Unmanned aerial vehicle (UAV) is at the heart of modern traffic sensing research due to its advantages of low cost, high flexibility, and wide view range over traditional traffic sensors. Recently, ...increasing efforts in UAV-based traffic sensing have been made, and great progress has been achieved on the estimation of aggregated macroscopic traffic parameters. Compared to aggregated macroscopic traffic data, there has been extensive attention on higher-resolution traffic data such as microscopic traffic parameters and lane-level macroscopic traffic parameters since they can help deeply understand traffic patterns and individual vehicle behaviours. However, little existing research can automatically estimate microscopic traffic parameters and lane-level macroscopic traffic parameters using UAV videos with a moving background. In this study, an advanced framework is proposed to bridge the gap. Specifically, three functional modules consisting of multiple processing streams and the interconnections among them are carefully designed with the consideration of UAV video features and traffic flow characteristics. Experimental results on real-world UAV video data demonstrate promising performances of the framework in microscopic and lane-level macroscopic traffic parameters estimation. This research pushes off the boundaries of the applicability of UAVs and has an enormous potential to support advanced traffic sensing and management.
Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, ...accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors’ detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy Formula Omitted-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network.
Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities of roads and transportation systems have not developed in an equivalent way to efficiently cope with the ...number of vehicles traveling on them. Due to this, road jamming and traffic correlated pollution have increased with the associated adverse societal and financial effect on different markets worldwide. A static control system may block emergency vehicles due to traffic jams. Wireless Sensor networks (WSNs) have gained increasing attention in traffic detection and avoiding road congestion. WSNs are very trendy due to their faster transfer of information, easy installation, less maintenance, compactness and for being less expensive compared to other network options. There has been significant research on Traffic Management Systems using WSNs to avoid congestion, ensure priority for emergency vehicles and cut the Average Waiting Time (AWT) of vehicles at intersections. In recent decades, researchers have started to monitor real-time traffic using WSNs, RFIDs, ZigBee, VANETs, Bluetooth devices, cameras and infrared signals. This paper presents a survey of current urban traffic management schemes for priority-based signalling, and reducing congestion and the AWT of vehicles. The main objective of this survey is to provide a taxonomy of different traffic management schemes used for avoiding congestion. Existing urban traffic management schemes for the avoidance of congestion and providing priority to emergency vehicles are considered and set the foundation for further research.
In order to quantitatively analyze the influence of different traffic conditions on highway crash risk, a method of crash risk assessment based on traffic safety state division is proposed in this ...paper. Firstly, the highway crash data and corresponding traffic data of upstream and downstream are extracted and processed by using the matched case-control method to exclude the influence of other factors on the model. Secondly, considering the weight of traffic volume, speed and occupancy, a multi-parameter fusion cluster method is applied to divide traffic safety state. In addition, the quantitative relationship between different traffic states and highway crash risk is analyzed by using Bayesian conditional logistic regression model. Finally, the results of case study show that different traffic safety conditions are in different crash risk levels. The highway traffic management department can improve the safety risk management level by focusing on the prevention and control of high-risk traffic safety conditions.
As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied ...to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.
This paper presents a novel adaptive traffic signal control scheme that addresses a mixed manual-automated traffic scenario in a typically isolated intersection. The traffic signals are optimized in ...a receding horizon control framework that aims at minimizing the total crossing time of all vehicles, considering their dynamical states. The control scheme ensures comfortable crossing of manually driven vehicles by retaining the basic features of the traditional signal management systems. The optimal signal changing times are broadcasted one cycle ahead, which enables the automated vehicles to tune their speed in order to cross the intersection with minimum stop-delay. More specifically, the framework optimizes the green time of each signal without considering the existing cycle-split concept explicitly. The proposed signal control scheme is evaluated in microscopic traffic simulation considering the different proportion of turning traffic at the intersection and various penetration rates of the automated vehicles. It is observed that the optimization process usually results in the shortest possible green period of each signal that can be realized without reducing the capacity of the intersection at any traffic volumes. Consequently, the resulting short signal cycle which is adaptive to the traffic around the intersection improves the average speed and reduces both the traffic density and the number of idling vehicles. As a consequence, the fuel consumption efficiency and the rate of CO 2 emission around the intersection are also reduced. These results are compared with both the traditional fixed time and the actuated signal control schemes. As the portion of the automated vehicles increases in the case of the proposed scheme, the overall traffic flow performance further improves.
In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent ...impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved
machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.
