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An introduction to highway congestion high school edition

Correspondence should be addressed to Chyi-Ren Dow ; wt. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Existing intelligent transport systems ITS do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles IoV environments.

This paper proposes a traffic congestion monitoring system, an introduction to highway congestion high school edition includes data collection, segmented structure establishment, traffic-flow modelling, local segment traffic congestion prediction, and origin-destination traffic congestion service for drivers. Macroscopic model-based traffic-flow factors were formalized on the basis of the analysis results. Fuzzy rules-based local segment traffic congestion prediction was performed to determine the traffic congestion state.

To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine-Hugoniot condition and an origin-destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time. Introduction Over the last few decades, traffic congestion has become a serious problem in cities, which not only negatively affects the daily lives of humans but also impedes stable economic and societal development.

Traffic congestion increases air an introduction to highway congestion high school edition, travel time, and economic losses. Governments increasingly strive to monitor and resolve traffic congestion, but the task is difficult because of the complexity of the problem; specifically, traffic congestion is difficult to predict.

The complexity of traffic congestion is also reflected in its dynamic and interrelated characteristics. Traffic congestion can propagate from a congested road segment to neighboring road segments. Because of these complexities, fully automatic analysis of traffic congestion is difficult to achieve. Currently, two major challenges must be addressed to facilitate traffic congestion estimation and monitoring. The first challenge is formalizing the basic factors for estimating and forecasting traffic congestion on a large-scale road network.

Several researchers have proposed solutions to resolve the problem, which can be grouped into two approaches, namely, traffic congestion forecasting based on infrastructure equipment and traffic congestion forecasting based on Vehicular Ad Hoc Network VANET technology.

The first approach [ 1 ] mainly uses floating car data, namely, data from Global Positioning System- GPS- equipped vehicles. Some protocols use data from sensor equipment with the limitation, including loop detectors, video recording devices, and infrared technologies.

Although the infrastructure-based approach is the most widespread and features high reliability and accurate formalization, it lacks flexibility.

Based on the formalized information, researchers have adopted mathematical prediction algorithms to estimate traffic congestion levels. The advantage to this approach is that it can be implemented without deploying infrastructure sensors and has been demonstrated to operate effectively in various traffic and deployment scenarios. However, the approach is limited by network communication obstacles, including delayed and inaccurate traffic estimates [ 3 ], redundant data, bandwidth problems [ 4 ], and reliability problems [ 5 ].

These problems may cause insufficient precision or even failure in traffic congestion prediction. The second challenge is to guarantee the accuracy, instantaneity, and reliability of traffic congestion prediction. Existing systems do not fully address and resolve this challenge. The VANET-based approach involves considerable propagation delays and low reliability, whereas the infrastructure-based approach generally uses GPS data and data from limited number of sensors that lack flexibility.

Furthermore, most applications of the infrastructure-equipped approach use HTTP-based protocols for gathering and transferring data between a central computing unit and vehicles and equipment. This not only hinders integration with various types of sensor equipment but may also cause a serious overhead problem if the vehicle number increases markedly in a traffic congestion situation or if the amount of data transferred over a long period of time increases substantially.

This may result in false prediction [ 6 ]. Considering the aforementioned problems, this paper proposes a traffic congestion monitoring system using real traffic data based on Message Queue Telemetry Transport MQTT for investigating traffic congestion patterns; the proposed system has the advantages of both an infrastructure-based approach and MQTT techniques: This research also adopted a fuzzy rule-based method to address complex nondeterministic problems such as traffic congestion determination.

Furthermore, the proposed system is designed as a distributed system to eliminate computational bottlenecks and to avoid overhead problems, which is especially suitable for traffic congestion situations in which numerous communications are required due to the markedly increased number of vehicles. To provide a road segment-based traffic congestion monitoring service, the geographic map was converted to a segmented structure. Then, the authors defined the traffic congestion monitoring method and designed a mechanism that uses the segmented structure to predict local traffic congestion.

  1. The first application of these ideas may come in the Los Angeles area. Travel direction is used to determine lanes whose travel direction is the same.
  2. This idea is gaining increasing acceptance among those who build the roads. The formulas for segment determination can be expressed as follows.
  3. A number of proposals for new highways provide for truck lanes of 13 feet.

The remainder of this paper is organized as follows. Section 2 briefly describes the current relevant research and technology. Section 3 discusses the proposed traffic congestion monitoring method in detail.

Section 4 details an introduction to highway congestion high school edition implementation prototype. In Section 5the experimental results are demonstrated. Finally, Section 6 presents conclusions and future research directions.

The IoV plays as a part of the IoT, but it has distinctive characteristics. In the IoV, the mobility of vehicles is an important topic that needs to be paid attention. The IoV involves the way to gather information regarding sensors [ 9 ] on vehicles, roads, and their surroundings onto mobility platforms with the integration of the Internet. IoV technologies aim to use in intelligent transportation systems ITS which deliver intelligent traffic applications as the typical IoT applications in mobility environment.

In recent years, the IoV is proposed to provide more convenient services. The model adopts a phased approach to actively recommend to drivers by means of interacting with the considering of evaluation indicators.

