Handbook Of Transport Systems And Traffic Control Pdf

handbook of transport systems and traffic control pdf

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Such uniform system shall correlate with and so far as practicable conform to nationally accepted standards. On that date, legislation became effective which formally adopted the provisions of the National Manual on Uniform Traffic Control Devices. Combined, the two operate in conjunction as the manual and specifications for a uniform system of traffic control devices as required by Section a. Please note that the National MUTCD has some new sections and the number of some of the signs and sections has changed accordingly.

Traffic Engineering

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks.

The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles.

Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.

The amount of motor vehicles and correspondent travel demand are continuously increasing with economic and social development.

The frequent occurrence of traffic congestion in urban road network has negative impacts on economy and environment. Due to the limited land resources of large cities and restrictions to transportation infrastructure construction from socioeconomic factors, to apply traffic management and control measures in a reasonable and effective way, improve the efficiency of existing transportation facilities, and accommodate the growing traffic demand in big cities have become significant research contents for counteracting urban traffic congestion.

Traffic control is one of the most important technical means to regulate traffic flow, improve the congestion, and even reduce emissions. Its progress and development has always been accompanied by the development of information technology, computer technology, and system science. The self-adaptive control system can adjust the signal timing parameters in real time according to the control target of the manager such as the minimum delay of the intersection and the arrival characteristics of the traffic flow at the intersection.

Compared with timing control and actuated control, the self-adaptive control system can make better use of the overall traffic capacity of the road network and effectively improve the efficiency of road network traffic. The traffic data collected by the current traffic control system using induction loop detector and other existing sensors is limited.

With the advancement of the wireless communication technologies and the development of the vehicle-to-vehicle V2V and vehicle to infrastructure V2I systems, called Connected Vehicle or V2X, there is an opportunity to optimize the operation of urban traffic network by cooperation between traffic signal control and driving behaviors. This dissertation proposed a series of cooperative optimization methods for urban streets traffic control and driving assistant under the V2X concept.

In addition to the existing induction loop detector technology, the video, infrared, radar, floating cars, and other acquisition technologies and equipment provide urban traffic control system with a network of dynamic acquisition traffic flow status data and controller state data, which greatly enriched the information environment and provides more possibilities for the informationalized and intelligent application research.

Urban traffic control is entering the data-rich period of multisource holographic network traffic data from the period with only data of cross-section traffic flow. Recent advances in traffic control methods have led to flexible control strategies for use in an adaptive traffic control system [ 1 ]. Metropolitan road traffic digitized and informationalized infrastructure and related system construction has been developed rapidly in the past decade.

At the same time, the emergence of intelligent connected vehicles and automated vehicle jointly build a future traffic travel environment, whose abilities of individual information access and perception as well as the performance of response time and interactive behavior are significantly different from conventional artificial driving vehicles.

However, the current self-adaptive traffic signal control system cannot effectively utilize these abundant real-time traffic data, and its theory, methods, and techniques have clearly lagged far behind the progress of its key basic technologies [ 2 ]. Therefore, the research of data-driven feedback self-adaptive coordination control in data-rich environment is proposed and actively explored by researchers [ 3 ]. Moreover, with the continuous improvement of the theory and technology of intelligent control and nonmodel control, the concept of traffic control is changing under the new traffic data environment shown as Figure 1.

The researchers hope that the model of the control system is based on the data model identification rather than the existing mechanism model [ 4 ]. Besides, they hope that the system is based on real-time monitoring data rather than the traffic forecast data [ 5 ] and the control system can automatically adjust the control strategy instead of the manual intervention [ 6 ].

According to NCHRP, more than 20 self-adaptive traffic control systems have been developed by transportation research institutes and enterprises worldwide, but less than half systems have been put into use [ 7 ].

The first-generation self-adaptive control system adopts the multi-time timing control of fine division of period, or completely isolated self-adaptive control, to realize the simple regulation of traffic flow. Take the multi-period timing control system as an example, which divides the traffic flow arriving within a day into multiple periods such as peak, nonpeak , taking into account changes in daily traffic demand to optimize the signal timing scheme in different periods of time each day, using the comprehensive performance index method or green wave band timing method to optimize and generate a signal timing scheme library [ 10 ].

