Chapter- 7 GIS-T Applicationsnptel.ac.in/courses/105108073/module3/lecture7.pdf · Chapter-7: GIS-T...

Chapter-7: GIS-T Applications NPTEL Web Course (12th Aug. 2011)

Course Title: Geo-informatics in Transportation Engineering Course Co-ordinator: Dr. Ashish Verma, IISc Bangalore

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Chapter- 7

GIS-T Applications

Key words: GIS and transportation, matrix operations, SP analysis, network analysis.

In the past, many individual developments, both technological and conceptual led to the rise of

Transportation-GIS (GIS-T) especially during 1980s and 1990s. In this chapter, an attempt has

been made to review these individual developments which led to the rise of GIS-T, and also to

bring out the trends and future directions of research in this most exciting and useful field of GIS

endeavour.

7.1 Introduction

Transportation applications of GIS have become increasingly popular in recent years, so much so

that they are routinely referred to by the acronym GIS-T. This is mainly due to the fact that to

design an efficient transportation system, one needs to collect, manage, retrieve, and analyze a

large amount of data, which is not possible with the manual and heuristic approaches prevailing

till date. Hence, there is a need for an efficient database management system to handle such

large-scale data, and Geographical Information Systems (GIS) is one such efficient system,

which has been thoroughly described in previous chapters. But, GIS in transportation is more

than just one more domain of application of generic GIS functionality. GIS-T has several data

modeling, data manipulation, and data analysis requirements that are not fulfilled by

conventional GIS. To quote, Vonderohe et al (1993), “the necessary enhancement to existing

Transportation Information Systems (TISs) is the structuring of the attribute databases to provide

consistent location reference data in a form compatible with the GIS, which in turn has been

enhanced to represent and process geographic data in the forms required for transportation

applications.

Now-a-days, there are already many conferences which are fully devoted to GIS-T and even

many of the general GIS conferences have special sessions on GIS-T and closely related topics

such as infrastructure management. GIS-T is well represented in GIS journals such as computers,

Environment and Urban Systems, the International Journal of Geographic Information Science,

Geographical Systems, Transactions in GIS, GIS Development, and Geographic Information



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Sciences. There have been special issues of traditional transportation journals devoted to GIS-T

and special GIS-T journals such as the IVHS Journal. Finally, there are GIS packages like

TransCAD, which have been developed specifically for GIS-T, although much of the additional

functionality required in GIS-T may also be found in the industry leading generic GIS packages.

In short, it is possible to state unequivocally that GIS-T has „arrived‟ and now represents one of

the most important application areas of GIS technology, Maguire et al (1993).

7.2 Geographic Information Systems and Transportation

The four major components of a GIS, encoding, management, analysis and reporting, have

specific considerations for transportation:

Encoding. Deals with issues concerning the representation of a transport system and its

spatial components. To be of use in a GIS, a transport network must be correctly

encoded, implying a functional topology composed of nodes and links. Other elements

relevant to transportation, namely qualitative and quantitative data, must also be encoded

and associated with their respective spatial elements. For instance, an encoded road

segment can have data related to its width, number of lanes, direction, peak hour traffic,

etc.

Management. The encoded information often is stored in a database and can be

organized along spatial (by region, country, census units, etc.), thematic (for highway,

transit, railway, terminals, etc.) or temporal (by year, month, week, etc.) considerations. It

is important to design a GIS database that organizes a large amount of heterogeneous data

in an integrated and seamless environment such that the data can be easily accessed to

support various transportation application needs.

Analysis. Considers the wide array of tools and methodologies available for transport

issues. They can range from a simple query over an element of a transport system (what

is the peak hour traffic of a road segment?) to a complex model investigating the

relationships between its elements (if a new road segment was added, what would be the

impacts on traffic and future land use developments?).

Reporting. A GIS would not be complete without all its visualization and data reporting

capabilities for both spatial and non-spatial data. This component is particularly



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important as it offers interactive tools to convey complex information in a map format. A

GIS-T thus becomes a useful tool to inform people who otherwise may not be able to

visualize the hidden patterns and relationships embedded in the datasets (potential

relationships among traffic accidents, highway geometry, pavement condition, and

terrain).

Information in a GIS is often stored and represented as layers, which are a set of geographical

features linked with their attributes. In Figure - 7.1 a transport system is represented as three

layers related to land use, flows (spatial interactions) and the network. Each has its own features

and related data.

Figure - 7.1: Representation of Transportation System

7.3 Historical Development of GIS-T

The concept of GIS traces its root back to a handful of research initiatives in the US, Canada and

Europe during the late 1950s. It is widely acknowledged that the first real GIS was the Canada

Geographic Information System set up for the Canada Land Inventory. As far as GIS-T is



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concerned, many individual developments, both technological and conceptual, have contributed

to its rise. Technologically, GIS-T, like GIS as a whole, benefited from developments in

management information systems and database techniques in general, and relational databases in

particular. The development of „stand alone‟ packages for carrying out specific operations such

as shortest path analysis and location-allocation modeling (Rushton et al, 1973), and more

recently the McTrans programs (University of Florida, Transportation Research Center) all

contributed to the development of GIS-T. On the hardware side, the development of powerful

and low cost desktop computing hardware assisted the introduction of GIS-T into even smallest

government departments of transportation.

Many conceptual developments were also important in aiding the rise of prominence of GIS-T.

These developments included works in operations research and programming, which led to new

algorithms for shortest path analysis, routing procedures, solving the „transportation problem‟ of

linear programming, and the dynamic segmentation of links within the GIS-T, (Church and

Sorensen, 1996). Frequently, these algorithms became embedded first in the stand-alone

packages such as Ostresh‟s (1973) shortest path analysis routines and then in the fully developed

GIS and GIS-T packages such as ARC/INFO and TransCAD, respectively. Equally important

was the development of a systems approach to transportation planning (Stopher and Meyburg

1975, Wilson 1974) and the so-called four-step model of trip generation, trip distribution, modal

split and network assignment, as discussed in the previous chapter.

