The document discusses several approaches to improving urban infrastructure:
1. Wireless magnetic sensors can be used to monitor traffic flow more cheaply than inductive loops, providing data to optimize traffic signals and assess road damage.
2. An organic traffic control system and dynamic route guidance can significantly reduce traffic delays, especially during incidents, by optimizing traffic light patterns and advising drivers of the best routes.
3. An automated parking system stacks vehicles vertically using mechanical lifts, nearly doubling parking capacity while reducing the land area needed compared to a multi-story parking garage.
Intelligent Traffic System for kajang city, Malaysia.Yousef Abujubba
The presentation is illustrating the common traffic difficulties in Kajang area and show suggested intelligent urban design plan to be settled in the future to solve out the traffic jam and rebuild the city to be sustainable.
India is definitely developing at a rapid pace. Being native Bengalurians, we've seen the city grow from being called the Garden City of India to the Startup Capital of the world.
With advancement comes it's own set of challenges, and one that we face here is that of traffic management. While we weren't able to bring the solution to life, happy to share our deck and hope we can collaborate with someone who'd like to take this forward. Do reach out to me at me@suhasmotwani.com - Happy to forward our research!
Do let me know if I could help you on your Product and Growth journey > www.suhasmotwani.com
its help to learn about intelligent transportation system which we can implement in highway construction and its improve our economy , its reduce accidental rate on highways that save our time money and obviously human's life .through this presentation we improve our growth rate in construction in India.
Intelligent Transportation Systems (ITS) can be defined as the application of advanced information and communications technology to surface transportation in order to achieve enhanced safety and mobility while reducing the environmental impact of transportation. The addition of wireless communications offers a powerful and transformative opportunity to establish transportation connectivity that further enables cooperative systems and dynamic data exchange using a broad range of advanced systems and technologies.
Intelligent Traffic System for kajang city, Malaysia.Yousef Abujubba
The presentation is illustrating the common traffic difficulties in Kajang area and show suggested intelligent urban design plan to be settled in the future to solve out the traffic jam and rebuild the city to be sustainable.
India is definitely developing at a rapid pace. Being native Bengalurians, we've seen the city grow from being called the Garden City of India to the Startup Capital of the world.
With advancement comes it's own set of challenges, and one that we face here is that of traffic management. While we weren't able to bring the solution to life, happy to share our deck and hope we can collaborate with someone who'd like to take this forward. Do reach out to me at me@suhasmotwani.com - Happy to forward our research!
Do let me know if I could help you on your Product and Growth journey > www.suhasmotwani.com
its help to learn about intelligent transportation system which we can implement in highway construction and its improve our economy , its reduce accidental rate on highways that save our time money and obviously human's life .through this presentation we improve our growth rate in construction in India.
Intelligent Transportation Systems (ITS) can be defined as the application of advanced information and communications technology to surface transportation in order to achieve enhanced safety and mobility while reducing the environmental impact of transportation. The addition of wireless communications offers a powerful and transformative opportunity to establish transportation connectivity that further enables cooperative systems and dynamic data exchange using a broad range of advanced systems and technologies.
With increasing vehicle size in the luxury segment and crunching parking space, traffic congestion is increasingly becoming an alarming concern in almost all major cities around the world. Burning about a million barrels of the world’s oil every day, and considering cities are turning urban without a well-planned, convenience-driven retreat from the cars, these problems will only worsen.
Smart Parking systems is one of the latest disruptive technologies that help address this problem by generating real time contextual information about the available parking spaces particular geographical area to accommodate vehicles low-cost sensors, mobility-enabled automated payment systems, real-time data collection, Smart Parking systems is designed to aid drivers to precisely find a spot.
What’s more, Smart Parking also minimizes emissions from vehicle in urban centers when deployed as a system by decreasing the dependency of people; unnecessarily circling the blocks trying to identify parking space. Apart from this green cause, by employing a host of technologies such as M2M telematics, Smart Parking helps resolve one of the biggest problems when driving around in urban areas – which is illegal parking and identifying free parking space.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
This Presentation mentions the various ways in which transportation can be improved by use of "Intelligent Transportation System" and it also includes case study on "The Eastern Freeway, Mumbai."
