Dynamic traffic signaling using remote sensing technology

Abstract

Ahmedabad is growing in every possible dimension .The resulting economic growth has fuelled a jump in

four wheelers and two wheelers. Current traffic signaling systems installed in Ahmedabad are static - the

paper attempts to highlight advantages of incorporating RTMS(Remote traffic microwave sensors)

technology and GIS to make the traffic control dynamic and intelligent .Traffic behaviour is studied at

various central business districts of the city to study the feasibility of using RTMS in traffic control

systems. The study runs through various traffic patterns arising in the city at various junctions and tries

to suggest advantage of using RTMS in each case. The paper discuses the simplicity, effectiveness and

viability of such dynamic(technological) adaptation in current traffic systems in Ahmedabad .

Introduction and discussion

Maruti Suzuki , India’s leading carmaker sold over 76000 cars in November 2009,60% more than in the

dire month of November 2008 .This sharp recovery left my uncle with mixed feelings. As a proud owner

of Swift, a popular model, he is finding it increasingly difficult to spot his silver hatchback in Delhi’s

crowded car-parks. In the year 2009 Indian economy grew by 7.9%,far surpassing expectations, in fact a

robust economic indicator considering the fact that the country witnessed worst monsoon since

1972(23% below the historical average).

Such sharp growth has many downsides. The list of downsides includes traffic congestion .Not only

traffic congestion but in a global scenario the aspect of environmental price we pay must also be taken

care of. The number of vehicles in India is already equal to the number of vehicles in U.K.(which is a

developed nation) –it is a surprising fact however that road casualties in India in 2007 were 1.14 lakhs

while the same statistic for U.K. is under 3000 people.

Gujarat is one of the most developed and fastest growing markets in India. Ahmedabad is considered to

be the fastest growing markets for automobile sales. Most of the vehicular traffic in Indian cities is

disoriented and Ahemdabad is no exception. This disorientation brings with it some headaches

of modern day life in the form of Traffic blockages, low average traffic speed, collisions and

other issues.

Not only road safety but business gets directly affected by traffic menace. Businessmen find it hard to

reach their office let alone airports on time. Recently it was reported® that Indian B.P.O business is being

upstaged by Philippines due to ‘messy’ infrastructure here. Arranging employee transit becomes a huge

burden for firms onshore in India. Such basic infrastructure is taken granted in countries like Philippines.

Growth of traffic (all modal systems):(1951-2004) O.P. agarwal and Zimmerman ,towards a sustain able urban t ransit

As the graph suggests the urban vehicle population is growing at double digit rates and as is urban

population.

Growth in percentage(1991-2001)

Urban Population 31%

Urban Vehicle count 250%

The automobile industry in India is the ninth largest in the world with an annual production of over 2.3

million units in 2008.In 2015 it is expected to grow to 4 million units per year level. The World Bank

estimates that traffic in India's cities has grown by 15 percent a year for the last decade, reducing

average speeds during rush hour to five to 10 kilometres an hour in central areas. The internal

combustion engine that powers our transport system is most efficient @ 2000 rpm upwards, that means

a car stuck in congestion or slow moving traffic is spending more time and fuel. Such traffic congestions

are turning chronic by day. Less traffic sense in country worsens the situation in the cities.

India’s traffic infrastructure problems are impending and thus should be taken seriously. RTMS&GIS

technology is a valuable attempt towards improving the current situation.

† Intelligent variable signal timing (iVST)

Traffic behavior is non-linear and the vehicle flux is rarely uniform from all directions arriving at a

junction. There is no debate over the fact that vehicle flux is heterogeneous. Traffic signal is a device

which is meant to control the vehicle flux arriving at a junction. Traffic signal as a tool would be more

effective if we empower it to be dynamic and treat traffic as it behaves. The basic idea behind iVST can

be devised as follows

∆t α V {∆t =signal interval, V=incoming traffic volume}

A rough sketch showing traffic behavior. What is proposed is a spunch-like control system which does

not break owing to sharp and sudden changes-A system that adapts itself to the arising demand.

The tools that can make the proposal possible are RTMS and GIS.

RTMS

RTMS stands for Remote traffic microwave sensor.