One of the forerunners of the original GIS-T applications was the Geodata Analysis and Display

System (GADS) which was developed by the IBM Research Division in the mid 1970s to help

solve problems such as police beat and school boundary design, (Keen and Morton, 1978). While

GADS was not typical of the later generation GIS-T packages it did incorporate such datasets as

traffic pattern and transportation infrastructure; it was essentially a rudimentary GIS. Shortly,

after this, and totally independent of the American work, research in Sweden led to the

establishment of a road data bank which was used for transportation planning (Bydler and

Nilsson, 1977). In the Swedish system many of the fundamental problems and challenges in the

development of GIS-T, such as the node and link referencing system and the data content of the

system, were addressed and resolved for the first time.



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The above represents a brief history of some of the more important milestones which led to the

development of GIS-T in the 1980s and 1990s. A more detailed account may be found in

Simkowitz (1988).

7.4 The Structure of a GIS-T

7.4.1 The Relational Database Management Model

Today most GIS-T uses a relational database model for the storage of attributes. Grayson (1991)

discusses the use of an external relational database management system (RDBMS) which uses

Structured Query Language (SQL) in conjunction with the ARC/INFO GIS. This approach is

becoming less necessary as many GIS and GIS-T packages incorporate their own RDBMS.

Other database models such as the flat files have been used but they became less popular in the

1980s because of the greater conceptual and operational elegance of the relational database

(Healey, 1991). Maguire et al (1993) describe the RoMIS system developed by Oracle

Corporation as part of their highways application package and also the HERMIS system

developed for the Ingres RDBMS. While these systems allow for the efficient storage of

transportation network and attribute data, they do not provide any geographical analysis or

mapping capabilities. Such capabilities are provided by ESRI‟s ARCHIS (ARC/INFO Highways

Information System) which also includes an interface to Oracle‟s RoMIS.

7.4.2 Spatial Databases

Bydler and Nilsson (1977) were among the first to address the complexity of developing a

location referencing system for a GIS-T. Goodwin et al (1995) provides a summary of the more

common systems. These include: link Ids using either a planar or non-planar graph

representation; linear referencing systems where location is specified as distance from an

established node or even a non-topologically significant point along the road (Goodwin et al,

1995 note that the USA is working towards a national standard for a linear referencing system);

coordinate systems (Scarponcini, 1995 describes seven different coordinate systems and two

different datum in use at the Minnesota department of transportation); street addresses (which



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frequently suffer from the problem of ambiguity because similar names are located in close

proximity to one another), and finally cross street matching which defines points as offsets from

a given node and segments as the difference between two offsets.

7.5 Essential Operations and Capabilities Required for a GIS-T

A state-of-art review of the important capabilities of a GIS-T has been done by Nyerges (1989).

7.5.1 General GIS Operations Applied to Transportation-GIS

Many of the operations commonly found in commercial GIS-T packages or systems being run by

municipal governments or private firms (e.g. trucking companies) would incorporate the

standard capabilities to be found in any GIS. These operations include data editing, display and

spatial and conditional search functions.

One standard editing feature that is especially useful in GIS-T is the ability to manipulate

existing link attributes to produce entirely new attributes which have applications in

transportation planning. Thus it should be possible to divide the arc length by the speed limit in

order to estimate travel time (Rowell, 1996). The ability to edit data spatially is essential. For

example knowing the number of individuals with particular socioeconomic characteristics who

live within walking distance of a light rail transit station would help the transportation planner to

estimate the potential ridership. Display capabilities should allow for the import of raster images

as background information for the vector transportation database, thus allow users to orient

themselves to the real world.

Buffering capabilities are also a commonplace feature in generic GIS but they are particularly

important in GIS-T for determining the accessibility of population groups close to transportation

routes and services and for determining the environmental and noise impacts of these facilities

(Love, 1984; Aultman et al, 1997). It is also important to be able to combine spatial searches and

conditional queries within a GIS-T. For example, all narrow roads within 20 miles of a chosen

feature should be easily obtainable. Address geocoding is another critical feature for many GIS-T

applications (Arthur and Waters, 1995). Finally, the GIS-T should have report generating



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capabilities. These are important for accident analysis and road maintenance (Bydler and

Nilsson, 1977).

7.5.2 Matrix Operation in GIS-T

Generic GIS packages usually store attribute data in the tabular form associated with traditional

spreadsheet and database technology, which is commonly known as the data view (Waters,

1995). In a GIS-T an equally useful form of storage is the matrix in which both rows and

columns represent the same set of geographical features, either points such as cities or areas such

as traffic analysis zones (TAZs), (Nyerges, 1995). The rows are usually regarded as the origins

and the columns the destinations. Thus each element of the matrix can store such information as

traffic or commodity flows, travel time or distance measures, and migration patterns. This is

known as the matrix view.

Because such matrices are the basis of many forms of analysis in transportation research, Taaffe

et al (1996), the GIS-T should be able to create, modify and edit such matrices. Traditionally

such matrices are square but some GIS-T packages such as TransCAD allow user to select sets of

columns, which may not be identical to the rows, and thus rectangular matrices are also possible.

One form of display that is common in transportation research is a map of desire lines,

portraying the flows from one zone to all other zones. This is also used in location-allocation

modeling to show the allocation of users to a facility. The ability to create such maps directly

from a matrix view is a useful feature in any GIS-T. The GIS-T user should be able to edit cells,

rows, columns, ranges of cells, or indeed the entire matrix in a single operation. The user should

be able to perform the full range of mathematical operations on one or more of cells in the matrix

or to apply a formula to the selected cells. It is helpful if the GIS-T package can switch

automatically between the data view and the matrix view. Figure - 7.2 shows a typical matrix

created using GIS tool.



