Gps Design for Network Travel Time Study

GPS DESIGN FOR NETWORK TRAVEL TIME STUDY

1TRANSPORATATION DIVISION

GPS DESIGN FOR NETWORK TRAVEL TIME

STUDY

April-2008

ASEMINAR REPORT

ON


ABSTRACT

Memory decay, failure to understand or to follow survey

instructions, unwillingness to report full details of travel, and simple carelessness have all

contributed to the incomplete collection of travel data in self-reporting surveys. The regularity

and the variability of individual travel behavior over time has been one of the key issues in

travel behavior research for three decades. A deeper insight into the long-term mobility

patterns of persons and households has been so far restricted by the limited availability of

longitudinal data.

Travel time-based measure, such as mean travel time, is easy to

understand by both the professional transportation community and the traveling public. Travel

time-based measure is also applicable across modes and is a common measure of effectiveness

for all modes. These advantages make travel time-based measure extremely powerful, versatile,

and desirable.

The main objective is to investigate the statistical properties of

link travel time and the transform of the properties over the change of the traffic conditions and

to propose an efficient link travel time estimation method by small number of travel time

reports from the GPS technology. Global Positioning System (GPS) technology has been

used as a supplement in the collection of personal travel data. Previous studies confirmed the

feasibility o f applying GPS technology to improve both the accuracy and completeness of

travel data.



І

CONTENTS

ABSTRACT І

P.NO

1. INTRODUCTION …………………………………………………………………. 1

2. BACKGROUND ……………………………………………………………………. 2

3. TRAVEL SPEED FIELD DATA COLLECTION METHOD ………………. 3

3.1) MANUAL AND DISTANCE MEASURING INSTRUMENT TEST VEHICLE …………. 3

3.2) GPS TECHNOLGY ………………………………………………………………………. 3

4. DATA COLLECTION ……………………………………………………… 5

4.1) VEHICLE MILE TRAVEL METHOD …………………………………………………… 5

4.2) PROBE VEHICLE METHOD ……………………………………………………………… 5

4.3) TRAVEL ROUTE SELECTION AND ROUTE DATA PREPARATION …………… 5

4.4) GPS DATA COLLECTION DESIGN ………………………………………………….. 6

4.5) AUTOMATED DATA COLLECTION ……………………………………………… 8

4.6) ACCURACY AND VALIDITY OF FIELD GPS DATA ………………………………… 12

5. ADVANTAGES AND DISADVANTAGES OF GPS DATA COLLECTION 13

6. POST-PROCESSING SYSTEM DESIGN ………………………………. 14

6.1) FIELD DATA CONVERTION AND VALIDATION ……………………………… 15 6.2) LINK TOPOLOGY ……………………………………………….. 16 6.3) DATA ANALIYSIS AND REPORT MODULE ……………………….. 18

7. CONCLUSION …………………………………………………………………. 20

REFERENCE …………………………………………………………… 21



List of figures and Tables: - P.NO

Fig-1:- GPS Equipment. ………………………………………………………… 4

Fig-2:- Collection of Information from the Selected Route. ………………………… 8

Fig.3:- Automated GPS Data Collection. …………………………………………… 10

Fig-4:- Data Processing Flow Chat. ………………………………………………….. 14

Fig-5:-A GPS run was overlaid on the street base map for validity checking. ……….. 16

Fig-6:- PM travel time from airport. …………………………………………………. 19

Table-1:- Available data: GPS data and additionally collected information. ……………. 11



1) INTRODUCTION:-

Geographic Information Systems (GIS) are useful analysis and

presentation tools in the field of transportation planning, engineering, and management. When

combined with the capabilities of Global Positioning System (GPS) as a data collection tool and

the power of a relational data base system. GIS yield new solutions to area wide travel speed and

delay studies.

Travel speed is a direct measure of road network performance; low speeds

are an indication of congestion, delay, increased fuel use, and higher pollution emissions.

Decreasing auto travel speeds any signal a need for increased road or transit capacity or for

greater attention to travel demand management. Travel speed surveys are an important

component of Congestion Management Systems. Quality speed data results in better travel

demand and air qualify modeling.

Travel time-based measure, such as mean travel time, space mean speed,

and delay, is easy to understand by both the professional transportation community (transportation

engineers, planners, and administrators) and the traveling public (commuters, business persons,

and consumers). Travel time-based measure is also applicable across modes and is a common

measure of effectiveness for all modes. These advantages make travel time-based measure

extremely powerful, versatile, and desirable and an increasing number of transportation

agencies are switching to travel time measure to monitor traffic condition (Quiroga, 2000).

The use of Global Positioning System (GPS) technology to

compare traditional household survey methods’ results with passively recorded travel activity.

Vehicle Miles of Travel (VMT, where one mile is equivalent to 1.6 km) and Probe Vehicle

Method (PVs) are widely used as an indicator of the total level of travel in a region. It is also

an often-used output of travel demand models.



