Traffic Jam Clustering Analysis with Countermeasure Strategies During Traffic Congestion (original) (raw)
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Traffic Congestion Identification and Reduction
Wireless Personal Communications, 2020
Historical data is increasingly becoming more purposeful in the field of intelligent traffic systems. Traffic congestion is one of the major challenges in most cities around the world. It increases fuel wastage, monetary losses, and life-endangering. Historical trajectory data may be a suitable solution to reduce congestion on the road in VANET. This approach is a combination of data mining historical trajectory data to detect and predict traffic congestion and VANET in reducing the detected congestion events. The trajectories are first preprocessed before they are clustered using traj clustering algorithm. Congestion detection is then done for each cluster based on a certain speed threshold and also time duration of that particular event. The experiments are done on a data set which recorded traffic trajectories for one day and the same experiment is iterated on data sets for the day two and three respectively day and the detected congestion incidents are recorded in terms of their coordinates and time period in which they occurred. After detecting the congestion, we have also proposed a traffic congestion reduction solution. Results and simulations show that our proposed mechanism is suitable in VANET.
Increase the Safety of Road Traffic Accidents by Applying Clustering
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In terms of continual increase of number of traffic accidents and alarming trend of increasing number of traffic accidents with catastrophic consequences for human life and health, it is necessary to actively research and develop methods to combat these trends. One of the measures is the implementation of advanced information systems in existing traffic environment. Accidents clusters, as databases of traffic accidents, introduce a new dimension in traffic systems in the form of experience, providing information on current accidents and the ones that have previously occurred in a given period. This paper proposes a new approach to predictive management of traffic processes, based on the collection of data in real time and is based on accidents clusters. The modern traffic information services collects road traffic status data from a wide variety of traffic sensing systems using modern ICT technologies, creating the most accurate road traffic situation awareness achieved so far. Road...
Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks
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Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on urban road networks. In this paper we propose an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined as those LJTs that are greater than a threshold. The threshold is calculated by multiplying the expected LJTs with a congestion factor. To evaluate the effectiveness of the proposed NRC detection method, we propose two novel criteria. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be associated with incidents. The proposed NRC detection methodology is tested for London's urban road network. The optimum value of the congestion factor is determined by sensitivity analysis by using a Weighted Product Model (WPM). It is found out those LJTs that are at least 40% higher than their expected values should belong to an NRC; as such NRCs are found to maintain the best balance between the proposed evaluation criteria.
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Detection and recognition of the level of congestion at an intersection is a very important problem and a valuable source of information in traffic management. Although it is just one of all the aspects that make up a traffic management system, it seems to be a crucial point for gathering information. In this paper, we present a technique based on a k-means clustering algorithm for classification, which has been already successfully used in a number of pattern recognition problems, namely: as an algorithm for face recognition problems and in a number of medical diagnosis problems and it compares very well with the state of the art techniques.
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Road traffic congestion, a serious illness in developing regions, is one of the biggest problems in our day-today life, resulting in delays, wastage of fuel and money. In this paper, a new model is developed using Simulation of Urban Mobility (SUMO) simulator for simulating a realistic traffic scenario for a large city like Bhubaneswar where, traffic congestion is a critical issue. In a city, traffic congestion is characterised by many parameters such as rapid growth of population, number of four wheelers, inadequate and poor road infrastructures and shortage of physical plan to govern the developments, which are focused on enhancing the volume of the roads by raising the number of lanes, overpasses , underpasses and over-bridges at many junctions. However, for the success of these master plans to fully overcome the congestion issues, it is necessary to transmit the congestion information to vehicles coming towards a congestion area by using a Vehicular Ad-hoc Network. This paper analyzes clustering techniques in Vehicular Ad-hoc Networks to detect congestion in roads with the minimal infrastructural support. The raw data from vehicles are classified using cluster analysis. Out of a number of algorithms that are used to solve the congestion detection problem, three important algorithms such as Centroid based K-means, object based FCM and FKM algorithms are compared in this work on the basis of data points and number of clusters. The results of the algorithms are close to each other, but fuzzy techniques are preferable as the traffic situations are dynamic in nature.