The Remote Traffic Microwave Sensor is a low-cost advanced sensor for the detection and measurement

of traffic at intersections and on roadways. This compact true-presence detector provides per-lane

presence indication, as well as Volume, Occupancy, Vehicle Speed, and Classification information, in up

to eight lanes or detection zones simultaneously.

Output information is provided to existing controllers by contact closure and to other computing

systems by its serial communication port. A single RTMS can replace multiple inductive loop detectors

and the attendant controller.

The RTMS is a small radar operating in the microwave band. Mounted on road-side poles, it is easy and

safe to install and remove without traffic disruptions or lane closures. It is fully programmable to

support a variety of applications, using simple intuitive software running on a Notebook PC.

Provides presence indications as well as volume information at up to eight (8) discreet detection zones or lanes

Non-intrusive counting device Increases safety for motoring public

GiS stands for Geographic information system, the data that comes out of RTMS can be integrated

with GIS.GIS can be particularly helpful with

Predicting traffic volumes and alternate routing

Mapping traffic patterns and helping build dynamic traffic algorithm

Study AREA

Ahmedabad: Vroom,Vroom…..

Our study area is beautiful city of Ahmedabad which is the commercial heart of Gujarat . Considered the

second largerst industrial center of the country, Ahmedabad is a vibrant ,colorful city and the bursting

metropolis .The city is designated to ‘mega city’ status recently .Estimated population is 5.2 million in

2009,in an area spread over 200 sq. kms of urban space .The city is physically divided into eastern and

western parts by Sabarmati river. The western part recently witnessed infrastructure and real estate

boom. The western part of the city represents the office going corporate class population, while the east

has industrial pockets where small and medium industries exist. The famous nano project near Sanand is

just one of the many projects in and around Ahmedabad , in coming years Ahmedabad’s prosperity

seems to have no end in sight.

† Urban transport in Ahmedabad

Ahmedabad city is well connected by an expressway, several national and state highways, the broad-gauge and

meter-gauge railways and an international airport. The city transportation system is predominantly dependent

on roadway systems.Vehicular growth has been rapid. Every year about a lakh of vehicles are added in the city.

Of these about 20000 are cars and about 60000 are two wheelers. In fact the vehicle ownership rates are the

highest among the 4 million plus cities of India. Overall, the congestion levels have still not reached their critical

limits but just started to cross the critical limits in some central sectors.

Road map of ahmedabad. Study area marked in red. (Map not to scale)

†Method of data acquisition

A regular digital cam was used to count the vehicular traffic.

†Modal split of vehicles observed(averaged out)[5 cycles of traffic stops][4 junctions-

IIM, Panjrapole ,keshav bag, vijay cross roads][TB1]

MODAL SPLIT Observed % among total vehicle count

2 wheelers 58.3

Auto rickshaws 9.4

Passenger cars 20.8

Buses 2.5

Trucks/Tractors 2.1

Cycles/Others 6.9

Modal split chart

Based on Survey.

2 wheelers58%

Auto rickshaws9%

Passenger cars21%

Buses3% Trucks/Tractors

2%

Cycles/Others7% Modal Split

Peak hour data[TB2]

Based on survey

† Hetrogenity of traffic flux within the junction[TB3]

Junction: IIM(1900-2030 ,10 cycles)Arm1 ×Arm3 Arm2 ×Arm4

Junction Arm BY length(AVG)m BY rate(AVG) Length(min,MAX)

ARM1 70m 21/min (40,400)

ARM2 25m 9/min (20,70)

ARM3 40m 16/min (40,300)

ARM4 50m 14/min (20,250)

Based on survey

RTMS & GIS: FLEXIBLE TREATMENT TO FLEXING PROBLEM

We know that

Efficiency of a traffic system, η= , here the numerator has a random behavior

while the denominator is linear (incase of a static system).

Junction Peak hour

20 cycles each

Volume(est.)

Extrapolated

Recorded frequency

All 4 arms avg.

IIM 1850-2045 7500 65.21/min

Panjrapole 1915-2015 3000 50/min

Vijay cross roads 1930-2015 1000 22/min

Keshav baug 1915-2045 4000 44.44/min

The above graph explains efficiency of a traffic signal in the current system.

As shown above η is maximum at certain value x. Mean while it is less to the left side which is

underutilization and gets even lesser at the right side of the value x –can be said as overutilization.