With the development trend of the IoV, a huge number of sensors and vehicles tend to continuously connect to the Internet which may constitute the network bandwidth challenge. MQTT is one of the best developed candidates for this purpose. Yokotani and Sasaki [ 12 ] proved that the MQTT protocol is more scalable and reliable for applications, which requires very high data transmission frequency as in IoV environments. In a deeper research, Del Campo et al. The proposed architecture has been adopted for the home monitoring of patients with dementia.

The design aims to apply to crowd-sourcing based smart applications, such as smart travel planner or smart parking in the cities. Numerous research studies have highlighted the effect of traffic congestion, such as decreasing productivity and worsening pollution and increasing the transportation cost [ 15 ]. The traffic congestion effect can be seen in any country no matter it is a developing country or developed country.

Traffic congestion is classified into two categories [ 16 ]: Recurrent traffic congestion is a frequent basis type caused by various factors such as dramatic increases of traffic flow during peak hour [ 17 ] or repeated on-ramp and off-ramp road network [ 18 ]. Nonrecurrent congestion is an irregular type, which may happen due to road network disruptions such as road accidents and natural disaster [ 19 ].

To tackle with the traffic congestion problems, various scenarios have been proposed, which depended on various traffic flow detection techniques.

Gholve and Chougule [ 20 ] proposed a wireless sensor network-based traffic congestion detection system for highways, which has developed a protocol to guarantee the communication between sensor nodes and was prototyped on an Arduino embedded device. The proposed system used magnetic sensors for vehicle detection through proper signal conditioning and use of data processing. The proposed system archived promising results in using smart phones sensors for traffic congestion detection.

Data Collection Real data collection and analysis are vital requirements for forming a realistic system.

  • For most of us, the car is a time-saving machine that makes the humdrum tasks of daily life quicker, easier, and more convenient to accomplish;
  • The statistical probability of finding carpool matches people with similar origins and destinations at similar times will continue to diminish with the steady dispersion of jobs and more flexible job hours, just as the probability of finding convenient public transit is declining;
  • But there is pressure to make lanes wider for trucks;
  • Discovering the market value of a particular trip on a particular road and charging individual drivers accordingly are essential if we are to build our way out of perpetual congestion;
  • Plans for our major metro areas show projections for the year 2020, modeled after funded road improvements, in which average speeds on major arteries continue to decline in rush hours that extend throughout much of the working day.

In this subsection, the authors describe the collection of VD data and real-time vehicular data. The collected data are used to formalize fundamental factors according to macroscopic traffic-flow models, establish a segmented structure, and predict traffic congestion.

VD data analysis was performed to formalize the fundamental factors according to the macroscopic traffic-flow model that and used to extract information about the distribution of traffic flows. Furthermore, at the beginning of system development, more than 184 million historical VD records concerning traffic on the main roads of Taichung City, Taiwan, were sorted in ascending order of the recorded time. Table 1 illustrates the data fields. Historical VD Data Fields. The device ID is a number unique to each VD device used to identify which device is recording.

Traffic Congestion: A Solvable Problem

Longitude and latitude field indicates the location of the VD device. Longitude and latitude are also used to identify the road segment. For roads with more than one lane, lane order is the order number of the lane for which the traffic information has been obtained, and each record represents one lane of the road segment.

The date-time field is used to store the time stamp of the recorded traffic event. Historical VD records represent traffic events and were obtained at 5-minute intervals. The vehicle volume field indicates the number of vehicles occupying the current lane of the road segment. The average speed field indicates the average speed of vehicles, which was measured using the VD devices. Speed limit is used to define the maximum allowable speed of the road lane.

Travel direction is used to determine lanes whose travel direction is the same. Real-Time Vehicular Data Collection The insufficiency of just-in-time information is a major cause of traffic congestion. In this study, to manage traffic congestion problems, real-time movement-related vehicular data are focused. In this system, the authors presume that vehicles play two roles.

  • But there is no sign that this focus has stemmed solo driving either;
  • Fuzzy rules-based local segment traffic congestion prediction was performed to determine the traffic congestion state.

The first role, vehicles act as normal users that can subscribe to published topics to receive traffic congestion information using the MQTT model. In fact, each vehicle has a distinct origin-destination route. Thus, the proposed system provides vehicles the initiative in their origin-destination traffic congestion information. To handle origin-destination traffic congestion matter, each vehicle maintains a status table as shown in Table 2.

The vehicular ID is the unique vehicle registration plate number. The destination indicates the desired destination of predefined route. Longitude and latitude are used to identify the current road segment where the vehicle is located.

The date-time field is used to determine the time when the record was recorded. A vehicle acts the second role when the vehicle is selected as the header of a segment with a high possibility of an introduction to highway congestion high school edition to publish information to MQTT topics. Each header is assigned to estimate traffic-flow and predict local traffic within its road segment. This design intends to eliminate communication redundancy and computational bottlenecks.

Consequently, the overall performance and computational ability of the system should be improved. Segmented Structure In fact, each segment of a road network exhibits different traffic-flow levels, and traffic congestion usually occurs in high-traffic-flow road segments.

Traffic congestion starts at a specified road segment when the vehicle number reaches the capacity of the road segment. Thereafter, traffic congestion may propagate from a congested road segment to neighboring road segments. Because of this characteristic, the authors used historical VD data to investigate road network traffic-flow levels, which benefits local traffic congestion prediction.