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In this study, the effect of median guardrails in no-passing zone sections of a two-lane two-way highway was investigated. In this regard, the Firuzkuh-Qaemshahr highway in Mazandaran province of Iran which has 5.5 km median guardrails was selected. All types of crashes studied in two periods, before-and after-the implementation of median guardrails, were considered, and using crash predictive models for the two-way two-lane highway, the effect of implementation was explored. To evaluate the economic effects of this treatment, various crash cost factors were converted to monetary equivalents to compare the cost of crashes before-and after-applying the treatment. This study revealed that using median guardrails caused the monthly average of fatal and injury crashes to decrease by 84% and 29%, respectively. Moreover, the proportion of fatal and injury crashes to total crashes had a 55% drop, indicating that the severity of crashes was reduced, leading to fewer deaths or injuries. Also, this treatment had an impressive reduction in the average monthly cost of crashes, so that the largest reduction among all cost factors belonged to the cost of fatalities in traffic crashes with an 85% reduction, followed by the cost of lost time due to the traffic jams caused by fatal crashes with an 84% decrease. Furthermore, the economic analysis of the implementation of median guardrails revealed that this treatment reduced the average monthly traffic crash cost by 65%. Therefore, the median guardrails in no-passing zone sections of the two-way two-lane Firuzkuh-Qaemshahr highway had a remarkable economic benefit. [ABSTRACT FROM AUTHOR]

Centrality is a numerical measure that characterizes the structural properties of a graph. In the presented work, centrality is used to analyze the load of the graph of urban roads in the city of Petrozavodsk. In the paper, we describe the method used to construct the road graph, present a modified centrality measure that takes into account the features of the transport network and the distribution of passenger traffic, and demonstrate the results of numerical simulations. For the transport graph, betweenness centralities were calculated with and without regard to the distribution of passenger traffic; a connectivity analysis was performed to identify critical, overloaded and reserve roads, and the routes that make the greatest contribution to the centrality of the most loaded roads. The results show that centrality can be used for the analysis of the structural features of the graph of urban roads, modeling sustainability and planning the development of the transport network. [ABSTRACT FROM AUTHOR]

With advancing vehicular technology, there are challenges related to the computing capabilities of the deployed infrastructure. In a dense vehicular network, the system performance quickly degrades due to the scarcity of computing capacity and the heavy workload on the coordinating nodes. In situations of a road accident or slow-moving traffic, many trigger messages are generated to enable situational awareness. Aforesaid events may lead to network congestion across the vehicular environment resulting in higher packet loss. Moreover, emergency messages incur increased service delays rather than getting preference for servicing. In this work, we propose a clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads. We use queue optimization to categorize and execute tasks based on their priority level at each fog node. Moreover, threshold-based congestion detection is used to determine congested nodes, which, alongside clustering-based suitable node selection, is used for workload sharing. Furthermore, a re-routing mechanism is implemented to decrease packet loss, improving the delivery rate. The simulation results show that the proposed technique is reliable, effective, and robust in terms of packet loss, throughput, and delivery of emergency messages. [ABSTRACT FROM AUTHOR]

Road pavement maintenance and upgrades (RPU) need to be scheduled in a way that minimises congestion across the road network and matches the schedule with the availability of equipment and skilled labourers to ensure that they are completed on time. RPU should not introduce unbearable congestion around blocked lanes or roads, and increased traffic due to ongoing road upgrades needs to be transferred to alternative routes. In situations where multiple upgrades are planned together, the scheduled road closures should be coordinated to not block alternative routes. While extensive studies have been conducted separately on scheduling and routing, limited research has linked these processes together, and only in restricted scenarios. In this paper, we investigate how to minimise the disruption of a set of road maintenance tasks based on their interaction with other tasks. We propose a new approach for scheduling a set of road maintenance tasks, some at the same time and others at different times, to minimise overall disruption. The proposed approach has two phases: (1) identify the local area affected by the RPU, and (2) determine which upgrades can be planned together to minimise congestion. The comparison of the results shows how the novel optimized approach identifies a better schedule to minimise congestion. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

A macroscopic traffic flow model considering the effects of curves, ramps, and adverse weather is proposed, and nonlinear bifurcation theory is used to describe and predict nonlinear traffic phenomena on highways from the perspective of global stability of the traffic system. Firstly, the stability conditions of the model shock wave were investigated using the linear stability analysis method. Then, the long-wave mode at the coarse-grained scale is considered, and the model is analyzed using the reduced perturbation method to obtain the Korteweg-de Vries (KdV) equation of the model in the sub-stable region. In addition, the type of equilibrium points and their stability are discussed by using bifurcation analysis, and a theoretical derivation proves the existence of Hopf bifurcation and saddle-knot bifurcation in the model. Finally, the simulation density spatio-temporal and phase plane diagrams verify that the model can describe traffic phenomena such as traffic congestion and stop-and-go traffic in real traffic, providing a theoretical basis for the prevention of traffic congestion. [ABSTRACT FROM AUTHOR]

