Search

Highlights • A new method of constructing traffic state vectors is proposed. • Variables are selected using spatio-temporal correlations based on Mutual Information. • Weighted distance is used in selecting K nearest neighbors. • The proposed method can improve the prediction accuracy of data-driven models. Abstract Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction. [ABSTRACT FROM AUTHOR]

In emergencies cases, the main problem is how to arrive to an incident location in a minimum time. In such cases, using the classical distance based routing techniques does not always provide the optimal path in the time needed. In this paper, we present an enhanced routing technique which is based on Dijkstra's algorithm and Analytical Hierarchical Processing (AHP). Eight impendence factors that cause latency in emergency situations are analyzed using the AHP technique to give an overall weight for every road in Greater Cairo Region (GCR) road network. Then, the classical Dijkstra's algorithm applied on the weighted road network to give the best path between any two nodes in the network. The eight impedance factors considered are: Road Length, Travel Time, Road Type, Road Width, Velocity Limits, Traffic Volume, Mass Density, and Junction Delay. The suggested routing approach is implemented as a mobile application, and a comparison with the classical Dijkstra's algorithm is performed. The results show that the enhanced routing technique is more reliable than the classical one and more suitable for emergencies situations. [ABSTRACT FROM AUTHOR]

Realistic modeling traffic flow dynamics in urban traffic network remains a big challenge at present due to the complex nonlinear characteristics of traffic flow. In this paper, a novel and model-free iterative learning control (ILC) strategy for balancing traffic flow in urban traffic network is proposed. To tackle the randomly varying trial lengths in the iteration domain of traffic system, an iterative-average operator is introduced in the proposed ILC law for tracking tasks with non-uniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. The learning convergence condition of the ILC strategy in iteration-average and expectation is derived through rigorous analysis. The performance and the effectiveness of the ILC strategy are analyzed by simulations on a test road network. The results show that the proposed ILC strategy can homogeneously balance the accumulation in the network and improve the network mobility. [ABSTRACT FROM AUTHOR]

Highlights • Closed form formulas for green times (expectation/variance) and vehicle waiting time. • Numerical results are provided to support the findings. Abstract We study green extension of a two-phased vehicle actuated signal at an isolated intersection between two one-way streets. The green phase is extended by a preset time interval, referred to as critical gap, from the time of a vehicle actuation at an advance detector. The green phase switches if there is no arrival during the critical gap. We develop an exact model to study the intersection performance with traffic following Poisson processes. We further extend the model to approximate the case of general traffic. Our model in the general case works well compared with Monte Carlo simulation. A few major observations include: (1) The optimal critical gap decreases with the traffic; (2) The optimal critical gap can be much larger (up to 5 s) than the common presumption of 2–3 s; (3) Queue clearance policy is not nearly optimal in general even in the case of heavy traffic. [ABSTRACT FROM AUTHOR]

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km 2 ) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%–40% of vehicular emissions (e.g., CO 2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NO X ). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITS) - Connected Vehicle technology in the USA - where vehicle and roadside infrastructure communicates to deliver more intelligent traffic management, is one of a range of ITS technologies emerging as a key component in pursuit of the wider objectives of improved urban mobility. This paper presents the Compass4D project, which deployed a C-ITS system in seven European cities and coordinated the common evaluation of the technology for three services focusing specifically on safety and environmental objectives under real-world driving conditions. The significance of the Compass4D deployments and results provides some of the first evidence of the effectiveness of C-ITS in real world conditions. Both light and heavy vehicles showed efficiency savings of 2–6%. Equipped buses exhibited a variety of results with one pilot site showing a reduction of greater than 200gCO 2 per bus route per trip whilst other buses showed an increase in total emissions. The paper presents results from both field trials and microscopic simulation studies (to understand the network- or city-wide impacts of the technology). It discusses the results in detail before outlining the potential for further deployment in terms of the impact on energy efficiency and environmental objectives. Government and road operators will benefit from the results to gain an understanding of the potential impact of services given specific deployment characteristics. [ABSTRACT FROM AUTHOR]

Urban signal intersection as the key node in the urban road network, its operational efficiency directly affects the overall operational efficiency of the road network traffic. The existing urban road signal intersections cannot be broadened due to the limitation of road size and boundary lines of road. Some signalized intersections adopt straight-right lane design to improve the traffic efficiency of vehicles at intersections. This design can improve the traffic efficiency of straight-through vehicles during the green light phase. However, straight-through vehicles stopping in the straight-right lane during the red light will prevent right-turning vehicles from passing the intersection. This phenomenon causes delays for right-turning vehicles. In order to deal with the above issues, this paper takes the concept of separating straight and right-turning vehicles in spatial and temporal on the dynamic straight-right lane (DSRL). The organization form of vehicles entering DSRL is designed based on signal phase time. The control strategy for DSRL with the interaction of traffic lights and pre-signal is formulated integrated with the intersection design. The delay indexes of straight and right-turning vehicles under DSRL design are quantified using cellular automata model to calculate vehicle delays under the design form of DSRL. Finally, the test platform based on the actual intersection is established with MATLAB to simulate the operation of the vehicles under the DSRL design. The experiment shows that the delay reduction degree of straight-through vehicles is between 1.2% and 4.5%, and the reduction degree of right-turning vehicles delay is more than 66%, that the operation efficiency of intersection is improved. • In terms of design concept, this paper proposes DSRL design scheme, which separates straight-through vehicles and right-turning vehicles from temporal and spatial. Right-turning vehicles are not delayed during the red phase while both straight and right-turning vehicles can successfully pass the intersection during the green phase under DSRL. The traffic efficiency of vehicles at the intersection can be greatly improved. • In terms of control method, the control strategy proposed in this paper is more flexible than the original variable lanes. This paper proposes pre-signal and traffic signal control scheme under DSRL. The calculation method of signal timing parameters and suggesting parameters of signal timing are given according to the ideal operating conditions of the traffic flow. The signal control strategy and DSRL design cooperating together can make traffic flow operate better and fully exploit the lane traffic capacity. • In terms of test results, this paper builds a microscopic model to evaluate the performance of the DSRL. By systematically analyzing key parameters, DSRL design can effectively reduce vehicle delays in different traffic environments. This paper also lays the foundation for optimizing the indicators under DSRL design and provides guidance for the implementation of DSRL design. [ABSTRACT FROM AUTHOR]