Analyzing highway flow patterns using cluster analysis
Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005., 2005
Historical traffic patterns can be used for the prediction of traffic flows, as input for macroscopic traffic models, for the imputation of missing or erroneous data and as a basis for traffic management scenarios. This paper investigates the determination of historical traffic patterns by means of Ward's hierarchical clustering procedure. Days were clustered before and after a pre-classification into working days and non-working days, using two different definitions of a daily traffic profile. The results of the clustering after preclassification are clearly better than before classification. Moreover, working days are easier to classify into distinctive, recurrent traffic patterns than non-working days. Finally, clustering on the basis of 15 minutes traffic flows resulted in a better classification of working days than the two-step clustering that used the total daily traffic flow, peak flows, peak times and ratios. The clustering on the basis of 15 minutes traffic flows resulted in a classification into five clusters that show distinct daily flow profiles and are representative for the days within the clusters. The day of the week and vacation periods are determinative for the cluster a working day is classified to.
Problems and Queries Associated with Traffic Congestion
isara solutions, 2019
Traffic congestion is a condition on transport network that occurs as vehicular use increases and is characterized by slower speeds, longer trip times and vehicular queueing. When traffic demand is great enough the interaction between vehicles slows the speed of traffic stream, this results in traffic congestion. As demand approaches the capacity of a road traffic congestion sets in. When vehicles stand for periods of time, this is known as traffic jam or traffic snarl-up. Traffic congestion in recent world is a worldwide urban phenomenon and even the so called well managed developed nations have its prevalence. Only the Underdeveloped countries have managed to have escape from this problem primarily due to lack of vehicular usage owing to low Economic development. This paper attempts to explain the problems of traffic congestion and their related impacts whether in terms of economy or social aspects on administration and society respectively.
Traffic congestion is a dynamic phenomenon; it is not possible to determine the actual degree of congestion prevailing on the field using sharp boundaries of the influencing parameters. To overcome this, in this paper we have employed fuzzy concept to fuzzify the two influencing parameters viz. congestion index value and average speed that facilitated the categorization of the congestion status into five different classes i.e. highly congested, high-moderate congested, moderate congested, low congested, least congested as compared to the only two congestion classes determined through the traditionally used congestion index value of the influencing parameters. For each route, pre-defined membership values (between 0 and 1) were assigned to the congestion index value and average speed respectively based on the empirical observations made in the field. Using the same logic, knowledge-based weights were assigned to the five different classes of congestion. Subsequently, fuzzy OR operation was performed on the membership values of the two influencing parameters for each route separately. Finally, different routes of the study area were categorized as one of the five classes of congestion based on the resultant value of the fuzzy OR operation. The research demonstrated that application of the fuzzy concept and knowledge-based congestion weights can provide better realistic status of the congestion in the field as compared to traditionally used congestion index value of the influencing parameters. Theoretical and Empirical Researches in Urban Management Volume 9 Issue 4 / November 2014 nt congestion. If stop and go driving (congested conditions) is reduced then the increase in greenhouse gases can be controlled (Barth and Boriboonsomsin 2010). The degree of congestion in a route can be used as a parameter to indicate the risk of air and noise pollution in the route. Congestion has dynamic characteristics and it does not have a specific definition, the definition is contextual. The definition depends upon perception which can vary according to the situation. Delays and lower travel speeds are the parameters which characterize congestion or congestion can also be viewed as the queued vehicles, the prime reason for the blockage of roads (Zito et al. 1999). The decrease in speed, the increase in travel time and the increase of vehicle's queue on the road characterize congestion (Lee et al. 2008 & Boamah 2010). Traffic congestion is a dynamic phenomenon and the behavior of congestion depends upon the condition on the roads. Congestion is a growing problem of urban areas. According to Hanson (1995), because of the daily life activities and heterogeneous land use, congestion has spatio-temporal complexity.
The conceptual structure of traffic jams
Transport Policy, 1998
Area-wide traffic jams develop through the propagation of queues from link to link, a process that resembles the growth of branches on a tree. The process is not well understood. In this paper, simple models for jam growth arising from a single bottleneck are developed for an idealized grid network. Under these idealized conditions, it has been shown that there are essentially two possible spatial configurations for a traffic jam on the type of network considered, each having a characteristic form sharing some of the properties of a fractal. More important, the models highlight an interesting dilemma in traffic management. A strategy that aims to minimize the rate of growth of a jam by a suitable allocation of queue storage space will actually encourage gridlock at the heart of the congested area, and conversely, a strategy that aims to defer gridlock will result in queues spread over a wider area. Extensive channelization (normally advocated in the interests of efficient traffic flow and safety) will also encourage longer queues. With hindsight, these conclusions seem obvious for any network whether imaginary or real, but they do not seem to have appeared in the literature, and the models give some indication of the size of the effects involved.