Search

This paper aims to estimate the causal effect of accidents on traffic congestion and vice versa. In order to identify both effects of this two-way relationship, I use dynamic panel data techniques and open access ‘big data’ of highway traffic and accidents in England for the period 2012–2014. The research design is based on the daily-and-hourly specific mean reversion pattern of highway traffic, which can be used to define a recurrent congestion benchmark. Using this benchmark, I am able to identify the causal effect of accidents on non-recurrent traffic congestion. A positive relationship between traffic congestion and road accidents would yield multiplicative benefits for policies that aim at reducing either of these issues. Additionally, I explore the duration of the effect of an accident on congestion, the ‘rubbernecking’ effect, as well as heterogeneous effects in the most congested highway segments. Then, I test the use of methods which employ the bulk of information in big data and other methods using a very reduced sample. In my application, both approaches produce similar results. Finally, I find a non-linear negative effect of traffic congestion on the probability of an accident.

Summarization: Traffic congestion in urban networks may lead to strong degradation in the utilization of the network infrastructure, which can be mitigated via suitable control strategies. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that controls the traffic light parameters in an urban network to reduce traffic congestion. A nearly optimal control formulation is adopted to avoid the curse of dimensionality occurring in the solution of the corresponding Hamilton–Jacobi–Bellman (HJB) optimal control problem. First, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov function; then, the solution is updated at each iteration in such a way to approach the nearly optimal solution, using a close-to-optimality index and information coming from the simulation model of the network (simulation-based design). Simulation results obtained using a traffic simulation model of the network Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach, as compared with alternative traffic strategies based on a simplified linear model of the traffic network. It is shown that the proposed strategy can adapt to different traffic conditions and that low-complexity parametrizations of the optimal solution, a linear and a bimodal piecewise linear strategy, respectively, provide a satisfactory trade-off between computational complexity and network performance. Παρουσιάστηκε στο: Transportation Science

Traffic congestion has become one of the major problems in the urban cities according to the increasing number of vehicles in those cities, obsolete technologies used on the roads of those cities, inappropriate road design, and many other reasons. So, that has urged the need for a more accurate traffic light controlling system; one that will help in maintaining high stability at all levels of demand. This paper introduces a dynamic traffic light phase plan protocol for Single-Isolated Intersections. The developed controlling method was compared with four other methods and showed a good performance in terms of reducing the average and maximum queue lengths, optimizing the given green time amount as needed, and increased the intersections throughput (increased the given green time utilization). In addition, it maintained a good traffic light stability at all levels of demand., 21 pages Journal Paper

In this paper, a coupled map car-following model is extended to two-lane traffic system. Based on it, the numerical simulations for one stop and series stops situations are performed respectively under the open boundary condition. The evolution of traffic jam and corresponding energy consumption are then explored and compared each other. Additionally, the influences of the lane changing rule and control scheme on traffic jam and energy consumption are investigated separately. The results show that lane changing rule and control scheme are found not only to suppress the traffic jam but also to reduce the energy consumption. The control scheme exhibit better behavior than lane changing rule does in suppressing traffic jam and reducing energy consumption in two-lane traffic system. The outputs of these findings demonstrate the potential of the proposed model to be applicable to evaluate the control schemes for suppressing traffic jam and reducing energy consumption in real traffic condition.

Air Traffic Flow Management (ATFM) aims at structuring traffic in order to reduce congestion in airspace. Congestion being linked to aircraft located at the same position at the same time, ATFM organizes traffic in the spatial dimension (e.g. route network) and in the time dimension (e.g. sequencing and merging of aircraft flows taking off or landing at airports). The objective of this paper is to develop a methodology that allows the traffic to self-organize in the time and space dimensions when demand is high. This structure disappears when the demand diminishes. In order to reach this goal, a multi-agent system has been developed, in which aircraft cooperate to structure traffic. Multi-agent systems have several advantages, including a good resilience when confronted with disruptive events. In this system, three algorithms have been implemented, aiming at reducing traffic complexity in three different ways. The first algorithm allows aircraft agents flying on a route network to regulate speed in order to reduce the number of conflicts, a conflict occurring when two aircraft do not respect separation norms. The second algorithm allows aircraft to solve conflicts when the traffic is not structured by a route network. The third algorithm creates temporary local route networks allowing to structure traffic. The three algorithms implemented in this multi-agent system allow to decrease overall traffic complexity, which becomes easier to manage by air traffic controllers. This algorithm was applied on realistic examples and was able to structure traffic in a resilient way.