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Figure - 7.2: Typical Matrix (City Distance) Constructed using GIS

7.5.3 Various Modeling Procedures

7.5.3.1 TIGER:

By far the most prolific network structure for GIS-T applications has been the model accepted by

the U.S. Bureau of the Census for its Topologically Integrated Geographic Encoding and

Referencing (TIGER) files. The TIGER model had developed from some earlier network data

models, and it was marked by its adherence to the principle of planar enforcement. Planar

enforcement simply means that all lines in the network are forced into a single plane, and all

intersections of lines are defined in that plane. The planar enforced TIGER model presented

several difficulties. First, many transportation applications are not concerned with the polygons



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that may have transportation features as their boundaries. It is the transportation features

themselves that are of interest. Secondly, the planar enforcement that was needed to generate

polygons also had the effect of splitting transportation features into many small segments

whenever two features crossed in the plane. Therefore, there were many “intersections” in the

network data structure that didn‟t correspond to any actual intersection in the transportation

network at all.

7.5.3.2 Shortest Path Analysis (SPA):

SPA is an essential precursor to many GIS-T operations. It is critical for route location models

for determining the minimum environmental cost route (Lee and Tomlin, 1997; Thirumalaivasan

and Guruswamy, 2000), for determining allocations in a location-allocation model, for trip

assignment in transportation planning models, Nyerges (1995), and for automatic vehicle

dispatch systems (AVDS). Densham (1996) discusses the importance of SPA routines within his

visual interactive location system, which is part of an interface with the TransCAD GIS-T. A

major concern in the recent years has been the size of problems, which can be handled by

existing algorithms and the solution speed. Zhan (1996) has explored the use of fast shortest path

algorithms on extensive road networks.

Shortest path algorithms in a GIS-T should be highly flexible in order to handle the many

subtleties of the real world. It should be possible to solve the problem for one origin and one

destination or for many origins and many destinations. The GIS-T should be able to store a

variety of cost and other variables for each link in the network. Shortest paths in terms of time,

distance, and cost may all be relevant. The number of lanes, their height and width, and

restrictions such as those on hazardous materials should all be incorporated into the calculations.

The more sophisticated GIS packages (e.g. ARC/Info and TransCAD) allow impedance

functions to be specified for turns of various types, traffic lights, on-off ramps, and other traffic

controls and management devices. Algorithms for handling some of these features of the real

world were originally developed in the 1960s, Kirby and Potts (1969), although much more

comprehensive procedures have been developed, Ziliaskopoulos and Mahmassani (1996) which

are likely to be quickly incorporated into standard GIS-T packages. Behavioural work on



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specifying relevant attributes for route choice is reviewed by Bovy and Stern (1990). Figure - 7.3

shows typical shortest path analysis solved using GIS software.

Figure - 7.3: A Shortest Path Analysis Problem in GIS Tool

7.5.3.3 Vehicle (VRo) & Arc Routing (AR)

As also discussed in previous chapter, vehicle routing problem includes the development of

routes or tours for deliveries and/or pickups from one or more depots (warehouses) at one or

more stops (delivery or pick-up points). Such problems become more complex when there are

time constraints at either the depots or stops or both and when the service times are variable. In

some problems vehicle capacity is a serious constraints (e.g. delivering gasoline/petrol to a

filling station) whereas in other applications this is not the case (e.g. delivering a pizza).

TransCAD and some very few packages can handle most of these real world complexities but

not all of them- such as mixed fleets, Wirasinghe and Waters (1983), mixed products and open-

ended tour. In these cases, either the user can interface their own customized software with the

GIS-T or they can ask the GIS-T vendor for a customized solution.



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In the arc routing problems the GIS-T user is attempting to find routes which will allow for an

optimal (or at least an efficient) transversal of a set of arcs in the transportation network.

Applications include bus service and any residential delivery, pick-up or monitoring systems

such as mail delivery, solid waste collection, or meter reading, respectively. One of the aims of

such algorithm is to minimise the amount of „deadheading‟ which is the distance from the point

at which the service is completed back to the bus depot, mail sorting plant, or other base facility,

Waters et al (1986). Real world variations which a GIS-T should be able to handle, include

situations: where certain links may require service because they perhaps lack houses; where

service is required only on one side of a street; and where multiple passes may be required by

street cleaning vehicles. As with VRo problems there may be peculiar constraints such as time of

day restrictions, which may require customized solutions.

7.5.3.4 Spatial Interaction and Gravity Models

The gravity model is still used despite the limitations noted by Wilson (1974), although newer

forms of spatial interaction models such as the entropy maximising model popularised by Wilson

have largely replaced the more simplistic gravity model. GIS-T packages are now incorporating

these procedures as standard operations. Spatial interaction models are increasingly being used

as the basis for non-emergency applications of location-allocation models (Birkin et al, 1996;

Oppong, 1992).

7.5.3.5 The Urban Transportation Systems Planning (UTSP)

The four-step UTSP process of trip generation and attraction (how many trips?), trip distribution

(where do they go?), modal split (by what travel mode do they move?), and traffic or network

assignment (which route do they take?) is almost universally used in transportation planning

(Nyerges, 1995; Pas, 1995; Taaffe et al, 1996) and a GIS-T package should include procedures

for facilitating this process.

The aim of trip generation models is to predict the number of trips produced by each origin zone

or region. This task can be handled in many ways. The GIS-T analyst has to make choices

concerning the units of analysis, specifically, whether vehicle or person trips are being modeled



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and whether to work at household or individual level (Caliper Corporation, 1996b; Wilson et al,

1974). Trip distribution models are used to predict the flow values in the transportation zone,

origin-destination matrix. Modal choice can be determined either at the zonal (aggregate) level or

at the individual (disaggregate) level. Estimation procedures may involve either revealed or

stated preference, Louviere (1988) and these can be modeled using popular choice such as

multinomial, binary, and nested logit models. Akiva and Lerman (1985) and Horowitz (1995)

provide comprehensive accounts of these procedures. Traffic assignment models are used to

allocate traffic volumes to the network based on the different strategies, a sophisticated GIS-T

should allow for a variety of traffic assignment strategies. TransCAD employs the following

strategies: all-or-nothing assignment; incremental assignment; capacity constraint assignment;

user equilibrium assignment; stochastic user equilibrium; and, finally, a system optimum

assignment.