An intelligent transportation system (ITS) is an advanced application which, without embodying intelligence as such, 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.
intelligent transportation system ppt
intelligent transportation society of america
ieee intelligent transportation systems
intelligent transportation systems pdf
smart transportation systems
intelligent traffic system
intelligent transportation systems 2019
intelligent transportation systems in namibia
Optimized Traffic Signal Control System at Traffic Intersections Using VanetIOSR Journals
Abstract: Traditional Automated traffic signal control systems normally schedule the vehicles at intersection in
a pre timed slot manner. This pre-timed controller approach fails to minimize the waiting time of vehicles at the
traffic intersection as it doesn’t consider the arrival time of vehicles. To overcome this problem an adaptive and
intelligent traffic control system is proposed in such a way that a traffic signal controller with wireless radio
installed at the intersection and it is considered as an infrastructure. All the vehicles are equipped with onboard
location, speed sensors and a wireless radio to communicate with the infrastructure thereby VANET is formed.
Once the vehicles enter into the boundary of traffic area, they broadcast their positional information as data
packet with their encapsulated ID in it. The controller at the intersection receives the transmitted packets from
all the legs of intersection and then stores it in a temporary log file. Now the controller runs Platooning
algorithm to group the vehicles approximately in equal size of platoons. The platoons are formed on the basis of
data disseminated by the vehicles. Then the controller runs Oldest Job First algorithm which treats platoons as
jobs. The algorithm schedules jobs in conflict free manner and ensures all the jobs utilize equal processing time
i.e the vehicles of each platoons cross the intersection at equal delays. The proposed approach is evaluated
under various traffic volumes and the performance is analyzed.
Keywords Conflict graphs, online job scheduling, traffic signal control, vehicular ad hoc network (VANET)
simulation, vehicle-actuated traffic signal control, Webster’s algorithm.
Toward a resilient prediction system for non-uniform traffic data Osamu Masutani
We developed a traffic prediction system which enhances a traffic information service. The prediction method is based on time series analysis and is applicable to short to long term prediction. Traffic information system are real-time and real-world system therefore it suffers various kind of disturbance from environment. To preserve traffic prediction quality, we need fundamental treatment on overall system so that the prediction engine be tolerant toward incomplete traffic data feed or non-stationary traffic data. A solution for incomplete data feed is a combination of data for multiple links. A solution for non-stationary traffic is a traffic simulation dedicated to traffic accidents. With these enhancements toward cyber disturbance and physical disturbance, the system resiliency can be higher.
With increasing vehicle size in the luxury segment and crunching parking space, traffic congestion is increasingly becoming an alarming concern in almost all major cities around the world. Burning about a million barrels of the world’s oil every day, and considering cities are turning urban without a well-planned, convenience-driven retreat from the cars, these problems will only worsen.
Smart Parking systems is one of the latest disruptive technologies that help address this problem by generating real time contextual information about the available parking spaces particular geographical area to accommodate vehicles low-cost sensors, mobility-enabled automated payment systems, real-time data collection, Smart Parking systems is designed to aid drivers to precisely find a spot.
What’s more, Smart Parking also minimizes emissions from vehicle in urban centers when deployed as a system by decreasing the dependency of people; unnecessarily circling the blocks trying to identify parking space. Apart from this green cause, by employing a host of technologies such as M2M telematics, Smart Parking helps resolve one of the biggest problems when driving around in urban areas – which is illegal parking and identifying free parking space.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
This Presentation mentions the various ways in which transportation can be improved by use of "Intelligent Transportation System" and it also includes case study on "The Eastern Freeway, Mumbai."
An intelligent transportation system (ITS) is an advanced application which, without embodying intelligence as such, 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.
intelligent transportation system ppt
intelligent transportation society of america
ieee intelligent transportation systems
intelligent transportation systems pdf
smart transportation systems
intelligent traffic system
intelligent transportation systems 2019
intelligent transportation systems in namibia
Optimized Traffic Signal Control System at Traffic Intersections Using VanetIOSR Journals
Abstract: Traditional Automated traffic signal control systems normally schedule the vehicles at intersection in
a pre timed slot manner. This pre-timed controller approach fails to minimize the waiting time of vehicles at the
traffic intersection as it doesn’t consider the arrival time of vehicles. To overcome this problem an adaptive and
intelligent traffic control system is proposed in such a way that a traffic signal controller with wireless radio
installed at the intersection and it is considered as an infrastructure. All the vehicles are equipped with onboard
location, speed sensors and a wireless radio to communicate with the infrastructure thereby VANET is formed.