Smart traffic prediction system provides significant benefits in solving the city traffic congestion. However, existing smart transportation system needs a lot of real-time traffic data and accurate location information to display the traffic condition. We hope that we can use the data which is easy to be obtained, and then predict a reliable congestion time. To address this problem, this paper studied a smart traffic forecasting system based on SWARIMA model. The system includes three steps: 1) use the sliding windows to calculate and process real-time data stream; 2) establish the SWARIMA model and make regression analysis; and 3) from a statistical point of view, calculate the elastic interval and predict the congestion trend. Our system is capable of accepting the real-time traffic data stream for the congestion prediction, in addition, we reduce the actual running parameters to three attributes: 1) speed; 2) time; and 3) location information. When faced with the challenges of real-time traffic congestion, the system can timely and effectively calculate the congestion trends and provide three reliable elastic intervals: 1) warning; 2) congestion; and 3) mitigation, which has significance to improve traffic condition and alleviate urban road congestion.

Abnormal traffic behaviors are common traffic phenomena. Existing traffic simulators focus on showing how traffic flow develops after an anomaly occurs; however, they cannot depict the anomaly itself. In this paper, we introduce the concept of shadow traffic for modeling traffic anomalies in a unified way in traffic simulations. We transform the properties of anomalies to the properties of shadow vehicles and then describe how these shadow vehicles participate in traffic simulations. Our model can be incorporated into most existing traffic simulators with little computational overhead. Moreover, experimental results demonstrate that our model is capable of simulating a variety of abnormal traffic behaviors realistically and efficiently.

Considering traffic flow as a steady noise source is common practice when studying traffic alternatives and its impact on the sound environment. However, vehicle dynamics have a strong influence on both transport behaviour and noise emission. One of the most relevant elements of traffic design is the intersection, where replacement of crossing intersections with roundabouts is common. In order to understand the features of these two traffic configurations, microscopic approaches are needed, making it possible to study time-pattern fluctuations relevant for the urban sound environment perception. A model based on individual-vehicle characteristics as function of time is developed and implemented in a real case study at a development stage. The model incorporates state-of-art microscopic traffic simulation software combined with the recent noise emission model, CNOSSOS-EU, applied through an in-house developed dynamic traffic noise tool, including both internal combustion engine and all-electric vehicles at different traffic flows. The tool is described in general terms incorporating the randomisation of source power. The propagation considers a flat-city configuration up to 100 m range. The tool enables study of different statistical indicators, including descriptors of probability density functions, calm periods through the novel indicator Centre of Mass Time (CMT) and noise events. The outcomes are presented through graphs and maps explaining traffic disruptions, acceleration effects, vehicle configurations and flows, source strengths, contribution and difference maps. Among the results, it is shown that, for the signalised crossing, the acceleration of the simulated traffic has a large effect on the source strength. It is however also shown that, for an unbalanced roundabout intersection leading to congestions, it can become noisier than the signalised crossing. It is furthermore shown that, when reducing the traffic flow, the two intersection types behave more similarly; however the roundabout having the best performance for the majority of the studied analysis. Further results are shown, e.g. for removing heavy vehicles, removing also medium heavy vehicles and assuming only all-electric light vehicles, including analysis from using various indicators. A discussion about the presented tool, the current results and ideas for future work concludes the paper. The present paper goes along a series of studies with the overall intention to provide a more solid basis for justifying decisions in traffic planning regarding the outdoor sound environment.

Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations. Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-of-the-art methods.

In this paper, using discrete model of traffic flow the dynamics of traffic flow against traffic lights controlled road section points was analyzed. In the article is determined what is going on with the traffic flows when period of traffic lights switching was chosen. For the results, road points at different distances from traffic light-controlled road points were chosen. The flow, velocity and density dynamics of traffic flows are determined at different points before the traffic lights. Fundamental diagrams to determine the dependence of traffic flow and density are developed.

In this research we performed new bicycle and pedestrian experiments to supplement data extracted from existing follow-the-leader experiments in vehicles, bicycles and pedestrians, and studied their spacetime trajectories and flow-density (or spacing-velocity) phase diagrams. The strong similarities in the spacetime trajectories and the bi-variate phase plots as well as the relative consistence of the estimated proportionality parameter across all three types of traffic, suggest that a unified behavioral mechanism is at play in human-driven traffic. It is suggested that this mechanism is essentially a safety-driven behavior that vehicles, bicycles or pedestrians adopt a safe speed for a given spacing between them. This behavior is well described by a well-known model in vehicular traffic and it is shown in this paper that a scaled version of this model applies to all three types of traffic. A unified relaxation-driven social force traffic model is then proposed to incorporate this behavior mechanism. Simulations with the same setup as the real-life experiments were carried out for vehicle, bicycle, and pedestrian traffic using the unified traffic model and the simulated spacetime trajectories and fundamental diagrams show remarkable consistence with the experimental results.

Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to explore the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers through experiments. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. In addition, we found that traffic congestion correlation has obvious directionality and transmissibility. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.

A probabilistic methodology is developed for the evaluation of characteristic maximum traffic load on long-span bridges for which congestion is the governing condition. It considers all the congestion types that can occur such as stop-and-go waves and oscillating congested traffic. The approach uses site-specific traffic data, such as flow and incident data. Statistics on driver behaviour and traffic incident frequency are assembled from the literature. As an example application of the method, a calibrated traffic microsimulation model is used to obtain the input data for the proposed methodology. The results counter many of the prevailing assumptions for long-span bridge loading.

In a real traffic system, information feedback has already been proven to be a good way to alleviate traffic jams. However, due to the massive traffic information of real system, the procedure is often difficult in practice. In this paper, we study the effects of the amount of feedback information based on a cellular automaton model of urban traffic. What we found most interesting is that when providing the traffic information of a part of a road to travelers, the performance of the system will be better than that providing the road's full traffic information. From this basis, we can provide more effective routing strategy with less information. We demonstrate that only providing the traffic information of about first half road from upstream to downstream can maximize the traffic capacity of the system. We also give an explanation for these phenomena by studying the distribution pattern of vehicles and the detailed turning environment at the intersections. The effects of the traffic light period are also provided.

Traffic signals play a critical role in smart cities for mitigating traffic congestions and reducing the emission in metropolitan areas. This paper proposes a bi-level optimization framework to settle the optimal traffic signal setting problem. The upper-level problem determines the traffic signal settings to minimize the drivers’ average travel time, while the lower-level problem aims for achieving the network equilibrium using the settings calculated at the upper level. Genetic algorithm is employed with the integration of microscopic-traffic-simulation-based dynamic traffic assignment (DTA) to decouple the complex bi-level problem into tractable single-level problems, which are solved sequentially. Case studies on a synthetic traffic network and a real-world traffic subnetwork are conducted to examine the effectiveness of the proposed model and relevant solution methods. Additional strategies are provided for the extension of the proposed model and the acceleration of solution process in large-area traffic network applications.

Macroscopic fundamental diagram (MFD) describes the macro relationship between a network vehicle density and a network space mean flow, without requiring the mastery of complex origin to destination data. Thus, MFD provides an opportunity for the macro control of urban road network. However, most of the existing MFD control methods ignore the active role of traffic guidance in solving congestion problems. This study presents a traffic guidance–perimeter control coupled (TGPCC) method to improve the performance of macroscopic traffic networks. The method considers the optimal cumulative volume of a network as the goal and establishes a programming function according to the network equilibrium rule of traffic flow amongst multiple MFD sub-regions, which regards the minimum delay of network, as the objective. The Logit model for the compliance rate of driver route guidance is established by the stated preference survey. Moreover, the perimeter control (PC) method is proposed for adjusting the phase split of intersections. Finally, three schemes, namely, the TGPCC, PC and the method without PC and guidance are tested on a network with four well-defined MFD sub-regions. Results show that the TGPCC addresses the issue of congestion and decreases the total delay accordingly.

In road traffic, stalled vehicles, spilled loads and traffic accidents often result in traffic jams or secondary accidents. It is better for us to detect and locate these non-recurrent traffic incidents accurately as soon as possible, so that early warning, timely incident handling and speedy congestion evacuation will be better achieved. This paper proposes an automatic detecting and locating method of traffic incidents in a road segment based on lane-changing characteristics. At regular time intervals, for each traffic lane, the method calculates the lane-changing-out ratio of vehicles and judges whether a traffic incident has happened. For a traffic-incident lane, entry points and exit points of lane-changing vehicles are considered as positive and negative samples, respectively, then the traffic incident is confirmed further and its position is calculated by searching for the optimal separating surface of the two kinds of samples. Experimental results prove that the method is practicable and e...

Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_R by comprehensive comparison with existing approaches.