Abstract Anomaly detection is an important part of an Intelligent Transportation System. In this study, image processing and machine learning techniques are used to detect anomalies in vehicle movements. These anomalies include standing and traveling in reverse direction. Images are captured using CCTV cameras from front and rear side of the vehicle. This capability makes the results robust to the variations in operational and environmental conditions. Multiple consecutive frames are acquired for motion detection. Features such as edges and license plate corner locations are extracted for tracking purposes. Direction of the traffic flow is obtained from the trained classifier. K-nearest neighbor is chosen as the classifier model. The proposed method is evaluated on a public highway and promising detection results are achieved. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study the application of perimeter flow control by simulation of cellular automaton model on urban traffic system. We find that the relation of traffic flow and vehicle density (Macroscopic Fundamental Diagram, MFD) will have different shapes for the core area and the peripheral area. The MFD shows free-flow state, saturate flow state and congestion state. But the magnitude of maximal flow and the position for congestion transition are different for the whole system and the core area. We suggest to realize the perimeter control strategy by assigning a special prohibiting phase to the perimeter traffic lights for the roads entering the core area. The strategy is controlled by two critical densities ρ 1 and ρ 2 , which are determined by the MFD of the core area. Simulations show that both the average arrival rate and the average flow will be greatly improved with the perimeter flow control strategy. In addition, the perimeter flow control strategy can increase the critical density of traffic congestion. The probability of system-wide gridlock will decrease, and the system can perform well under both close and open boundary conditions. All the results indicate that the perimeter flow control strategy can effectively improve the performance of the traffic system. Highlights • MFD has different shape for the system and for the core area. • Parameters of perimeter control should be determined by the MFD of core area. • Perimeter control can reduce gridlock and increase the critical density. [ABSTRACT FROM AUTHOR]

Abstract Based on the cellular automaton model and the concept of ant pheromone, this paper proposes a new route guidance strategy, called the ant pheromone route guidance strategy, in which the vehicles are regarded as special types of ants and their traffic information is regarded as the ant pheromone. To evaluate its performance, the new route guidance strategy was applied and compared with other three typical route guidance strategies under three different route scenarios, respectively, with open boundary conditions based on the Nagel–Schreckenberg cellular automaton model. First, in a symmetrical two-route scenario with two exits, results showed that the new route guidance strategy and vacancy length route guidance strategy were optimal. They outperformed the other strategies in terms of the value, stability, and balance of vehicle number, and the average speed and average flux on each route. To understand the impact of the strategy on traffic states, flux–density evolution process was studied. In addition, the influence of evaporation rate was also investigated. Second, in an asymmetrical two-route scenario with one exit, the simulation results also proved that ant pheromone route guidance strategy and vacancy length route guidance strategy were the best. Finally, in a symmetrical two-route scenario with a traffic accident, we compared the new route guidance strategy with the vacancy length route guidance strategy. Results indicated that the new route guidance strategy has distinct advantages. In short, in all three route scenarios we investigated, the ant pheromone route guidance strategy performed best. Highlights • This paper proposes the ant pheromone route guidance strategy based on CA model. • The new strategy takes vehicles as ants and vehicles' data as ant pheromones. • Adopting the NS model, this strategy is applied to three two-route scenarios. • Results indicate that the new strategy outperforms other route guidance strategies. • In traffic accident, the new strategy can reduce congestion and save recovery time. [ABSTRACT FROM AUTHOR]

Abstract By means of the Nagel–Schreckenberg model, we have investigated into the maximum vehicular flow rate of traffic processing bottlenecks. The evaluated analytical form of this flow rate is found to give quantitative insights into the underlying physics of collective vehicular motions constrained by these bottlenecks. Our analysis shows that for large-scale expansion, a new class of processing bottleneck known as the serial bottleneck is more efficient than the conventional parallel bottleneck in the absence of human driving behavior. When characteristics such as slow-to-start is considered in the model, the consequential delay due to human reaction time not only degrade the overall efficiency, it also diminishes the efficacy of serial processing such that a serial bottleneck is no longer tenable for traffic processing. These results point to the fundamental importance of optimizing traffic efficiency, which we illustrate by elucidating the detailed mechanisms with which vehicles interact collectively in the bottlenecks. In particular, we demonstrate that by constructing combinations of serial and parallel bottlenecks, optimal efficiencies are achieved via configurations with few (many) lanes of a large (small) number of serial units when the processing time is short (long). A direct implication of these results is that autonomous self-driving vehicles could serve to improve the transportation capacity for the densely populated urban cities of the future, due to the intrinsically more efficient collective vehicular motions through these bottlenecks. Highlights • Capacity of serial and parallel bottlenecks have been compared using full analytical solutions. • Analysis shows that the difference in the constrained motion results in the difference in capacity. • Novel combination of serial and parallel expansions was proposed. • The efficacy of these bottlenecks in relevance of real-world traffic have been discussed. [ABSTRACT FROM AUTHOR]