Although it implies that the four-step model is strictly sequential, in practice the process is

usually iterative: in particular, the output from the traffic assignment may be used as input to the

earlier parts of the model (Nyerges, 1995; Miller and Storm, 1996). In addition, the GIS-T

should be used to evaluate the efficiency and effectiveness of the forecast in terms of system

parameters such as performance, safety, level of service, environmental concerns, and financial

considerations.

7.5.3.6 GIS in Land Use Modeling

GIS is an enabling technology for land use modeling, and typically are either linked to or is used

to feed data to most current land use models. This useful connection between GIS and land use

models was recognized early on. One of the first uses of the SYMAP, a computer mapping

software was to visualize the outputs of land use simulators.

Although none are in widespread use, there are many land use models that have been developed.

Thirteen are described by Wegener (1995). Many of the more recent models have been linked to

some form of GIS for gathering inputs or the presentation of results. The California Urban

Futures Model (Landis, 1994) is built upon a GIS platform, and predicts housing developments

for sites: however, it does not treat transportation as a determinant of land use. In models that do



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include transport, both zonal (polygonal) and grid cell structures have been used, and historically

most models were aggregate in nature, as were the prevalent transport forecasting models in use.

In general, land use models have not been very detailed geographically, and tend to be coarser in

detail than the transport models that are used. There are computational limits, and some data may

not be available at more detailed geographic scales (Green and Flowerdew, 1996).

With the maturation of choice models, land use models are becoming more disaggregate and

behavioral in nature, and will require and benefit from the greater geographic detail that GIS

technology can provide. For example, the UrbanSim model development effort incorporates

disaggregate data which is aggregated to a grid layer with square cells that are 150 m on a side.

Parcel-based land use models can be envisioned, which may be the most natural level for

modeling.

GIS technology offers the prospects of providing great volumes of data for modeling from

existing data systems and tools for obtaining and creating other data needed for land use models.

Aerial photography and remote sensing can provide measurements of variables that are important

but are not captured in the municipal GIS. Buffering and polygon overlay can be used to create

new variables. A GIS has many facilities for managing spatial data of diverse types, and thus

offers useful data management for the entities of interest in a land use model, be the roads,

households, residential, dwellings, land use polygons, shopping centers, or employment sites.

Any data of interest can be associated with these polygons, lines, or points.

7.5.3.7 Freight Modeling

The scale independence of a GIS-T based modeling system has facilitated national and inter-

regional fright transport model applications. Among these are multimodal models that include

shipment by truck, rail, air, and sea. In USA, GIS-T (TransCAD) has been used to synthesize

modal freight networks and apply a traffic model for freight modeling for capacity analysis.

Extensive use of GIS-T technology was made to integrate state level data sets, national data sets,

freight data, and traffic information in a consistent network. Figure - 7.4 shows typical freight

demand analysis.



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Figure - 7.4: Freight Modelling using GIS

7.5.3.8. Public Policy Making

In the policy making process, different studies can be carried out using GIS. One of the examples

of this is freight traffic analysis using GIS. This includes integrating truck count data that can be

visualized with GIS in a number of ways: by volume, by percentage of total traffic and by

percentage growth. This type of data can be used to evaluate truck impacts and design mitigation

measures such as designing truck routes, and where to impose truck restrictions.

Likewise GIS are an especially important tool when environmental impacts have to be estimated.

These data have spatial extents, so overlaying them with traffic volumes in GIS is an excellent

method. Another important issue in public planning is to find patterns in the locations of logistic

centers for an optimal planning of public infrastructure for freight transport. These GIS based

routing systems can also be used to evaluate the impacts of major incidents that may close down

a section of highway.



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7.6 Some Specialized Studies Done using GIS-T

Following are some of the examples, illustrating the studies done using GIS-T. They can be

broadly classified into, Transport Management Systems, Travel Demand Modeling, Route

Planning, and Other Studies.

7.6.1 Transport Management Systems Studies

Many studies have been attempted in GIS-T environment. Simkowitz (1990) points out that a

pavement management system (PMS) should address all aspects of the pavement management

process from planning and programming through project development and implementation.

Simkowitz shows how GIS technology can be used to expand and enhance each of these PMS

components. All reported concepts and results have been incorporated into TransCAD, the GIS

for transportation software. His conclusions are that the coupling of appropriate GIS technology

with stand-alone PMS can result in a greatly enhanced PMS process and also that TransCAD has

all the tools for comprehensive PMS/GIS. Partheeban & Santhakumar (1999) described the

development of a database for highways around Tiruchirapalli, India (GIS/HAT) using GIS

developed in Visual BASIC language. They have tried to present GIS in VB as a viable

alternative to the existing GIS software, which are costly to be purchased by planners from

developing countries. Kharola and Gopalkrishna (1999) have presented GIS as a powerful

analytical tool and discuss its introduction into urban transport undertakings especially with

regard to fleet management. Singh (1999), developed the GIS database in TransCAD and used it

for the planning and management of regional road network. A study region of five districts

around Mumbai Metropolitan Region was considered for this purpose. The objective functions of

his study were: a) to develop the GIS database structure for the regional road network based on

the organizational hierarchy of the PWD in the country, and b) to develop GIS based procedure

for road network management, rural road planning and, road safety analysis. The main drawback

with his work is that the database is based on the secondary data obtained from PWD, which is

incomplete and inaccurate at many places. Dueker and Butler (2000) developed a framework and

principles for sharing transportation data, both of which are based on an enterprise GIS-T model

that defines relations among transportation data elements. The data model guards against



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ambiguities and provides a basis for the development of the framework and principles for sharing

transportation data. A model to synchronies the management and query of temporal and spatially

referenced transportation data in GIS has been described by Sutton and Wyman (2000). The

model employs a method referred to as dynamic location, which facilitates spatial intersect

queries from geographic shapes without the use of topological relationships.

