Vehicular Density Research Articles

Capacity Analysis for Cooperative Vehicular Networks with Different Fading Channels

The cooperative vehicular networks (CVNs) enable vehicles to obtain information of remote infrastructures through cooperative vehicles. Nevertheless, the tradeoff between the cooperative vehicular ratio and transmission capacity of CVN is a challenge. In this paper, an analytical framework is developed to study the impacts of cooperation, interference and channel fading on the system capacity. Moreover, based on the proposed analytical framework, a closed-form expression is derived to reveal the relationship between transmission capacity and other network parameters, such as outage probability, vehicular density and adjacent infrastructures distance. Numerical results show that an optimal cooperative vehicular ratio leads to the maximum capacity. The results can also provide a preferable guidance for the design of CVNs.

Future liasing of the lockdown during COVID-19 pandemic: The dawn is expected at hand from the darkest hour

The lockdown during COVID-19 pandemic has converted the world into new experimental laboratories, which may reveal temporal or spatial comparative analysis data. However, some startling information is gathered in terms of reduced premature mortality cases associated with air and water quality improvement, enhanced e-learning on a broader platform, work from home, and successful e-health. The decline in vehicular density on roads and congestion leads to reduced energy consumption and associated greenhouse gases (GHG) and other pollutants emission. The lockdown has also been identified as a possible emergency measure to combat severe air pollution episodes. Similarly, industrial pollution has been recognized as one of the primary causes of water resource pollution and would, therefore, bring change in policy vis-à-vis groundwater pollution control. Our findings suggest that the results of successful e-learning and work from home would be a permanent shift from conventional modes in the near future due to a drastic reduction in socio-economic cost. Our critical analysis also highlights that with such temporary lockdown measures acute/chronic ill-effects of anthropogenic perturbations on planet earth can be effectively estimated through sociocultural, socioeconomical and socio-political/sociotechnological nexus.

Traffic flow on percolation-backbone fractal

Abstract We study the urban-scale macroscopic traffic flow in fractal network which represents a percolation backbone. We show where and how the traffic densities and currents vary with increasing mean density in the fractal network. The fractal network is transformed to the cell-transmission graph where a node corresponds to a road. The dynamic equations of vehicular densities on all nodes (roads) are presented by using the speed-matching model on the cell-transmission graph. The recursion formulas are obtained for the coefficients of density equations. By solving the density equations numerically, the densities on all roads are derived at a steady state. The urban-scale macroscopic fundamental (current-density) diagrams are obtained numerically in the fractal network. We clarify how the geometrical structure of fractal network affects the traffic characteristics.

Traffic Signal Data for Emergency Vehicles using C-Means and SVM Classification

Traffic related troubles include not just traffic jamming due to increase in vehicular density, but also complexity for passage of emergency vehicles, violation of rules such as red signal jumps, vehicle breakdowns and accidents causing blockage of roads and loss of lives. Nowadays lot of people losing their lives due to delay of emergency vehicle service. By providing ambulance service timely and accurate can reduce the deaths. By avoiding the unnecessary time delay near traffic jams during an emergency situation. Clustering is a machine learning procedure that includes the gathering of targeted information which is a strategy for unsubstantiated learning and is a typical procedure for factual information investigation utilized in numerous fields. Fuzzy C-means logic is a technique for clustering which enables one bit of information to have a place with two or more clustering. The proposed Fuzzy C-Means (FCM) algorithm technique is often utilized calculation, to inspect the different types of information with the frequent data sets. The Support Vector Machine (SVM) classification method is obviously used classification model which classifies the data entirely however the size is in a common manner. In this paper, a set of datasets is implanted and the experimental clustering report is verified with the frequent parameters such as overlapping, data partitioning, high dimensional data and irrelevant data clustering. On comparing with existing clustering processes, this proposed approach shows the high efficiency than other clustering models with approximate effective results on the association rules.

Detecting false messages in vehicular ad hoc networks based on a traffic flow model

In vehicular ad hoc networks, inside attackers can launch a false information attack by injecting false emergency messages to report bogus events such as traffic accidents. In this article, a false message detection scheme is proposed and evaluated. First, traffic flow theory is employed to analyze vehicular behavior under a traffic accident scenario. It shows that a “bottleneck” phenomenon is triggered because the road capacity is reduced by blocked lanes at an accident site. The traffic parameters, such as vehicular density, exhibit a distinct statistical property compared to an accident-free scenario. Based on this, a false message detection algorithm is proposed in which the traveling vehicles are exploited as witnesses to collect traffic parameters, and their observation data are used as evidence to feed a traffic flow model. A Bayesian theorem–based method is used to calculate the likelihood for each traffic scenarios, and the actual traffic condition is estimated to determine whether the reported accident has actually occurred. Finally, the performance of the proposed scheme was verified through simulations in a realistic traffic scenario. It was shown that a higher detection accuracy could be obtained compared to previously proposed approach.