This paper proposes a methodology to categorize drivers’ behaviors at a congested signalized intersection. As a discrete event system model, timed Petri nets (TPNs) are used in this paper to formally define two kinds of behaviors: non-jam-induced driving behavior and jam-induced one. In order to systematically assess the performances of both behaviors, a new urban traffic network model is built: a cell transmission model is used to depict the road link traffic that is consistent with the kinematic property of traffic flow, and TPNs are used to model the behaviors and the conflicting traffic flow at the intersection. Some simulation results are given to evaluate the impact of driving behavior on the traffic delay.

Traffic congestion is a critical concern in most cities. Inefficient traffic control wastes time and fuel, and causes harmful carbon emissions, road accidents, and many economic problems. This paper proposes a cooperative traffic control framework for optimizing the global throughput and travel time for multiple intersections. Adjacent intersections are considered in analyzing their joint passing rates and attempting to maximize the number of vehicles traveling through a road network. The proposed framework provides fairness for each road segment and realizes the green wave concept for arterial roads. This paper extends previous studies by considering the passing rates of continuous road segments and coordinating traffic signals of multiple intersections. The simulation results show that the approach outperforms existing schemes in that it achieves a high global throughput, reduces the average waiting time, lowers the total travel time, and decreases average CO2 emissions. To verify the feasibility of the proposed framework, a wireless access in vehicular environments/dedicated short-range communications-based prototype for lane-level dynamic traffic control is designed and implemented.

The paper presents the modelling of a mixed traffic flow in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. In a simulation-based analysis it is shown that the fundamental diagram of the traffic network changes. A new mixed-traffic model incorporates the nonlinearity of the traffic dynamics using a polynomial formula, in which the look-ahead cruise control is considered with a parameter-dependent form. Using these nonlinear forms the impact of the look-ahead vehicles on the stability of the traffic is examined through controlled invariant sets. In this analysis the sum-of-squares programming method is applied. The results are demonstrated through simulation examples using the VISSIM simulation environment.

Congestion in the air traffic network is a problem with an increasing relevance for airlines costs as well as for airspace safety. One of the major issue is the limited capacity of the air network, that is the limited maximum number of aircraft that can coexist on some points of the network. This value can also vary during the day for safety constraints (weather and visibility). In this work an Autonomous Agent approach is proposed to solve in real time the problem of air traffic congestion. The air traffic network is divided in different sectors each one modeled with a graph. Each sector has its own control decision agent managing the air traffic involved in it. When a congestion occurs in a sector the control decision agent has to reschedule and/or reroute the flights with the aim of respecting capacity constraints and of limiting delays. In this paper rerouting solutions are obtained by applying an iterative procedure based on a generalized Dijkstra algorithm on the graph. We test the effects of different levels of cooperation between agents controlling adjacent sectors while solving the congestion problem.

Nowadays, traffic light system is very important to avoid car crashes and arrange traffic load. In the Sulaimani City / Iraq, there are many traffic problems such as traffic congestion or traffic jam and the amount of time provided manually to the traffic light system. This is the main difficulty that we try to solve. The traffic lights exist but still do not manage traffic congestion due to the fixed time provided for each lane regardless of their different load. Therefore, we are proposing to change the traditional traffic system to smart traffic system (adaptive system). This paper Focuses on the existing system (fixed system), then propose the adaptive one. The main crucial side effects of the existing system are: Emergency cases: congested traffics might block the way of emergencies for instance ambulance, which transports people to the hospital Wasting time of people generally and specially Delays, which lead people to not to be punctual, this means people arrive late to the work Wasting more fuels as staying more in the traffics, which affects the environment by increasing pollution.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

Intelligent Transportation Systems (ITS) focus on increasing the efficiency of existing surface transportation systems through the use of advanced computers, electronics, and communication technologies. In order to perform advanced traffic management and provide travel information, dynamic traffic assignment models need to be developed to provide time-dependent estimates of traffic flows on networks in order to efficiently utilize possible advanced traffic information as well as traffic control measures. Traffic assignment distributes Origin-Destination (OD) trips in a network and determines the flow patterns in a traffic network. This research aims at developing simulation-based algorithm for dynamic traffic assignment problems under mixed traffic flow considerations. Four different physical vehicle types are explicitly considered and modeled, including car, bus, motorcycle, and truck. Four different behavioral rules, pre-specified-path driver, user-equilibrium driver, system-optimization driver, and real-time information driver, are considered in the solution procedure. The DTA algorithm consists of an inner loop that incorporates a direction finding mechanism for the search process for System Optimization (SO) and User Equilibrium (UE) classes based on the simulation results of the current iteration, including experienced vehicular trip times and marginal trip times. In order to understand tripmaker acceptance toward route guidance, a survey is conducted to explore possible behavioral classifications and associated percentages. Numerical experiments are conducted in a test network and a real city network to illustrate the capabilities of the simulation-based DTA procedures, and to observe how system performs under multiple user class’s conditions, including multiple user behavior rules and multiple physical vehicle classes.

