Search

Cities around the world are inundated by cars and suffer traffic congestion that results in excess delays, reduced safety and environmental pollution. The interplay between road infrastructure and travel choices defines the level and the spatio-temporal extent of congestion. Given the existing infrastructure, understanding how the route choice decisions are made and how travellers interact with each other is a crucial first step in mitigating traffic congestion. This is a problem with fundamental importance, as it has implications for other limited supply systems where agents compete for resources and reach an equilibrium. Here, we observe the route choice decisions and the traffic conditions through an extensive data set of GPS trajectories. We compare the actual paths followed by travellers to those implied by equilibrium conditions (i) at a microscopic scale, where we focus on individual path similarities, and (ii) at a macroscopic scale, where we perform network-level comparison of the traffic loads. We present that non-cooperative or selfish equilibrium replicates the actual traffic (to a certain extent) at the macroscopic scale, while the majority of individual decisions cannot be reproduced by neither selfish nor cooperative equilibrium models. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field. [ABSTRACT FROM AUTHOR]

Taxis are an important component of urban passenger transport. Research on the daily dispatching of taxis and the utility of governmental management is important for the improvement of passenger travel, taxi driver income and environmental impacts. However, urban taxi management is a complex and dynamic system that is affected by many factors, and positive/negative feedback relationships and nonlinear interactions exist between each subsystem and variable. Therefore, conventional research methods can hardly depict its characteristics comprehensively. To bridge this gap, this paper develops a system dynamics model of urban taxi management, in which the empty-loaded rate and total demand are selected as key factors affecting taxi dispatching, and the impacts of taxi fares on driver income and travel demand are taken into account. After the validation of the model, taxi operations data derived from a prior analysis of origin–destination data of Beijing taxis are used as input for the model to simulate the taxi market in Beijing. Finally, economic and environmental implications are provided for the government to optimise policies on taxi management. [ABSTRACT FROM AUTHOR]

The paper investigates a traffic-responsive control method applicable at isolated signalized intersections. The proposed strategy involves three basic parts: a traffic model, a reconfigurable regulator, and a congestion detection filter. Road traffic dynamics is modeled by the well-known store-and-forward approach. The controller is based on the efficient Linear Quadratic Regulator algorithm. The filter is designed by using the modified version (for discrete time case) of the Fundamental Problem of Residual Generation. The main achievement of the system is the ability to deal with a time-varying model parameter, namely the saturation flow rate of the road links. To this end, an error term is estimated continuously by appropriate fault detection algorithm. The predicted error term is further used by the reconfigurable controller which finally aims to mitigate the number of vehicles waiting at the stop line, i.e. the delay caused by the intersection. A simulation study is also carried out to demonstrate the effectiveness of the controller extended by congestion detection filter. [ABSTRACT FROM AUTHOR]

In recent years, the number of motor vehicles in the country has risen rapidly. Traffic demand has continued to grow, while land resources, capital and energy have become increasingly tense. The development trend of urban transport system is not optimistic. Especially in the face of major events, vehicle evacuation suddenly increased, which is a serious planning for the evacuation of a serious challenge. Reasonable and accurate implementation of the emergency evacuation plan is to make the evacuation time at least, to minimize the major traffic congestion protection. Among them, route selection and traffic flow distribution are the core contents of emergency evacuation plan. In this paper, traffic emergency evacuation of major activities is taken as the research object, and the state vector of each road is introduced into the navigation system according to the linking analysis algorithm thought. The network model of evacuation route selection is established by using the theory of spatial diversity and the theory of minimum cost maximum flow, and an empirical analysis is made on the road network. Based on the research, the emergency evacuation plan for large-scale activities is proposed, and the theory and method system of emergency evacuation are enriched, which can help with formulating more reasonable and effective traffic management policies according to the characteristics of emergency evacuation, meanwhile we provide decision-making basis for urban emergency evacuation transportation planning and management. An experimental evaluation is also conducted with the real data from city of Beijing, in aspects of effectiveness and efficacy. [ABSTRACT FROM AUTHOR]

Many congestion charging projects charge traffic only within part of a day with predetermined congestion tolls. Demand peaks have been witnessed just around the time when the charge jumps up or down. Such peaks may not be desirable, in particular (a) when the resulting peaks are much higher than available capacities; (b) traffic speeding up to get into the charging zone causes more incidents just before the toll rises up to a higher level; or (c) traffic slowing down or parking on the roadside decreases road traffic throughput just before the toll falls sharply. We term these types of demand peaks 'boundary effects' of congestion charging. This paper investigates these effects in a bottleneck scenario and aims to design charging schemes that reduce undesired demand peaks. For this purpose, we observe and analyse the boundary effects utilising a bottleneck model under three types of toll profiles that are indicative of real charging schemes. The first type maintains a constant toll across the charging period, the second type allows the toll to increase from zero to a given maximum level and then decrease back to zero and the third type allows the toll to rise from zero to a given maximum level, remain at this level for a fixed period and then fall down to zero. This investigation shows that all three types of toll profiles can produce greater boundary peak demands than the bottleneck capacity. A significant contribution of this work is that instead of designing an optimal traffic congestion pricing scheme we analyse how existing sub-optimal congestion pricing schemes could be improved and suggest how observed problems may be overcome. Hence, we propose a set of extra requirements to supplement existing principles or requirements for design and implementation of congestion charging, which aim to reduce the adverse consequences of boundary effects. Concluding remarks are made on implications of this investigation for the improvement of existing congestion charging projects and for future research. [ABSTRACT FROM AUTHOR]