Once the vehicles enter into the boundary of traffic area, they broadcast their positional information as data
packet with their encapsulated ID in it. The controller at the intersection receives the transmitted packets from
all the legs of intersection and then stores it in a temporary log file. Now the controller runs Platooning
algorithm to group the vehicles approximately in equal size of platoons. The platoons are formed on the basis of
data disseminated by the vehicles. Then the controller runs Oldest Job First algorithm which treats platoons as
jobs. The algorithm schedules jobs in conflict free manner and ensures all the jobs utilize equal processing time
i.e the vehicles of each platoons cross the intersection at equal delays. The proposed approach is evaluated
under various traffic volumes and the performance is analyzed.
Keywords Conflict graphs, online job scheduling, traffic signal control, vehicular ad hoc network (VANET)
simulation, vehicle-actuated traffic signal control, Webster’s algorithm.
Toward a resilient prediction system for non-uniform traffic data Osamu Masutani
We developed a traffic prediction system which enhances a traffic information service. The prediction method is based on time series analysis and is applicable to short to long term prediction. Traffic information system are real-time and real-world system therefore it suffers various kind of disturbance from environment. To preserve traffic prediction quality, we need fundamental treatment on overall system so that the prediction engine be tolerant toward incomplete traffic data feed or non-stationary traffic data. A solution for incomplete data feed is a combination of data for multiple links. A solution for non-stationary traffic is a traffic simulation dedicated to traffic accidents. With these enhancements toward cyber disturbance and physical disturbance, the system resiliency can be higher.
Fédération des Promoteurs Immobiliers : observatoire 2015Monimmeuble.com
BILAN 2015 : EMBELLIE DES VENTES GRÂCE AUX INVESTISSEURS ; L’ACCESSION À L’ARRÊT
Globalement, l’année 2015 aura été celle du retour des ménages investisseurs vers la pierre (+43,8%), alors que l’accession aura légèrement reculé (‐1,7% par rapport à 2014) et que les ventes en bloc, malgré le rattrapage de fin d’année, n’auront crû sur un an que de +3,7%. Au total, les ventes auront atteint le chiffre de 122 781, en nette croissance par rapport à 2014 (+13,6%) mais inférieur aux 134 520 ventes enregistrées en 2010.
Par ailleurs, bien qu’en progression, l’offre nouvelle de logements (+6,3% à 96 715 logements) ne se développe pas au rythme de la demande. Pour les professionnels, le marché demeure fragile : les difficultés, qui pèsent sur les délais de réalisation restent nombreuses et la reprise de l’accession n’est pas encore au rendez‐vous.
Autonomous Traffic Signal Control using Decision Tree IJECEIAES
The objective of this paper is to introduce an effective and efficient way of traffic signal light control to optimize the traffic signal duration across each lanes and thereby, to minimize or completely eliminate traffic congestion. This paper introduces a new approach to resolve the traffic congestion problem at junctions by making use of decision trees. The vehicle count in the real time traffic video is determined by Image Processing technique. This information is fed to the decision tree based on which the decision is made regarding the status of traffic signal lights of each lane at the junction at any given instant of time.
Traffic Light Controller System using Optical Flow EstimationEditor IJCATR
As we seen everyday vehicle traffic increases day by day on road is causing many issues. We face many traffic jams due to the inefficient traffic controlling system which is unable to cope up with the current scenario of traffic in our country. To overcome such drastic scenario and looking at current traffic volume we need to develop a system which works on real time processing and works after determining the traffic density and then calculating the best possibility in which the traffic on particular cross road is dissolved. Also, it helps in saving time as on traffic roads. In present traffic control system when there is no traffic on road but the static signal not allow traffic to move to cross and it changes after at fixed interval so at every cycle this amount of time is wasted for unused traffic density road and if one road is at high traffic it continuously grows till human intervention. The basic theme is to control the traffic using static cameras fixed on right side of the road along top of the traffic pole to check the complete traffic density on other side of the road. This system will calculate number of vehicles on the road by moving detection and tracking system developed based on optical flow estimation and green light counter will be based on the calculated number of vehicles on the road.