Map matching combining vehicle Global Positioning System (GPS) with road attribute information has a significant impact on the traffic route guidance. In the current work, an advanced topological map matching algorithm based on the D-S (Dempster-Shafer) theory is proposed to improve the application for the high-density road network. Data cleaning is carried out to delete the apparent errors of original data and to reduce the data sources. Matching features of the GPS point stated four factors, namely the road speed limit, heading, proximity and spatial correlation. These factors are in line with the vehicle travel characteristics. The D-S theory is applied to dynamically estimate the weight of each factor and further to calculate the fusion result in order to increase the proposed algorithm flexibility. An optimization method based on trajectory shape matching is also proposed to filter the candidate point set to enhance the algorithm accuracy. The results established that the data cleaning can eliminate the invalid data up to more than 5% of the collected data; where using the four factors can guarantee the basic demand of the data matching, while the D-S theory and shape matching can effectively improve the matching accuracy. The proposed system achieved an overall algorithm matching accuracy up to 96.5%. [ABSTRACT FROM AUTHOR]

Short-term traffic flow forecasting is a key problem in the area of intelligent transportation systems (ITS). Timely and accurate traffic state prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. In this paper, a new hybrid model for short-term traffic flow forecasting, which is built based on multifractal characteristics of traffic flow time series, is proposed. The hybrid model decomposes traffic flow series into four different components, namely a periodic part, a trend part, a stationary part and a volatility part, to unearth the traffic features hidden behind the data. Four parts are treated and modeled separately by using different methods, such as spectral analysis, time series and statistical volatility analysis, to further explore the underlying traffic patterns and improve forecasting accuracy. Performance of the proposed hybrid model is investigated with traffic flow data from freeway I-694 EB in the Twin Cities. The experimental results indicate that the proposed model outperforms in capturing nonlinear volatility and improving forecasting accuracy than traditional forecasting methods, especially for the multi-step ahead forecasting. Compared with the ARIMA-GARCH model, it gets an improvement of 8.23% in RMSE for one-step ahead forecasting and 10.69% for ten-step ahead forecasting. It is better than the hybrid model newly proposed in literature (Zhang et al. Transp Res Part C: Emerg Technol 43(1):65-78 2014) and gets an improvement of 1.27% in forecasting accuracy. [ABSTRACT FROM AUTHOR]

Road traffic speed prediction is a challenging problem in intelligent transportation system (ITS) and has gained increasing attentions. Existing works are mainly based on raw speed sensing data obtained from infrastructure sensors or probe vehicles, which, however, are limited by expensive cost of sensor deployment and maintenance. With sparse speed observations, traditional methods based only on speed sensing data are insufficient, especially when emergencies like traffic accidents occur. To address the issue, this paper aims to improve the road traffic speed prediction by fusing traditional speed sensing data with new-type “sensing” data from cross domain sources, such as tweet sensors from social media and trajectory sensors from map and traffic service platforms. Jointly modeling information from different datasets brings many challenges, including location uncertainty of low-resolution data, language ambiguity of traffic description in texts, and heterogeneity of cross-domain data. In response to these challenges, we present a unified probabilistic framework, called Topic-Enhanced Gaussian Process Aggregation Model (TEGPAM), consisting of three components, i.e., location disaggregation model, traffic topic model, and traffic speed Gaussian Process model, which integrate new-type data with traditional data. Experiments on real world data from two large cities validate the effectiveness and efficiency of our model. [ABSTRACT FROM AUTHOR]

Travel time is an important performance measure used to assess traffic operational quality of various types of facilities. Previous efforts to estimate travel time have also compared model-estimated travel times to field-measured travel times using various sources of data. Given the variety and diversity of travel time measurement methods, it is important to evaluate the accuracy of the data obtained by each of them and to develop recommendations regarding their suitability in the validation of travel time estimation models. The research objective for this paper was to collect field data along several freeways and arterials and to evaluate the travel times obtained by STEWARD (Statewide Traffic Engineering Warehouse for Regionally Archived Data), INRIX, BlueTOAD (Bluetooth Travel-time Origination and Destination), and HERE. Benchmark data were collected with the use of an instrumented vehicle at five freeway segments and two arterial segments in Florida. The field-measured travel times were statistically compared with the travel times provided through various methods. The results suggest that the HERE traffic data provide better freeway travel time estimates compared to the remaining methods. In oversaturated conditions, STEWARD, INRIX and BlueTOAD data seem to underestimate travel times, while HERE data were found to be more accurate. For undersaturated freeways, STEWARD, INRIX and BlueTOAD were found to perform better than HERE. At the arterial sites BlueTOAD and HERE travel time data were analyzed and the analysis suggests that none of the methods is accurate, possibly due to the small sample size. [ABSTRACT FROM AUTHOR]

Inter-vehicles Safety Warning Message (SWM) exchange is used to remind surrounding vehicles to avoid potential danger and considered as an effective means to enhance road safety level. For the high happening frequency of sub-health vehicles (SHV), the traditional flooding mechanism, which is adapted in emergency warning message (EWM) transmission, is not applicable for SWM delivery. In this paper, a communication demand matrix (CDM) is defined to illustrate inter-vehicles relationship, while a novel channel allocation algorithm based on communication demand matrix is proposed. Moreover, a CDM based SWM transmission mechanism is designed. Both simulations and experiments are done to inspect performance of proposed method. The simulation and experimental results show that the proposed mechanism has much higher ratio of fulfilled communication demand in the same time slots than flooding based on communication demand relationship (FD) methods in low, medium, and high vehicle density scenario. [ABSTRACT FROM AUTHOR]