This study examines the economic implications of traffic congestion and travel time variability on the productivities of commuters in Ilorin, Nigeria. The study covered three routes (Maraba, Amilegbe and University) at the peak periods of morning (7:00am-9:00am) and evening (4:00pm-6:00pm). A total of 450 commuters were sampled equally from the three routes, and 396 (89%) were returned valid. Descriptive statistics were employed in analysing the data collected. The major factors found responsible for congestion on the selected routes are inadequate road capacity, illegal on-street parking, poor driving habit, encroachment on the right of way, too many taxis/buses and poor traffic control management. While the effects are lateness to work and school, tiredness, hike traveling cost and pollution. The implication of these is that excessive travel delay, particularly during peak hours negatively affect commuters' productivity. The study therefore recommends a strict implementation of policies through the town planning authorities with strict adherence to erections of retail stall on the right of way. Functional, fast, affordable and reliable mass transit which can convey a large number of people should be provided by the government or employers to replace the use of taxicab as this will provide sustained relief on our roadways. [ABSTRACT FROM AUTHOR]

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR. [ABSTRACT FROM AUTHOR]

Station inflow control (SIC) is an important and effective method for reducing recurrent congestion during peak hours in the Beijing, Shanghai, and Guangzhou subway systems. This work proposes a practical and efficient method for establishing a static SIC scheme in normal weekdays for large-scale subway networks. First, a traffic assignment model without capacity constraint is utilized to determine passenger flow distributions on the network. An internal relationship between station inflows and section flows is then constructed. Second, capacity bottlenecks are identified by considering the transport capacity of each section. Then, a feedback-based bottleneck elimination strategy is established to search target control stations and determine their control time and control strength. To validate the effectiveness of the proposed approach, a decision support system coded in the C# programming language was developed, and the Beijing subway was used as a case study. The results indicate that the proposed method and tool are capable of practical applications, and the generated SIC plan has better performance over the existing SIC plan. This study provides a practical and useful method for operation agencies to construct SIC schemes in the subway system. [ABSTRACT FROM AUTHOR]

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions. [ABSTRACT FROM AUTHOR]

Purpose: Freight vehicle parking facilities at large urban freight traffic generators, such as urban retail malls, are often characterized by a high volume of vehicle arrivals and a poor parking supply infrastructure. Recurrent congestion of freight parking facilities generates environmental (e.g. pollution), economic (e.g. delays in deliveries) and social (e.g. traffic) negative externalities. Solutions aimed at either improving or better managing the existing parking infrastructure rely heavily on data and data-driven models to predict their impact and guide their implementation. In the current work, we provide a quantitative study of the parking supply and freight vehicle drivers' parking behaviour at urban retail malls. Methods: We use as case studies two typical urban retail malls located in Singapore, and collect detailed data on freight vehicles delivering or picking up goods at these malls. Insights from this data collection effort are relayed as data stories. We first describe the parking facility at a mall as a queueing system, where freight vehicles are the agents and their decisions are the parking location choice and the parking duration. Results: Using the data collected, we analyse (i) the arrival rates of vehicles at the observed malls, (ii) the empirical distribution of parking durations at the loading bays, (iii) the factors that influence the parking duration, (iv) the empirical distribution of waiting times spent by freight vehicle queueing to access the loading bay, and (v) the driver parking location choices and how this choice is influenced by system congestion. Conclusions: This characterisation of freight driver behaviour and parking facility system performance enables one to understand current challenges, and begin to explore the feasibility of freight parking and loading bay management solutions. [ABSTRACT FROM AUTHOR]

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

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

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. [ABSTRACT FROM AUTHOR]

In the article the construction of a modular mobile overcrossing is offered. Calculation of its constructive elements is performed and the optimum length of one module is determined. The purpose is the development of the technique and calculation for the new construction of a mobile bridge overcrossing intended for reduction of traffic jams. Methods: The methods uses are mathematical analysis, method of finite elements, method of finite differences, and analytical method of relocation. Dependencies for determination of the optimum length of the module of bridge overcrossing are identified. The calculation of the constructive-orthotropic plate for the carriageway of the bridge overcrossing using numerical methods of finite differences and finite elements is performed; the reliability of results is confirmed with coincidence of deflection values. The solution matrix of the method of finite differences developed in this work allows calculation of arbitrary plates with a wide variety of geometrical sizes, and also for different values of flexural stiffness properties of the plate and reinforcing elements. The calculation of the spatial frame of the bridge overcrossing is performed by the precise analytical method of relocation taking into account the bend and torsion of its elements. [ABSTRACT FROM AUTHOR]