Improvement of Traffic Monitoring System by Density and Flow Control For Indi...IJSRD
The growth and scale of vehicles today makes management of traffic a constant problem. The existing traffic control system works based on a timing mechanism, meaning an equal time slot is provided for each junction. This is inefficient for non-uniform flow of vehicles. Hence there is a need for a system which is adaptive in nature. Routes should have an option of being granted more time slots depending on the requirements for the given route. This paper proposes a traffic congestion control system which would be adaptive in nature and provide time slot to each route based on traffic density.
Improving traffic and emergency vehicle clearance at congested intersections ...IJECEIAES
Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.
Sensor based System for Computation of Speed, Momentum of Vehicle, Traffic De...iosrjce
This sensor based system can compute the speed of vehicle. It also computes the momentum of
vehicles and the traffic density on the approach road. This system will also detect any vehicular problems such
as brake failure, accidents on the roads and also detect the section which is affected or will be affected. The
system can be easily rectified in case there is a failure in the sensors. The system can also be used for estimating
any obstruction in between two ends of the road.
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Traffic Density Control and Accident Indicator Using WSNIJMTST Journal
Now a day’s many of the things get controlled automatically. Everything is getting controlled using the mechanical or the automated systems. In every field machines are doing the human works. But still some area is controlled manually. For example traffic controls, road control, parking controlling. Keeping these things in mind we are trying to develop the project to automate the traffic tracking for the square. To make any project more useful and acceptable by any organization we need to provide multiple features in a single project. Keeping these things in consideration proposed system is less with multiple methodologies which can be used in traffic control system It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphasized. However, further analysis is needed in order to obtain the useful information for traffic management such as real time traffic density and number of vehicle types passing these roads. This paper presents emergency vehicle alert and traffic density calculation methods using IR and GPS
Similar to Restore and Improve Urban Infrastructure (20)
Traffic Density Control and Accident Indicator Using WSN
Restore and Improve Urban Infrastructure
1. Date: 11/14/2013
1
Restore and Improve Urban Infrastructure
By Lucas Smith, Shahmeer Baweja and Chris Wiggs
Background:
What is it?
It is the repairing and the finding out of the best method of making the basic structures of the city
such as roads and railway systems more public friendly.
Why should we?
The world’s population is increasing exponentially. This growth will put enormous strains on
infrastructures including roads, bridges and tunnels.
What was my motivation?
The basic infrastructures such as traffic systems, roads and railways are the building blocks of
the city. They need to be constantly maintained as well as to be improved in order to increase
our living standards in an urban city.
What are the challenges today?
1. Highways are becoming increasingly congested. Increased use of our roadways is
occurring at a time where many facilities require expensive rehabilitation, repairs, and
maintenance.
2. The land spaces are becoming less while the human population is increasing. A
conventional car parking takes up lots of space and is less secure as well. A long queue is
formed when searching for empty parking lots causing traffic jam.
Techniques and Approach:
1. Reduce traffic congestion and assess road damage with relatively cheap alternatives to
inductive loops.
2. We use dynamic route guidance with organic traffic control to guide vehicles and
maximize traffic flow. The organic traffic control optimizes the traffic signal patterns
and dynamic route guidance enhances this by giving drivers instructions on which route
to take to reduce delays caused by obstructions.
3. We stack up cars using automated parking system in which the cars are elevated on top of
another inside tall structures, and we use limit sensors to detect empty parking spaces.
Wireless Magnetic Sensors
Wireless magnetic sensors provide an alternative to inductive loops, used in traffic
surveillance. The goal of a traffic surveillance system is to assess the number of cars on a road,
the speed of cars, and ideally the size and type of car. Intelligent Transport Systems (ITS) use the
data provided to direct traffic efficiently and obtain necessary data for determining the condition
of roadways (Cheung and Varaiya 2007). Currently, inductive loops perform these functions, but
they have a high cost and disruptive installation though it is one of the most accurate methods of
detection. Wireless magnetic sensors address those issues while providing data that is as
accurate.