7.6.2 Route Planning Studies

Lepofsky et al (1993), describes the methods employing GIS-T that can provide the capability to

perform transportation hazard analysis and incident management. Incident management

considerations include those of emergency response deployment and rerouting to bypass the

affected area. Transportation hazard analysis also addresses dynamic routing and emergency

preparedness in the case of hazardous-materials transport release, and involves comprehensive

risk assessment and evacuation planning. The paper concludes with a discussion of how the GIS-

T approach to incident management may be extended to address dynamic management in an

intelligent-vehicle-highway-system environment. Sadek et al (1999) developed a decision-aid

tool using GIS for multicriteria evaluation of route alignments. Possible alignments are evaluated

based on community disruption and environmental, geotechnical and geometric design criteria.

Results of the case study demonstrates the advantages of the decision-aid tool and highlighted its

potential in providing a quick, multicriteria screening evaluation of possible route alignments.

Thirumalaivasan and Guruswamy (2000) used the route module available in Arc-Info (a GIS

based software) to find optimal routes between two given points for emergency services, such as

ambulance and fire services, based on minimum travel time criteria. Southworth and Peterson

(2000) describe the development and application of a single, integrated digital representation of a

multimodal and transcontinental freight transportation network. The paper focuses on the routing

of the tens of thousands of intermodal freight movements reported in United States Commodity

Flow Survey.

7.6.3 Travel Demand Modeling

Miller and Storm (1996), reports on the development of a prototype geographic information

system design to support network equilibrium-based travel demand models, which is an attempt



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to advance the standard practice of sequential approach. The key features of GIS design includes

(a) realistic representation of the multimodal transportation network, (b) increased likelihood of

database integrity after updates, (c) effective user interfaces, and (d) efficient implementation of

network equilibrium solution algorithms. Souleyrette and Anderson (1998) presented tools and

recommended steps for developing urban transportation models using desktop GIS. Taking the

case study of Ames, Iowa, these procedures and tools are shown to facilitate integration of

spatially referenced data for efficient model development. An integrated transit-oriented travel

demand modeling procedure within the framework of GIS has been presented by Choi and Jang

(2000). Focusing on transit network development, this paper presents both the procedure and

algorithm for automatically generating both the link and line data for transit demand modeling

from the conventional street network data using spatial analysis and dynamic segmentation. Rao

(2000) has done a comparative study of various traffic assignment techniques using TransCAD.

7.6.4 Other Studies

Pathan et al (1993), described the results from a study about growth trends of the urban areas in

the Bombay metropolitan region using multi-date remote sensing data and ARC/INFO GIS

package. The spatial growth trends are examined in relation to the population and the population

density has been computed for different periods. Based upon these densities, the extent of land

required for urban development for the year 2001 has been calculated. Soni et al (1996)

described some basics of GIS technology and its applications and suitability for transportation

engineers. Cesar and Darcy (1998) described a new methodology for performing travel time

studies using Global Positioning System (GPS) and geographic information system (GIS)

technologies. The data collection procedure uses GPS receivers to automatically collect time,

local coordinates, and speed at regular sampling periods, for example every one second. The data

reduction procedure filters and aggregates GPS data to compute travel time and speed along

highways. The data reporting procedure uses a GIS-based management information system to

define queries, tabular reports, and color-coded maps to document travel time data along these

highway segments. O‟sullivan et al (2000) investigated the application of existing desktop GIS

to the assessment of accessibility by public transport. Two approaches to the measurement of



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accessibility- aggregate accessibility measures and the space-time geography framework- were

described. It has been suggested by authors that isocrones (lines of equal travel time) are a

natural way to combine these approaches in a GIS setting. Thill (2000) placed the concept of

GIS-T in broader perspective of research in GIS and Geographic Information Science. He

emphasised on the requirements specific of the transportation domain of application of this

emerging information technology as well as on core research challenges. Ziliaskopoulos and

Waller (2000) developed an internet-based GIS that brings together spatio-temporal data, models

and users in a single efficient framework to be used for a wide range of transportation

applications- planning, engineering and operation. They also discussed the implementation issues

and necessary models needed to support the system. Xiong (2000) described a three-stage

matching algorithm (node matching, segment matching, and edge matching) that combines

bottom-up and top-down procedures to carry out the network matching computation. Kwan

(2000) describes several GIS-based three-dimensional (3D) geovisualisation methods for dealing

with the spatial and temporal dimensions of human activity-travel patterns at the same time while

avoiding the interpretative complexity of multivariate pattern generalisation or recognision

methods. Bachman et al (2000) presented a GIS-based modeling approach called the Mobile

Emission Assessment System for Urban and Regional Evaluation (MEASURE). MEASURE

provides researchers and planners with a means of assessing motor vehicle emissions reduction

strategy. The authors have discussed the benefits and challenges related to mobile source

emissions modeling in a GIS framework and identifies future GIS mobile emissions modeling

research needs. You and Kim (2000) developed and evaluated a hybrid travel time forecasting

model with GIS technologies for predicting link travel times in congested road networks. Taylor

et al (2000) described a case study application of developing an integrated GIS-GPS for

collecting on-road traffic data from a probe vehicle for traffic congestion studies. Peng and

Huang (2000) presents a web-based transit information system design that uses Internet

Geographic Information Systems (GIS) technologies to integrate Web serving, GIS processing,

network analysis and database management.