Due to its geographical situation, the port of Ploče has become a junction of land and sea routes within the Transport Corridor Vc, as a part of the Mediterranean Transport Corridor, passing through Bosnia and Herzegovina, Croatia and Hungary. The paper deals with the analysis of the connecting roads leading from the port of Ploče as well as with the evaluation of the respective road traffic volume. Although the congestion on certain road sections is related to the tourist transit traffic, the road traffic volume at the Transport Corridor Vc within Bosnia and Herzegovina indicates the need for the construction of a motorway to stretch out the Corridor. The planned modernisation will fundamentally improve the main road infrastructure on the Transport Corridor Vc that will facilitate the transport of goods and enable shorter travel time from the port of Ploče to the hinterland. Considering that the major portion of the freight transport from the port of Ploče uses road transport services, the envisaged modernisation will significantly increase competitiveness of the port, mostly due to the intermodal transport mode. [ABSTRACT FROM AUTHOR]

VCC leverages the underutilized storage and computing resources of vehicles to collaboratively provide traffic management, road safety, and infotainment services to end users, such as drivers and passengers. It is a hybrid technology that improves the resource utilization on vehicles and is able to perform complex computing tasks that cannot be handled by a single vehicle. Despite the appealing advantages, security and privacy threats are severe in VCC due to the sharing of resources among unfamiliar vehicles. In this article, we identify security goals for the interoperability with VCC and provide an AKA framework for VCC. Specifically, we first present the research challenges and open problems for designing a reliable AKA with strong security guarantees for VCC. Then we propose an integrated AKA framework that integrates the single-server 3-factor AKA protocol and the non-interactive identity-based key establishment protocol, and evaluate its performance based on a simulated experimental platform. Finally, several interesting issues are discussed to light up the further research directions on AKA for VCC. [ABSTRACT FROM AUTHOR]

A wide variety of Intelligent Transportation Systems (ITS) are developed to increase travel safety, improve traffic management and minimize environmental impact. C-ITS (cooperative ITS) are a subclass of ITS based on the communication between the vehicles and/or communication between the vehicles and the infrastructure. C-ITS are promising solutions to improve transport systems thanks to a local information processing. In that way, the information processing latency is minimal, the information is up to date and relevant to the current road environment context. Nevertheless the deployment of these systems in the vehicular ad-hoc network (VANET) raises many difficulties. Our goal is to help the designer of a C-ITS to focus only on the expertise that is required to satisfy his objectives and not on the technical issues that the deployment in a VANET implies. To reach this objective, we propose a platform which consists of a set of modules ITS (m-ITS), each of these m-ITS relies on a generic architecture hosting on stationary and mobile agents dedicated to the support of C-ITS in VANET. The objective in deploying mobile agents is reinforcing the local processing of the information. Finally an example of C-ITS for contextual speed management illustrates our proposal. [ABSTRACT FROM AUTHOR]

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented. [ABSTRACT FROM AUTHOR]

In case of railway disruptions, traffic controllers are responsible for dealing with disrupted traffic and reduce the negative impact for the rest of the network. In case of a complete blockage when no train can use an entire track, a common practice is to short-turn trains. Trains approaching the blockage cannot proceed according to their original plans and have to short-turn at a station close to the disruption on both sides. This paper presents a Mixed Integer Linear Program that computes the optimal station and times for short-turning the affected train services during the three phases of a disruption. The proposed solution approach takes into account the interaction of the traffic between both sides of the blockage before and after the disruption. The model is applied to a busy corridor of the Dutch railway network. The computation time meets the real-time solution requirement. The case study gives insight into the importance of the disruption period in computing the optimal solution. It is concluded that different optimal short-turning solutions may exist depending on the start time of the disruption and the disruption length. For periodic timetables, the optimal short-turning choices repeat due to the periodicity of the timetable. In addition, it is observed that a minor extension of the disruption length may result in less delay propagation at the cost of more cancellations. [ABSTRACT FROM AUTHOR]

As vehicular networking has recently been developed and commercialized, vehicular cloud computing has received much attention in various research areas, such as intelligent transportation systems and vehicular ad hoc networks. An efficient intersection traffic control using vehicular cloud computing is one of the key research topics in intelligent transportation systems. To efficiently deal with intersection traffic control via vehicle-to-vehicle communications, we design a distributed mutual exclusion algorithm that does not rely on broadcast, which introduces communication overheads; instead, our algorithm use point-to-point messages sent between the vehicles to keep network traffic load lower. In our algorithmic design, to pass an intersection, the lead vehicle on a lane must get permissions from a subset of other vehicles and its following vehicles on the same lane can follow the lead vehicle without permissions unlike the previous research. To evaluate the performance of our distributed mutual exclusion algorithm, we conduct extensive experiments. The results show that our algorithmic design is both effective and efficient. [ABSTRACT FROM AUTHOR]

Transport telematics systems integrate IT with telecommunications in applications for different transport systems. Through their implementation, the use of transport infrastructure and means of transport increases, while increasing the level of safety of vehicles and facilities performing transport tasks. The implementation of modern Intelligent Transport Systems in road transport necessitates their evaluation (including reliability and operation) and the rational selection for planned criteria. The article presents the author's work on criteria and sub-criteria that will be used to evaluate technical and teleinformatic solutions for ITS services. [ABSTRACT FROM AUTHOR]

Recently, multirotor Unmanned Aerial Vehicles(UAVs) or drones have become increasingly popular for a vast variety of civil applications. Efficient traffic data collection and extraction of various flow parameters are some of the futuristic applications of this technology. However, such applications still need to be streamlined and thoroughly explored for varying traffic and infrastructural conditions. In this paper, the focus is on the authentication of the application of small multirotor UAVs for traffic data collection and subsequent analysis of traffic streams at urban roundabouts. This paper presents an analytical methodology to evaluate the performance of roundabouts by extracting various parameters and performance indicators. The performance evaluation methodology is based on: (i) determining traffic volume via OD matrices for each leg, and (ii) analyzing drivers’ behavior via gap-acceptance analysis. The overall analytical process is principally based on the authors’ previously proposed automated UAV video-processing framework for the extraction of vehicle trajectories. The extracted trajectories are further employed to extract useful traffic information. The experimental data to analyse roundabout traffic flow conditions was obtained in the city of Sint-Truiden (Belgium). The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. With the significant increase in the usage of UAVs expected in the coming years, such studies could become a useful resource for practitioners as well as future researchers. The future research will mainly focus on further extensions of the UAV-based traffic applications. [ABSTRACT FROM AUTHOR]