Wireless magnetic sensors cost about half as much as inductive loops. The exact numbers
are found in Figure 1. Installing these sensors turns out to be quite easy. The VSN240 wireless
2. Date: 11/14/2013
2
magnetic detector is composed of several sensor nodes and an access point that processes data
and sends it to ITS (Cheung and Varaiya 2007). Installation of each sensor node takes around ten
minutes.
The VSN240 works by connecting an access point to several sensor nodes through radio.
The magnetic sensors detect cars and their size by their significant disruption of Earth’s magnetic
field and send the count to the access point (Figure 2). The sensors do not detect bikes or
skateboards. Two sensor nodes placed a distance apart from each other allow the access point to
determine the length and speeds of cars as they drive over the sensors (Cheung, Ergen, and
Varaiya 2005) (Figure 3).
Of course, one of the possible issues with a wireless system is powering the sensor nodes
that provide all of the data as well as the access point. The access point of the VSN240 is
powered by a wired connection, ensuring that a dead battery never stops it. The sensor nodes rely
on battery, but the batteries power the sensor nodes for ten years, about as long as an inductive
loop is likely to last. The low power consumption of radio transmission of data makes this long
battery life possible (Cheung and Varaiya 2007).
The resulting data from wireless magnetic sensors is almost as accurate as video and about
as accurate as inductive loops, but not nearly as expensive or fragile to weather. With a
combination of data about number of cars, sizes of cars, and speeds of cars, reliable inferences
can be made about road damage in areas with sensor nodes to determine the areas with the most
immediate need for repair while also providing the necessary data for traffic control.
Dynamic Route Guidance and Organic Traffic Control
The Organic Traffic Control (OTC) system optimizes traffic signals, and speeds traffic flow.
Organic computing systems are capable of adapting to changing environments. Current traffic
light systems work on a fixed-time basis that does not react to the traffic situations as they
happen. The OTC traffic control reconfigures the traffic light controller (TLC) based on traffic
conditions. The experimental setup used a traffic simulator to model two intersections in
Hamburg, Germany, called K3 and K7. Reference figures came from real world traffic
information for those intersections (Figure 4). On K7 the average reduction in delays over the
reference was twelve percent over three days (Figure 5). On K3, the average reduction in delay
was eight percent (Figure 6). This proves that the OTC system can significantly reduce the
delay (Prothmann, Holger, et al., 2008).
The Dynamic Route Guidance (DRG) mechanism guides vehicles to their destination and
improves traffic in case of blockages. DRG extends the OTC system by finding the quickest
route to a destination. The observer/controllers of the OTC are extended by a routing component
that determines the best route to the destination and updates it according to traffic flow
information. This route is given to drivers either by digital road signs or by infrastructure-to-car
communication. This system was evaluated comparing OTC intersections with and without the
DRG in a simulated network of 25 intersections. Two test scenarios were implemented: one
“regular” scenario and an “incident” scenario with three 40 minute road blockages at 15, 45, and
75 minutes. The regular scenario resulted in larger delay reductions in the first hour, but then a
smaller difference the rest of time due to optimization (Figure 7a). The incident scenario
showed a larger reduction of delays whenever there were blockages by helping drivers avoid
congested or blocked areas (Figure 7b) (Prothmann, Holger, et al., 2011).
Limitations of the OTC system include the fact that it would require new devices at every
intersection, which could be very expensive, and a method of communication between traffic
3. Date: 11/14/2013
3
nodes would need to be implemented. Limitations of DRG also include the necessity for new
devices at many intersections, which would likely be expensive, and in regular traffic flow there
was not a large delay reduction. However, both systems do reduce delays in traffic and would
definitely improve traffic flow.
Smart Car Parking
An automated parking system (APS) shown in Figure 8 is a mechanical system designed
to minimize the area or volume required for parking cars. An APS provides parking for cars on
multiple levels stacked vertically to maximize the number of parking spaces while minimizing
land usage. The APS, however, utilizes a mechanical system to transport cars to and from
parking spaces (rather than the driver) in order to eliminate much of the space wasted in a multi-
story parking garage.
How it works? The driver will park his vehicle on a pallet at the platform of the car park.
Then the sensor will detect the available empty parking spaces and display them on the control
panel. After the driver selects the desired parking space on the control panel, the vehicle will be
transported to that parking space. In order to retrieve the vehicle, the driver will select the
location of his vehicle on the control panel. The system will retrieve the vehicle from the parking
space and send it back to the original position where the driver is waiting (Lina Lo 2008).
