Published on Sep 03, 2023
In smart cities, the wide variety of use cases spans from traffic management to water distribution.This is an important issue for managing traffic in an urban environment like smart cities.For solving this, Internet of Things(IoT) should be used.IoT used network of physical objects that feature an IP address for internet connectivity.Reservation-based system,Connected and Automated Vehicles(CAV) and smart parking system are proposed as a part of traffic management by using IoT.Sensing and classifying roadway obstacles provides accident free environment and also a smooth drive to the vehicles.
This paper is a survey on traffic management in smart cities which is useful for traffic management in smart cities. Survey involves different traffic management schemes by using IoT.
Smart City is an urban environment that provides a new level of innovative and interactive services for all over the social activites in the urban area such as transportation, energy distribution, health care, environmental monitoring, business, commerce, emergency response and water distribution.From a technological point of view, Smart City uses information and communication technology(ICT) and IoT in a effective and secure manner to access the physical objects roads, buildings, as well as the location and status of city resources.IoT is the network of physical objects that featuers an IP address for internet connectivity, the communication between these objects and other systems that have internet accessability. IoT has a major role in the traffic control in smart cities.
The.IoT have major role in smarter urban management for Cities and counties. In smart cities traffic related problems are controlled by using IoT in right way.Reservation-based system, Connected and Automated Vehicles(CAV) and smart parking system are proposed as a part of traffic management by using IoT.Sensing and classifying roadway obstacles provides accident free environment and also a smooth drive to the vehicles.
In smart cities traffic related problems are controlled by using IoT in right way.Reservation-based system, Connected and Automated Vehicles(CAV) and smart parking system are proposed as a part of traffic management by using IoT.Sensing and classifying roadway obstacles provides accident free environment and also a smooth drive to the vehicles. The 2014 revision of world urbanization prospects, which contains the latest estimates of the urban and rural populations of 233 countries.
Population censuses are the most commonly used sources of data,although estimates obtained from population registers or administrative statistics.Classifyingan area as urban may be based : a minimum population threshold; population density; proportion employed in non-agriculturalsectors; the presence of infrastructure such as paved roads, electricity, piped water or sewers; and the presence of education or health services.In compiling information on city population size, the Population Division has endeavoured to use data or estimates based on the concept of urban agglomeration.
The method to project city populations as the last observed city growth rate converges towards an expected value, estimated on consistent and timely data on global trends in urbanization and city growth are critical for assessing current and future needs with respect to urban growth and for setting policy priorities to promote inclusive and equitable urban and rural development.Successful sustainable urbanization requires competentancy. K. Dresner[2] propose a reservationbased system for alleviating traffic congestion, specifically at intersections, and under the assumption that the cars are controlled by agents.
A custom simulator is created to measure the different delays associated with conducting traffic through an intersection. A precise metric for evaluating the quality of traffic control at an intersection. This reservation-based system can perform two to threehunderd times better than traffic lights and it can smoothly handle much heavier traffic conditions.This system very closely approximates an overpass, which is the optimal solution for the problem.
Current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. K. Dresner[3] describe an autonomoud intersection management system.Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol. Demonstrate in simulation that new mechanism has the potential to significantly outperform current intersectioncontrol technology—traffic lights and stop signs. It subsumes the most popular current methods of intersection control. the basis of the city population and the growth rate of the overall urban population in the country.Globally, more people live in urban areas than in rural areas.Levels of urbanization vary greatly acrossregions.
Most megacities and large citiesare located in the global South. One in five urban dwellers worldwide lives in a medium-sized city with 1 million to 5 million inhabitants.Some cities have experienced population decline since 2000, most of which are located in low-fertility countries of Asia and Europe with stagnating or declining populations.Diversified policies to plan for and manage the spatial distribution of the population and internal migration are needed.Policies aimed at a more balanced distribution of urban growth.
Accurate, This article also presents two extensions to the mechanism,The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles.described the construction of the simulator itself, as well as the communication protocol, the intersection manager, the driver agent, and several intersection control policies. The first policy, FCFS is only for fully autonomous vehicles.FCFS-Light extends FCFS to allow human interoperability using existing traffic lightinfrastructure. The last policy, FCFS-Emerg, extends FCFS to give priority to emergency vehicles without significant increasing delays for other vehicles
The second gives priority to emergency vehicles without significant cost to civilian vehicles.In this there is no switch among several different policies, learning from reservation historieswhich policy is best suited to particular traffic conditions, could significantly improveperformance. There is no light model that could react not react to the presence of individual vehicles, might better be able to exploit the abilities of autonomous vehicles, without adversely affecting human drivers.
A.de La Fortelle[4]present a framework designed initially for cybercars (fully automated cars) but that could also be applied -though with major differences -to human driven cars. It is a world where vehicles have to reserve pieces of roads to cross a junction. This work is an enhancement of a previous work that demonstrated the feasibility of such a reservation algorithm. S. Huang[5] designs and evaluates a reservation-based approach to intersection control that is designed to take full advantage of the unprecedented connectivity that the connected vehicle initiative promises to provide.