Traffic congestion is a phenomenon which occurs when road traffic is disturbed. In Galati city, traffic congestion increases rapidly due to an increase in the number of vehicles on the road. This work is an attempt to analyze the effects of traffic congestion by applying the waiting theory. Traffic congestion is frequent during the day, and the locals accept it as a daily routine. Urbanization in developing countries indicates that more people live in cities than before. The trend of urbanization, the population growth and the increasing number of registered vehicles put pressure on traffic and make life in the urban area more difficult. General data congestion and congestion management are the mitigation measures proposed but they are labor intensive and a heavy investment would be needed. Therefore, in order to find feasible measures, latest technologies such as GIS and GPS help analyze the live traffic situation and suggest cost effective measures to alleviate congestion. A previous attempt was effective in data collection, data analysis and display of results [1, 2]. A Geographic Information System (GIS) is a computer-based mapping tool that geographically analyses events and phenomena occurring on Earth. GIS technology integrates common database operations such as query and statistical analysis with unique viewing and the benefits of geographic analysis provided by maps [2]. In Galati, the GIS system is still not applicable because it has not been purchased by the competent authorities. In Romania, the GIS system is partially implemented. The traffic congestion has begun to be solved globally through GPS positioning technology with vehicle data collection, transport and operation. Using GPS Vehicle will provide direct and objective data on the behavior and status of the trip and the transport system that influences this behavior [3]. [ABSTRACT FROM AUTHOR]

Wireless network sensors and their use in traffic monitoring, traffic density determination or vehicle speed detection and classification have recently been the focus of interest for researchers. This article describes how a new sensor circuit was designed to deliver instantaneous, real-time and novel solutions as a vehicle detection system, which is more powerful than the nodes used in other studies, and gives results with smaller error margins due to its serial communication qualification. With the proposed logic algorithm, it was possible to categorise the instantaneous traffic status of a road in four levels: no traffic, mild traffic, heavy traffic and very heavy traffic. Additionally, with the nodes placed at the beginning and the end of the road, the number of vehicles per hour for a day was determined and traffic was analysed. Then, vehicles passing by were classified with a proposed classification algorithm and magnetic signature length (MSL) parameter as cars, minibuses, buses and trucks, and an accuracy rate of 95% was obtained. As the last application, the direction of motion of the vehicle on the x-axis as well as left-to-right or right-to-left directions was determined, and the result was 94% accurate. The simplicity of the proposed algorithms, the absence of any complex mathematical calculations, the low cost of the sensor node and circuit and the low power consumption of the communication system demonstrate the superiority of this system in comparison with other studies. [ABSTRACT FROM AUTHOR]

There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition. [ABSTRACT FROM AUTHOR]

One of the worst traffic problems today is the existence of huge traffic jams in almost any big city, produced by the large number of commuters using private cars. This problem has led to an increase in research on the optimization of vehicle occupancy in urban areas as this would help to solve the problem that most cars are occupied by single passengers. The solution of sharing the available seats in cars, known as carpooling, is already available in major cities around the world. However, carpooling is still not considered a safe and reliable solution for many users. With the widespread use of mobile technology and social networks, it is possible to create a trust-based platform to promote carpooling through a convenient, fast and secure system. The main objective of this work is the design and implementation of a carpool system that improves some important aspects of previous systems, focusing on trust between users, and on the security of the system. The proposed system guarantees user privacy and measures trust levels through a new reputation algorithm. In addition to this, the proposal has been developed as a mobile application for devices using the Android Open Source Project. [ABSTRACT FROM AUTHOR]

An innovative idea of vehicle tracking for video-based intelligent traffic management system is known to bring significant socioeconomic impact. A successful vehicle tracking method is always in demand to monitor different traffic parameters such as the average speed, strange movements, and congestion of vehicles or even to detect accidents automatically on highways or freeways. The challenges of traditional video-based vehicle tracking methods include the initialization of tracking to tackle an unknown number of targets and the reduction of the drift sensitivity of targets from true positions mainly caused by the variations in lighting condition, occlusions and camera position. To address these challenges, this paper presents a novel vehicle tracking method for a traffic management system that introduces the multiple time-spatial images (MTSIs)-based detection in the stochastic filter-based tracking. The MTSI-based tracking employs the concept of multiple numbers of key vehicular frames (KVFs) for each of the vehicular-objects in the traffic. These KVFs provide highly accurate positional information of the vehicles due to the fact that the shape and texture of the vehicles are comparable on the same scale and do not depend on the speed of the traffic. The spatial correspondence of a vehicle in successive KVFs is then incorporated as a low-complexity data association technique to alleviate the common problem of drifting in the stochastic filter-based method and thereby increasing the accuracy in tracking trajectory. Comprehensive experimentations are carried out using two publicly available video databases (EBVT and GRAM-RTM) that have traffics of varying environments to evaluate the vehicle tracking performance of the proposed method as compared to the existing methods. Experimental results demonstrate that the introduction of MTSIs not only automates the initialization of tracking, but also significantly increases the accuracy of the tracking trajectories of the vehicles on roads evaluated both in the presence and absence of ground truths. [ABSTRACT FROM AUTHOR]

