Real-time Traffic Conditions Research Articles

PROBE VEHICLE LANE IDENTIFICATION AT SIGNALIZED INTERSECTION USING SUPPORT VECTOR MACHINE METHOD

Transportation problems in Indonesia, especially in urban areas, can be solved by applying Intelligent Transportation Systems (ITS), one of which is by using a Global Positioning System (GPS) based probe vehicle system. This system is growing because smartphones can be used as instruments for investigators' vehicles. This system provides information on the location and speed of all investigator vehicles every second, making it possible to estimate the real time traffic condition on a road segment passed by the probe vehicle. The implementation of this system in the city of Manado can help improve traffic conditions because so many motorists carry smartphones while driving. However, the problem is that the GPS on this smartphone has a low accuracy of 3 to 15 meters, so this GPS data cannot be used to solve transportation problems at the micro level such as at intersections. The purpose of this study was to obtain a method for identifying probe vehicle lanes through GPS data from smartphones. The steps in achieving the research goal are 1) building a microscopic transportation model using software to obtain data 2) building a data processing algorithm for vehicle lane identification using traffic flow theory and machine learning methods which in this case is Support Vector Machine, and 3) perform model validation. The results showed that the individual queue shock wave from each probe vehicle could be used to identify the lane of the probe vehicle. This study also confirms that the Support Vector Machine method can be used to predict the lane position of the probe vehicle.

Optimal scheduling of electric vehicle charging operations considering real-time traffic condition and travel distance

As the number of electric vehicles (EVs) increases rapidly, the problem of electric vehicle charging has widely become a concern. Therefore, considering the fact that charging time for one EV cannot be shortened quickly and the number of charging stations will not expand rapidly, how to schedule charging operations of electric vehicles in urban areas becomes a very important issue, since it can improve charging efficiency and relieve charging anxiety of EV users. Up to now, there is no scheduling software tool for practical use in this field. Based on the analysis of electric vehicle charging behavior characteristics, this paper investigates the EV charging problem at the scheduling level. First, a mathematical model for coordinated charging of EVs is proposed to minimize the total charging time for a given number of vehicles. Second, an earliest finish charging scheduling algorithm is presented to solve the charging problem. Then, by considering the combinatorial nature and practical applications with large number of EVs, two practical swarm-optimization-based EV charging scheduling algorithms are proposed. A real-life case study is presented to illustrate the proposed approaches.

Communication Quality Prediction for Internet of Vehicle (IoV) Networks: An Elman Approach

With the help of the new generation information technology, the Internet of Vehicle (IoV) networks have become widespread. IoV can improve the automatic driving ability, and provide users with intelligence, comfort, safety, energy saving and efficiency traffic services. However, the IoV networks face serious challenges due to the complex wireless environment. The vehicles cannot obtain the real-time traffic condition and early warning information, which leads to the decrease of link quality and the failure of information transmission. To evaluate the communication quality of IoV networks, the outage probability (OP) is commonly employed as a metric. This paper considers mobile IoV networks, and investigates communication quality prediction. Novel OP expressions are derived, which can analyze the OP performance. Then, to predict OP in real time, an intelligent OP prediction approach with an Elman model is proposed. This is evaluated with data generated using the OP expressions. In terms of computational complexity and prediction accuracy, the results obtained show that the Elman-based approach provides better forecasting effect than other methods. For prediction accuracy, the proposed Elman approach is increased by 84.6%. For computational complexity, the execution time is reduced by 79.9%.

An eco-driving algorithm based on vehicle to infrastructure (V2I) communications for signalized intersections

Efficient operations of traffic signals are of critical importance in urban areas, as signalized intersections prevent the smooth flow of traffic and cause delays. This paper devises an eco-driving algorithm based on connected vehicle technologies, with basic kinematic wave and car-following models. The objectives of the proposed algorithm are to increase the throughput of signal intersections and decrease fuel consumption. Specifically, we focus on a signalized intersection under mixed traffic flows with connected and autonomous vehicles (AVs) and human-driven vehicles (HVs). Through the proposed algorithm, the vehicle speeds at the intersection (i.e., the intersection control speed) and signal timings can be adjusted in response to the real-time traffic conditions. According to the signal timing and the speed at the intersection, the algorithm estimates the time points of each vehicle entering the intersection. An advisory speed limit approach is formulated for each AV, making the vehicle enter the intersection at the allocated timing with the control speed. An onboard alert is set for each HV to stop or pass through. The algorithm is evaluated under various market penetration rates of AVs, different congestion levels, and with signal actuation. The results indicate that the eco-driving algorithm can increase the throughput and average travel speed at signalized intersections in addition to gaining fuel savings.

