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Signalized Traffic Networks | Research Paper Writing Help Online

Abstract

The control and management of traffic signals contribute a significant throughout the movement of cars on the road. It therefore becomes essentials to formulate a model that enhances this movement by minimizing the waiting time of people by offsetting the periods between signals. The model presented in the paper utilizes mixed-integer programming to achieve the coordination between signals. Moreover, we present a microsimulation that indicates the high level of coordination that can be achieved by the traffic using limited on low efforts. With such a system, the coordination is achieved through distinct computations and rather not by mere human capability making the process more feasible and practical. The paper presents a simulation achieved by analyzing the flow of people at the intersection. The main aim is to provide an analysis on the intersection classification and geometry associated with the movement of traffic and therefore present a model to engineers that could enhance their ability to design more integrated systems and roads using the shortest movement period, relieve congestion in urban regions and enhance the road networks capacity.

Keywords: Traffic, Networks, Arterials, Intersections

 

 

Signalized Traffic Networks

A computer simulation is regarded as one of the most significant and effective approaches to analysis the geometry of road and systems that form an integral part of an urban system. With such a simulation, specialists can easily study the structure of the traffic system and thus eliminate congestion of the roads. Most of the models presented in previous instances are aimed at establishing the optimization of cycle lengths and green time fractions (g/c). This is a result of the frequently used solution approaches. Through the use of quicker technique, a closer integration would be achieved between network coordination and demand modelling. Overtime, the traffic on the urban regions is continually rising. For this reason, engineers in the field of traffic are continually striving to deliver computations that could enhance increased the level of movement and rather minimize the waiting time. This requires high levels of intelligence and capabilities other than just adding up the movement space as this could, in most instance, a very limited and costly option. In order to eliminate the issue of perceived and objective impediment on the traffic that could arise as a result of increased stops and longer waiting periods, signal timings should be offsets at various junctions to ensure that that cars are progresses within the network with limited or impedance. The operation and coordination of signal controls serve a fundamental purpose in facilitating the movement of a traffic network enhancing the flow of vehicles. The capacity to coordinate the traffic network is essential to assist in making the flow of vehicles more efficient, reduce time wastage in heavy traffic, minimize chances of accidents occurring, and improve time efficiency. The development of signalized traffic networks is applied in areas that experience heavy traffic and especially the movement of many roads on the road (Coogan et al. 2017). Traffic assignment models are divided into three main categories, depending on the temporal dimension: static traffic assignment (STA), dynamic traffic assignment (DTA), and semi-dynamic traffic assignment (Bliemer et al., 2017).

STA models have the advantage of mathematical simplicity and computational efficiency, which makes them a favorite for transportation planners. They are largely applied in the management and design of road networks that are environmentally sustainable. Despite being simple and computationally efficient, STA models have various limitations, including the incapability to model traffic dynamics such as speed variation and queue spillback. Such traffic dynamics are related to emissions. For instance, fast acceleration results in increased rate of emissions. The DTA model has time steps for network loading within each time period for route choice. DTA models are able to capture traffic flow characteristics that are more realistic, such as queue spillback, expansion waves, and shock waves (Szeto & Lo, 2006). This capability shows a huge potential of improving the emission estimation accuracy, which explains why DTA models are starting to receive increased attention in the optimization of transportation systems.

The theory of traffic signals that aims at the estimation of delays has received much attention since the pioneering work by cycles and queue lengths that have resulted to the adoption of different control strategy at an individual intersection and the analysis tools and intersection control throughout the world. Within a traffic network, coordination of the signal controls has a great impact on journey times and throughput of the cars in the network (Gordon, Tighe and Siemens, 2005).

Ideally, an intersection is considered as a location whereby two or more roads carrying traffic streams in varying directions cross. This space which is common at all these roads is termed as intersection. At this location diverse traffic streams strive to use the common space of the intersection. Thus, if left uncontrolled, the flow at the intersection becomes chaotic while the efficiency and safety at such location tends to be low. Hence there have been carious control strategies that have been used so as to improve the efficiency and safety of traffic flow (Prothmann et al., 2009). Among these strategies that are relied upon, signalization is the most common.  Relatively, models that have applied both the stochastic and determinist components of the traffic performance have been very appealing to a broad range of traffic intersections as well as to a variety of signal control.