Vehicular traffic is endlessly increasing everywhere in the world and can cause terrible traffic congestion at intersections. Most of the traffic lights today feature a fixed green light sequence, ...therefore the green light sequence is determined without taking the presence of the emergency vehicles into account. Therefore, emergency vehicles such as ambulances, police cars, fire engines, etc. stuck in a traffic jam and delayed in reaching their destination can lead to loss of property and valuable lives. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the measurement of the distance between the emergency vehicle and an intersection using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. The distance between the emergency vehicle and the intersection is calculated for comparison using Euclidean distance, Manhattan distance and Canberra distance techniques. The experimental results have shown that the Euclidean distance outperforms other distance measurement techniques. Along with visual sensing techniques to collect emergency vehicle information, it is very important to have a Medium Access Control (MAC) protocol to deliver the emergency vehicle information to the Traffic Management Center (TMC) with less delay. Then only the emergency vehicle is quickly served and can reach the destination in time. In this paper, we have also investigated the MAC layer in WSNs to prioritize the emergency vehicle data and to reduce the transmission delay for emergency messages. We have modified the medium access procedure used in standard IEEE 802.11p with PE-MAC protocol, which is a new back off selection and contention window adjustment scheme to achieve low broadcast delay for emergency messages. A VANET model for the UTMS is developed and simulated in NS-2. The performance of the standard IEEE 802.11p and the proposed PE-MAC is analysed in detail. The NS-2 simulation results have shown that the PE-MAC outperforms the IEEE 802.11p in terms of average end-to-end delay, throughput and energy consumption. The performance evaluation results have proven that the proposed PE-MAC prioritizes the emergency vehicle data and delivers the emergency messages to the TMC with less delay compared to the IEEE 802.11p. The transmission delay of the proposed PE-MAC is also compared with the standard IEEE 802.15.4, and Enhanced Back-off Selection scheme for IEEE 802.15.4 protocol EBSS, an existing protocol to ensure fast transmission of the detected events on the road towards the TMC and the comparative results have proven the effectiveness of the PE-MAC over them. Furthermore, this research work will provide an insight into the design of an intelligent urban traffic management system for the effective management of emergency vehicles and will help to save lives and property.
•We model the mixed traffic of traditional vehicles (TVs) and connected vehicles (CVs).•We examine the mixed traffic flow efficiency and safety.•We incorporate human factors, particularly driver ...compliance.•CVs can enhance traffic flow efficiency and safety.•CV’s spatial distribution is important.
In the foreseeable future, connected vehicles (CVs) will coexist with traditional vehicles (TVs) resulting in a complex mixed traffic environment and the success of CVs will depend on the characteristics of this mixed traffic. Therefore, before the large scale deployment of CVs, it is important to examine how CVs will influence the characteristics of the resultant mixed traffic. To achieve this aim, this study models the mixed traffic of TVs and CVs, and examines the traffic flow disturbance, efficiency, and safety. Intelligent Driver Model (IDM) with estimation errors is utilised to model TVs since it incorporates human factors such as estimation errors. Whereas, connected vehicle driving strategy integrated with IDM is utilised to model CVs because it incorporates driver compliance, a critical human factor for the success of CVs. Moreover, two classes of drivers based on their compliance levels are considered, namely the high-compliance drivers and the low-compliance drivers, to comprehensively investigate the impact of driver compliance on the mixed traffic of CVs and TVs. Two simulation experiments are performed in this study. The first experiment is used to measure traffic flow disturbance and safety while the second is used to measure the traffic flow efficiency. Furthermore, a total of 7 mixed traffic environments are generated in each experiment via different combinations of TVs, CVs with low compliance drivers, and CVs with high compliance drivers. Another important point considered in the simulation is the spatially distribution of CVs in the platoon. As such, three platoon policies are investigated. In the first policy i.e., the best case, the CVs are spatially arranged with a motive to maximise benefits from CVs whereas in the second policy i.e., the worst case, the CVs are spatially arranged with a motive to minimise benefits from CVs. Finally, in the third platoon policy i.e., the random case, the CVs are distributed randomly in the platoon. This study demonstrates the importance of the spatial arrangement of CVs in a platoon at a given penetration rate and its impact on traffic flow disturbance, efficiency, and safety. Moreover, findings from this study underscores that CVs can supress the traffic flow disturbance, and enhance traffic flow efficiency, and safety; however, traffic engineers and policy makers have to be cautious regarding how CVs are distributed in a traffic stream when deploying these vehicles in the real world traffic environment.
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