According to the number of weeks and control period, traffic controller can directly select the appropriate offline scheme from the scheme library. The second-generation traffic signal control system dynamically adjusts the parameters of the signal timing scheme signal period, green signal ratio, and phase difference. Compared with the timing and induction coordination control system, the second-generation system greatly improved the flexibility and adaptive adjustment ability of the control system.

The third-generation control system uses the similar idea as the second generation to dynamically adjust the signal timing parameter in response to the fluctuation of the time-varying traffic flow at the intersection.

Kosmatopoulos et al. The main conclusion drawn from this high-effort inter-European undertaking is that traffic-responsive urban control is an easy-to-implement, interoperable, low-cost real-time signal control strategy whose performance, after very limited fine-tuning, proved to be better or, at least, similar to the ones achieved by long-standing strategies that were in most cases very well fine-tuned over the years in the specific networks [ 15 ].

The fourth-generation self-adaptive traffic signal control system is an integrated traffic management and control system, which can realize the integrated management of network traffic and maximize the technical and performance advantages of multiple subsystems [ 16 ].

It integrates self-adaptive traffic signal control system and other ITS traffic management systems with system hardware and software integration technology, like dynamic process models of combined traffic assignment and control with different signal updating strategies [ 17 ]. It is committed to building an efficient urban traffic control integrated management system to achieve the integration of mobile network management so that it can provide better decision support for local government decision-making [ 18 ].

The fifth-generation self-adaptive traffic signal control system is based on the abilities of self-learning and high efficiency calculation in automated vehicles and regular vehicles environment [ 19 ]. Based on the empirical information and real-time traffic condition, the fifth-generation adaptive traffic signal control system learns the traffic control knowledge independently and reduces the computational burden of decision optimization intelligently.

As of June , InSync system has been applied in intersections in more than cities across the United States and has become the fastest growing self-adaptive traffic control in the United States, which is also recommended by the FWHA currently [ 20 ]. Manolis et al. Most importantly, this new methodology, called adaptive fine-tuning AFT , achieved to improve the performance of the system and compensate the effect of the continuous changes of its behavior that may be due to either internal or external factors.

The results from AFT real-life application demonstrated that it was capable of significantly improving the performance of the system in a safe and robust manner. Moreover, the real-life results exhibited the capability of AFT to efficiently adapt and compensated in cases of changes in the system behavior, even if these changes were significant [ 21 ].

Each generation system not only inherited the excellent characteristics of the previous generation traffic system, but also moves forward continuously to promote the evolution of traffic control technology under the support of the key basic technology and the guide of the new traffic control strategy.

However, there are some shortcomings of the existing self-adaptive traffic control theory, method, and technology with fixed period, as follows: 1 The existing model of static traffic prediction and timing scheme does not have learning ability.

Therefore, the relevant departments will recalibrate the model parameters only when the network traffic patterns have significantly changed. Many of the existing self-adaptive traffic control systems use the traffic model to predict the evolution of the network traffic flow under the condition of limited traffic flow data and then use the comprehensive index method to optimize the signal timing parameters.

Therefore, volume prediction is an essential part. Associated with the prediction are two aspects: resolution and accuracy. It is imperative to study the relationship and tradeoff between the control strategy, prediction resolution, and its associated error, which are crucial to the development of self-adaptive traffic control systems. In a word, it is the inevitable option to study the theory and method of urban road traffic adaptive control in the future traffic data-rich environment.

The composition of the regular vehicle RV traffic flow is also changed by the emergence and mixing of the Connected Vehicle CV and the autonomous vehicle AV. It is foreseeable that the car traffic flow will consist of conventional vehicle, CV, and AV in the next few decades. The United States establishes seven test sites to promote the intelligent connected vehicles testing and large-scale demonstration. Now, Nevada, Michigan, and so forth have allowed driverless vehicles to enter public road for testing.