A multi-hop broadcast wave approach for floating car data collection in vehicular networks

Inter-Vehicle Communication (IVC) is bringing connected and cooperative mobility closer to reality. Vehicles today are able to produce huge amounts of information, known in the literature as Floating Car Data (FCD), containing status information gathered from sensing the internal condition of the vehicle and the external environment. Adding networking capabilities to vehicles allows them to share this information among themselves and with the infrastructure. Collecting real-time FCD information from vehicles opens up the possibility of having access to an enormous amount of useful information that can boost the development of innovative services and applications in the domain of Intelligent Transportation System (ITS). In this paper we propose several solutions to efficiently collect real-time FCD information in Dedicated Short-Range Communication (DSRC)-enabled Vehicular Ad Hoc Networks (VANETs). The goal is to improve the efficiency of the FCD collection operation while keeping the impact on the DSRC communication channel as low as possible. We do this by exploiting a slightly modified version of a standardized data dissemination protocol to create a backbone of relaying vehicles that, by following local rules, generate a multi-hop broadcast wave of collected FCD messages. The proposed protocols are evaluated via realistic simulations under different vehicular densities and urban scenarios.

Data Redundancy Mitigation in V2X Based Collective Perceptions

Collective perception is a new paradigm to extend the limited horizon of individual vehicles. Incorporating with the recent vehicle-2-x (V2X) technology, connected and autonomous vehicles (CAVs) can periodically share their sensory information, given that traffic management authorities and other road participants can benefit from these information enormously. Apart from the benefits, employing collective perception could result in a certain level of transmission redundancy, because the same object might fall in the visible region of multiple CAVs, hence wasting the already scarce network resources. In this paper, we analytically study the data redundancy issue in highway scenarios, showing that the redundant transmissions could result in heavy loads on the network under medium to dense traffic. We then propose a probabilistic data selection scheme to suppress redundant transmissions. The scheme allows CAVs adaptively adjust the transmission probability of each tracked objects based on the position, vehicular density and road geometry information. Simulation results confirm that our approach can reduce at most 60% communication overhead in the meanwhile maintain the system reliability at desired levels.

A Review on Various Issues, Challenges and Different Methodologies in Vehicular Environment

Vehicular Adhoc NETwork (VANET) can be used for the safety as a security and comfort applications. When the channel or say the communication path becomes overloaded, congestion occurs which is directly proportional to the number of the vehicles for the specific area. Because of continuously movement of vehicles and network topology, the vehicular density varies. In this paper, we describe various issues related to vehicular network system and their respective methodologies. Deliberation of various techniques and corresponding performance metrics for improving the system performance are described here. Vehicular network are inseparable component due to its several applications. Applications vary from safety purpose to entertainment. IEEE 802.11P and IEEE 1609 standards help to achieve secure and reliable communication between vehicle nodes and roadside units. VANETs are considered recently that complement the Intelligent Transport System (ITS). Vehicular clouds have also been introduced to facilitate VANET application in more efficient way. Vehicular network have comparatively low bandwidth than fixed network such as LAN and have many more benefits and various applications.

Performance enhancement of in-network probabilistic aggregation (PEPA) for vehicular ad-hoc networks

Abstract In the current scenario of vehicular communication, it is very difficult to believe that the aggregated packet of information is not malicious. The algorithm is developed to allocate probability to the packet transmitted by the cluster head depending upon, the number of vehicles involved in creating a clustered information. The aggregated packet with the highest value of probabilistic correctness is considered by the receiving-end cluster head when multiple packets of similar information with related timestamps are received. The performance of the technique is tested under varying vehicular densities and data-sending rates. This scheme provides robust aggregation performance in comparison to the existing state-of-the-art structured and structure-free aggregation techniques.