Attention-guided lightweight generative adversarial network for low-light image enhancement in maritime video surveillance

AbstractBenefiting from video surveillance systems that provide real-time traffic conditions, automatic vessel detection has become an indispensable part of the maritime surveillance system. However, high-level vision tasks generally rely on high-quality images. Affected by the imaging environment, maritime images taken under poor lighting conditions easily suffer from heavy noise and colour distortion. Such degraded images may interfere with the analysis of maritime video by regulatory agencies, such as vessel detection, recognition and tracking. To improve the accuracy and robustness of detection accuracy, we propose a lightweight generative adversarial network (LGAN) to enhance maritime images under low-light conditions. The LGAN uses an attention mechanism to locally enhance low-light images and prevent overexposure. Both mixed loss functions and local discriminator are then adopted to reduce loss of detail and improve image quality. Meanwhile, to satisfy the demand for real-time enhancement of low-light maritime images, model compression strategy is exploited to enhance images efficiently while reducing the network parameters. Experiments on synthetic and realistic images indicate that the proposed LGAN can effectively enhance low-light images with better preservation of detail and visual quality than other competing methods.

No-queue road pricing: A comprehensive policy instrument for Europe?

European roads have become increasingly congested over the last decades. Addressing congestion is a multi-faceted issue. In the short term, adequate incentives may shift peak car travel to other times, roads or modes. In the longer term, investment projects should be prioritized to provide an efficient supply of road capacity. This also touches upon funding issues. This paper eclectically reviews the literature to demonstrate the potential of no-queue road pricing (NQRP, i.e., setting tolls responsive to real-time traffic conditions) to contribute to a European policy framework and address three challenges: (i) congestion management; (ii) investment project prioritization; and (iii) sustainable road funding. The limitations of NQRP and reasons why it has not been implemented anywhere in Europe yet are examined. Potential implementation issues are discussed, and a conceptual framework for socially acceptable NQRP is drafted.

Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic

Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 – 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Real-Time Incident-Responsive Signal Control Strategy under Partially Connected Vehicle Environment

The performance of the traffic system can drastically drop when nonrecurrent congestion caused by incidents occurs. Early detection and clearing of traffic incidents will enable the mitigation of the congestion and early restoration of normal traffic conditions. The research in this paper utilized the vehicle information from the recent technological advancement in transportation systems, connected vehicles (CV), and loop-detector information for nonconnected vehicles (NCVs) and developed a novel algorithm to (1) control traffic signals for normal traffic conditions in the absence of incidents, (2) detect traffic incidents using CV/NCV information, and (3) control traffic signals during the occurrence and dissipation of incidents. All the 3 strategies were integrated into one algorithm, which runs as per the real-time traffic conditions, in the presence or absence of incidents. Space-mean speeds of the vehicles on nonincident lanes and throughput maximization criteria were taken as the indicators for the activation of specific signal timings directed at the incident-affected approach. Diverse incident scenarios were tested on a four-legged isolated intersection using the VISSIM simulation tool. Incident detection results showed a higher detection rate and lower mean detection time at higher CV penetration and higher traffic volumes, and at the incident locations nearer to the stop-line. The proposed incident-responsive signal control strategy at 40% and higher CV penetration showed better performance over EPICS adaptive signal control solution, in reducing average travel time delay and the average number of stops per vehicle.

Traffic Signal Control Using End-to-End Off-Policy Deep Reinforcement Learning

An efficient transportation system can substantially benefit our society, but road intersections have always been among the major traffic bottlenecks leading to traffic congestion. Appropriate traffic signal timing adapted to real-time traffic may help mitigate such traffic congestion. However, most existing traffic signal control methods require a huge amount of road information, such as vehicle positions. In this paper, we focus on a particular road intersection and aim to minimize the average waiting time. We propose a traffic signal control (TSC) system based on an end-to-end off-policy deep reinforcement learning (deep RL) agent with background removal residual networks. The agent takes real-time images at the road intersection as input. Upon sufficient training, the agent can perform (near-) optimal traffic signaling based on real-time traffic conditions. We conduct experiments on different intersection scenarios and compare various TSC methods. The experimental results show that our end-to-end deep RL approach can adapt to the dynamic traffic based on the traffic images and outperforms other TSC methods.

Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions

Personalized route recommendation aims to recommend routes based on users’ route preference. The vast amount of GPS trajectories tracking driving behavior has made deep learning, especially inverse reinforcement learning (IRL), a popular choice for personalized route recommendation. However, current IRL studies assume that the traffic condition is static and approximate the expected state visitation frequencies to update the neural network. This study improves the IRL to recommend personalized routes considering real-time traffic conditions. We also improve the expected state visitation frequency calculation based on characteristics of ride-hailing and taxi trajectories to calculate the gradient of the neural network. In addition, the graph attention network is employed to capture the spatial dependencies between road segments. Numerical experiments using real ride-hailing trajectories in Chengdu, China validate our model. At last, a statistical test is conducted, and route preferences reflected by the same driver’s empty trajectories and occupied trajectories are found to have significant differences.

A personalized recommendation system for multi-modal transportation systems

Recommendation system has recently experienced widespread applications in fields like advertising and streaming platforms. Its ability of extracting valuable information from complex data makes it a promising tool for multi-modal transportation system. In this paper, we propose a conceptual framework for proactive travel mode recommendation combining recommendation system and transportation engineering. The proposed framework works by learning from historical user behavioral preferences and ranking the candidate travel modes. In this framework, an incremental scanning method with multiple time windows is designed to acquire multi-scale features from user behaviors. In addition, to alleviate the computational burden brought by the large data size, a hierarchical behavior structure is developed. To further allow for social benefits, the proposed framework proposes to adjust the candidate modes according to real-time traffic states, which is potential in promoting the use of public transport, alleviating traffic congestion, and reducing environmental pollution.

Cost-and-Quality Aware Data Collection for Edge-Assisted Vehicular Crowdsensing

In view of the rapid development of edge computing and vehicular network, edge-assisted vehicular crowdsensing system has brought significant benefits to Intelligent Transportation Systems (ITS). To satisfy the requirements of ITS applications, a substantial number of sensed data are generated continuously, which incurs considerable communication and computation delays. In addition, with fast-moving vehicles and the sheer amount of data, data loss becomes an issue during the process of data uploading. To deal with these challenges, this paper aims to propose a cost-and-quality aware data collection scheme for edge-assisted vehicular crowdsensing system, so as to satisfy the accuracy and timeliness requirements of ITS applications without overloading the network. Particularly, we design a functional architecture for edge-assisted vehicular crowdsensing system, where vehicular sensing, data uploading and sensing parameter adjustment interwork to enable ITS applications. Based on the architecture, two effective algorithms are proposed, which are adaptive clustering and online sensing parameter adjustment. Adaptive clustering algorithm can support reliable and timely data uploading by taking into account the cluster stability and the communication reliability during the process of clustering vehicles. Online sensing parameter adjustment algorithm can guide the actions of intelligent vehicles based on the real-time traffic condition, which can achieve a tradeoff between sensing accuracy and communication overhead. In order to evaluate the performance of our scheme, we develop a simulation platform in which all the components in the system are built. By conducting extensive simulation, the superiority of the proposed scheme is validated.

Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas

On-ramp merging areas are deemed to be typical bottlenecks for freeway networks due to the intensive disturbances induced by the frequent merging, weaving, and lane-changing behaviors. The Connected and Autonomous Vehicles (CAVs), benefited from their capabilities of real-time communication and precise motion control, hold an opportunity to promote ramp merging operation through enhanced cooperation. The existing CAV cooperation strategies are mainly designed for single-lane freeways, although multilane configurations are more prevailing in the real-world. In this paper, we present a flow-level CAV coordination strategy to facilitate merging operation in multilane freeways. The coordination integrates lane-change rules between mainstream lanes, proactive creation of large merging gaps, and platooning of ramp vehicles for enhanced benefits in traffic flow stability and efficiency. The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions. The impacts of tunable model parameters on the produced control plan are discussed in detail. The efficiency of the proposed multilane strategy is demonstrated in a micro-simulation environment. The results show that the coordination can substantially improve the overall ramp merging efficiency and prevent recurrent traffic congestions, especially under high traffic volume conditions.