Likewise, GIS has been tried for many other applications to transportation like, pedestrian

accessibility, highway management, prediction of urban traffic air pollution, regional planning,



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road environmental impact, transportation improvement site selection, land use planning, land

use-transport planning etc. The following sub-sections describe few of the studies in greater

detail.

7.6.5 Design and Development of Interactive Trip Planning for Web-Based Transit

Information Systems

Peng and Huang (2000) developed a Web-based transit information system that allows transit

users to plan a trip itinerary and to query service-related information, such as schedules and

routes using Internet Geographic Information Systems (GIS) technologies. A unique feature of

the system is that it integrates Internet GIS into the system design so that the user interface is

map-based. The user can interact with the transit network and street maps, conducting query,

search, and map rendering. The interactive map-based user interface also allows the system to

incorporate other information, such as shops, theatres, parks, and other local attractions. This is

very important for visitors who may want to explore these sites around their destinations. A path

finding algorithm for transit network is proposed to handle the special characteristics of transit

networks, e.g., time-dependent services, common bus lines on the same street, and non-

symmetric routing with respect to an origin/destination pair. The algorithm takes into account the

overall level of services and service schedule on a route to determine the shortest path and

transfer points. A framework is created to categorize the development of transit information

systems on the basis of content and functionality, from simple static schedule display to more

sophisticated real time transit information systems.

7.6.6 IMPACT: An Integrated GIS-Based Model for Simulating the Consequences of

Demographic Changes and Population Ageing on Transportation

Maoh et al, (2009) developed an application of IMPACT (integrated model for population

ageing consequences on transportation), a GIS-based model capable of assessing the

ramifications of demographic changes, including population ageing, on the performance and

usage of urban transportation systems. Unlike existing operational models, IMPACT possesses

unique and novel features that enable it to model demographic changes and urban travel demand



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for various age cohorts. The development of IMPACT follows the modular approach. The result

is a versatile, stand-alone GIS-T model with an extensive set of policy handles that can be used

to simulate scenarios relating to changes in (1) vital statistics (fertility, mortality, and migration

rates), (2) land use characteristics per TAZ, including employment, new residential development,

dwelling rent values, availability of schools, parks, recreational facilities, and the existence of

commercial and industrial land uses, and (3) transportation infrastructure such as the

enhancement of the level of service for existing road infrastructure or building new roads.

Furthermore, the system can be used to simulate scenarios that pertain to changes in the socio-

economic characteristics (e.g. people with driving licenses, car ownership levels, employment

status, and household structure) of the driving population in the city.

7.6.7 A GIS-based approach for the screening assessment of noise and vibration impacts

from transit projects

Hamed and Effat (2007) presented a GIS-based tool for the assessment of airborne-noise and

ground-borne vibration from public transit systems, and its application to an actual project. The

tool was based on the US Federal Transit Administration‟s (FTA) approach, and incorporates

spatial information, satellite imaging, geo-statistical modeling, and software programming. They

presented a methodology of using spatial modeling, Geographic Information System, Visual

Basic modeling, and satellite images to assess the environmental impacts from transit projects. A

case study was presented on the application of the model for assessing the air-borne noise and

ground-borne vibration impact resulting from a LRT project in an urban center in the Middle

East. Specifically, the assessment was used in a multi-criteria analysis to evaluate two alternative

alignments for the rail tracks. The method fully automates the assessment process; allows the

evaluation of a very large number of potential receptor points; and allows the presentation of the

results in a graphical format.

7.6.8 Geographic Information Systems (GIS) Based Model of Dairy Manure

Transportation and Application with Environmental Quality Consideration

Paudel et al, (2009) used the survey information to develop a minimum cost spatial dairy manure

transportation model where environmental quality and crop nutrient requirements were treated as



170

constraints. The GIS model incorporated land use types, exact locations of dairy farms and

farmlands, road networks, and distances from each dairy farm to receiving farmlands to identify

dairy manure transportation routes that minimized costs relative to environmental and other

constraints. Analyses indicated that the characteristics of dairy manure, its bulk and relatively

low primary N2, P2O5 and K2O nutrient levels limit the distribution areas or distances between

the farms and the land over which the manure can be economically spread. Physical properties of

the land limit the quantities of nutrients that can be applied because of excess nutrient buildup in

soil and potential to harm nearby water bodies and downstream people and places. Longer

distances between dairy and farmland favor the use of commercial fertilizers due to the high cost

of manure transportation. The GIS-based model made it possible to determine a least- cost dairy

manure application distance for Louisiana‟s major dairy production area. An optimal

transportation route identified is a real world path with minimum cumulative impedance between

the dairy farm origin and farm and pasture land destination using the existing road network. The

analysis identified the shortest and cheapest existing road network by calculating and cumulating

the contributions of all cost elements involved in the loading and transportation of manure from

its origin to the point of final destination including the farm application cost.

7.6.9 A GIS-Based Environmental Modeling System for Transportation Planners

Brown and Affum (2002) developed a GIS-based environmental modeling system, termed

TRAEMS, for use by transport planners in assessing the environmental effects of road traffic

plans. The system utilises capabilities of GIS to integrate the output from a transport planning

activity with land use information to model the environmental impacts of different road traffic

scenarios. TRAEMS enables planners to test transport related environmental impacts at the same

time as they are testing the traffic carrying efficiencies of network plans. TRAEMS operates

entirely within the MapInfo environment, as an additional item in the MapInfo main menu, in the

form of pull down menu. It is user-friendly, simple to use and requires no prior knowledge of

MapInfo. All input data and output are maintained in MapInfo format, keeping everything

simple and uniform. The various components are designed as individual programs in modular

form, linked together through a common controller. Separate sub-modules have been designed



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for each of the environmental factors of interest, namely: traffic noise, air pollution, energy

consumption and storm water pollution. Through the identification of total pollutant load, and the

location of this load in the network, the model has the potential to allow transport and land use

planners to examine environmental consequences, not just transport consequences of proposals.