Data Collection

Data was collected at various intersections and evaluated at different periods of the day. The raw data was recorded in Microsoft excel and simulations done to establish the analysis done to establish the link between data. The worksheet presented below presents the data obtained from the activity. It presents the signalized, traffic and geometric conditions. The traffic flow interruption at a signalized intersection is deterministic and orderly. For instance, take the following situation. The signal has changed to red for a specific movement or stream. All the cars on this movement come to s atop and will remain stopped forming a queue until the lights are turned green. Once the light changes to green the first car in the queue departs followed by the other vehicles and the movement continues until the amber where the vehicle that is close to the intersection proceeds while the rest halt or stop. This pattern is followed for every stream to ensure efficiency and flow of vehicles at intersection.  The fixed time control for the isolated intersection works properly for the low volume of traffic present at the intersection (Zheng and Liu, 2017). Considerably, the signal timing at the isolated intersection is not interrupted by various transactions and thus will offer flexibility in setting the time cycle needed for the signal. The time needed for a car to cross the passage can be managed through proper timing of the signal for isolated intersections. The signal for timing at the isolated intersections will have impact on the transportation network that is at the intersection.

 

Isolated Intersections

The intersection is regarded as one of the major causes that have a huge impact on the degree of saturation, capacity, performance index, stop, queues, level of service and travel time. The application of isolated intersections comes into effect without having thorough consideration of adjacent signalized intersections. The isolated intersection falls into place following the interchange of closely spaced intersections through a change of lanes to ease on the traffic flow and traffic networks. This mechanism lacks the use of a traffic light system which provides the management of proper signal timing on a section of the road. The time cycles provided by the traffic light system are amiss in the isolated intersections. Instead, the section is often controlled using zebra crossing markings that signal for the road users when it is clear, safe, and necessary to pass through the section (Simões & Ribeiro 2014). The markings are provided to determine sections of human traffic and when vehicle drivers should give respect and courtesy to other road users or when to proceed if it is clear and safe to do so without impeding or inconveniencing other road users. Isolated intersection control is considered traffic control that fails to consider adjacent signalized intersections. This means that the adjacent intersections are not used to coordinate isolated intersections. Most of the isolated intersections in the universe tend to function through a fixed time mechanism. Ideally, these approaches are believed to assume presence of signal cycle that offers one accomplishment of the basic arrangement of signal groupings within the intersection (Olariu and Weigle, 2009). Isolated traffic signals control strategy can be divided into two major sections which include the strategy for selection of phase and the phase timing strategy. The strategy for phase timing controls the length of the green period in phase by the use of logical decisions based on the information from the detector with respect to the flow of the vehicles and pedestrians. The strategy for phase selection is important in selecting the best possible phase in every period or offers a better combination of the green signals for every traffic moment. The need for this strategy has been high as a result of the increased channelized larger transactions.

They play a significant role in the realization of the objectives, which aim at overcoming traffic problems and reducing the overall wait-time. In this case, road networks should be carefully analysed to achieve the objectives of signal coordination. One of the most important elements of developing the isolated intersections focus on reducing the wait-time and length of traffic delays, which have been identified as a major economic problem in many developing countries (Amini, Coogan, Flores, Skabardonis, & Varaiya, 2018). Signal timings can be positioned in specific intersections where drivers can be directed to use during peak hours and other periods where congestion might affect the accessibility in major town areas. By pursuing this development, it is imperative for individuals to recognize the issues that can be addressed through the achievement of independence where vehicles can reach their destinations within the expected timeframe.

Fig 1.0 A graphical representation of an isolated intersection.