To design and evaluate the “intelligent intersection,” a novel simulation test bed for connected vehicle applications is developed. The test bed integrates a microscopic traffic simulator with a network simulator and an emission analyzer. Using the integrated simulator, the mobility and environmental benefits : of the intelligent intersection approach, compared with those of traditional control methods, are evaluated on two case studies1) an isolated intersection 2)a real-world transportation network with multiple intersections. proposed control approach offers significant mobility and environmental benefits.using observed traffic volumes, the intelligent intersection reduced the average vehicle delay by 85%, fuel consumption by 50%, and emissions by 39%-50%.
Autonomous passing-through intersections has been becoming one important research problem , especially with the real emerging of driverless vehicles. Including the lane, path, critical section and vehicle, are modeled with considering relations among their physical and kinetic characters. Abstract some basic actions of this passing procedure, and K. Zhang[6] propose a universal state-based action model.The procedure will be equal to the switching between these actions and their states. Propose a new centralized scheduling algorithm that is reservation-oriented, and can guarantee the higher request to be responded preferentially. Finally, this algorithm is simulated , especially for vehicles with high priority.
Each vehicle can be installed with a RFID tag. This RFID tag would store all the information regarding the vehicle such as the vehicle number, etc. RFID tags can be used in identifying each vehicle uniquely and also help the driver to receive some traffic messages. The existing signaling system can be coupled with the RFID controller. As described in figure 1, each signal can have the information regarding every vehicle that passes by it. Thus when a vehicle passes by a signal, the signal can automatically keep the count of the vehicles passing by it, and help in detection of traffic congestion.
Each signal should be stored with a threshold value for which it should be red and green. Now depending upon the frequency of the vehicles passing by the signal per second, the timer can be dynamically controlled. Each controller of the signal should be stored with a value of minimum frequency of the vehicles passing by the signal. As soon as this minimum frequency is reached, the controller should send a command to the signal to turn red.
Thus the signal is controlled dynamically. For example, suppose for a signal, maximum time for which a signal can be red is set to be 30 seconds and maximum time for which the signal can be green is set as 20 seconds. The controller is stored with the value of minimum frequency of vehicles passing by it per second as 5. Now suppose the signal turns green, the timer starts with a maximum value of 20. Initially the frequency of the vehicles passing the signal per second is 10, after 10 seconds this frequency reduces to 5, and then automatically the RFID controller sends a command to the signal to turn red.
Thus the signal turns red and its adjacent signal in that junction turns green. This process continues in a cycle. Thus dynamic controlling of the signal helps in reducing the wastage of time. This also helps in avoiding traffic congestion as priority is given to a high vehicular traffic road. This system helps in detection of traffic congestion. If the frequency of the vehicles passing the signal per second remains higher than the value set even though the maximum value of the timer is reached, then the congestion has occurred at that point.
Once the congestion has been detected, the RFID controller can send a message to its preceding signal’s controller notifying it to temporarily stop traffic along that stretch. After receiving the message from its successor signal the RFID controller will put ON the red signal for that stretch towards that congested crossing point for a predefined time period.
When the congestion is released at the crossing, the respective signal’s controller will send another message to its earlier controller indicating to resume the traffic flow again in that direction. Accepting this message the controller of the preceding signal put the red light OFF and green signal ON and restart the signal cycle as before.
Congestion In addition to the earlier method of traffic congestion detection, one more method can be used. A server can be maintained which can receive certain crucial data calculated by the Controller of the signals. The main aim is to implement a system that would trace the travel time of individual cars as they pass the roadside controllers and compute an average trip time using a rule-based system to decide whether the area is congested or uncongested. If congestion is sensed then system would control traffic signals / generate automatic re-routing messages to selected approaching vehicles.
Violation We can use this technique to calculate the speed of a motorist and to detect if he violates the prescribed/set speed limit. If the motorist violates the rule, a warning message will be sent to the motorist via audio and/or video interface and penalty will be calculated in the server and billed monthly to the vehicle owner [14] .
Automatic toll collection and automatic ―core area charge‖ collections are also done using the same framework. Controller unit will be placed at toll-booth and along the motor able roads around the core area which will detect each individual vehicle uniquely within its zone by capturing their device ids and will keep records of the time during which the vehicle was seen by those Controllers within its reading zone. This information will be sent to a main server. Accordingly the main server will calculate the charges and raise bills against the vehicle ids
The proposed work focuses on Smart Traffic management System using RFID which will eliminate the drawbacks of the existing system such as high implementation cost, dependency on the environmental conditions, etc. The proposed system aims at effective management of traffic congestion. It is also cost effective than the existing system. Furthermore, the study presents the problems in metropolitan areas all over the world caused by congestions and the related sources. Congestions developed to a problem, which affects economies worldwide.
Particularly metropolitan areas are worst hit under these conditions. Congestions have a negative impact on the financial situation of a country, on the environment and hence the overall quality of life. The proposed system can be enhanced by using any other powerful communication network other than GSM.
[1]Economic Social Affairs, New York, NY,USA, (Jul. 2014).World Urbanization Prospects. [Online]
[2]K. Dresner and P. Stone, “Multiagent traffic management: A reservationbased intersection control mechanism” in Proc. 3rd Int. Joint Conf. Auto.Agents Multiagents Syst., 2004, pp. 530537.
[3] K. Dresner and P. Stone, “A multiagent approach to autonomous intersection management,” J. Artif. Intell. Res., vol. 31, pp. 591656, Mar. 2008.