7.7 Software Available for GIS-T

There are many generic or multipurpose GIS packages, which can be used for GIS-T. Idrisi is a

low cost, raster-oriented package with some vector analysis capabilities suitable for GIS-T

applications, including the ability to calculate minimum cost paths, Eastman (1995). ESRI has

been gradually increasing the functionality of its low-end ArcView software as it has released

newer versions. ArcView Version 3.0 introduced an extension called Network Analyst who has a

variety of automated routing functions, ESRI (1996). Among the high-end commercial packages

are Intergraph‟s Microstation, GeoMedia and ESRI‟s ARC/INFO system, both of which have

full network analysis functionality, Rodcay (1995). The other main generic software available

are (CSDMS, 2000): AutoCAD Map software which is based upon AutoCAD Release 14;

MapInfo which is a comprehensive desktop mapping software with a powerful graphical display

program; ILWIS which uses an object-oriented approach in which maps, such as point, segment,

polygon and raster maps are data objects; TNTmips which is MicroImages flagship product for

geospatial analysis; GRAM++ which has been developed by CSRE IIT Bombay and which

includes modules for import/export of different format data, map editing, raster analysis, vector

analysis, network analysis etc. (Venkatachalam et al, 2001). But, among all these, probably the

best known specialised GIS-T software package is Caliper Corporation‟s TransCAD, a superset

of their GIS Plus system. It includes all the standard GIS-T operations discussed above. Caliper

also produces the in-expensive Maptitude desktop GIS package, which has extensive GIS-T

functionality.

7.7.1 TransCAD

TransCAD is the first and only Geographic Information System (GIS) designed for use by

transportation professionals to store, display, manage, and analyze transportation data.



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TransCAD combines GIS and transportation modeling capabilities in a single integrated

platform, providing capabilities that are unmatched. TransCAD can be used for all modes of

transportation, at any scale or level of detail. With TransCAD, you can create high-quality map

output using many of thematic mapping styles and options, unlimited colors, and fully-scalable

line styles and true type map symbols. With a few clicks of the mouse, automatic mapping

technology helps to create color and pattern coded maps, dot-density maps, scaled-symbol maps,

and maps with integrated pie charts and bar charts. TransCAD also provides specialized mapping

functions for transportation applications:

TransCAD is a state-of-the-art GIS that can be used to create and customize maps, build and

maintain geographic data sets, and perform many different types of spatial analysis. TransCAD

includes sophisticated GIS features such as polygon overlay, buffering, and geocoding, and has

an open system architecture that supports data sharing on local and wide-area networks.

TransCAD is the only software package that fully integrates GIS with demand modeling and

logistics Functionality. The various advantages in TransCAD are:-

GIS makes it possible for models to be much more accurate. Network distances and

travel times are based on the actual shape of the road network and a correct

representation of highway interchanges. Also, with networks we can specify complex

road attributes such as truck exclusions, delays at intersections, one-way streets, and

construction zones.

The entire modeling process is more efficient. Data preparation is greatly facilitated and

the database and visualization capabilities catch errors before they cause problems.

The third advantage is the GIS itself. In TransCAD, different modeling equations can

easily be derived and applied for different geographic sub areas. Similarly, TransCAD

brings new and much-needed capabilities for measuring geographic accessibility.

Lastly, the GIS approach provides a graphical solution that is easily understood. Users

can convey highly technical information to the non-practitioner in a very straightforward

and understandable manner.



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7.7.2 MapInfo

MapInfo provides location intelligence solutions through combining software, data (both spatial

and non spatial) and consultancy with project management, systems design and development,

training and support. MapInfo produces a wide range of software including Spatial Cartridges for

databases (Spatial Ware), Routing, Geocoding (Mapmaker), Site Analysis, Risk Analysis,

Market Analysis, and the Envies web services suite along with the more traditional Geographic

Information System (GIS) software.

7.8 Future Developments

7.8.1 Use of Expert Systems (ES) and Other Techniques

In the near future GIS-T is likely to benefit from an infusion of techniques related to ES.

Heikkila et al (1990) suggest that ES can be used to determine which infrastructure

improvements are appropriate in the light of changes within the GIS-T. The GIS-T can then be

used to evaluate the effects of the infrastructure change and this information can be entered into

the ES. Taylor (1990) has developed design criteria for knowledge based route guidance advisors

which take into account not only the characteristics of the system but also the characteristics of

the users to whom the advice is targeted. Spring and Hummer (1994), have demonstrated the use

of engineering knowledge regarding accident causation to identify hazardous locations.

Panchanathan and Faghri (1994), discussed the development of a knowledge-based geographic

information system for managing and analyzing safety-related information for rail-highway

grade crossings. Besides this, in future, there will be an increased use of advance techniques like

Genetic Algorithm (GA), Simulated Annealing (SA), Decision Support Systems (DSS) etc. or

some customized software, with GIS-T to get optimal solutions or to solve specific problems

which are at present cannot be solved with present GIS-T tools.

7.8.2 Internet GIS-T

In future, data and maps and software will all be available on the internet for downloading or

simply viewing, Maguire et al (1993). Many organisations are already making extensive use of



174

the internet and this is particularly true of municipal transportation departments which frequently

make traffic service maps available over the map. Peng and Huang (2000) have done such a

study to develop a web-based transit information system. Bertazzon and Waters (1996) have

built a web site, which incorporates GIS functionality for determining shortest paths and provide

route guidance and weather information on the web in real time.

7.8.3 Intelligent Transportation Systems (ITS) and GIS-T

As we all will agree, Intelligent Transportation Systems (ITS) is the future of transportation and

there is bound to be an increase in the research in this key area, which is mainly the integration

of GIS-T with GPS technologies. Two main components in this area are Intelligent Vehicle

Highway Systems (IVHS), and Automatic Vehicle Location Systems (AVLS).