Here ηmax is a constant which represents a certain count of vehicles(Vmax) in given time that a junction

can handle efficiently. defined by

ηmax = Vmax/∆t

ηmax depends upon road width and hence constant.

WHERE RTMS WILL HELP?

Underutilization [V<< Vmax]

Increase in efficiency by being dynamic using RTMS.

When η<< ηmax

ηu α {where ηu is efficiency during underutilization}

hence the ratio,

ηu/ ηmax = Vmax / V

IF Vmax is more than V and if we change ∆t accordingly we’ll get a positive change in efficiency according

to the above equation. Surely Data from RTMS will tell us about behavior of V helping us improve the

efficiency overall. Refer to table 3 many times it happens so that only few arms of junction have

underutilized capacity. In that sense we optimize the time on underutilized lanes so that remaining

crowded lanes are serviced more efficiently. IIM is one of the intersection where most hetrogenity is

found.

Hence the total stoppage time is a result of individual functions in its arms

Fa(∆t) + Fb(∆t)+ Fc(∆t)+ Fd(∆t)= ∆ttotal {where A,B,C,D are individual arms of junction}

Each individual function may have different parameters but they depend more or less on the same

factors,

F(∆t)=f (V, ,H,R)<SL { V=vehicle volume, rate of incoming traffic, H=hetrogenity mix,R=Road width

SL is an function which calculates operating maxima for F(∆t) based on traffic models that take care of

optimum efficiency of other three arms}

V, ,H is the data that can be provided by RTMS.

RTMS DATA

Above is the example of RTMS data from HAITI. Customized data can be extracted . RTMS data can be

interpolated/customized to match the requirements of various patterns of Traffic and modal splits.

Overutilization [V> Vmax]

Crowding at an intersection is generally responsible for slow moving traffic. Slow moving traffic can

potentially develop into a jam or gridlock. Many countries have developed dynamic systems to control

such situations.

Mostly such systems are powered by some sort of sensors and switching times are computed using

powerful algorithms. This approach considers traffic to be moving smoothly and hence does not require

any management or monitoring. Traffic behavior is extremely complex , when some unpredictable

situation arises sensor-based systems fail to manage it. There are systems with video cameras where

traffic is controlled from a station full of human experts –these systems remain extremely expensive to

operate and take a lot of computing time which is not feasible.

WHERE WILL GIS HELP?

The best way to tackle crowding at a junction is to avoid it.

†UNCERTAINITY IN ROAD TRAFFIC

Mostly sudden occurrences are not manageable. The uncertainty of events in traffic behavior is surely a

matter of huge concern. Traffic behavior is a stochastic process. Each individual event occurs randomly

and is governed by undefined laws. Its very hard to be proactively control such events. Traffic

synchronization becomes a headache not only because of sudden arrival of huge volumes but also

because of undeterministic nature of vehicle flux arriving at a junction .Just imagine if you’d be able to

determine exactly the amount of flux to received you get ample time to react adaptively. The problem

of synchronization multiplies as the number of junctions to be managed increases and also it depends

on factors like average speed,collisions etc.

GIS and CERTAINITY (ARRESTING SUDDEN CHANGES)

Vehicle movement in an urban space is a mixed function.

Total vehicle volume=Regular volume + Random volume

Vtotal = Vreg + Vrndm

Where Vreg ≥ 0 , Vrndm ≥0 at any given point of time.

Regular traffic

People movement is pre-empt at some period of intervals. More acceptable example is that of people

going to their offices and returning .Part of the trips that people make are periodic which are made at

almost identical times and identical routes.

Regular traffic is the most predictable traffic ,still undeterministic in nature. That means Regular traffic is

pseudo-random. All people that commute dialy ,hold choice of changing the trip-time, trip-route, trip-

mode or choose to not make a trip at-all. But most of the people are bound to do their work ,go to

schools etc.

ESTIMATION OF V, , H through GIS

DIAGRAMATIC REPRESENTATION OF A CITY BLOCK

Estimation of V

A city block may consists of different kinds of built up area. Most of the properties can be categorized

into residential or commercial. We may gather origin-destination details of each individual in each

property and know the demand of transport in each sector of the city. We can feed this data into GIS.