In order to improve operational efficiency of traffic road, it is necessary to carry out the exploration on the reliability of road network. The connectivity reliability is the critical index to calculate the reliability of the road network. Based on the joint failure modes of fatigue cracking and permanent deformation, the connectivity reliability model of road network was established, which was further introduced into the reliability analysis of road network system. By considering the influences of different OD (Origin-Destination) pairs and their weighting coefficients on the system reliability, the reliability model of complex road network of multiple OD pairs was finally constructed. Furthermore, connectivity reliability of complex road network was calculated on the basis of the Monte Carlo method and the breadth-first search method. The influences of the different road elements and nodes on the system reliability were analyzed by the numerical examples. In addition, the influences of reinforcement and expansion of the road network on the system reliability were also evaluated. Results show that, although the presented models based on multiple OD pairs and its different weighing coefficients are closer to the actual road network system, it is safer to define the road network reliability by single OD pair. Moreover, the failure of the nodes, especially those adjacent to the starting node, causes more damages to the road network system than the road section does, which decrease the reliability of the road network system. The recovery of the road sections adjacent to the starting nodes can not only effectively improve the reliability of the system but also reduce economic costs. [ABSTRACT FROM AUTHOR]

Vehicular traffic is endlessly increasing everywhere in the world and can cause terrible traffic congestion at intersections. Most of the traffic lights today feature a fixed green light sequence, therefore the green light sequence is determined without taking the presence of the emergency vehicles into account. Therefore, emergency vehicles such as ambulances, police cars, fire engines, etc. stuck in a traffic jam and delayed in reaching their destination can lead to loss of property and valuable lives. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the measurement of the distance between the emergency vehicle and an intersection using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. The distance between the emergency vehicle and the intersection is calculated for comparison using Euclidean distance, Manhattan distance and Canberra distance techniques. The experimental results have shown that the Euclidean distance outperforms other distance measurement techniques. Along with visual sensing techniques to collect emergency vehicle information, it is very important to have a Medium Access Control (MAC) protocol to deliver the emergency vehicle information to the Traffic Management Center (TMC) with less delay. Then only the emergency vehicle is quickly served and can reach the destination in time. In this paper, we have also investigated the MAC layer in WSNs to prioritize the emergency vehicle data and to reduce the transmission delay for emergency messages.We have modified the medium access procedure used in standard IEEE 802.11p with PE-MAC protocol, which is a new back off selection and contention window adjustment scheme to achieve low broadcast delay for emergency messages. A VANET model for the UTMS is developed and simulated in NS-2. The performance of the standard IEEE 802.11p and the proposed PE-MAC is analysed in detail. The NS-2 simulation results have shown that the PE-MAC outperforms the IEEE 802.11p in terms of average end-to-end delay, throughput and energy consumption. The performance evaluation results have proven that the proposed PE-MAC prioritizes the emergency vehicle data and delivers the emergency messages to the TMC with less delay compared to the IEEE 802.11p. The transmission delay of the proposed PE-MAC is also compared with the standard IEEE 802.15.4, and Enhanced Back-off Selection scheme for IEEE 802.15.4 protocol [EBSS, an existing protocol to ensure fast transmission of the detected events on the road towards the TMC] and the comparative results have proven the effectiveness of the PE-MAC over them. Furthermore, this research work will provide an insight into the design of an intelligent urban traffic management system for the effective management of emergency vehicles and will help to save lives and property. [ABSTRACT FROM AUTHOR]

The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a "tailgating effect" between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments. [ABSTRACT FROM AUTHOR]