Installing a traffic light controller at major roads and highways enables individuals to manage and direct vehicles remotely in a manner that addresses issues that affect people’s expectations. While the main objective is to improve the vehicular movement accessing and leaving major towns, there is a need to oversee the issues that can be introduced in the later stages to solve the traffic menace. Newer versions that aim at improving the existing solutions have been developed and are preferable than the traditional models of addressing the traffic problem (Royani, Haddadnia, & Alipoor, 2010). Minimizing delays is the main objective that can be achieved by embracing this model of traffic because of its impact on the perspectives of individuals using vehicles in their immediate environment. Addressing traffic is a collective responsibility that should be coordinated by the local and federal government where individuals can solve problems that interfere with their expectations in the world today. However, adjusting various aspects such as genetic algorithm to surpass the conventional methods of traffic signalling is the only way to achieve positive results and effectiveness.

The isolated intersections rely on the use of lane allocation that includes the approach and exit lane numbers. Also, the lane numbers determine the lane markings which determine the lane approach and the lane exit to follow through in an isolated intersection. The main purpose of such dynamics of traffic control is focused on improving the capacity of an intersection to handle enormous traffic flow more efficiently and effectively (Yu et al. 2017). In turn, the capacity of effective utilization of approaching and exit lanes induce the optimization of lane allocations to facilitate the movement of traffic. As well, this often ignores the fluctuations in traffic demand (TD) that may hinder or prompt unnecessary lanes change either on approaching or on exit (Yu et al. 2017). The isolated intersections rely on the adherence of the law and common decency to ease the traffic flow resulting in efficiency and timeliness of traffic movement.

Arterials

Arterials roads main aim is to deliver traffic from expressways, freeways and collector roads and between urbans regions with the highest level of service possible. In the United States, the hierarchy of roads nomenclature comprises of freeways (expressways) arterials, collectors, and the local roads. The analysis of arterials indicates their significant impact in the flow of traffic in urban centers (Birjs & Pirdavani 2018). Also referred to as the arterial thoroughfare road marks a section of the road with high-capacity traffic. The arterial roads primary function in urban centers is to efficiently deliver the flow of traffic from collector roads to the expressways. As well, the arterials deliver the flow of traffic between urban centers at the highest level of efficiency in service delivery possible. The utilization of arterials thoroughfare is a critical section of the road in facilitating the flow of traffic in urban centers. The existence of the capacity to accommodate high traffic is critical towards making the traffic flow more efficient (Wang & Cheng 2012). The arterials are often dual carriage infrastructure that delivers traffic flow in one direction. The arterials connecting expressways and the collector roads makes it possible to ease traffic flow more efficiently, effectively, and in a timely manner. Urban networks are made up of arterial compositions, which comprise the entire traffic coordination. By using this aspect, it is possible for governments and relevant authorities to realize their objectives and expose the public to an efficient traffic system where movement and accessibility to the major towns is guaranteed. By pursuing this innovative traffic aspect, it is possible to discover the various elements that can be pursued to overcome issues that take place in the business environment (Jrew, Msallam, & Momani, 2019). Arterial streets act as major points for the resolution of problems that occur in the business environment. On the same note, streets with arterial labels are marked with a green band that directs individuals from the busy roads, which lead to the outskirts of the city.

Communication and transport system are part of the economic and social life because of their impact on the perspectives of individuals towards work. Accessibility and mobility is a concept that should be undertaken by governments to enable the public to overcome problems that affect their lives in the immediate environment. Demand for effective and well-defined transport networks is an aspect that should be pursued by governments and other relevant authorities in the world today. Developing defined road networks is one of the procedures that should be followed by individuals to accomplish their objectives (Islam, Vu, & Panda, 2019). On the same note, public authorities should explore alternatives that can be used by governments to improve the lifestyles of individuals in their immediate environment. However, maintaining the systems to ensure that they achieve full and total functionality is a concept that should be realized by individuals in the world today.

The Arteria traffic control offers a progressive traffic flow through the arterial. In this case, the control is typically done through coordination of various traffic signals. This is carried out through the traffic signal control systems that are used as the primary tools that are used in the management of the flow traffic within the arterial street systems. The primary aim of these traffic control systems is to improve enhance the traffic networks, enhance safety and lower the traffic delays (Zheng and Liu, 2017). The traffic signals, detectors and other means of communicating the information to the vehicles are used in the management of the traffic. These systems make use of the various information systems that collect information through traffic surveillance devices that facilitate smooth traffic flow.  Thus, to maintain the flow of traffic, arterial traffic controller system has to coordinate timing of the adjacent intersections. This is done through establishment of a time relationship between the start of an arterial green at one intersection and the beginning of the other in the next intersection. This allows continuous which lowers the delay where better performance is achieved.