The concept of intelligent connected vehicle was formed in the s, known as cooperative infrastructure vehicle in the beginning. Michigan, California, and other states gradually established connected vehicle test platform from These systems and projects have entered the stage of large-scale system applications and related technology policy development. With the continuous development of related fields such as mobile Internet and Internet of Things, cooperative infrastructure vehicle system and its application have become the new trend of the intelligent transportation system.

Besides, the necessity of carrying out research on relevant theories, technologies, standards, policies, and regulations has become a broad consensus. In , Carnegie Mellon University developed the autonomous vehicle Navlab-5, completing a self-driving experiment of nearly 5, kilometers across the US, of which In , Stanford University developed the driverless vehicle Junior, which can independently plan the path and realize its precise positioning, perceive other social vehicles and interact, and can achieve driving behaviors such as lane changing, U-turn, and parking [ 30 ].

Besides, Google, Nissan, Tesla, GM, Ford, and other companies are also involved in the study of autonomous vehicle, but the technical details of the study are usually not disclosed [ 33 ].

In the theoretical study, Levinson et al. Following the methods of earliest fixed signal timing and offline delay calculation proposed by Webster [ 35 ], the traffic signal control system has evolved from offline to online control, from point to network control, from fixed-time to self-adaptive control. With the development of intelligent transportation system, the research of a new generation of traffic control technology based on multisource heterogeneous data has been gradually started [ 36 ].

In recent years, the research on signal control based on cooperative infrastructure vehicle system has become the frontier field of domestic and foreign traffic control theory and application [ 37 ]. The research of intelligent connected vehicle was mainly focused on the optimization methods of traffic safety, such as collision warning [ 38 ] and lane changing assistance [ 39 ]. With the concept of active safety and traffic signal control problems being put forward, driving optimization strategy for efficiency and emission reduction, such as the speed guidance strategy considering the signal light state [ 40 ], eco driving strategy [ 41 ], and so on, has been widely studied.

Besides, to meet the special needs like emergency rescue vehicles and bus priority, the multimode signal priority control system considering the real-time status of special vehicles has also been put forward and achieved initial implementation [ 42 ].

Automatic driving research mainly focuses on the data collection and forecasting problem of mixed traffic flow [ 43 ] and the local optimization method based on the rolling optimization strategy [ 44 ].

Most of the optimization targets adopt efficiency-related indicators such as the least delay, the least number of stops, or the shortest across time [ 45 ]. About the control effect evaluation, most of the research output optimization control effect based on the secondary developed traditional simulation software [ 46 ]. Researches show that the traffic control which considers the mixed traffic flow of the connected vehicle and autonomous vehicle can effectively improve the traffic efficiency of the intersection, compared to the conventional traffic flow control [ 43 ].

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. The traffic signal control method is evolved along with the combination of modern control theory, artificial intelligence theory, traffic information technology, and traffic engineering technology.

Because the modern control theory is based on the basic assumption that the mathematical or nominal model of the controlled object is precisely known, the method is collectively referred to as Model Based Control MBC theory and method [ 47 ]. In the last 20 years, Artificial Intelligence AI theory and methods, which were represented by agents, neural networks, fuzzy logic, and group intelligence, were gradually mature.

Diakaki et al. Based on a store-and-forward modeling of the urban network traffic and using the linear-quadratic regulator theory, the design of TUC led to a multivariable regulator for traffic-responsive coordinated network-wide signal control that is particularly suitable also for saturated traffic conditions [ 48 ]. Arrival—Discharge Process signal control algorithm is based on dynamic programming and the optimization of signal policy is performed using a certain performance measure involving delays, queue lengths, and queue storage ratios [ 51 ].

Storage-Forward Response Control using a real-time monitoring data of arrival and leaving traffic flow to simulate the movement of the vehicle platoon and realize the predictive control [ 52 ]. According to the different control targets, the traffic signal MBC control method can be divided into a coordinated control method based on comprehensive performance index and a coordinated control method based on green wave band. The comprehensive index method, represented by TRANSYT [ 53 ], considers the delay, the number of stops, and the length of queues to obtain the best overall efficiency of the network.