Development of speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining the individual vehicle speed. Estimation of passenger car equivalent (PCE) which is essential in converting the mixed traffic volume into its equivalent homogeneous also requires the speed information of individual vehicle categories at varying traffic conditions on the road. The present research was carried out to model the individual vehicle speed and to study the effects of traffic volume and its composition on individual speed and PCE in the context of urban mixed traffic. Traffic data on classified traffic volume and speed information were collected at six-lane divided arterial mid-block road sections in New Delhi, India. The methodology of artificial neural network was adopted to develop a volume-based speed prediction model for individual vehicle category. Validation results showed a great deal of agreement between the predicted and the observed speeds. Then, the sensitivity analysis was performed utilizing the model developed in order to examine the effects of traffic volume and its composition on individual speed and corresponding PCE.

Due to increase in traffic in cities and on major roads, it has become a necessity to have an efficient traffic management system to handle such scenarios. Present traffic management system performs mere traffic monitoring and event handling which cannot be a viable system for highly populous country like India and China. In this paper, we propose a system that will predict the densely populated roads based on the present and past traffic congestion. This system also suggests the alternate paths for the given source and destination. A simulation of live stream of online data is performed on legacy traffic data set which is processed incrementally. Density based clustering is applied after Fuzzification of data to assign weightage for the densely congested path on the route map. The weightage for the path on the given time helps to decide the best route form the source to destination. Floyd’s algorithm is also applied to find the shortest set of alternate path for the given source to destination.

Modeling dynamics of heterogeneous traffic flow is central to the control and operations of today’s increasingly complex transportation systems. We develop a macroscopic heterogeneous traffic flow model. This model considers interplay of multiple vehicle classes, each of which is assumed to possess homogeneous car-following behavior and vehicle attributes. We propose the concepts of road capacity split and perceived equivalent density for each class to model both lateral and longitudinal cross-class interactions across neighboring cells. Rather than leveraging hydrodynamic analogies, it establishes pragmatic cross-class interaction rules aspired by capacity allocation and approximate inter-cell fluxes. This model generalizes the classical Cell Transmission Model (CTM) to three types of traffic regimes in general, i.e. free flow, semi-congestion, and full congestion regimes. This model replicates prominent empirical characteristics exhibited by mixed vehicular flow, including formation and spatio-temporal propagation of shockwaves, vehicle overtaking, as well as oscillatory waves. Those features are validated against numerical experiments and the NGSIM I-80 data. Realistic class-specific travel times can be computed from this model efficiently, which demonstrates the feasibility of applying this multi-class model to large-scale real-world networks.

This article addresses the problem of modeling and estimating traffic streams with mixed human operated and automated vehicles. A connection between the generalized Aw Rascle Zhang model and two class traffic flow motivates the choice to model mixed traffic streams with a second order traffic flow model. The traffic state is estimated via a fully nonlinear particle filtering approach, and results are compared to estimates obtained from a particle filter applied to a scalar conservation law. Numerical studies are conducted using the Aimsun micro simulation software to generate the true state to be estimated. The experiments indicate that when the penetration rate of automated vehicles in the traffic stream is variable, the second order model based estimator offers improved accuracy compared to a scalar modeling abstraction. When the variability of the penetration rate decreases, the first order model based filters offer similar performance.

Traffic jams often occur without any obvious reasons such as traffic accidents, roadwork, or closed lanes. Under moderate to high traffic density, minor perturbations to traffic flow (e.g., a strong braking motion) are easily amplified into a wave of stop-and-go traffic. This is known as a phantom jam. In this paper, we aim to mitigate phantom jams leveraging the three-phase traffic theory and vehicle-to-vehicle (V2V) communication. More specifically, an efficient phantom jam control protocol is proposed in which a fuzzy inference system is integrated with a V2V-based phantom jam detection algorithm to effectively capture the dynamics of traffic jams. Per-lane speed difference under traffic congestion is taken into account in the protocol design, so that a phantom jam is controlled separately for each lane, improving the performance of the proposed protocol. We implemented the protocol in the Jist/SWAN traffic simulator. Simulations with artificially generated traffic data and real-world traffic data collected from vehicle loop detectors on Interstate 880, California, USA, demonstrate that our approach has by up to 9% and 4.9% smaller average travel times (at penetration rates of 10%) compared with a state-of-the-art approach, respectively.

