of building emissions, personnel cooling loads, and traffic densities were introduced. Finally, the temporal and spatial dynamic rules of the AHE were revealed. Results showed the AHE was subject to a large changing amplitude. The diurnal AHE values of 64% of the blocks ranged from 93 to 498 W/m2, especially in some core commercial areas, the value could reach above 1000 W/m2 during the peak stages. Compared with previous research, this study dynamically evaluates the temporal and spatial heterogeneity of the AHE under different emission scenarios with a short update cycle and high spatial resolution.

A Dynamic Pyramid Tilling Method for Traffic Data Stream Based on Flink

Traffic guidance, traffic management and emergency vehicle traffic all require keeping abreast of traffic status. Intelligent Transportation Systems (ITS) is highly expected to provide real-time traffic condition information service. To achieve this, the capability of handling dynamic data stream collected from multi traffic monitoring sources and serving the public with information timely is essential for ITS. With the wide spread of Internet of Things technology, not only the amount, but also the spatial and temporal resolutions of real-time traffic data have explosive growth, thereby enhancing the difficulty of real-time traffic data processing in ITS. Web pyramid map tiles is wide accepted for massive spatial data service, and the latency of tile generation significantly reduces the timeliness of information transmission and the reliability of services. A Flink-based method for dynamic pyramid tile generation and updating is proposed here. Take advantages of combining grid indexes, employing data partition and window selection mechanisms, and applying iterative computational characteristics for resampling, the distributed dynamic pyramid map tile generation algorithm (DPTG), can quickly visualize real-time spatial traffic data with digital map tiles. Taking the national highway road data from China as an example, the experimental results show that the Flink-based DPTG method has high efficiency and scalability in both batch processing and stream processing mode, which highlights the capability of the proposed method to support real-time traffic monitoring data processing for timely large-scale public service in ITS.

An improved fuzzy ant colony system for route selection based on real time traffic condition

<span id="docs-internal-guid-43a74def-7fff-f5f6-9f3c-4828e591ebd1"><span>With city conditions that often have congestion, a driver need to find a route of the many possible routes that may occur from origin to destination by considering several factors. The weaknesses of the ant algorithm is the dependency of required parameter values and must be set manually. This paper will make improvements to the fuzzy ant colony system (FACS) with minimize parameter dependency by using fuzzy logic to determine the probability of the next node visited by ants. There are four criteria or input variables for fuzzy inference system, that is “Pheromone Intensity”, “Distance”, “Vehicle Intensity”, and “Average Speed”. The routes generated by improved FACS (I-FACS) are more varied than the FACS algorithm. The best condition obtained by I-FACS was at point O/D= 112/34 where at 05:00, I-FACS was able to obtain the route with the best length compared to ACS and FACS. The result of the comparison of the route distance obtained shows that I-FACS is able to produce a better route by 4.16% than FACS by looking at the average distance difference on ACS. This method is expected to be a reference for the development of smart transportation in support of smart mobility, which is one of the components in the smart city concept.</span></span>

A Survey on Novel Estimation Approach of Motion Controllers for Self-Driving Cars

The motion planning framework is one of the challenging tasks in autonomous driving cars. During motion planning, predicting of trajectory is computed by Gaussian propagation. Recently, the localization uncertainty control will be estimating by Gaussian framework. This estimation suffers from real time constraint distribution for (Global Positioning System) GPS error. In this research article compared novel motion planning methods and concluding the suitable estimating algorithm depends on the two different real time traffic conditions. One is the realistic unusual traffic and complex target is another one. The real time platform is used to measure the several estimation methods for motion planning. Our research article is that comparing novel estimation methods in two different real time environments and an identifying better estimation method for that. Our suggesting idea is that the autonomous vehicle uncertainty control is estimating by modified version of action based coarse trajectory planning. Our suggesting framework permits the planner to avoid complex and unusual traffic (uncertainty condition) efficiently. Our proposed case studies offer to choose effectiveness framework for complex mode of surrounding environment.