Intelligent transportation system (ITS) is widely employed in dynamic traffic management to alleviate roadside congestion and increase traffic efficiency for solving the increasingly critical traffic congestion problems. With the advancement in ITS, real-time data collection of vehicles on the road has become a reality. A vast amount of traffic data ensures that the state of the road network may be analysed and predicted in real time for vehicle detection. Decision support system plays a significant role in the early decision making based on some defined criteria again available options. If the decision is made in the right and precise way, then eventually, it lead to success. This research investigates and designs a vehicle recognition algorithm and the road environment discrimination algorithm, which greatly increase the accuracy of highway vehicle detection, using a deep learning framework. In this work, we collect the highway video surveillance's images in various environments, create an original database, build a deep learning model of environment discrimination, and train the classification model to achieve real-time highway environment as the basic condition of vehicle recognition and traffic event discrimination. The proposed work uses a decision support system which is feed with basic information for vehicle detection and selection. Labeling the vehicle target and sample preparation of various environments are carried out to improve the accuracy of detecting the vehicles on a highway. The vehicle recognition algorithm is investigated in this context, and a vehicle detection technique based on weather environment recognition and a rapid RCNN model is presented. The performance of the vehicle recognition algorithm developed in this study is then verified by comparing detection accuracy with the existing state-of-the-art approaches. The comparison with these approaches in terms of accuracy, sensitivity, and F-score shows that our algorithm outperforms these approaches for detecting and classifying vehicles on the highway. [ABSTRACT FROM AUTHOR]

To investigate highway petrol station replenishment in initiative distribution mode, this paper develops a mixed-integer linear programming (MILP) model with minimal operational costs that includes loading costs, unloading costs, transport costs and the costs caused by unpunctual distribution. Based on discrete representation, the working day is divided into equal time intervals, and the truck distribution process is decomposed into a pair of tasks including driving, standby, rest, loading and unloading. Each truck must execute one task during a single interval, and the currently executing task is closely related to the preceding and subsequent tasks. By accounting for predictive time-varying sales at petrol stations, real-time road congestion and a series of operational constraints, the proposed model produces the optimal truck dispatch, namely, a detailed task assignment for all trucks during each time interval. The model is tested on a real-world case of a replenishment system comprising eight highway petrol stations, one depot, one garage and eight trucks to demonstrate its applicability and accuracy. [ABSTRACT FROM AUTHOR]

Traffic network is basically a "network of networks" consisting of mainly two types of networks: road network and a travel network. Due to drastic increase in population of vehicles, traffic congestion in metro cities of India is a severe problem. To resolve this use, we have proposed an approach to dynamic and automatic road traffic light management system. The approach is based on arithmetic mean theorem. The input to the control interface of traffic light of proposed approach are six different parameters : velocity of traffic units (v), queue length (l), inter-arrival time between vehicles (t), centrality measures value(c) and predicted value (prdt v) of traffic congestion by historical database and output is level of congestion. On the basis of congestion level, we build inference rules. The proposed approach automatically updates duration of green and red light as per the level of congestion at a particular junction. Proposed approach follows forward reasoning of If–Then rules. A study of this approach is done in this paper on various traffic situations of Delhi, India depending on queue length of vehicles. [ABSTRACT FROM AUTHOR]

In order to analyze topological properties and vulnerable elements of urban road complex network, 98 lines and 128 nodes are selected by taking Shiyan city in China as an example. Firstly, the definitions of the urban road network's running state vulnerability and structural vulnerability are proposed in this paper, and the assessment indexes are given respectively. From the perspective of the urban road network topology's structural characteristics and the traffic demand characteristics, the internal relationship of the running state vulnerability and the structural vulnerability is introduced in order to show the necessity and reasonableness of the urban road network vulnerability assessment. Secondly, the concepts of the urban road network element's structural-running state parameters are proposed to identify the source of network vulnerability. The Original Method is used to acquire the topology of the urban road network. Finally, with static vulnerability assessment principle, the structural vulnerability index is calculated on the premise that the cascading failure is not considered. With dynamic vulnerability assessment principle, the running state vulnerability index is calculated on the premise that the propagation effects of traffic congestion are considered. The results show that the vulnerability of road network is influenced by two factors: network topology structure and network running state. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems are becoming more and more important nowadays. In these systems, vehicles and possibly the infrastructure communicate with each other by vehicular ad hoc networks (VANETs). VANETs are being deployed and widely used in urban as well as in highway applications. Several standard use cases have been identified over the last decade (i.e., alert messages, car following support, data exchange between vehicles). In this paper, we focus on the alert message propagation on the highway. We derive the stationary and the transient solution of the message propagation distance by constant vehicle speed. Since these messages frequently indicate an accident on the road leading to a traffic jam, we extend the model to take the queueing system due to the traffic jam also into consideration. Our analytical results are compared with SUMO-/Veins-based simulations. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is one of the major problems of the developing countries. In routine practice, traffic managers use their knowledge and exposure to address this urban mobility menace. This heuristic approach based on individual experience results in inconsistency causing further delays and an increase in travel time. The aim of this research is to incorporate urban mobility dynamics of the developing countries in a microsimulator for real-time decision making. A pilot-scale research was conducted in Peshawar, one of the ten largest cities of Pakistan. Traffic volume, travel time, vehicle specifications and geometry parameters were determined in the field as input for the microsimulation model. Calibration and validation of the model were done, and results of traffic volume and travel time were compared with actual field data and statistical similarity confirmed through paired t-test. The analysis of queue delay, travel time and traffic flow indicates that in most of the cases congestion was found near the intersections and U-turns which was verified from the field data as well. The options of a roundabout and a flyover were assumed in the analysis; resultant queue delay and travel time decreased. With the provision of the roundabout and flyover, the capacity of traffic flow was also improved. [ABSTRACT FROM AUTHOR]

Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a proof of concept, when we lowered the speed limit or increased the capacity of road segments in the targeted road clusters, we found that both the number of congested roads and extra travel time were effectively reduced. In addition, the proposed modeling framework provided new insights on the optimization of transport efficiency in any infrastructure network with a specific supply and demand distribution. [ABSTRACT FROM AUTHOR]

In this paper we study the Indian highway network as a complex network where the junction points are considered as nodes, and the links are formed by an existing connection. We explore the topological properties and community structure of the network. We observe that the Indian highway network displays small-world properties and is assortative in nature. We also identify the most important road-junctions (or cities) in the highway network based on the betweenness centrality of the node. This could help in identifying the potential congestion points in the network. Our study is of practical importance and could provide a novel approach to reduce congestion and improve the performance of the highway network. [ABSTRACT FROM AUTHOR]

Due to the rapidly growing population of the city of Ankara (Turkey) and increased traffic congestion, it has become necessary to widen the Ankara-Eskişehir (E-90) highway connecting the newly built areas west of the city to the city center. During widening, several cut slopes were formed along the highway route. As a result, some instability problems (small-sized rock falls/sliding, sloughing, raveling) produced detachment zones along a cut slope in highly jointed, folded and sheared limestone, causing local degradation of the cut slope. Identification of the areas that are likely to detach from the cut slope in the future is considered to be very important for the application of remedial measures. For this purpose, the relationships between the existing detachment zones and various parameters (e.g., point load strength index, weathering, block size, daylighting, shear zone) were investigated using GIS-based statistical detachment susceptibility analyses in order to predict the further aerial extension of the detachment zones with time. During the overlay analyses, statistical index and weighting factor methods were used. The outcomes of the analyses were compared and evaluated with the field observations to check the reliability of the methods and to assess the detachment zones that may develop in the future. The detachment susceptibility map without the block-size layer gives the best result and indicates some risky zones where detachments are likely to occur in the future. Recommendations on remedial measures of the cut slope should consider these risky zones. [ABSTRACT FROM AUTHOR]

This paper deals with the continuum modeling of traffic dynamics for freeways with two lanes where the faster vehicles are allowed to travel on both lanes while the slower vehicles are allowed to travel on one lane only. The speed gradient-based momentum equation is used to develop the traffic models for each lane. Using the proposed models, some nonequilibrium phenomena such as small disturbance instability and stop-and-go waves, together with results from numerical tests are investigated. The conditions for keeping the models' linear stability are presented. [ABSTRACT FROM AUTHOR]

This paper shows that road pricing can be regressive, progressive or neutral, and refutes the generalised idea that road pricing is always regressive. The potential distributional impacts of a road pricing scheme are assessed in three English towns. It is found that impacts are town specific and depend on where people live, where people work and what mode of transport they use to go to work. Initial impacts may be progressive even before any compensation scheme for losers is taken into account. When the situation before the scheme is implemented is such that majority of drivers entering the area where the scheme would operate come from households with incomes above the average, it can be expected that, once the scheme is implemented, these drivers coming from rich households will continue to cross the cordon and will be prepared to pay the charge. In such a case the overall effect will be that on average, rich people will pay the toll and poor people will not. [ABSTRACT FROM AUTHOR]

When drivers do not have complete information on road travel time and thus choose their routes in a stochastic manner or based on their previous experience, separate implementations of either route guidance or road pricing cannot drive a stochastic network flow pattern towards a system optimum in a Wardropian sense. It is thus of interest to consider a combined route guidance and road pricing system. A road guidance system could reduce drivers' uncertainty of travel time through provision of traffic information. A driver who is equipped with a guidance system could be assumed to receive complete information, and hence be able to find the minimum travel time routes in a user-optimal manner, while marginal-cost road pricing could drive a user-optimal flow pattern toward a system optimum. Therefore, a joint implementation of route guidance and road pricing in a network with recurrent congestion could drive a stochastic network flow pattern towards a system optimum, and thus achieve a higher reduction in system travel time. In this paper the interaction between route guidance and road pricing is modeled and the potential benefit of their joint implementation is evaluated based on a mixed equilibrium traffic assignment model. The private and system benefits under marginal-cost pricing and varied levels of market penetration of the information systems are investigated with a small and a large example. It is concluded that the two technologies complement each other and that their joint implementation can reduce travel time more efficiently in a network with recurrent congestion. [ABSTRACT FROM AUTHOR]

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. [ABSTRACT FROM AUTHOR]