Traffic control is an effective and efficient method for the problem of traffic congestion. It is necessary to design a high-level controller to regulate the network traffic demands, because traffic congestion is not only caused by the improper management of the traffic network but also to a great extent caused by excessive network traffic demands. Therefore, we design a demand-balance model predictive controller based on the macroscopic fundamental diagram-based multi-subnetwork model, which can optimize the network traffic mobility and the network traffic throughput by regulating the input traffic flows of the subnetworks. Because the transferring traffic flows among subnetworks are indirectly controlled and coordinated by the demand-balance model predictive controller, the subnetwork division can variate dynamically according to real traffic states, and a global optimality can be achieved for the entire traffic network. The simulation results show the effectiveness of the proposed controller in improving the network traffic throughput. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the main problems in large urban cities. The evaluation of the performance of any traffic facility, such as basic freeway segments, merging sections and diverging sections, depends mainly on investigating the main relationships such as speed-flow and flow-occupancy. This study has focused on the fundamental relationships of traffic stream, including flow-speed, flow-density, speed-density (Greenshield relationships), headway-flow and flow-occupancy relationships. 24-hour data, each minute, from loop detectors has been adopted to produce the above mentioned relationships. Field data has been collected from two different sites in Manchester city in the UK. These are three-lane and four-lane sections. In some cases of the current data, the behaviour has been investigated under 1min, 5min and 15min periods to show the difference between various periods of data collection. The analysis of this data has revealed different behaviours at different sections for each lane. The main use of this data is to provide the critical values which are the basis for Intelligent Transportation System (ITS) applications, such as simulation models. This data could be used for the calibration and validation of simulation models, such as S-Paramics or VISSIM, as well as other developed models. [ABSTRACT FROM AUTHOR]

Urban transportation systems are multimodal, sociotechnical systems; however, while their multimodal aspect has received extensive attention in recent literature on multiplex networks, their sociotechnical aspect has been largely neglected. We present the first study of an urban transportation system using multiplex network analysis and validated Origin-Destination travel demand, with Riyadh’s planned metro as a case study. We develop methods for analyzing the impact of additional transportation layers on existing dynamics, and show that demand structure plays key quantitative and qualitative roles. There exist fundamental geometrical limits to the metro’s impact on traffic dynamics, and the bulk of environmental accrue at metro speeds only slightly faster than those planned. We develop a simple model for informing the use of additional, “feeder” layers to maximize reductions in global congestion. Our techniques are computationally practical, easily extensible to arbitrary transportation layers with complex transfer logic, and implementable in open-source software. [ABSTRACT FROM AUTHOR]

The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system—a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset. [ABSTRACT FROM AUTHOR]

For a predetermined set of an upper bound of link flows, this paper proposes a price-based congestion control scheme for achieving such a restraint target of traffic flow that evolves from day to day. On each day, drivers have to pay a toll selected from a feasible set. The tolls on each day are determined by the link flows and toll charges on the previous day and the predetermined upper bound of link flows. Several properties of the dynamical system model with the control scheme are analyzed, including the invariance of its evolutionary trajectories; the equivalence between its stationary state and user equilibrium under toll charge; the uniqueness, existence, and boundedness of its stationary state; and the convergence of its evolutionary trajectories. A special case of the model and implementation of the control scheme for several alternative targets are also given. Finally, application of the model to a traffic network is demonstrated with a numerical example. The study is helpful for better understanding the mechanism of congestion control under day-to-day traffic flow dynamics. [ABSTRACT FROM AUTHOR]

The majority of fuel consumed in traffic on urban arterials is related to driving in congested traffic, characterized by frequent speed fluctuations and stops at signalized intersections. A range of traffic control strategies was suggested in the past to decrease fuel consumption and emissions in urban networks. The research presented in this study aims to fill the gap in the existing knowledge by proposing a novel combination of an ecology-based performance index and an evolutionary method for optimization of traffic signal settings. An optimization problem is defined to find the best values for traffic light control parameters on the street network. The ecology-based performance index is used as the criteria for optimization. The defined problem is solved by the Bee Colony Optimization (BCO) technique. The geometry of the subject network has been inserted into a Vissim model calibrated by using relevant traffic demand and field-measured travel times. The Vissim has been used as the tool for the evaluation of the proposed BCO, TRANSYT-7F (T7F) and field signal timing plans. The methodology is tested on the field-real-like network of the City of Kragujevac, Serbia. Results show that in the case of the overall network ecology performance index, BCO makes a decrease of 11.95 % and 8.47% compared to the signal timings from field and T7F, respectively. • Proposed an ecology-based fixed-time control logic for street networks. • The model uses a BCO algorithm and derives control parameters. • A VISSIM simulation estimates the quality of the proposed control algorithm. • TRANSYT-7F and field signal plans are used for comparison with the BCO algorithm. • The model is tested on the field-real-like network of the City of Kragujevac. [ABSTRACT FROM AUTHOR]

Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is considered as one of the most critical issues in Intelligent Transportation Systems (ITS). Among the proposed AI -based approaches, Deep Reinforcement Learning (DRL) has been largely applied while showing better performances. This paper proposes a new DRL -based cooperative approach for controlling multiple intersections. The problem is modelled as a Multi-Agent Reinforcement Learning (MARL) system, while each agent is trained to select the best action to control an intersection by obtaining information about its local lanes state. The cooperation aspect is manifested in this approach by considering the effect of the state, action and reward of neighbour agents in the process of policy learning. An intersection controller applies a Deep Q-Network (DQN) method, while transferring state, action and reward received from their neighbour agents to its own loss function during the learning process. The experimental results under different scenarios shows that the proposed approach outperforms many state-of-the-art approaches in terms of three metrics: Average Waiting Time (AWT), Average Queue Length (AQL) and Average Emission CO 2 (AEC). In addition, the cooperation between the different trained DRL -based controllers allows the system to continuously learn and improve its performance by interacting with the environment, particularly when the traffic is congested. [ABSTRACT FROM AUTHOR]

Congestion avoidance mechanisms are used to prevent the saturation of the gateway which represents a bottleneck of the system. Recently, a new method for the congestion avoidance has been proposed: the smart access point with limited advertised window (SAP-LAW). The main idea is to hijack at the gateway the acknowledge packets in the transmission control protocol (TCP) connections in order to artificially reduce the advertised destination window according to some bandwidth allocation policy. Therefore, the flux control mechanism is artificially exploited to control the congestion at the bottleneck. The advantage of this approach is that it does not drop any packet and does not require any modification in the TCP implementations at the clients. While the most popular congestion avoidance mechanisms are the explicit congestion notification (ECN) and the random early detection (RED). In this paper, we propose stochastic models for the ECN/RED and SAP-LAW mechanisms in order to compare their performances under different scenarios. The models are studied in mean field regime, i.e. under a great number of TCP connections and UDP-based transmissions. Different from previous work for the ECN/RED, in this paper, we consider the presence of UDP traffic with bursts and the case of not greedy TCP connections. The models for SAP-LAW are totally new. The comparison is performed in terms of different performance indices including average queue length, system throughput, and expected waiting time. [ABSTRACT FROM AUTHOR]

This paper proposes a self-adaptive interactive navigation tool (SAINT), which is tailored for cloud-based vehicular traffic optimization in road networks. The legacy navigation systems make vehicles navigate toward their destination less effectively with individually optimal navigation paths rather than network-wide optimal navigation paths, particularly during rush hours. To the best of our knowledge, SAINT is the first attempt to investigate a self-adaptive interactive navigation approach through the interaction between vehicles and vehicular cloud. The vehicles report their navigation experiences and travel paths to the vehicular cloud so that the vehicular cloud can know real-time road traffic conditions and vehicle trajectories for better navigation guidance for other vehicles. With these traffic conditions and vehicle trajectories, the vehicular cloud uses a mathematical model to calculate road segment congestion estimation for global traffic optimization. This model provides each vehicle with a navigation path that has minimum traffic congestion in the target road network. Using the simulation with a realistic road network, it is shown that our SAINT outperforms the legacy navigation scheme, which is based on Dijkstra's algorithm with a real-time road traffic snapshot. On a road map of Manhattan in New York City, our SAINT can significantly reduce the travel delay during rush hours by 19%. [ABSTRACT FROM PUBLISHER]

Modeling traffic flow and gathering accurate traffic congestion information are two challenging problems in smart transportation systems. Most of the traffic flow models and velocity estimation methodologies that have been proposed so far gather the data from GPS-equipped smart phones and extract the flow model based on GPS sampling. However, these approaches tend to fail in real life scenarios due to the insufficient vehicle data and unpredictable dynamics of the flow. Furthermore, utilization of GPS sensor leads to a battery drainage and hence reduces the overall system performance. In this paper, we propose a new battery-friendly data acquisition model to obtain the raw data. We then evaluate our model under various traffic conditions to determine its feasibility in vehicle speed estimation. The proposed model results in 88% location accuracy whereas it reduces the battery consumption by half. [ABSTRACT FROM AUTHOR]

Every increase in traffic volume on road network in towns can lead to overcrowding of road network. This results in undesirable external costs such as traffic congestions, which cause high loses in time during transportation, increased fuel consumption and thus higher production of greenhouse gases and noise. This all ultimately reduces the attractiveness of the area. The increase of traffic volume and therefrom derived traffic problems are needed to be solved during traffic-capacity assessment of every larger investment. The software can help to assess increased traffic in solved area and thus help authorities to make a right decision during approving of the investment plan. This article is focused on comparison of two software - Aimsun and OmniTrans, and calculations according to Webster and technical regulations for assessing junction capacity in the Slovak Republic. The packages outputs are also compared to the measured data at the assessed junction in this article. The analysis showed that outputs of various tools differ, generally all packages showed higher delays compared to measured data at the main road and lower delays compared to measured data at the side roads. [ABSTRACT FROM AUTHOR]