The green wave band method is designed to maximize the number of nonstop platoons of the main line, and the typical arterial coordinated control method based on green wave band includes the maximum green wave band method MAXBAND and the multi-green wave band with variable band width method MULTIBAND [ 54 ].

Some researchers think that optimization of traffic lights in a congested network is formulated as a linear programming problem [ 55 ]. However, considering the complexity of the internal structure of the urban regional traffic system and the external operation environment, it is impossible to establish the precise mathematical model.

There are many challenges in effectively integrating signal timing tools with dynamic traffic assignment software systems, such as data availability, exchange format, and system coupling [ 56 ]. However, little effort has been put in developing control frameworks that are aimed not only at improving the average performance of the system, but also at improving the system robustness and reliability. In the past 10 years, artificial intelligence computing technology simulates human reasoning and learning process and controls the optimal control strategy in the process of interaction between traffic controller and road environment.

Fuzzy logic, group intelligence algorithms, and neural network control dominate the many traffic control methods based on intelligent computing.

The fuzzy control of urban traffic signal is one of the effective connotative solutions to solve the urban traffic problem. Pham et al. This fuzzy logic diamond interchange FLDI comprises three modules: fuzzy phase timing FPT module that controls the green time extension of the current phase, phase logic selection PLS module that decides the next phase based on the predefined phase sequence or phase logic, and fuzzy ramp-metering FRM module that determines the cycle time of the ramp meter based on current traffic volumes and conditions of the surface streets and the motorways [ 57 ].

Zhao et al. A genetic algorithm GA based heuristic is used to yield meta-optimal solutions to the model.

Traffic Control

The purpose of work zone traffic control is to provide a safe work area for workers within the roadway, while facilitating the safe and orderly flow of all road users motorists, bicyclists and pedestrians including persons with disabilities in accordance with the Americans with Disabilities Act of through the work zone. This manual is intended to provide New York State Department of Transportation NYSDOT employees, utility companies, municipalities, and contractors who are involved with the design, set-up and maintenance of highway work zones, or anyone working within the state right-of-way, with the basic principles and elements constituting a safe work zone. This manual includes basic information on work zone traffic control, including a description of traffic control devices, illustrations of acceptable, commonly used devices, and the proper flagger attire and methods. Color diagrams typical applications depicting typical traffic control set-ups for two-lane and multilane highways are intended to show the minimum requirements for a safe work zone set-up. Traffic control or protection can be enhanced for situations that may require additional measures such as high traffic or pedestrian volume, high speeds, restricted sight distance, poor or confusing alignment. This is a "living document" that will evolve as recommendations are received from the Regions. Your browser does not support iFrames.

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Handbook of Transport Systems and Traffic Control | Editors: Kenneth J. Button, David A. Hensher.

Traffic Control

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Intelligent transportation system

Agency Directory Online Services. This manual in combination with the Federal Highway Administration's MUTCD provides guidance on the installation and proper use of traffic control devices. The WMUTCD pages are for the guidance of design engineers, technicians, inspection personnel, contractors, municipalities, counties, townships and others who are involved in highway design, construction, maintenance and operations. The goal is to provide uniform application of traffic control devices and other related items used on the Wisconsin highway system. Table of Contents and Introduction. Part 1 - General. Chapter 2A - General.

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The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles.

An intelligent transportation system ITS is an advanced application which aims to provide innovative services relating to different modes of transport and traffic management and enable users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. Some of these technologies include calling for emergency services when an accident occurs, using cameras to enforce traffic laws or signs that mark speed limit changes depending on conditions. Many of the proposed ITS systems also involve surveillance of the roadways, which is a priority of homeland security. Further, ITS can play a role in the rapid mass evacuation of people in urban centers after large casualty events such as a result of a natural disaster or threat. Much of the infrastructure and planning involved with ITS parallels the need for homeland security systems.

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Traffic Control Devices


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