Vehicle detection based on real-time traffic condition and adaptive pixel segmentation

Vehicle detection based on real-time traffic condition and adaptive pixel segmentation

Virtual vehicle based on incremental learning for navigation service

In internet of vehicles (IoVs), drivers always use navigation systems to plan out routes and optimally navigate real-time road congestion. However, the navigation problem cannot be solved nicely by the present approach due to the emphasis put on one point of view on this problem only. The intelligent transportation systems do not consider the drivers' preferences adequately, and driverless cars do not consider real-time traffic conditions. To solve this dispersion, in this paper, we first configure an image of driver and vehicle, named virtual vehicle, to replace the driver making some decisions in IoV. Then, we propose an incremental learning approach for virtual vehicles based on negative correlation learning algorithm, called divided negative correlation learning algorithm, to obtain the drivers' preference knowledge. In the proposed algorithm, the trained ensemble is divided into three parts, where the first part is trained on a new dataset, the second part is used to retrain the old dataset, and the third part is retained. In the end of the proposed algorithm, we combine the three parts to form a new ensemble. Finally, the experimental results show that virtual vehicles can obtain drivers preference knowledge effectively.

Automatic Vehicle Tracking with LiDAR-Enhanced Roadside Infrastructure

Abstract Vehicle tracking technology is a prerequisite for the connected-vehicle (CV) system. However, a mixture of CV and unconnected vehicles will be under normal conditions on roads in the near future. How to obtain the real-time traffic status of unconnected vehicles remains a challenge for traffic engineers. The roadside Light Detection and Ranging (LiDAR) sensor provides a solution for collecting real-time high-resolution micro traffic data of all road users (CV and unconnected vehicles). This article developed a systematic procedure for vehicle tracking using the roadside LiDAR sensors. The procedure can be divided into five major parts: point registration, background filtering, point clustering, object classification, and vehicle tracking. For each step, the corresponding data processing algorithms were provided. A field test was conducted to evaluate the performance of the proposed method. Compared to the state-of-the-art method, the proposed methods can track vehicles with higher accuracy and lower computation loads.

Capacity Improvements Based on Dynamic Signal Timing at Signalized Road Intersections Using Genetic Algorithm

Due to lack of public transportation, economic growth and the rapid increase in the number of populations, the number of vehicles in Erbil city has recently increased dramatically. This increment caused congestion on the roads and posed most of the loads on the traffic intersections, which already have limited capacity. As the intersection volume increases, the degree of saturation (X) and delay increases and, as a consequence, the Level-of Service (LOS) decreases, leading to instability and platoon formation at the intersection approaches. One of the reasons of congestion at intersections is due to the fixed signal timing during the day, while the traffic volume at the intersections varies from one hour to another. This research suggests solutions to vehicle delays at intersections through using a Genetic Algorithm (GA) based optimization model to adjust the signal parameters to fit the real time traffic condition. The suggested model tries to optimize the cycle length besides the green time per phase for different time lags throughout the day. The results showed distinct and remarkable improvements in the intersection capacity with 2 to 13 % along with an average delay of 28- 64 %.

Optimal Planning of Real-Time Bus Information System for User-Switching Behavior

Seoul Metropolitan City’s buses cater to more than 50% of the average daily public transportation use, and they are the most important transportation mode in Korea, together with the subway. Since 2004, all public transportation records of passengers have been stored in Seoul, using smart transportation cards. This study explores the environmental and psychological factors in implementing a smart transportation system. We analyze the switching behavior of traffic users according to traffic congestion time and number of transfers based on public transportation data and show that bus-use behavior differs according to the traffic information of users and the degree of traffic congestion. Information-based switching behavior of people living near bus stops induces people to change routes during traffic congestion. However, in non-congested situations, the original routes are used. These results can guide the formulation of policy measures on bus routes. We made it possible to continuously change the routes for certain buses, which were temporarily implemented due to traffic congestion. Moreover, we added a service that posts the estimated arrival time to major stops while reflecting real-time traffic conditions in addition to the bus location and arrival time information through the global positioning system.