RSU-Assisted Traffic-Aware Routing Based on Reinforcement Learning for Urban Vanets

In urban vehicular ad hoc networks (VANETs), the high mobility of vehicles along street roads poses daunting challenges to routing protocols and has a great impact on network performance. In addition, the frequent network partition caused by an uneven distribution of vehicles in an urban environment further places higher requirements on the routing protocols in VANETs. More importantly, the high vehicle density during the traffic peak hours and a variety of natural obstacles, such as tall buildings, other vehicles and trees, greatly increase the difficulty of protocol design for high quality communications. Considering these issues, in this paper, we introduce a novel routing protocol for urban VANETs called RSU-assisted Q-learning-based Traffic-Aware Routing (QTAR). Combining the advantages of geographic routing with the static road map information, QTAR learns the road segment traffic information based on the Q-learning algorithm. In QTAR, a routing path consists of multiple dynamically selected high reliability connection road segments that enable packets to reach their destination effectively. For packet forwarding within a road segment, distributed V2V Q-learning (Q-learning occurs between vehicles) integrated with QGGF (Q-greedy geographical forwarding) is adopted to reduce delivery delay and the effect of fast vehicle movements on path sensitivity, while distributed R2R Q-learning (Q-learning occurs between RSU units) is designed for packet forwarding at each intermediate intersection. In the case of a local optimum occurring in QGGF, SCF (store-carry-forward) is used to reduce the possibility of packet loss. Detailed simulation experimental results demonstrate that QTAR outperforms the existing traffic-aware routing protocols, in terms of 7.9% and 16.38% higher average packet delivery ratios than those of reliable traffic-aware routing (RTAR) and greedy traffic-aware routing (GyTAR) in high vehicular density scenarios and 30.96% and 46.19% lower average end-to-end delays with respect to RTAR and GyTAR in low vehicular density scenarios, respectively.

Traffic Avoidance System for High Priority Vehicles

India and most of the developing countries have unplanned cities in which roads are narrow and unkempt. Due to increasing vehicular density, repair of roads and traffic rules violation, traffic congestion is increasing at an alarming rate. Delay in arrival of High Priority Vehicles (HPV) like ambulances and fire engines cause severe loss to life and property. Although most HPVs are equipped with sirens, it is not efficient to guarantee a clear road to HPVs due to its audibility range. Hence, we propose a system by which the vehicles near the traffic junction will be alerted about the oncoming of HPV much in advance of its arrival so that they can make way for the HPV. In this system, every HPV is provided with an active RFID tag. Few kilometers before the main signal, an active RFID reader would be placed which would detect the vehicle and send this information to the display screen placed near the traffic junction signal via LoRa wirelessly, starting to make way for the HPV. Therefore, this project proposes to streamline the traffic and allow HPV to pass easily.

Modelling vehicular behaviour using trajectory data under non-lane based heterogeneous traffic conditions

The present study aims to understand the interaction between different vehicle classes using various vehicle attributes and thereby obtain useful parameters for modelling traffic flow under non-lane based heterogeneous traffic conditions. To achieve this, a separate coordinate system has been developed to extract relevant data from vehicle trajectories. Statistical analysis results show that bi-modal and multi-modal distributions are accurate in representing vehicle lateral placement behaviour. These distributions help in improving the accuracy of microscopic simulation models in predicting vehicle lateral placement on carriageway. Vehicles off-centeredness behaviour with their leaders have significant impact on safe longitudinal headways which results in increasing vehicular density and capacity of roadway. Another interesting finding is that frictional clearance distance between vehicles influence their passing speed. Analysis revealed that the passing speeds of the fast moving vehicles such as cars are greatly affected by the presence of slow moving vehicles. However, slow moving vehicles does not reduce their speeds in the presence of fast moving vehicles. It is also found that gap sizes accepted by different vehicle classes are distributed according to Weibull, lognormal and 3 parameter log logistic distributions. Based on empirical observations, the study proposed a modified lateral separation distance factor and frictional resistance factor to model the non-lane heterogeneous traffic flow at macro level. It is anticipated that the outcomes of this study would help in developing a new methodology for modelling non-lane based heterogeneous traffic.