IVHS is a generic term for a number of GIS-T related applications, Johns (1990), including:

automatic vehicle identification and billing (AVI); weighing in motion; collision warning and

guidance; driver information and route guidance through advanced travel orientation systems

(ATOS); advanced trip planning systems (ATPS); advanced travel conditions systems (ATCS);

advanced traffic signal control and operation; automatic incident detection; and automobile

vehicle spacing among a number of others. Goodwin (1994) notes that ORNL in the USA is

developing an information infrastructure to support data sharing across all IVHS applications and

uses.

AVLS systems are designed to locate vehicles at all times. Klesh (1989) noted that there are as

many categories of AVLS as there are modes of travel. Concentrating on road vehicle

applications AVLS were divided into two distinct categories: dispatch and stand-alone systems.

The latter, often known as AVNS, are in-vehicle systems which are used to determine the

shortest route between the vehicle‟s present location and an intended destination, and have been

discussed in detail by Jeffrey (1987) and White (1991). Klesh notes that the dispatch category

(AVDS or AVMS) supports variety of applications in the private sector (e.g. application for

taxicab companies, delivery and collection, services, and security agencies) and in the public



175

sector (e.g. police, fire department, ambulance, transit services). AVDS may employ GPS, a

beacon, or some other technology to fix the vehicle‟s position, but in addition this information is

relayed to a central site from which additional resources may be dispatched, if and when

necessary. Holland (1990) provides a detailed review of the use of AVLS technology in many

urban transit systems and notes how it can be integrated with scheduling programs developed for

transit operations. The next chapter will discuss ITS in much greater detail.

7.8.4 Temporal GIS-T

The construction of temporal GIS databases is one of the ongoing challenges for the GIS

community. Such formulations are particularly useful in the transportation sector. A series of

recent papers (Clark et al, 1996; Crawford-Tilley et al, 1996; Masuoka et al, 1996) discuss the

design of a temporal GIS-T for the Baltimore-Washington region.

The Flow Chart in Figure - 7.5 summarizes the application of GIS trends.

.



176

GIS-T applications

Prevailing trends Future developments

Service area analysis; O’Sullivan et.al(2000).

Pavement management system; Simkowitz(1990).

Transportation hazard analysis and incident management; Lepofsky et.al(1993).

Urban growth trend analysis; Pathan et.al(1993).

Urban transportation systems planning and management; Stopher & Meyburg(1975), Wilson(1974), Nyerges(1995), Pas (1995), Taaffe et.al(1996),

Miller & Storm(1996), Souleyrette & Anderson(1998).

Travel time studies; Rowell(1996), You & Kim(2000), Cesar & Darcy(1998).

Evaluation of route alignments; Sadek et.al(1999).

Evaluation of assignment techniques; Rao(2000).

Planning & management of road network; Grayson (1991)

Network information system; Bydler & Nilsson(1977), Partheeban & Santhakumar(1999).

Routing of transit & other services; Waters et.al(1986), Church & Sorenson(1996), Wirasinghe & Waters(1983).

Shortest path analysis; Church & Sorensen(1996), Ostresh(1973), Lee & Tomlin(1997), Thirumalaivasan & Guruswamy(2000), Zhan(1996), Kirby & Potts(1969), Ziliaskopoulos & Mahmassani(1996), Bovy(1990).

Transportation data sharing; Dueker & Butler(2000), Sutton & Wyman(2000).

Network matching procedures; Xiong(2000).

Freight transportation planning; Southworth & Peterson(2000).

Human activity travel pattern studies; Kwan(2000).

Traffic congestion studies; Taylor et.al(2000).

Pollution emission assessment systems; Bachman et.al(2000).

Transit information systems; Kharola & Gopalkrishna(1999).

Accident analysis; Bydler & Nilsson(1977).

Pedestrian accessibility studies; Aultman et.al(1997).

Regional planning; Nyerges(1995), Singh(1999).

Environmental & noise impact studies; Love(1984).

Transportation improvement site selection.

Land use planning.

Integrated landuse-transport planning; Horowitz(1995).

Location-allocation modelling; Rushton et.al(1973), Densham(1996), Birkin et.al(1996), Oppong(1992).

Review studies; Maguire et.al(1993), Nyerges(1989), Simkowitz(1988), Soni et.al(1996), Thill(2000).

Knowledge based GIS; Spring & Hummer(1994), Panchanathan & Faghri(1994), Heikkila et.al(1990), Taylor(1990).

Web based GIS; Bertazzon & Waters(1996), Peng & Huang(2000), Ziliaskopoulos & Waller(2000).

Intelligent Transportation Systems (ITS); Johns(1990), Klesh(1989),

Goodwin(1994), Jeffrey(1987), White(1991), Holland(1990).

Mass transit planning; Choi & Jang(2000).

Temporal GIS; Clark et.al(1996), Crawford-Tilley et.al(1996), Masuoka

et.al(1996).

Knowledge based GIS

Web based

GIS

GIS coupled

with

customized

software or

advanced

optimization

technique.

Integration of GIS and GPS

for Intelligent

Transportation

Systems (ITS).

Integrated

urban

transport /

land use

transport

planning

Facility

location planning.

Real time

traveler

information

systems.

Cargo fleet

management

& routing.

Temporal GIS

Figure – 7.5:

GIS-T Review



177

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Exercises

1. Explain the structure of GIS in Transportation.

2. What are the essential capabilities of GIS in transportation?

3. Explain how Shortest Path (SP) analysis is performed in GIS.

4. Write a brief note on matrix operations in GIS.

5. Categorize the applications of GIS in different fields of transportation.

Assignment

1. Perform the conventional Travel demand modeling procedure (preferably using tutorials or

example given in software) using TransCAD or any other GIS software.

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