A driving simulation study to investigate the information threshold of graphical variable message signs based on visual perception characteristics of drivers

Graphical variable message signs (VMSs) are infrastructure-based advanced traveler information systems widely used to provide drivers with real-time traffic condition information about a road section or area. However, there is a lack of research on the suitable volume of information to be shown on graphical VMSs. In practice, an overload of VMS information commonly exists, especially on China’s highways. Building on our earlier findings obtained through surveys and static cognitive experiments, this study introduces the use of dynamic simulation experiments to assess the influence of the volume of information (i.e., number of roads displayed) on graphical VMSs from the perspective of drivers’ visual perception characteristics. Thirty-two drivers participated in the driving simulation experiment and questionnaires. Five indexes, including legibility speed, legibility distance, legibility time, comprehension accuracy, and driver subjective scoring, were thoroughly analyzed to evaluate their relationships to different volumes of information (i.e., four, five, and six roads shown on a VMS). The results show that the legibility distance notably decreased with increasing volumes of information. The comprehension accuracy decreased significantly when the number of roads shown increased to six. The legibility speed, legibility time, and subjective scoring also deteriorated as the number of roads displayed on the VMS increased. The index scores were evaluated, in combination with the data of the drivers’ subjective scoring, data-based statistical analyses, and comprehensive evaluations using the TOPSIS method, to recommend that five is the recommended maximum number of roads to be shown on a graphical VMS. The results of this study support the goal of providing understandable and effective messages for drivers by addressing issues relating to how much information should be displayed on a VMS. These findings provide a basis for policy development to ensure consistent and practical designs of graphical VMSs on highways.

A Novel Method about the Representation and Discrimination of Traffic State.

The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city’s central area was given.

Self-learning adaptive traffic signal control for real-time safety optimization

Adaptive traffic signal control (ATSC) is a promising technique to improve the efficiency of signalized intersections, especially in the era of connected vehicles (CVs) when real-time information on vehicle positions and trajectories is available. Numerous ATSC algorithms have been proposed to accommodate real-time traffic conditions and optimize traffic efficiency. The common objective of these algorithms is to minimize total delay, decrease queue length, or maximize vehicle throughput. Despite their positive impacts on traffic mobility, the existing ATSC algorithms do not consider optimizing traffic safety. This is most likely due to the lack of tools to evaluate safety in real time. However, recent research has developed various real-time safety models for signalized intersections. These models can be used to evaluate safety in real time using dynamic traffic parameters, such as traffic volume, shock wave characteristics, and platoon ratio. Evaluating safety in real time can enable developing ATSC strategies for real-time safety optimization. In this paper, we present a novel self-learning ATSC algorithm to optimize the safety of signalized intersections. The algorithm was developed using the Reinforcement Learning (RL) approach and was trained using the simulation platform VISSIM. The trained algorithm was then validated using real-world traffic data obtained from two signalized intersections in the city of Surrey, British Columbia. Compared to the traditional actuated signal control system, the proposed algorithm reduces traffic conflicts by approximately 40 %. Moreover, the proposed ATSC algorithm was tested under various market penetration rates (MPRs) of CVs. The results showed that 90 % and 50 % of the algorithm’s safety benefits can be achieved at MPR values of 50 % and 30 %, respectively. To the best of the authors’ knowledge, this is the first self-learning ATSC algorithm that optimizes traffic safety in real time.

GPS navigation apps and the price of anarchy

Folded paper road maps are found next to sextants in the pile of obsolete navigation tools. GPS navigation apps like Waze and Google Maps, accurate to within a few metres, are available on all smartphones and most new cars. These apps provide drivers with real time traffic conditions and suggest minimum drive time routes, giving drivers the ability to avoid congestion and delays caused by heavy traffic, accidents, road construction and other hindrances.