This study evaluated operators' mental workload while monitoring traffic density in a city traffic control center. To determine the mental workload, physiological signals (ECG, EMG) were recorded and the NASA-Task Load Index (TLX) was administered for 16 operators. The results showed that the operators experienced a larger mental workload during high traffic density than during low traffic density. The traffic control center stressors caused changes in heart rate variability features and EMG amplitude, although the average workload score was significantly higher in HTD conditions than in LTD conditions. The findings indicated that increasing traffic congestion had a significant effect on HR, RMSSD, SDNN, LF/HF ratio, and EMG amplitude. The results suggested that when operators' workload increases, their mental fatigue and stress level increase and their mental health deteriorate. Therefore, it maybe necessary to implement an ergonomic program to manage mental health. Furthermore, by evaluating mental workload, the traffic control center director can organize the center's traffic congestion operators to sustain the appropriate mental workload and improve traffic control management. [ABSTRACT FROM AUTHOR]

This paper presents the stages in stochastic ship traffic stream model creation, applied to determine the influence of liquefied petroleum gas (LPG) tanker introduction to Police Port on the Świnoujście-Szczecin waterway. The model is based on the Monte Carlo methodology, and is microscopic (which means that each ship's model is treated as a separate object possessing given attributes). The model is applied here in order tofind the influence of ships with dangerous cargo (LPG tankers in the case study) on regular ship traffic, and hence to establish whether special traffic solutions are necessary. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major and growing problem in urban areas across the globe. It reduces the effective spatial interaction between different locations. To mitigate traffic congestion, not only the actual status of different routes needs to be known but also it is imperative to determine network congestion in different spatial zones associated with distinct land use classes. In the present paper, a new formula is proposed to quantify traffic congestion in the different spatial zones of a study area characterized by distinct land use classes. The proposed formula is termed the Traffic Congestability Value (TCV). The formula considers three major influencing factors: congestion index value, pedestrian movement and road surface conditions; since these parameters are significantly related to land use in a region. The different traffic congestion parameters, i.e. travel time, average speed and the proportion of time stopped, were collected in real time. Lower values of TCV correspond to a higher degree of congestion in the respective spatial zones and vice-versa and the results were validated in the field. TCV differs from the previous approaches to quantifying traffic congestion since it focuses on the causes of network congestion while in previous works the focus was generally on link flow congestion. [ABSTRACT FROM AUTHOR]

There is an incompatibility between how transport engineers think drivers behave in roadworks and how they actually behave. As a result of this incompatibility we are losing approximately a lane's worth of capacity in addition to those closed by the roadworks themselves. The problem would have little significance were it not for the fact a lane of motorway costs approx. £30 m per mile to construct and £43 k a year to maintain, and that many more roadworks are planned as infrastructure constructed 40 or 50 years previously reaches a critical stage in its lifecycle. Given current traffic volumes, and the sensitivity of road networks to congestion, the effects of roadworks need to be accurately assessed. To do this requires a new ergonomic approach. A large-scale observational study of real traffic conditions was used to identify the issues and impacts, which were then mapped to the ergonomic knowledge-base on driver behaviour, and combined to developed practical guidelines to help in modelling future roadworks scenarios with greater behavioural accuracy. Also stemming from the work are novel directions for the future ergonomic design of roadworks themselves. [ABSTRACT FROM AUTHOR]

Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like 'what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?', and (2) a probability-threshold query like 'what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?'. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network G( V, E) by analyzing uncertain trajectories of moving objects. Based on G( V, E), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP queries are verified by extensive experiments on real and synthetic spatial data. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of intelligent transportation system around the world, which helps to relieve urban traffic congestion problems. For instance, many mega-cities in China have devoted a large amount of money and resources to the development of intelligent transportation system. This poses an intriguing and important issue: how to measure and quantify the contribution of intelligent transportation system to the urban city, which is still a puzzle. This paper proposes a matching difference-in-difference model to calculate the contribution rate of intelligent transportation system on traffic smoothness. Within the model, the main effect indicators of traffic smoothness are first identified, and then the evaluation index system is built, and finally the ideas of the matching pool are introduced. The proposed model is illustrated in Guangzhou, China (capital city of Guangdong province). The results show that introduction of ITS contributes 9.25% to the improvement of traffic smooth in Guangzhou. Also, the research explains the working mechanism of how ITS improves urban traffic smooth. Eventually, some strategy recommendations are put forward to improve urban traffic smooth. [ABSTRACT FROM AUTHOR]

The traffic assignment problem aims to calculate an equilibrium route flow vector, generally by seeking a zero of an appropriate objective function. If a continuous dynamical system follows a descent direction for this objective function at each nonequilibrium route flow vector, the system converges to equilibrium. It is shown that when this dynamical system is discretized with a fixed step length, the system eventually approaches close to equilibrium provided that the objective function is continuously differentiable and that the rate of descent is bounded below. The method of successive averages is widely used in traffic assignment; it has a decreasing step size at each iteration. With the same conditions as above, it is shown that the resulting dynamical system converges to equilibrium. In the steady-state model, the necessary conditions are shown to be satisfied, provided that the route cost vector is a continuously differentiable monotone function of the route flow vector. However, continuous differentiability of the cost function is shown not to hold in the dynamic queueing model. [ABSTRACT FROM AUTHOR]