Implementation of Dynamic and Efficient Virtual Channel Router for Network on Chip with Virtual Channel Arbitration Reduction and Parallel Switch Allocation Unit

— Network on chip (NoC) is a new method of interconnecting modules in system on chip (SoC). Now a day, it is believed that this new communication subsystem will replace the bus-based architecture and will become dominant in SoC design technology. The performance of NoC mainly depends on the router so it is considered as the crucial part in the NoC design. This paper describes the different factors which affect the NoC performance and provides solution to solve these problems. The proposed router has three architectural modifications which makes it dynamic and efficient as compare to generic virtual channel router. This paper introduced the dynamic buffer allocation scheme in which virtual channels and buffer slots are selected and allocated dynamically depending on the network traffic conditions in real time which in turn increases the throughput. In this paper, we have focused on optimizing the virtual channel arbitration (VA) unit design and switch allocation (SA) unit design as well as focusing the maximum utilization of buffer so that it will result in minimizing the latency, area and power. The source code of the router is written in VHDL hardware description language and the simulation and synthesizing the router is done in Xilinx ISE Design Suite 13.1. The parameters like area, frequency, delay, power, latency and throughput are calculated. The results of this modified virtual channel router are compared with the generic virtual channel router. Keywords—Intellectual Property (IP), Network on Chip (NoC), Network Interface (NI), Switch allocation (SA), System-on-Chip (SoC), Virtual channel (VC), Virtual channel arbitration (VA)

Impacts of Real-Time Traffic State on Urban Expressway Crashes by Collision and Vehicle Type

With the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. Variation of traffic flow is considered to have significant impact on the safety performance of expressways. Therefore, the motivation of this study is to explore the mechanism of how the variation of traffic flow measurements such as average speed, speed variation and traffic volume impact the crash risk. Firstly, the crashes were classified according to crash type and vehicles involved: and they are labeled with rear-end collisions or side-impact collisions, they are labeled with heavy-vehicle related collisions or light-vehicle related collisions as well. Then, the corresponding crash data were aggregated based on the similarity of traffic flow conditions and types of crashes. Finally, a random effect negative binomial model was introduced to consider the heterogeneity of the crash risk due to the variance within the traffic flow and crash types. The results show that the significant influencing factors of each type of crashes are not consistent. Specifically, the percentage of heavy vehicles within traffic flow is found to have a negative impact on rear-end collisions and light-vehicle-related collisions, but it has no obvious correlation with side-impact collisions and heavy-vehicle-related collisions. Average speed, speed variation and traffic volume have an interactive effect on the crash rate. In conclusion, if the traffic flow is with higher speed variation within lanes and is with lower average speed, the risk of all types of crashes tends to be higher. If the speed variation within lanes decreases and the average speed increases, the crash risk will also increase. In addition, if the traffic flow is under the conditions of higher speed variation between lanes and lower traffic volume, the risk of rear-end collisions, side-impact collisions and heavy-vehicles related collisions tend to be higher. Meanwhile, if the speed variation between lanes decreases and the traffic volume increases, the crash risk is found to increase as well.

Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks

As the general mobile edge computing (MEC) scheme cannot adequately handle the emergency communication requirements in vehicular networks, unmanned aerial vehicle (UAV)-assisted vehicular edge computing networks (VECNs) are envisioned as the reliable and cost-efficient paradigm for the mobility and flexibility of UAVs. UAVs can perform as the temporary base stations to provide edge services for road vehicles with heavy traffic. However, it takes a long time and huge energy consumption for the UAV to fly from the stay charging station to the mission areas disorderly. In this paper, we design a pre-dispatch UAV-assisted VECNs system to cope with the demand of vehicles in multiple traffic jams. We propose an optimal UAV flight trajectory algorithm based on the traffic situation awareness. The cloud computing center (CCC) server predicts the real-time traffic conditions, and assigns UAVs to different mission areas periodically. Then, a flight trajectory optimization problem is formulated to minimize the cost of UAVs, while both the UAV flying and turning energy costs are mainly considered. In addition, we propose a deep reinforcement learning(DRL)-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Simulation results demonstrate that our proposed method can obtain an optimal flight path and deployment of UAV with lower energy consumption.