Traffic congestion is not only a cause of nuisance for general commuters but also a factor that has a measurable impact on the economy if not handled proactively. When congestion increases, the waiting time for commuters increases which results in wasted fuel and wasted time. Wasted fuel adds to the import bill of a country and lost time results in loss of productivity. Traffic can be regulated at various points in order to reduce congestion and eliminate bottleneck areas. In this paper, we propose the use of conventional scheduling to regulate traffic at intersections in order to reduce congestion. We propose minimum destination distance first (MDDF) and minimum average destination distance first (MADDF) algorithms and compare them with some of the relevant existing scheduling algorithms. The proposed algorithms can not only be easily implemented on low cost hardware but also show better performance and outperform the existing algorithms that are considered, based on simulation results. Simulation results show that the MDDF and MADDF algorithms reduce the traffic congestion at intersections by up to 80% in some cases compared to static traffic lights.

For a growing number of developing cities, the capacities of streets cannot meet the rapidly growing demand of cars, causing traffic congestion. Understanding the spatial–temporal process of traffic flow and detecting traffic congestion are important issues associated with developing sustainable urban policies to resolve congestion. Therefore, the objective of this study is to propose a flow-based ranking algorithm for investigating traffic demands in terms of the attractiveness of street segments and flow complexity of the street network based on turning probability. Our results show that, by analyzing the topological characteristics of streets and volume data for a small fraction of street segments in Taipei City, the most congested segments of the city were identified successfully. The identified congested segments are significantly close to the potential congestion zones, including the officially announced most congested streets, the segments with slow moving speeds at rush hours, and the areas near significant landmarks. The identified congested segments also captured congestion-prone areas concentrated in the business districts and industrial areas of the city. Identifying the topological characteristics and flow complexity of traffic congestion provides network topological insights for sustainable urban planning, and these characteristics can be used to further understand congestion propagation.

manuscipt submitted under different title: “Simultaneous optimization of traffic congestion and noise exposures“, This study elaborates on the interrelation of external effects, in particular road traffic congestion and noise. An agent-based simulation framework is used to compute and internalize user-specific external congestion effects and noise exposures. The resulting user equilibrium corresponds to an approximation of the system optimum. For traffic congestion and noise, single objective optimization is compared with multiple objective optimization. The simulation-based optimization approach is applied to the real-world case study of the Greater Berlin area. The results reveal a negative correlation between congestion and noise. Nevertheless, the multiple objective optimization yields a simultaneous reduction in congestion and noise. During peak times, congestion is the more relevant external effect, whereas, during the evening, night and morning, noise is the more relevant externality. Thus, a key element for policy making is to follow a dynamic approach, i.e. to temporally change the incentives. During off-peak times, noise should be reduced by concentrating traffic flows along main roads, i.e. inner-city motorways. In contrast, during peak times, congestion is reduced by shifting transport users from the inner-city motorway to smaller roads which, however, may have an effect on other externalities.

Traffic congestion is one of the major issues in urban areas. To mitigate this problem a centralized and personalized traffic control system is essential. This paper proposes a cloud-enabled real-time congestion free vehicular path planning system using Vehicular Ad-hoc Networks (VANETs). In this work, a novel algorithm has been proposed for finding the fastest (i.e. less density) congestion free path using real-time traffic information. This algorithm also predicts the future road traffic density and takes into account in order to find the near future congestion free path. This algorithm can be made available as Software as a Service (SaaS) on the cloud.

This paper makes local and aggregate estimates of the effects of 152 level crossings in the Melbourne metropolitan area, Australia on traffic congestion in the morning peak period (7 am–9 am). A new method, including micro-simulation of a range of rail crossing configurations, is used to inform a network model which makes aggregate estimates of impacts on all traffic. Relationships between train frequency and percentage change in vehicle travel time and volume were identified. These equations can predict change in travel time/traffic flow caused by rail level crossings based on rail crossing closures and train frequency. Overall, Melbourne's level crossings result in an average increase in travel time of 16.1% for vehicle traffic on links with a level crossing. However on average a level crossing reduces the volume of vehicles on these links by 5.9% as a result of traffic diversion. These values are higher in middle suburbs where train arrivals and crossing closure times at level crossings are more frequent. The aggregate effect of all 152 level rail crossings on all traffic in Melbourne is a travel time change from 1.81 to 1.82 min/km (an increase of around 0.3%). The number of congested links in Melbourne increases by 0.9% while the total delay increases by 0.7%. These network wide effects are not large compared to localised effects because road links affected directly by crossings represent a very small part of the overall network. Additionally, network effects also include traffic diversion impacts which will counteract some of the immediate impacts on a localised scale. However, it is significant that the very small element of the system can have even a measurable effect on aggregate traffic congestion.