Urban Networks

The application of signalized traffic networks is achieved in both dual or single carriage roads to serve the integral purpose of easing the traffic network movement. This can be applied in different intersections of the road using either the traffic light signals or the zebra crossing markings on the entry of every road. These sections are designed to mark areas of the road where motor vehicle drivers and human traffic are required to stop and take the necessary step in a coordinated traffic network.

This model shows how traffic spreads out on a road network, governing the network performance on the basis of travel time. It is also referred to as a traffic flow model and can have sub-models such as link model and node model. The output of network loading model in DTA is influenced largely by the type of link model and node model selected. Network loading models may be macroscopic, microscopic, or mesoscopic (Rakha & Tawfik, 2009). Traffic flow simulation software is also referred to as a network loading model of DTA. The macroscopic traffic flow models detail traffic from a collective vehicle flow perspective using variables that are macroscopic, such as link flows.

Grandinetti et al. (2015), “the roads of an urban network are a collection of three sets Rin, R, Rout, with associated the following relations.

prev: RRout →Rin R (1)

next: Rin R→RRout, (2)

Where,

  • R is the set of the inner roads of the network;
  • Rin (Rout) is the set of the roads entering (exiting) the network;
  • Next(r) (prev(r)) is the set of roads connected downstream (upstream) to the road r.”

Figure 1: An Example of Signalized Traffic Network

The analyzing of the dynamics of traffic flow at the signalized intersections can be best understood by the assessment of the following aspects of traffic networks including isolated intersections, arterials, and urban networks. These are critical aspects which elaborate on the significance of traffic networks and traffic flow coordination impacting to efficiency and effectiveness of vehicular and human traffic on the road (Sun 2014). Thus, culminate in good road usage and impacts to social-economic aspects of a nation through efficiency on the utilization of the road facilities.

In urban networks, signalized intersections contribute a significant part and care should be taken to ensure accurate and reliable systems have been developed. The signaling and coordination of the vehicles requires high attention and care as the split’s impacts capacity and cycle length plays a huge part on this coordination. The urban networks in urban centers exist to ease and facilitate the speed of the flow of traffic – goods, vehicles, people, and ideas (Glaeser et al. 2015). The utilization of urban networks in easing traffic flow provides the most effective approach to making traffic movement more efficient. The urban networks comprise of more advanced features of the road integrated to form a massive network of roads that make traffic movement more efficient both in terms of time and money. The control and coordination of traffic in an urban network takes a high-level of sophistication which determines the movement of traffic in the cities. This combines expressways, arterials in the urban center, and collectors in different corners of the city. Also, the roads are bound to incorporate heavy traffic lights control system that facilitates the movement of traffic across the urban center (Glaeser et al. 2016).

Many of the approaches that are developed by governments to solve congestion is the designing of effective road networks, which enable individuals to overcome problems revolving around their immediate environment. Having links that motorists can pursue when exiting or accessing the city is an accomplishment that cannot be matched with any other outcome in the world today. Designing convergences and other aspects of nature that outline the different issues that can be pursued to minimize the various problems attached to traffic congestion is a milestone that can be realized through the involvement of various stakeholders in the modern environment (Ethier, 2016). However, it should be observed that urban networks are different from social networks because of their limited dependence on having links, which expose individuals to free areas.

Figure 1 Urban Network Traffic Capacity

Whenever individuals mention urban networks, the aspect of transport comes into play because of its realization that compels individuals to question the role of government in creating an accessible system where people can access various regions within major towns. However, the influence of technology has compelled government and other relevant authorities to focus on developing innovative solutions where individuals can realize their objectives in the business environment. For instance, using the node, density, and accessibility-based concepts, it is possible to develop a seamless transport system where individuals can focus on different approaches where congestion is eliminated (Zhou, Fang, Thill, Li, & Li, 2015). Developed nations can share their approaches, which were used to solve the problems. Using different patterns, it is possible for individuals to discover their potential and accomplish a wide range of objectives, which influence the outcomes of events in the contemporary environment.