Urban Air Quality: Assessing Traffiffic and Building Architecture Impacts using Portable Measuring Devices in Toronto, Ontario

Vehicle emissions are one of the largest local contributors to poor urban air quality. High emissions are often associated with traffic congestion, and pollution may also become trapped between tall buildings creating a street canyon effect. The spatial variability of traffic-related air pollutants in microenvironments should be considered in evaluating changes in urban planning. This study focuses on assessing the air quality and commuter exposure in Toronto, Ontario, Canada, specifically focusing on the effect of the King Street Pilot Project on local urban air quality by reducing traffic. Increased vehicular density is expected to contribute to higher urban pollution levels and tall buildings may trap these contaminants. Field measurements were made within the King Street Pilot area during weekday rush hours to capture the best representation of peak activity and pollutant levels when there were similar average wind speeds and directions for the sampling dates. A suite of portable devices was carried along predesigned and timed routes through traffic dense areas to measure vehicle-related air pollutants including black carbon (BC), ultrafine particles (UFP, particles smaller than 0.1 μm), and particulate matter (PM2.5, particles smaller than 2.5 μm). Data was normalized, corrected and analyzed using centralized pollutant while considering meteorological site measurements located about 1.5 km away from the study area. Results indicated higher BC and UFP levels during peak commuting times between 8 am to 10 am and relatively increased pollution levels within the area of tall buildings versus the area with shorter buildings. Strong spatial variations of BC and UFP were found, while PM2.5 levels remained relatively constant in the downtown area. Elevated levels of BC and UFP were observed around nearby construction sites. This study contributes to establishing a baseline to evaluate the King Street Pilot Project’s air quality impact as well as proposing potential methods of detailed data collection within microenvironments to observe the air quality of urban centres.

Ambient Air Quality Monitoring Studies in Four Specific Location of Tamilnadu, India

A comprehensive study on the air quality was carried out in four locations namely, Tiruchengode Bus Stand, K.S.R College Campus, Pallipalayam Bus Stop and Erode Government Hospital to assess the prevailing quality of air. Ambient air sampling was carried out in four locations using a high volume air sampler and the mass concentrations of PM10, PM2.5, SO2, NOX and CO were measured. The analyzed quality parameters were compared with the values suggested by National Ambient Air Quality Standards (NAAQS). Air quality index was also calculated for the gaseous pollutants and for Particulate Matters. It was found that PM10 concentration exceeds the threshold limits in all the measured locations. The higher vehicular density is one of the main reasons for the higher concentrations of these gaseous pollutants. The air quality index results show that the selected locations come under moderate air pollution.

Performance Analysis of Various Routing Protocols for VANET Environments

Vehicular Adhoc Network (VANET) is a fast emerging and new technology that are derived from adhoc networks to provide intelligent vehicle to vehicle communication and to improve the road safety and other on road facilities. VANETs are distinct from other networks in terms of high speed movements of vehicles, unpredictable nature of vehicular density over time and specific mobility patterns associated with different scenarios. Hence designing a suitable routing protocol for VANET applications is a very critical and specific task and it is different from that of MANETs even though VANETs are derivatives of the former. In this work we discuss and compare the effectiveness of routing protocols such as, AODV, DSR and DYMO with respect to three parameters namely, throughput, average end-end delay and average jitter. The simulations were carried out in Qualnet 5.0 network simulator. From the obtained results it is concluded that DYMO edges out the other two protocols in terms of the above mentioned metrics when it comes to VANET scenarios.

An effective handover management based on SINR and software‐defined network over urban vehicular ad hoc networks

AbstractUrban vehicular ad hoc network (UVANET) is a modality of high‐topology variation MANET where vehicles travel across constrained layout of the roads and tall buildings, which eventually results in shadowing effects. One of the critical challenges in UVANET is the handover process that happens when a vehicle changes its association with the roadside unit. Urban area suffers from high vehicular density at traffic signals, and vehicle slowdowns caused by speed limits, frequent link disconnection caused by signal attenuation due to obstacles. Thus, it is difficult for UVANET to disseminate the packets to their destinations located in a specified area. This paper presents an effective handover management that calculates the signal‐to‐interference‐plus‐noise ratio (SINR) to ultimately determine the optimal cluster head for seamless handover in UVANET. It also utilizes the software‐defined network to resolve the problem of handover failure in UVANET. The finding in the experimental results shows that the developed association clustering mechanism based on SINR significantly outperforms the baseline association mechanism. More importantly, it can resolve different types of handover problems that improved the quality of service in terms of throughput, handover latency, packet loss ratio, and handover failure ratio, when compared to the baseline handover mechanisms.

Traffic flow stabilized by matching speed on network with a bottleneck

We study the urban-scale traffic flow on city networks when there is a bottleneck on a road. Directed cycle graphs with and without bypasses are considered for the city traffic network. The dynamic equations of vehicular densities on all roads are presented on the networks. When vehicles flow into the connected road, the drivers are matched their speed to that at the connected road. The outflow is given by a function depending on both densities at the road and the downstream. While the outflow depends only on the density at the road in the conventional traffic model. The traffic flow is stabilized by matching the speed at the downstream. When there is a bottleneck on a road, the congestion at the bottleneck propagates over the network. The urban-scale macroscopic fundamental diagrams are derived on the graphs numerically. When there is a bottleneck on the cycle graph with and without bypasses, the fundamental diagrams are reproduced successfully.