This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP and market penetration scenarios, changes in mobility in terms of VMT and average travel time, as well as changes in energy consumption are estimated. The state-of-the-art integrated activity-based modeling (ABM) and traffic simulation software POLARIS is used to model the mobility impact. Moreover, POLARIS is coupled with AUTONOMIE to analyze the resulting fuel consumption. [ABSTRACT FROM AUTHOR]

Network path selection defines the methodology of selecting the best routes and forwarding traffic in a network service provider (NSP). NSPs use routing protocols that are optimized for a single arbitrary metric (i.e., administrative weight), which is commonly calculated according to the link state information, to select network paths. Despite the advantages, link-state protocols lack the ability to select network paths by considering the states of network devices such as the effect of routers for network path selection. Apparently, studying possible techniques for selecting network paths based on the state information of network devices, e.g., routers, has become obligatory. This paper studies the effectiveness of using network device state information for network path selection by presenting the Extended Server Link Router state Routing Protocol (ESLR). ESLR uses the state information of network devices as parameters to calculate a composite route metric; ESLR selects the network paths based on the cumulative propagation delay. By simulating ESLR using an ISP topology, the proposed protocol is examined for the effectiveness of using network device state information for network path selection. [ABSTRACT FROM AUTHOR]

The availability, accuracy and relevance of real world input data are essential for developing a reliable traffic simulation model. Large-scale traffic simulation models, in particular, require data from many sources and in great detail. Though it is now possible to obtain detailed field data with the advent of new technologies such as GPS, cellular phones, RFIDs, it is still a challenge to gather all available data, especially traffic flow data, in the required spatial and temporal accuracy. The central theme of this paper is the calibration and validation (C&V) development of a large-scale traffic simulation model using data from multiple sources. [ABSTRACT FROM AUTHOR]

In this paper, we present necessary premises for the deployment of the Internet of Vehicles (IoV) integrating Big Data analytics of road network traffic measurements of the city of Mohammedia, Morocco. Thus, we introduce an architecture based on three main layers such as IoV, Fog Computing and Cloud Computing Layer. We specifically put more focus on Fog Computing layer in which we develop a framework for a real-time collecting and processing events generated by intelligent vehicles as well as visualizing traffic state on each road section. Furthermore, we consider deployment and test of the proposed framework using events retrieved from a Vanets -type micro simulation. Finally, we present and discuss the first obtained results as well as the advantages and limitations of the proposed architecture. [ABSTRACT FROM AUTHOR]

Decision support systems are of paramount importance to propose traffic control strategies and reliable information to road users. They usually take benefits from data-driven methods which represent a reliable way to gather real time traffic information and make predictions for recurring situations. Data Assimilation (DA) consists in considering both observed data and a traffic flow model to monitor and forecast traffic state. Traditionnaly, Kalman filtering methods with macroscopic traffic models have been widely used. More recently, mesoscopic approaches proved to be relevant for large scale network applications. However, errors on traffic state is a key information for DA methods. The paper proposes a DA framework that accounts for two types of error: (i) errors from the data collection system and (ii) errors that are propagated and amplified in time and space by the dynamic traffic flow model. When applied on a basic network, the proposed framework demonstrates its ability to track errors and the traffic state information is enriched with a level of uncertainty. It paves the way for new traffic indicators and new solutions for traffic forecast applications. [ABSTRACT FROM AUTHOR]

This study presents a novel sequential modeling framework of shared autonomous vehicle (SAV) operation in the Halifax transport network. The Halifax regional transport network model is used to generate business-as-usual traffic demand in the morning peak hours. A new module of SAV assignment upon trip request is introduced and integrated with the traffic microsimulation model to simulate the SAVs’ occupied and empty trips in the network. The proposed framework demonstrates the capability to evaluate service performance of SAVs with different level of fleet sizes in the network. Model results suggest that fleet size of 900 SAVs serves 20% of the total morning commute trip requests. Traffic condition is improved for the first hour of peak periods as average speed increases and total travel time requirement decreases during operation of SAV fleet in Halifax. The results provide insights into SAV system planning in accordance to anticipated challenges of SAV adoption in transportation network. [ABSTRACT FROM AUTHOR]

The number of people killed in traffic accidents in Morocco reached 3593 during 2016, compared to 3565 in 2015, registering a slight increase of 0.79%. Thus, the total of traffic accidents over the past year amounted to 81827 against 78864 in 2015, an increase of 3.76%, according to provisional statistics of traffic accidents in 2016. These statistics show that Morocco is one of the countries with the highest mortality rate. In order to decrease these numbers and put an end to this dilemma, we are working on developing vehicle-to-vehicle communication (V2V) in order to provide information and assistance required to avoid collisions, traffic-jam, etc. Vehicular Ad-Hoc Networks (VANETs) have great potential for providing safety and comfort applications for Intelligent Transport Systems (ITS) and to improve traffic safety on roads. In this paper, we have consecrated our study on Casablanca, the largest city in Morocco which has a highway passing through it. Thus, we took two scenarios, first the highway and trunk roads and second the other roads (primary, secondary, tertiary and residential roads). To demonstrate these scenarios, we used various tools such as OpenStreetMap and SUMO to generate a realistic mobility model that gives aspects of real vehicular traffic. Generated scenarios are added to NS-2.35 (Network Simulator) in order to analyze the performance of 802.11a (Wi-Fi) and 802.11p (DSRC/WAVE). We evaluated the performance based on three parameters: Packet Delivery Ratio, End-To-End Delay and Throughput. The results showed that 802.11p is better and more efficient than 802.11a under both scenarios. In addition, 802.11p gives better results in the second scenario than in the first one, and this is due to the number of cars, the authorized speed and the distance between the cars in each scenario. [ABSTRACT FROM AUTHOR]