As well, the use of isolated intersections that express the free movement of traffic with courtesy of road users to follow and respect the high code and rule of law. Urban networks rely on the use of pedestrian crossing sections and footbridges to isolate human traffic and vehicular traffic flows. In turn, the impact on decongesting the city more effectively (Cheng et al. 2013). Furthermore, the existence of pedestrian only, cycling only, among other designated pathways make the flow of traffic in the urban center more effective and efficient (Gil 2012). Hence, urban centers rely on the use of a more sophisticated mechanism to control and coordinate the flow of traffic in the city.

Conclusion

The model presented lays an emphasis on the performance and capacity analysis methods for isolated and signed controlled intersections through the application of integrated modelling system. Intersections forms an integral part in the control of traffic and several software have been developed to aid in the design and analysis of the movement of people in within the traffic. Future studies should be focused at establishing the level of increase in traffic and how the total population could directly or indirectly affect the presented models. This could be associated with modification of the system in ways that enhances traffic flow and eliminates congestion.

 

 

References

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Brijs, T., & Pirdavani, A. (2018). Urban and Suburban Arterials. In Safe Mobility: Challenges, Methodology, and Solutions (pp. 85-106). Emerald Publishing Limited.

Cheng, J., Bertolini, L., Le Clercq, F., & Kapoen, L. (2013). Understanding urban networks: Comparing a node-, a density-and an accessibility-based view. Cities, 31, 165-176.

Coogan, S., Kim, E., Gomes, G., Arcak, M., & Varaiya, P. (2017). Offset optimization in signalized traffic networks via semidefinite relaxation. Transportation Research Part B: Methodological, 100, 82-92.

Ethier, G. (2016). Connecting the Dots: How Digital Culture Is Changing Urban Design. Contour Journal1(2).

Gil, J. (2012). Integrating public transport networks in the axial model. In Proceedings of the 8th International Space Syntax Symposium, January 3-6, 2012, Santiago de Chile, Chile. Editors: Greene, M., Reyes, J., Castro, A. PUC.

Glaeser, E. L., Ponzetto, G. A., & Zou, Y. (2015). Urban Networks: Spreading the Flow of Goods, People, and Ideas.

Glaeser, E. L., Ponzetto, G. A., & Zou, Y. (2016). Urban networks: Connecting markets, people, and ideas. Papers in Regional Science, 95(1), 17-59.

Gordon, R. L., Tighe, W., & Siemens, I. T. S. (2005). Traffic control systems handbook (No. FHWA-HOP-06-006). United States. Federal Highway Administration. Office of Transportation Management.

Grandinetti, P., de Wit, C. C., & Garin, F. (2015, July). An efficient one-step-ahead optimal control for urban signalized traffic networks based on an averaged Cell-Transmission model. In 2015 European Control Conference (ECC) (pp. 3478-3483). IEEE.

Islam, T., Vu, H. L., & Panda, M. (2019). System optimal dynamic traffic assignment: solution structures of the signal control in non-holding-back formulations. Transportmetrica B: Transport Dynamics7(1), 967-991.

Jrew, B., Msallam, M., & Momani, M. (2019). Strategic Development of Transportation Demand Management in Jordan. Civil Engineering Journal5(1), 48-60.

Olariu, S., & Weigle, M. C. (2009). Vehicular networks: from theory to practice. Chapman and Hall/CRC.

Prothmann, H., Branke, J., Schmeck, H., Tomforde, S., Rochner, F., Hahner, J., & Muller-Schloer, C. (2009). Organic traffic light control for urban road networks. International Journal of Autonomous and Adaptive Communications Systems2(3), 203-225.