Characteristics of tail pipe (Nitric oxide) and resuspended dust emissions from urban roads – A case study in Delhi city

IntroductionPersonal exposure to elevated vehicle exhaust and non-exhaust emissions at urban roadside leads to carcinogenic health effects, respiratory illness and nervous system disorders. In this paper, an attempt has been made to investigate the exhaust and non-exhaust emissions emitted from selected roads in Delhi city. MethodsBased on the vehicular density per hour and speed, three categories of roads have been considered in the present study: (a) low density road (≤1000 vehicles/hour, V ≥ 10 m/s); (b) medium density road (>1000 vehicles/hour but ≤ 2000 vehicles/hour, V ≥ 7.5 m/s < 10 m/s); and (c) high density road (>2000 vehicles/hour, V < 7.5 m/s). At the selected roads, real-world exhaust emissions were measured using AVL DiTEST 1000 analyser. The silt load measurements were also carried out as per EPA AP-42 methodology at the selected roads. ResultsResults indicated real-world NO exhaust emissions of 0.5 g/m3 (2.03 g/km) on high-density roads and 0.23 g/m3 (0.67 g/km) on low and medium density roads. These values were significantly higher than the Bharat Standard (BS) IV (0.25 g/km). The silt load on the different types of roads indicated 3, 25 and 44 g/m2 -day dust deposition on, low, medium and high-density road, respectively. PM2.5 and PM10 emission rates were measured using US-EPA AP-42 methodology and were found to be least at low-density roads with values of 0.54 and 2.22 g/VKT (VKT -Vehicle Kilometer Travelled) respectively, and highest for high density roads with values of 12.40 and 51.25 g/VKT respectively. ConclusionThe present study reveals that both tailpipe (exhaust) and resuspend able road dust (non-exhaust) emissions contributes significantly and deteriorates local air quality. Although there exists emission standards, but there are no enforced regulations for non-exhaust emissions (resuspension of road dust). Hence, there is need to regulate non-exhaust emissions on urban roads.

Latency performance analysis for safety‐related information broadcasting in VeMAC

AbstractThe performance analysis is a powerful tool to reveal the features of protocols. We can find hints to optimize protocols through performance analysis. Ultralow latency transmission is one of stringent requirements for event‐driven safety‐related message transmission in vehicular medium access control (VeMAC) protocol. In this paper, we focus on analyzing the latency performance for event‐driven safety‐related application in VeMAC. As analyzed, the optimization of MAC layer latency depends on minimization of the slot acquisition latency. Then, we derive the mean slot acquisition latency according to the behavior of nodes. There are two typical protocol versions, with the split parameter equals to zero and infinity, respectively. From the analytical model, although the protocol version with split parameter equal to infinity can reduce the merge collision through split slots with in a frame, it prolongs the mean slot acquisition latency since the block‐wised slots placement. Then, we develop an interleaved frame structure that can reduce the mean slot acquisition latency and merge collision simultaneously. The interleaved frame structure can adapt to various vehicular densities through the proposed slots allocation algorithm. The derived theoretical mean latency shows the advantages of the proposed frame structure. At last, we present simulation results to verify the analytical model and show that the proposed scheme can provide desired performance.

Multiple-vehicle collision influenced by misjudgment of space headway in traffic flow under fog weather condition

The multiple-vehicle collision easily happens under fog weather condition. To illustrate the effect of misjudgment of space headway on the multiple-vehicle collision in traffic flow, this paper derives collision criterion of the car-following model including risk illusions in fog weather (RIFM). Numerical simulations are carried out by varying driver’s sensitivity, vehicular density and initial velocity. This paper obtains the region map for the multiple-vehicle collision when a vehicle stops suddenly in traffic flow. The results indicate that although under low density conditions, the collision will happen just because of the misjudgment of space headway. In general, when drivers are affected by the misjudgment of space headway, the number of crumpled vehicles has positive correlation with initial velocity and vehicular density, and negative correlation with driver’s sensitivity. Therefore, in order to ensure traffic safety, the initial velocity and vehicular density should be reduced, and driver’s sensitivity should be increased under fog weather condition.