Traffic congestion has always been an impending challenge for drivers as well as traffic authorities. It causes frustrations to millions of passengers. The estimated financial cost is $2,200 billion per year in developed countries worldwide. In this paper, we propose an intelligent routing algorithm to minimize the traffic jam occurrence through directing the paths of multiple vehicles cooperatively. According to Kerner's breakdown minimization principle, we can claim that the traffic network optimum has been achieved if the probability for spontaneous traffic jam occurrence over the entire road network during a given observation time period is minimized. The proposed multivehicle routing approach is fully scalable and distributed, which essentially makes it directly applicable to real traffic networks such as that in Singapore. Through numerical studies, the proposed algorithm is much faster in terms of convergence speed than that of state-of-the-art distributed computation approaches. Moreover, our approach always maintains a feasible route guidance solution during the computation process, which is applicable to scenarios with real time decision making requirements, i.e., the reaction time must be within seconds. Simulation results in arbitrarily large road networks with realistic settings show the effectiveness of the proposed algorithm.

Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles' efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.

A practical approach for predicting the congestion boundary due to traffic incidents was proposed. Based on the kinematic wave theory and Van Aerde single-regime flow model, a model for estimating the congestion propagation speed for the basic road segment was developed. Historical traffic flow data were used to analyze the time variant characteristics of the urban traffic flow for each road type. Then, the saturation flow rate was used for analyzing the impact of the traffic incident on the traversing traffic flow at the congestion area. The base congestion propagation speed for each road type was calculated based on field data, which were provided by the remote traffic microwave sensors (RTMS), floating car data (FCD) system and screen line survey. According to a comparative analysis of the congestion propagation speed, it is found that the expressway, major arterial, minor arterial and collector are decreasingly influenced by the traffic incident. Subsequently, the impact of turning movements at intersections on the congestion propagation was considered. The turning ratio was adopted to represent the impact of turning movements, and afterward the corresponding propagation pattern at intersections was analyzed. Finally, an implementation system was designed on a geographic information system (GIS) platform to display the characteristics of the congestion propagation over the network. The validation results show that the proposed approach is able to capture the congestion propagation properties in the actual road network.

The mobile observer method has been used by traffic engineers since the middle of the last century to estimate traffic velocity, flow and density. Although simple and intuitive, the method suffers from two major issues. First, the vehicle must traverse a given road segment both in the direction of the traffic whose parameters are of interest and also in the opposite direction, essentially driving in a loop. Second, to get accurate results, several runs must be taken and the results aggregated. Our variant utilizes the communication capabilities in present-day vehicles to mitigate both these issues, thus enabling faster and more accurate traffic maps and traffic routing applications. Extensive simulation results have confirmed that our variant of the mobile observer method is comparable in accuracy with the stationary observer method even at lower flow rates.

Transport delays due to traffic jams are manifested in many urban areas worldwide. To make road traffic networks more efficient, intelligent transport services are currently being developed and deployed. In order to mitigate (or even avoid) congestion, vehicle-to-vehicle and vehicle-to-infrastructure communications provide a means for cooperation and intelligent route management in transport networks. This paper introduces the novel predictive congestion minimization in combination with an A * -based router (PCMA * ) algorithm, which provides a comprehensive framework for detection, prediction, and avoidance of traffic congestion. It assumes utilization of vehicle-to-X communication for transmission of contemporary vehicle data such as route source and destination or current position, as well as for provision of the routing advice for vehicles. PCMA * further contains a component for intelligent selection of vehicles to be rerouted in case of a congestion, as well as an A * -based routing algorithm, taking into consideration the current road conditions and predicted future congestion. We prove the performance by dynamic microscopic traffic simulations in a real-world and an artificial road network scenario. Due to the well-performing prediction, the results reveal substantial advantages in terms of time and fuel consumption compared not only with situations with no active rerouting system but also with simple rerouting algorithms and more sophisticated approaches from literature.