A modified lattice hydrodynamic model considering the driver’s predictive and honk effect

Vehicular honk behavior is not uncommon in real traffic. Meanwhile, with the advent of sensors and communication technology, the real-time traffic state can be readily available to drivers. With such information, drivers can estimate the future traffic status and take countermeasures in advance. In this study, we propose a new lattice hydrodynamic model by jointly considering the vehicular honk and driver’s predictive effect. In the linear stability analysis section, neutral stability curve of the model is given, and the modified Korteweg de Vries (mKdV) equation is obtained in nonlinear stability analysis. The kink–antikink soliton solution is obtained by solving the above mKdV equation, which can be used to describe the phase transition of the traffic flow near the critical point. Results show that the honk and the driver’s predictive effect contribute to the traffic flow stability.

Urban Traffic Congestion State Recognition Supporting Algorithm Research on Vehicle Wireless Positioning in Vehicle–Road Cooperative Environment

Vehicle–road cooperative technology applies wireless communication and a new generation of internet technology to urban traffic management, providing an effective way to solve urban traffic congestion and improve traffic efficiency. This article researches the vehicle wireless positioning fusion algorithm, suitable for the actual vehicle–road collaborative environment, which is an important step of urban traffic congestion state recognition. First, based on the error correction of existing wireless positioning algorithms, a weighting indicator considering distance and positioning compound errors is designed, and a vehicle wireless positioning fusion algorithm based on error weighting to eliminate line-of-sight (LOS) and non-line-of-sight (NLOS) error is proposed. Secondly, the wireless positioning fusion algorithm is verified based on accuracy evaluation indicators such as root mean square error (RMSE), Cramer Rao lower bound (CRLB), geometric differentiation of precision (GDOP), and cumulative distribution probability (CDP), and the sensitivity of the distance propagation model parameters to the positioning error is analyzed. The verification results show that the local vehicle wireless positioning fusion algorithm proposed in this article could be useful to locate vehicles in an actual vehicle–road collaborative environment. The positioning accuracy could reach 46.31 m with 67% probability, while the positioning accuracy could reach 122.53 m with 95% probability. The average positioning accuracy could reach 39.97 m. Compared with the two types of wireless positioning methods based on ranging and non-ranging methods, the positioning accuracy is improved by 7.74% and 17.69%. The algorithm can either use the roadside base stations to carry out the individual vehicle positioning or cooperate with GPS positioning and trilateral positioning to make up for the positioning blind spots caused by the lack of signal, interference, or base station overload in the urban complex road environment and, furthermore, improves the robustness of vehicle positioning. The results could assist in all-day real-time traffic congestion state recognition and other actual scenarios.

POINT: Partially Observable Imitation Network for Traffic Signal Control

• This is the first study in the area of traffic signal control that combines the optimization-based signal control algorithm with imitation learning network to generate optimal signal plans in real time. • The proposed model does not need 100% penetration rate of CVs or necessity of complete traffic information in online system. Instead, only partial observable traffic information collected in real time are required for the POINT model. • The proposed model accounts for the stochastic features of traffic flows and diversities of driving behaviors in offline expert signal plan collection. • The proposed model has largely reduced the online computational burden and ready to be implemented in real world. Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. The objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.

Low-carbon VRP for cold chain logistics considering real-time traffic conditions in the road network

PurposeThis paper studies low-carbon vehicle routing problem (VRP) for cold chain logistics with the consideration of the complexity of the road network and the time-varying traffic conditions, and then a low-carbon cold chain logistics routing optimization model was proposed. The purpose of this paper is to minimize the carbon emission and distribution cost, which includes vehicle operation cost, product freshness cost, quality loss cost, penalty cost and transportation cost.Design/methodology/approachThis study proposed a mathematical optimization model, considering the distribution cost and carbon emission. The improved Nondominated Sorting Genetic Algorithm II algorithm was used to solve the model to obtain the Pareto frontal solution set.FindingsThe result of this study showed that this model can more accurately assess distribution costs and carbon emissions than those do not take real-time traffic conditions in the actual road network into account and provided guidance for cold chain logistics companies to choose a distribution strategy and for the government to develop a carbon tax.Research limitations/implicationsThere are some limitations in the proposed model. This study assumes that there are only one distribution and a single type of vehicle.Originality/valueExisting research on low-carbon VRP for cold chain logistics ignores the complexity of the road network and the time-varying traffic conditions, resulting in nonmeaningful planned distribution routes and furthermore low carbon cannot be discussed. This study takes the complexity of the road network and the time-varying traffic conditions into account, describing the distribution costs and carbon emissions accurately and providing the necessary prerequisites for achieving low carbon.