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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

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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

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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.

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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)

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Figure 2: These data are compared to show the potential for magnetic sensor to determine size of vehicle (Cheung, Ergen, and Varaiya 2005).

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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)

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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)

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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)

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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)

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Figure 10: This is the ladder logic method used for programming PLC (Programmable Logic Controller PDF)

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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)

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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.

Kumar, P.Sai, K.Aravind, K.Manoj Reddy, and K.Rakesh Babu. “Smart Car Parking.”

Gokaraju Rangaraju Institute of Engineering and Technology, 2011. PDF file.

Lina, Lo. "Automated Parking System." (2008).

"Programmable Logic Controller." PDF file.

http://claymore.engineer.gvsu.edu/~jackh/books/plcs/chapters/plc_intro.pdf

Prothmann, Holger, et al. "Organic control of traffic lights." Autonomic and Trusted Computing.

Springer Berlin Heidelberg, 2008. 219-233.

Prothmann, Holger, et al. "Decentralised route guidance in organic traffic control." Self-Adaptive

and Self-Organizing Systems (SASO), 2011 Fifth IEEE International Conference on. IEEE, 2011.

Sumathi, V., NV Pradeep Varma, and M. Sasank. "Energy Efficient Automated Car Parking

System." International Journal of Engineering and Technology (2013).