Qi, L., Zhou, M., & Luan, W. (2016). Impact of driving behavior on traffic delay at a congested signalized intersection. IEEE Transactions on Intelligent Transportation Systems18(7), 1882-1893.

Royani, T., Haddadnia, J., & Alipoor, M. (2010, August). Traffic signal control for isolated intersections based on fuzzy neural network and genetic algorithm. In Proceedings of the 10th WSEAS international conference on signal processing, computational geometry and artificial vision (pp. 87-91).

Simões, M. L., & Ribeiro, I. M. (2014). Optimal signal timing signalized intersection by global optimization. In Proceedings of the 1st International Conference on Engineering and Applied Sciences Optimization (pp. 664-679).

Sun, J. (2014). Vehicle routing problems in signalized traffic networks.

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Zheng, J., & Liu, H. X. (2017). Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C: Emerging Technologies79, 347-362.

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APPENDIX

Recorded Data Table

intersectionsspeed_avgspeed_stddata_rows
0370102201037.7419555125.11822754381845
1370102200724.9584049314.0782598690203
2370102200672.1791818922.1156502549118
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5370102266119.150178039.261609614354426
63701022662001053425
7370102202047.1893686115.3293644520336
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937010220170.656807045797.712829684486
10370102215325.3174638321.989982691671907
11370102266618.83484689.27675183377389
12370102266500659467
13370102202466.1172681321.37603264274610
14370102202328.377751115.865854998
15370102202226.3559639511.61861337265378
16370102202843.4111891120.67761657377921
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22370102204333.3545334727.280381643223
23370102210533.8169285433.518681581878485
24370102215028.514394618.07669411175858
25370102211330.1344503228.018601411750364
2637010226674.7789082688.117885889708837
27370102215738.5395108433.79474743700175
28370103311026.2371648925.34440856846584
29370103311135.4411795524.746615641118123
30370102212442.0890893121.645647311425895
31370102210739.8794834223.350693341778776
32370102214825.4734780311.58264181651065
33370102214970.2457501829.34351061955787
34370102210623.4352017428.71999611910576
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37370103310713.4075746510.85169338764471
38370103311256.7587266831.0403016608393
39370103310811.175881426.171088577156452
40370103310944.3147794519.085932661292308
413701022685001309617
423701022684001990167
43370103311317.4393225113.686680131638161
44370102204041.501354514.93876409252861
45370102203913.823824799.22932062816529
46370102203818.0616408511.40254555297968
47370102202644.3438435823.8567682611354
48370102202500176283
49370102202742.4488001417.29822237333331
50370102210427.53000523183.1764024838054
51370103310152.0459072639.19165039740188
52370103314135.1353825218.95740071462951
53370103314247.7379417921.90712797552839
54370103304600177828
55370103304500134706
56370103314347.0555732133.687701941721081
57370104411528.5219966712.97063421157766
58370104411621.772796148.766527573821126
5937010330480065413
60370103313735.2435718213.939338231365745
6137010330509.21886212215.550837395853
62370103313658.3219550429.552883791320175
63370103300613.35583839.75071879896201
64370103300739.3913852134.12632322209686
65370103300534.886418968.531262443252639
6637010330088.4373992484.93708421814888
67370103311435.7389178120.134814182063446
68370104412131.7054619417.67256413977473
69370103313921.3328896512.67316216884257
70370103316037.86094754291.0251723486694
713701111002003221543
72370103312719.077498688.972635868492731
73370103313837.6020779220.78143025747287
74370103310631.3309568325.57761576468194

 

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Signalized Traffic Networks | Research Paper Writing Help Online . (2022, September 01). Essay Writing . Retrieved September 27, 2022, from https://www.essay-writing.com/samples/signalized-traffic-networks/
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Signalized Traffic Networks | Research Paper Writing Help Online . [online]. Available at: <https://www.essay-writing.com/samples/signalized-traffic-networks/> [Accessed 27 Sep. 2022].
Signalized Traffic Networks | Research Paper Writing Help Online [Internet]. Essay Writing . 2022 Sep 01 [cited 2022 Sep 27]. Available from: https://www.essay-writing.com/samples/signalized-traffic-networks/
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