We present a novel data-driven approach to populate virtual road networks with realistic traffic flows. Specifically, given a limited set of vehicle trajectories as the input samples, our approach first synthesizes a large set of vehicle trajectories. By taking the spatio-temporal information of traffic flows as a 2D texture, the generation of new traffic flows can be formulated as a texture synthesis process, which is solved by minimizing a newly developed traffic texture energy. The synthesized output captures the spatio-temporal dynamics of the input traffic flows, and the vehicle interactions in it strictly follow traffic rules. After that, we position the synthesized vehicle trajectory data to virtual road networks using a cage-based registration scheme, where a few traffic-specific constraints are enforced to maintain each vehicle's original spatial location and synchronize its motion in concert with its neighboring vehicles. Our approach is intuitive to control and scalable to the complexity of virtual road networks. We validated our approach through many experiments and paired comparison user studies. [ABSTRACT FROM AUTHOR]

Understanding factors that influence the duration time of incidents are important for traffic incident management agencies to design effective mitigation strategies. Previous studies have proposed different approaches for examining the impact of influential factors on incident clearance and response time. However, very few studies have considered incident clearance and response time as two dependent variables and used a joint modeling framework. The objectives of this paper are to investigate the dependence between incident clearance and response time and to examine the applicability of the copula approach to the joint analysis of these two variables. To demonstrate advantages of the proposed copula modelling framework, incident clearance and response time data collected on freeway road sections in Seattle, Washington State are examined. Parameter estimation and prediction results from the proposed copula models are presented and compared with the conventional accelerated failure time model. The modeling results suggest that the proposed copula model can better describe the estimated conditional survival probability of incident clearance time, and can provide marginally more accurate prediction results. The proposed model can also provide different inferences about effects of factors on incident clearance and response time data. Overall, the findings in this paper provide a framework for jointly modeling incident clearance and response time by considering their dependence. [ABSTRACT FROM AUTHOR]

This study presents a thorough microscopic simulation investigation of a recently developed model-based approach for per-lane density estimation, as well as on-ramp and off-ramp flow estimation, for highways in the presence of connected vehicles. The estimation methodology is mainly based on the assumption that a certain percentage of vehicles is equipped with Vehicle Automation and Communication Systems (VACS), which provide the necessary measurements used by the estimator, namely vehicle speed and position measurements. In addition, a minimum number of conventional flow detectors is needed. In the investigation, a calibrated and validated, with real data, microscopic multi-lane model is employed, which concerns a stretch of motorway A20 from Rotterdam to Gouda in the Netherlands. It is demonstrated that the proposed methodology provides satisfactory estimation performance even for low penetration rates of connected vehicles, while it is also shown that the method is little sensitive to the parameters (two in total) of the model utilized by the estimator. [ABSTRACT FROM AUTHOR]

Sight distance analyses require careful and detailed field measurements to facilitate proper engineering decision making regarding the removal of obstructions, establishment of regulatory and advisory speed limits, and the location of new access points, among numerous other purposes. However, conventional field measurements for these analyses present safety concerns because they require personnel to be in or adjacent to traffic lanes. They can also be time consuming, costly, and labor intensive. Furthermore, the predominantly two-dimensional (2D) methods involve simplifying assumptions such as a “standard” vehicle heights and lengths without considering the wide range of vehicles and drivers present on the road. Recently, many transportation agencies worldwide have begun to acquire mobile lidar data to map their roadway assets. These data provide a rich three-dimensional (3D) environment that enables one to virtually visit a site at any frequency and efficiently evaluate sight distances from the safety of the office. This study investigates advanced safety analysis methodologies for drivers’ sight distance based on high resolution lidar data. The developed simulation method enables users to virtually evaluate available sight distances in a 3D context considering a variety of objects, vehicle types, and multi-modal forms of transportation (e.g., bicycle, pedestrian). The feasibility of this technique was analyzed with a case study at an intersection located in Corvallis, Oregon, USA. The experimental results demonstrated the ability of the proposed methodology to capture significantly more detail on visibility constraints when compared with conventional measurements as well as provide more flexibility in the analysis. [ABSTRACT FROM AUTHOR]

This study proposes a model that estimates unobserved highway link speeds by a machine learning technique using historical probe vehicle data. For highway traffic monitoring, probe vehicle data is one of the most promising data source. However, since such data do not always cover an entire study area, we cannot measure traffic speeds on all links in a time-dependent manner; quite a few links are unobserved. To continuously monitor speeds on all links, it is necessary to develop a technique that estimates speeds on unobserved links from historical observed link speeds. For this purpose, we extend the current Gaussian graphical model so as to use two or more multivariate normal distributions to accurately estimate unobserved link speeds. In general, since the number of unknown model parameters (mean parameters and covariance matrices) is enormous and also unobserved links always exist, the EM algorithm and the graphical lasso technique are employed to determine the model parameters. Our proposed model was applied to the Bangkok city center in Thailand as well as to the Fujisawa city in Japan. We confirmed that the model can estimate the unobserved link speeds quite reasonably. [ABSTRACT FROM AUTHOR]