Towards real-time density estimation using vehicle-to-vehicle communications

Traffic state estimation is a fundamental task of Intelligent Transportation Systems (ITS). Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real time within reach. Recognizing this, the US-DOT started promoting the Connected Vehicles (CV) initiative. By using wireless connectivity between the vehicles participating in the traffic, the CV initiative aims to promote an increased awareness of real-time traffic conditions and, as a result, to reduce the number and severity of crashes.A number of recent papers have proposed CV-based approaches to estimating traffic state parameters including density and flow. However, virtually all the CV-based approaches for density estimation also rely on communication with stationary detectors and other pre-deployed roadside infrastructure. This assumption is problematic since such infrastructure is often not available.The main contribution of this paper is to propose a simple and easy to implement real-time traffic density estimation method that uses only vehicle-to-vehicle communications and the on-board sensing capabilities of present-day vehicles. In our method, using their on-board devices, vehicles maintain a tally that keeps track of the difference between the number of times other vehicles pass them and the number of times they pass other vehicles. Notice that since vehicles may vary their speed as they please, they may pass and be passed by the same vehicle multiple times and, consequently, maintaining a correct tally is a non-trivial task. We show that the tallies computed by vehicles relate, in an interesting way, to traffic density.We provide a detailed proof of our method using techniques that avoid the use of common simplifications inherent to visual time-space traffic diagrams. Furthermore, we demonstrate the accuracy of our method through extensive simulations using real NGSIM traffic traces along with SUMO-generated synthetic traffic traces.

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

AbstractThe traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond t...

An Efficient Cloud-Based Traffic Signal Manipulation Algorithm for Path Clearance

Beside many challenges that urban cities have to face, one of them is increasing traffic. Unfortunately, in developing countries like, for example, Pakistan, the traffic management infrastructure does not scale accordingly. This leads to two types of problems: congestion and long queues at traffic signals. This makes it difficult for emergency vehicles (EV) such as ambulances to reach their destination on time. Therefore, in this article, the authors have developed an intelligent path clearance system for emergency vehicles. The particular focus is on long queues at traffic signals. Given the GPS coordinates of an EV, a destination, a map, and the traffic light grid system, our system provides a signal free corridor to the priority vehicle by automatically manipulating traffic signals that fall in its path using cloud computing. The idea is to clear the path of the vehicle. The proposed system also makes decision based on the time of the day and current traffic conditions in real time. In case of multiple options, it also calculates the shortest path to the destination.

Load Balancing Routing for Wireless Mesh Network With Energy Harvesting

Centralized routing is beneficial as it provides a global network view to plan and achieve the best and effective path for real-time traffic. With the rapid development of software defined networking, centralized routing has aroused more and more concern of researchers. An effective centralized load balancing routing (LBR) for wireless mesh network considering energy harvesting is proposed in this letter. LBR can provide flexible and optimal routes for real-time traffic. To minimize the long-term weighted sum cost which includes load and energy condition, LBR considers the queue length, channel condition, energy cost and energy harvesting. The problem of making dynamic decisions based on real-time traffic conditions is solved by the dynamic programming approach. Simulation results demonstrate the advantages of LBR.

Design and Implementation of Traffic Signal Control System Based on Parallel Simulation Technology

A traffic signal control system based on parallel simulation technology is designed in this article. Based on the second-level acquisition of intersection data, this system builds a control algorithm matching rule base based on offline algorithm adaptive analysis. In addition, the system can select the signal control algorithm that matches the real-time traffic status online, and realize the online evaluation of the control algorithm through the online parallel simulation and control algorithm evaluation platform, and provide a decision basis for further implementation of the algorithm. This system improves some existing deficiencies in signal control systems, and also provides decision support for the application of complex control algorithms. The system is applied to the control of actual signalized intersections. Practice shows that the system can better adapt to the actual situation of signalized intersections in District, improve the capacity of intersections and the efficiency of the entire road network.

Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

Origin-destination (OD) travel time estimation is of paramount importance for applications such as intelligent transportation. In this work, we propose a new solution for OD travel time estimation, with road surveillance camera data. The surveillance information supports accurate and reliable observations at camera-equipped intersections, but is associated with missing and incomplete surveillance records at the camera-free intersections. To overcome this, we propose a modified version of multi-layer graph convolutional networks. The camera surveillance data is used to extract the traffic flow of each intersection, the extracted information serves as the input of the multi-layer GCN based model, based on which the real-time traffic status can be predicted. To enhance the estimation accuracy, we address the effects of various features for the travel time estimation with encoder-decoder networks and embedding techniques. We further improve the generalization of our model by using multi-task learning. Extensive experiments on real datasets are done to verify the effectiveness of our proposals.