How the system does save time? The car parking system allots unique parking slots to the
cars and the system utilizes sensors for detecting the presence of cars. The prototype shown in
Figure 9 consists of two lanes and two slots in each of them. The slot nearest to the entrance has
a higher priority and is allotted first to an incoming car thus saving the time for parking. Each
slot is equipped with an indicator lamp which is switched ‘on’ if it is allocated thus indicating the
driver to park in that particular slot (Sumathi, Varna and Sasank 2013)
All the movements needed to transport a vehicle in the automated parking system are
controlled using Programmable Logic Controller (PLC). Programming for PLC is done in
software named CX-Programmer by using ladder logic method (Lina Lo, 2008) (Figure 10). We
develop the control application program and store it within the PLC memory. The program helps
PLC monitor input signals to detect changes from devices such as push buttons and sensors.
Based on the status of input signals, PLC will react by producing output signals to drive output
devices like motors, relays, alarm and contactors to on or off state which in turn enables the
system to transport cars to and from the parking spaces (Figure 11). However, there are some
limitations. Major problems of PLC are the complexities of the high level programming and its
application is only on specialized machines, and the problem in recognizing smaller vehicles.
Since then, there have been a markedly 30-50 % increase in available land spaces.
Compared to a multi-storage parking garage, approximately twice the number of cars could be
parked using APS in nearly half of the area occupied by the garage
Besides utilizing land spaces efficiently, APS ensure vehicle safety and security since no
public is allowed inside and saves time, money and fuel since no searching for the car is
required. All in all, the system minimizes land requirement and maximizes efficiency and
profitability in the long term.
4. Date: 11/14/2013
4
Figure 1: This table examines the cost of various possible technologies, including Inductive Loops and Wireless
Sensor Networks, for controlling traffic flow (Cheung and Varaiya 2007)
5. Date: 11/14/2013
5
Figure 2: These data are compared to show the potential for magnetic sensor to determine size of vehicle
(Cheung, Ergen, and Varaiya 2005).
6. Date: 11/14/2013
6
Figure 3: Magnetic sensor nodes (SN) prove to be almost as accurate as video in determining speed while costing
significantly less and functioning no matter what the weather and lighting conditions are (Cheung, Ergen, and
Varaiya 2005).
Figure 4: The traffic demands for K3 and K7 in vehicles per hour. (Prothmann, Holger, et al., 2008)
7. Date: 11/14/2013
7
Figure 5: Comparison of OTC approach and reference solution for the K7 intersection (Prothmann, Holger, et al.,
2008)
Figure 6: Comparison of OTC approach and reference solution for the K3 intersection (Prothmann, Holger, et al.,
2008)
8. Date: 11/14/2013
8
Figure 7: The DVR (or DRG) shows a small reduction in network-wide travel times and stops on the regular
scenario, and a larger reduction on the incident scenario. (Prothmann, Holger, et al., 2011)
9. Date: 11/14/2013
9
Figure 8: In this automated parking systems, a car is being lifted to its selected parking slot (Kumar, P.Sai,
K.Aravind, K.Manoj Reddy, and K.Rakesh Babu 2011)
Figure 9: This is the layout of the prototype (Sumathi, V., NV Pradeep Varma, and M. Sasank 2013)
10. Date: 11/14/2013
10
Figure 10: This is the ladder logic method used for programming PLC (Programmable Logic Controller PDF)
11. Date: 11/14/2013
11
Figure 11: This is a PLC with relays. The two input push buttons are imagined to be activating the 24V DC relay
coils. This in turn drives an output relay that switches 115V AC, which will turn on a light indicating whether a
parking space in available, or may operate a device used to move the vehicles (Programmable Logic Controller
PDF)
12. Date: 11/14/2013
12
Works Cited
Cheung, Sing-Yiu, and Pravin Pratap Varaiya. “Traffic surveillance by wireless sensor networks:
Final report”. California PATH Program, Institute of Transportation Studies, University of
California at Berkeley, 2007.
Cheung, Sing Yiu, Sinem C. Ergen, and Pravin Varaiya. "Traffic surveillance with wireless
magnetic sensors." Proceedings of the 12th ITS world congress. 2005.
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