In transportation planning and design studies it is customary to consider the network in its equilibrium state. Equilibrium conditions in the presence of Advanced Traveller Information Systems (ATIS) and recurrent congestion have been formulated either according to some behavioural principle derived from Wardrop’s assumption, or as fixed-point states of day-to-day dynamic assignment processes. In the latter approach only, a differentiation has been introduced between predictive ATIS and ATIS providing static information, but the impacts on equilibrium travel times are relatively unexplored. In addition, the route choice updating model is formulated without consideration of state-dependent effects, such as inertia to change. This forms the motivation for the present paper. According to traffic information sources, three classes of users are considered: (a) users with predictive information, (b) users with static information and compliant, (c) users not equipped with ATIS or noncompliant. Users of the three classes make choices in a stochastic manner based on a logit model subject to inertia. Users of the first class have lower perception variance in view of the higher information quality. The compliance rate is endogenous and dependent on the information accuracy of the static ATIS. The fixed points in the class-specific route flows of the dynamic day-to-day processes characterise a new concept of network equilibrium, referred to as Mixed User Equilibrium (MUE), where, if each user shifts from her currently used route to her newly chosen route, the observed class-specific route flows do not change. A variant of the method of successive averages is proposed for computing MUE. The model is illustrated by an example related to the Nguyen-Dupuis network. The impacts on equilibrium travel times of the market penetration of ATIS with different functionalities as well as of inertia are examined. [ABSTRACT FROM AUTHOR]

Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation systems. While there are several studies on the missing traffic data recovery in the last decade, it is still an open issue of making full use of spatial-temporal traffic patterns to improve recovery performance. In this paper, due to the multi-dimensional nature of traffic speed data, we treat missing data recovery as the problem of tensor completion, a three-procedure framework based on Tucker decomposition is proposed to accomplish the recovery task by discovering spatial-temporal patterns and underlying structure from incomplete data. Specifically, in the missing data initialization, intrinsic multi-mode biases based traffic pattern is extracted to perform a robust recovery. Thereby, the truncated singular value decomposition (SVD) is introduced to capture main latent features along each dimension. Finally, applying these latent features, the missing data is eventually estimated by the SVD-combined tensor decomposition (STD). Empirically, relying on the large-scale traffic speed data collected from 214 road segments within two months at 10-min interval, our experiment covers two missing scenarios – element-like random missing and fiber-like random missing. The impacts of different initialization strategies for tensor decomposition are evaluated. From numerical analysis, a sensitivity-driven rank selection can not only choose an appropriate core tensor size but also determine how much features we actually need. By comparison with two baseline tensor decomposition models, our method is shown to successfully recover missing data with the highest accuracy as the missing rate ranges from 20% to 80% under two missing scenarios. Moreover, the results have also indicated that an optimal initialization for tensor decomposition could suggest a better performance. [ABSTRACT FROM AUTHOR]

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbour (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This study proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A realworld dataset with more than one year traffic records is used to conduct experiments. The results show that the DP-kNN can perform better than the manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement. [ABSTRACT FROM AUTHOR]

Purpose: Road traffic incidents cause delay, affect public safety and the environment. The CEDR PRIMA project aims to extend practical guidance for traffic managers in pro-active Traffic Incident Management (TIM) techniques to reduce the impacts and associated costs of incidents. Methods: The paper describes modelling methods used in the project for assessing the effect of different management techniques on incident duration and travel delay under various scenarios, including collision, adverse weather, heavy vehicle breakdown and other obstruction, assuming various management strategies and generic impacts of novel technologies. Macroscopic simulations of 178 variations of 13 basic scenarios have been performed using a flexible and computationally efficient macroscopic queue model, results being verified by simulation using a velocity-based Cell Transmission Model (CTM-v). Results: The results of the two modelling methods are broadly consistent. While delays estimated by the two methods can differ by up to 20%, this is small compared to the factor of 30 range of modelled delays caused by incidents, depending on their nature and circumstances, and is not sufficient to affect general conclusions. Under the peak traffic conditions assumed, the most important factor affecting delay is whether running lanes can be kept open, but quick clearance of carriageway is not always feasible. Conclusions: Comparison of two very different modelling methods confirms their consistency within the context of highly scenario-dependent results, giving confidence in the results. Future research and data needs include further validation of the models, potential application to traffic flow and conflict prediction and incident prevention, and more complete and consistent recording of incident timelines and impacts. [ABSTRACT FROM AUTHOR]

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels. [ABSTRACT FROM AUTHOR]

One of the most critical issues in modern cities is transportation management. Issues that are encountered in this regard, such as traffic congestion, high accidents rates and air pollution etc., have pushed the use of Intelligent Transportation System (ITS) technologies in order to facilitate the traffic management. Seen in this perspective, this paper brings forward a road traffic management system based on wireless sensor networks; it introduces the functional and deployment architecture of the system and focuses on the analysis component that uses a new extension of batches Petri nets for modeling road traffic flow. A real world implementation of visualization and data analysis components were carried out. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of road traffic sign detection from complex outdoor scene images, which is of great importance for intelligent transportation system. We illustrate the structure of road traffic sign detection system, which is made up of two steps. In the detection step, images are segmented to obtain candidate blobs, and then shape recognition of traffic sign is implemented via blob tracking. In the recognition step, SVM classifier is used to convert the road traffic sign detection problem to a multi-class classification problem. Furthermore, 2DPCA algorithm is used to implement the work of dimension reduction for feature vectors. Afterwards, the blob tracking operations are executed to promote the performance of road traffic sign detection. Based on the above operations, SVM classifier is utilized to detect road traffic signs from the tracked blobs. Finally, experimental results demonstrate that our proposed algorithm can achieve higher recognition rate accuracy under complex outdoor environments. [ABSTRACT FROM AUTHOR]