VOLUME PROBABILITY DISTRIBUTION AT A REFRENCE POINT

P(V)= N(I)Sd {K is the statistical mean of arrivals,𝝀 is total expected number of

arrivals, N(I) is network performance factor ranging as 0-1, Sd is measured deviation from

average speed}

𝝀 can be directly measured by GIS application

Estimation of H

Hetrogenity in demand and modal split can be estimated with information regarding modal choice

pattern of each individual .

HETROGENITY PROBABILITY DISTRIBUTION AT A REFRENCE POINT

P(H)α P(V)

A very vague representation of P(H) can be done as

+ + + =1 {here P(c),P(m),P(t),P(o) represent average estimated traffic according to the

data collected of cars, mopeds ,truck/buses and other modal choices respectively. P(v) is estimated

volume.}

Estimation of

Speed distribution of vehicles depends on many macroscopic and microscopic properties. It is hard to

estimate such quantities accurately ,however not impossible to measure ,can be vaguely represented as

P(S)= AVG SPEED * *N(I) {F-S is the difference between fastest and slowest vehicle on the road and

N(I) is the network performance.}

P(S) is the corrected speed of total volume of cars.

D(S)=P(S)±d {d is the required interval of accuracy}

CATCHING THE PATTERN AND GIS

Traffic behavior is a sum of independent events and is an extremely complex to model it probabilisticly.

Although it is found that under a long series of observations and large proportion of time an event

occurs the probabilities of random quantities approach a constant. For example the probability of

vehicle volume at a refrence point becomes constant after n cycles of observation.GIS can measure

these quantities and store them at each observation point. When the need arrives the GIS system

should use these quantities to make decisions that help traffic flow by recognizing the pattern in current

environment. We should not forget that traffic pattern is spatially related.

V=

ALTERNATE ROUTING AND GIS

Alternate routing is also a dynamic way of controlling traffic. Refer table 2 you’ll see that at IIM junction

the volume of vehicles is 7500.Clearly from site observation it can be said that it is a large volume to

handle.IF we device a mechanism to route the traffic in other direction then may be part of vehicle users

may choose to deviate from their planned path. For example in our case we can make traffic efficient at

IIM junction if we distribute the extra traffic to other junctions.

Alternate Routing becomes practical only if the vehicles are provided with routing information well

before they enter a congested route. We should remember that our primary objective is to avoid the

congestion and not manage it once it has occurred. Practically alternate routing involves well modeled

algorithm. Although algorithm efficiency depends upon the input we feed into it. IF we have P(V),P(H)

and D(S) known at each junction then the effectiveness of the routing increases manifold. Knowing

these values and quick traffic pattern detection saves times which is advantageous in being proactive

while alternate routing.

CALCULATING LEAST OBJECTIONABLE ALTERNATE ROUTE

GIS system will have real time route statistics of each intersection.This information can be refrenced

while dynamically routing the vehicles. There are numerous factors to be considered to make an

efficient as well as acceptable choice.

There are n possible routes that a person can choose but least objectionable route can be calculated

dynamically using multivariate calculus. Further study needs to be carried out however to reach at

operating standard.

COMMUNICATION WITH USER

Communication with users can be done using digital signboards on each intersection. The choice of

alternatively routing the trip remains with the user. If the system is accurate and really saves time of the

user he’ll accept the system and rely on it in future trips. Communication on individual basis through

mobile telephony can be worked out, but it may increase the complexity of situation.

DIAGRAMATIC REPRESENTATION OF PROPOSED SYSTEM

Conclusion

There are good number of examples where sensor based intelligent traffic systems are involved. More

or less many traffic systems fail when extremely large volumes of vehicles gather at the same junction.

People forget that traffic movement is spatial .It depends on spatial factors.GIS thus becomes essential

tool in enhancing the efficiencies of sensor based systems. The paper suggests the direction of

involvement of GIS in traffic related infrastructure. Further study in this direction can be carried out to

figure out exact algorithms of on field operation.

Road infrastructure is a life-line of Ahmedabad city. Good vehicle volume is an great indicator of

prosperity. Economists believe that increase in traffic problems is a direct indicator of strength of

economy but care should be taken that traffic problems should not increase to a proportion that they

start giving negative effects on economy instead.