There has been extensive research on the optimal design of road congestion pricing schemes based on traffic network equilibrium models. Since travel demands are closely related to the locations of workplace and residence, it is of both theoretical and practical importance to extend the optimization approach to treat location choices as well as travel route choices. This paper addresses the problem of optimal location of housing supply and transportation network pricing in an integrated location and transportation model with heterogeneous households that have different values of travel time. In the model, housing exhibits external economies and diseconomies of scale, depending on the population density in residential zones. It is shown that in such a model the marginal cost pricing principle applied to internalize these externalities in general results in local optima of social surplus. Optimization algorithms based on sensitivity analysis are proposed to find combinations of housing supply patterns and road tolls that optimize social surplus under various conditions. A numerical example is given to illustrate the proposed method. The potential policy implications of the optimization approach are examined and compared with that of the marginal cost pricing principle. [ABSTRACT FROM AUTHOR]

As society has developed, the number of vehicles has increased and road conditions have become complicated, increasing the risk of crashes. Therefore, a service that provides safe vehicle control and various types of information to the driver is urgently needed. In this study, we designed and implemented a real-time traffic information system and a smart camera device for smart driver assistance systems. We selected a commercial device for the smart driver assistance systems, and applied a computer vision algorithm to perform image recognition. For application to the dynamic region of interest, dynamic frame skip methods were implemented to perform parallel processing in order to enable real-time operation. In addition, we designed and implemented a model to estimate congestion by analyzing traffic information. The performance of the proposed method was evaluated using images of a real road environment. We found that the processing time improved by 15.4 times when all the proposed methods were applied in the application. Further, we found experimentally that there was little or no change in the recognition accuracy when the proposed method was applied. Using the traffic congestion estimation model, we also found that the average error rate of the proposed model was 5.3%. [ABSTRACT FROM AUTHOR]

To identify network bottlenecks of urban expressway effectively is a foundational work for improving network traffic condition and preventing traffic congestion. This study proposes a methodology to estimate the impact of traffic incident on urban expressway on the basis of modified cell transmission model. The metastable state was taken into account in the proposed method to reflect the actual operating state of traffic flow on urban expressway as much as possible. Regarding the location of traffic incident, the method of cell restructuring settings was discussed. We then proceed to introduce a new concept of the effected length in a given time period as the evaluation indicator to directly depict the influence of traffic incident. The proposed method was tested on a 6516-meter urban expressway section of west second ring road in Beijing. The simulation results indicated that the proposed methodology performs well to predict the impact of traffic incident on urban expressway. [ABSTRACT FROM AUTHOR]

Pedestrian overflow causes queuing delay and in turn, is controlled by the capacity of a facility. Flow control or blocking control takes action to avoid queues from building up to extreme values. Thus, in this paper, the problem of pedestrian flow control in open outdoor walking facilities in equilibrium condition is investigated using M/M/c/K queuing models. State dependent service rate based on speed and density relationship is utilized. The effective rate of the Poisson arrival process to the facility is determined so as there is no overflow of pedestrians. In addition, the use of the state dependent queuing models to the design of the facilities and the effect of pedestrian personal capacity on the design and the traffic congestion are discussed. The study does not validate the sustainability of adaptation of Western design codes for the pedestrian facilities in the countries like Bangladesh. [ABSTRACT FROM AUTHOR]

The traffic loading of long-span bridges is governed by congestion. Real-world observations show that congestion can take several different forms. Nevertheless, most previous studies on bridge traffic loading consider only queues of vehicles at minimum bumper-to-bumper distances. In fact, such full-stop queues are rare events, while in most cases congestion waves propagate through the traffic stream, so that on a bridge there are periodically times of closely-spaced vehicle concentrations and times of flowing traffic, where vehicles are more distant. In this paper, an acknowledged traffic micro-simulation model is used for generating congested traffic on a single-lane roadway encompassing two bridges (200 and 1000 m long). Two truck percentages are considered (20% and 50%) and different congestion patterns are analysed in relation to their traffic features and effects on bridge loading. It is found that for the case of 200 m span and 20% trucks slow-moving traffic results in greater loading than full-stop conditions. Finally, the frequency of occurrence of different forms of congestion is taken into account based on recent available data, rather than being assumed as in most previous research. It is found that considering only the widely-used full-stop conditions leads to an over-estimation of the characteristic total load by about 10% for the cases of 200 m span with 50% trucks, and 1000 m with 20% trucks; for the case of 1000 m span with 50% trucks, the over-estimation drops to nearly 5%. However, for the case of 200 m span with 20% trucks, considering only the full-stop conditions leads to a slight under-estimation of the total load. [ABSTRACT FROM AUTHOR]