Advances in autonomous vehicular technology facilitate the development of intelligent traffic regulation systems. Such a system aims to configure and regulate vehicular mobility patterns to enhance in-road transportation safety and efficiency. To effectively function, the autonomous system must enable high-rate communications and rapid dissemination of vehicle-to-vehicle messaging flows. Instead of applying classical mobility models to capture human driver behaviors, the use of autonomously controlled driverless vehicles brings up another design dimensionality: the regulation and shaping of vehicular flows. The induced joint impact of the vehicular flow process on the message communications networking system, on the vehicular throughput rate, and on on-ramp waiting times for highway systems, has not been addressed by the existing studies. In this paper, we investigate the integrated design of these aspects. We synthesize and study methods that are used to optimally group autonomously controlled vehicles to travel along a highway in platoons. Vehicular formations are structured to yield effective autonomous mobility operation and to realize high-performance multihop dissemination of multiclass messaging flows. We then investigate an on-ramp traffic flow control mechanism that serves to regulate the admission of vehicles into the highway. We characterize the tradeoffs available to the system's designer in attaining high message communication throughput rates, accounting for time delays experienced by on-ramp waiting vehicles, while also striving to enhance the highway's capacity for accommodating high vehicular flow rate levels. [ABSTRACT FROM PUBLISHER]

The implementation of Intelligent Transport Systems (ITS) nowadays is associated with a strong need to solve problems related to the high accident rate, as well as low efficiency of transportation process management. The topic of this paper is the characterisation of selected intelligent transport services and the determination of their influence on road traffic safety and efficiency. Two services implemented within the framework of the National Traffic Management System in Poland were distinguished, namely: transmitting information to drivers and incident detection. A general analysis of the relationship between the functioning of ITS services and safety on national roads, as well as traffic efficiency therein was conducted. [ABSTRACT FROM AUTHOR]

Data-driven traffic management and control has attracted much attention recently. This paper conducts a series of coherent analyses based on geocoded data to understand the distribution characteristics of bus operational speed and to explore the potential applications of speed distributions. First, an original bipartite model is adopted for capturing instantaneous speed where the suspended and moving states are considered separately and a two-component mixed Weibull distribution is used to model the speed distribution in moving states. The mixed Gaussian distribution with variable components is found to be capable of expressing the speed distribution patterns of different road sections. Second, elaborate analyses on the basis of speed distribution modelling are conducted: (i) regression analyses are conducted to explore the correlations between parameters of instantaneous speed distributions and traffic related factors; (ii) a powerful clustering method using Kullback-Leibler divergence as the distance measure is proposed to grade the road sections of a bus route. These results can be utilized in fields such as bus operations management, bus priority signal control and infrastructure transformation aiming to improve the efficiency of bus operations systems. [ABSTRACT FROM AUTHOR]

In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, we summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies. [ABSTRACT FROM AUTHOR]

As one of the essential components of Intelligent Transport Systems (ITS), Vehicular Ad Hoc Network (VANET) plays a significant role in enabling various on-road applications, most of which primarily rely on two-hop Vehicle-to-Infrastructure communications. To make such communications reliable and secure, it is significant to ensure that only the trustworthy vehicles are selected as relays. To tackle this challenge, this paper proposes a robust trust-based relay selection scheme, called PTRS, which, based on Dirichlet distribution, systematically integrates a set of unique features of VANET, e.g., hybrid architecture, high dynamics, into the traditional reputation system, with an objective to effectively differentiate the trust levels of the vehicles, meanwhile, preserving robustness. Besides, the location privacy of vehicles are preserved in our proposed scheme by using the technique of pseudonyms and trust levels instead of explicit reputation scores. Detailed security analysis is conducted, which shows that the proposed PTRS scheme is secure and robust against several sophisticated attacks in VANETs. In addition, a set of extensive simulations are carried out, demonstrating its effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

In Cooperative Intelligent Transport Systems (C-ITS), vehicles and Road Side Units (RSU) exchange various messages about safety, traffic control, and weather conditions. These messages are broadcast to all neighbors. Each message should be authenticated and should protect users' privacy, mainly by hiding their locations. The authentication is achieved by signing messages using a private key related to the Pseudonym Certificate (PC), provided by a Trusted Authority (TA). The PC is delivered together with messages so that receivers can authenticate the messages. Privacy is ensured by changing PCs many times during a journey, so that trackers cannot get drivers' traces. This paper proposes a novel approach to manage PC switching periods between vehicles. The proposed method uses a Common PC (CPC) during a short period before switching to a new PC. Vehicles use the same shared PC during this period to sign their messages. Simulations have been conducted in an OMNET++ environment, showing significant improvement in privacy protection compared to well-known privacy schemes. [ABSTRACT FROM AUTHOR]

Internet of Vehicle (IoV) dominates the traditional concept of Vehicular Ad-hoc Network (VANET) to boost the capabilities of Intelligent Transportation System (ITS). Frequent deployment of high-speed vehicles with more enhanced features by vehicle manufacturers expands the variety of challenges for ITS. This technological enhancement enables vehicles to exchange information across other vehicles and existing communication infrastructures. When a vehicle detects any critical trouble, it transmits an emergency message (EM) to nearby vehicles and to the traffic control office as an alert message to take necessary actions. The main issue is congestion if each vehicle is authorized to broadcast warning messages constantly. This congestion may lead to delays in the transmission of critical emergency messages with the redundant receiving of the same EM at the traffic control office. In our proposed scheme, called Beacon-oriented Emergency Message Dissemination (BEMD), we proposed a novel technique to enhance coverage and distribution of EM with the least delay. We aim to implement beacon-oriented communication for traffics with high mobility and sparse density on highways. We furthermore fixated on the least dependence of density on EM dissemination. As compared to the latest existing schemes, BEMD shows enhanced simulated results of more than 13%, 26% and 4% in terms of information coverage, delay and packet distribution ratio (PDR) respectively. [ABSTRACT FROM AUTHOR]