Drivers often change lanes on the road to maintain desired speed and to avoid slow vehicles, pedestrians, obstacles and lane closure. Understanding the effect of lane-changing on the traffic is an important topic in designing optimal traffic control systems. This paper presents a comprehensive study of this topic. We review the theory of microscopic dynamic car-following models and the lane-changing models, propose additional lane-changing rules to deal with moving bottleneck and lane reduction, and investigate the effects of lane-changing on the traffic efficiency, traffic safety and fuel consumption as a function of different variables including the distance of the emergency sign ahead of the lane closure, speed limit, traffic density, etc. Extensive simulations of the traffic system have been carried out in different scenarios. A number of important findings of the effect of various factors on the traffic are reported. These findings provide guidance on the traffic management and are important to the designers and engineers of modern highway or inner city roads to achieve high traffic efficiency and safety with minimum environmental impact.

This paper attempts to reproduce the empirical phenomena of congested traffic flow with an on-ramp through a microscopic traffic model. First, an improved two-lane lattice hydrodynamic traffic flow model is proposed, which is capable of avoiding vehicles backward moving in original lattice hydrodynamic model. Then, the deterministic and stochastic on-ramps are designed and mapped into the new lattice model to reproduce the empirical phenomena. For the stochastic case, many empirical congested patterns are reproduced, such as moving localized cluster, triggered stop-and-go traffic (TSG), pinned localized cluster (PLC), oscillating congested traffic (OCT) and homogeneous congested traffic. For the deterministic case, a number of combination patterns of HST, PLC, TSG and OCT are found. Taken together, these results suggest that the present model is able to predict the congested traffic patterns.

Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.

Road urban transport and traffic engineering in general are long-term discussed topics. Traffic problems are solved from many aspects, such as transport infrastructure, transport organization, high share of car traffic, a negative impact on air quality, emissions etc. Finally, the problem of traffic congestion on urban roads is also highly topical. With increasing economic development of individual states the numbers of cars are increasing as well − the traffic volumes are increasing at the expense of road capacity. The paper deals with the formation of congestion particularly on urban roads, where the driver reaction time influences the size of congestion as well. In the first part of the paper the issues of traffic flow and congestion formation are theoretically discussed. The second part of the paper outlines in practice an influence of human factor (the driver's Perception − Response Time) on the congestion formation.

The factors affecting traffic intensity have been determined, and the situations of controlling traffic flows at city highways have been discussed taking into account actual traffic capacity of the roads. A concept of adaptability with respect to road traffic provision (RTP) has been given. The RTP system has been presented as a «black box» in the context system analysis. A dependence of determining indicator of adaptability of a signal-controlled intersection to the changed traffic flows has been proposed and the quantitative intervals making it possible to select intersections on the basis of actual duration of traffic-light control cycle have been acquired.

Recent research in Intelligent Transportation System (ITS) focuses on Automatic Incident Detection (AID) techniques. Using advances in wireless networking and sensor technologies, modern vehicles have the ability to communicate with each other as well as with roadside infrastructure units (RSUs) in order to increase road safety. These new innovations in transportation technology provide traffic management system the ability to use data collected from the vehicles on the road to detect congestion and traffic incidents. Lately, many techniques were developed to alert drivers in advance about traffic incidents and to enable them to avoid congestion. In this paper, we discuss probabilistic collection of traffic data through vehicle-to-infrastructure (V2I) communications and present two novel techniques for automatic detection of traffic incidents in a highway scenario that are based on the use of distance and time for changing lanes, respectively vehicle speed changes over time. The proposed methods, which are illustrated with numerical results obtained from simulations, outperform alternative AID techniques through higher incident detection rates, about 25% shorter peak queue values and 20% faster dissipation of roadway congestion.

The task of enhancing efficiency of traffic lights operation demands optimization of the throughput capacity of a regulated street and road network junction including rational and full use of an operation cycle of traffic lights. Oftentimes, the existing calculation system for an operation cycle of traffic lights at intersections does not provide a jam-free and safe route for transport in rush hours. In this article the authors suggest a new calculation methodology for an operation cycle of traffic lights based on the expert calculation which considers the necessity for keeping the possibility of entering a crossing by all vehicles that have arrived to a crossing from each of conflicting directions (including the most loaded one) during one cycle; the use of vehicles slow-down rate called in the traffic rules as “without emergency braking”; the use of phase coefficients at the calculation of the main cycles of the less loaded directions.

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.