2) BACKGROUND

Geographic Information Systems (GIS)/data base systems

approach to process the field data collected with Global Positioning system (GPS) units and

portable computers after the brief discussion of travel route selection and preparation and a brief

comparison of the several different travel time survey techniques using the test vehicle method.

The Three system modules are described: the Field Data Conversion and Validation module uses

the data base approach to combine the GPS data (position and speed)and event data into a data

base format and uses ARC/INFO to check GPS run validity; the Link Topology definition

module extract topological information of the selected travel routes by data base programming

and ARC/INFO’s Dynamic segmentation: the Data Analysis and Reporting module structures

the continuous GPS data on link-by-link basis and calculates travel time, delay ,and speed

information for each link. This module also feeds the results to a SAS program for statistical

analysis and to ARC/INFO for generating graphics report.

Traffic congestion reduces utilization of the transportation infrastructure

and increases travel time, air pollution and fuel consumption, and thus is becoming a

common world-wide problem. Due to the constraint of the physical space and the

environmental impact, Intelligent Transportation System (ITS) is considered as an

alternative approach to reduce congestion rather than building new roads (Pan et al., 2007).

Advanced Traveler Information Systems (ATIS) is one important components of IT’S and the

data collection method used in an ATIS is critical for the reliability of the ATIS.

Traditionally, travel time measure is collected “indirectly”. That

is, traffic data such as volume, occupancy, and spot speed were obtained directly, and

travel time is calculated using a function of these parameters. However, the general relation

among these parameters has been explored widely and the specific coefficients in the function

are most likely site specific. Further, this general relation might not stand during



saturated/oversaturated flow condition (Chen and Chien, 2001).

3) TRAVEL SPEED FIELD DATA COLLECTION METHOD:-

Area wide travel speed studies consist of collecting, processing, and presenting automobile travel

speed and delay information for a street and highway network. There are many methods

available for producing travel time surveys. The test vehicle method in its various forms is used

most of often when a comprehensive evaluation of travel time in a transportation network is

needed.

3.1) MANUAL AND DISTANCE MEASURING INSTRUMENTS TEST VEHICLE METHOD:

The test vehicle method requires two data collectors, one for driving the

vehicle, the other for recording time and events using a stop watch and tally sheet . This result in

labor-intensive data collection and data reduction processes. The invention of Distance

Measuring Instruments (DMI) offered an automated way to collect and process travel time

information using the test vehicle method. The DMI mounted on the test vehicle automatically

logs travel distance and time, and the driver codes events that occur during the survey process.

However, there are two major disadvantages of using DMI. First, it gives no spatial information

about the route the vehicle travels on. This makes it difficult to have travel data mapped into GIS

coverage or to check whether the driver followed the designated travel time is needed on a link-

by-link basis, the driver may need to keep track of the time the test vehicle passes each major

intersection along the route, in addition to recording other events. The more human interface

required, the greater the likelihood of errors.

3.2) GPS Technology

GPS is a positioning and navigational system. This space-age-

technology has found a variety of applications in natural resource management, urban



development and analysis, agriculture, and social sciences. A GPS consists of three segments:

SPACE, CONTROL, and the USER. The space segment consist of 24 Navistar satellite that

broad-cast a signal providing information on their orbital path. The control segment is the U.S.

Department of Defense, which monitors the movement of the satellites and transmits data to each

satellite to ensure that they are broadcasting accurate information on their orbital path to earth.

The user segment is a receiver, which receives the signal and uses information contained in the

signal to calculate latitude, longitude, and altitude.

Data collected using a test vehicle equipped with a GPS unit gives both temporal

and spatial information about the traveling vehicle. Thus naturally lending it to GIS-based post

processing. The data collection method makes validity checking of the travel runs possible by

overlaying GPS runs on the GIS base map, in addition to allowing the driver to concentrate on

driving and recording of other incidents. GPS was used in test vehicles to collect travel sped data

for the Maricopa Association of Government (MAG) travel speed study.GPS receiver shown in

fig-1

Fig-1 GPS Equipment



4) DATA COLLECTION

4.1) Vehicle Mile Travel Method

VMT and travel time are related measures of urban mobility.

Congestion and transportation network characteristics are important factors in the relationship

between VMT and travel time. It is not clear how measured and modeled estimates of VMT and

travel time compare. This is followed by a description of the data collection area, processing

methods and GPS study results. This is followed by a description of the data collection area,

processing methods and GPS study results.

4.2) Probe Vehicle Method

By probe vehicle (PV) technique, point-to-point travel time can be measured directly and

thus there is a growing attention to PV technique with the progressive implementation of

Advanced Traveler Information Systems (ATIS). PVs can be instrumented with different types

of electronic equipment to locate vehicle, including Signpost-Based Automatic Vehicle Location, AVI

tag (Automatic Vehicle Identification), Ground-Based Radio Navigation, Cellular Phone

Tracking and Global Positioning System (GPS).

4.3) Travel route selection and route data preparation

The selection of routes for the travel speed survey was based on a

procedure agreed on by the Maricopa Association of Governments Transportation Planning

Office (MAGTPO) and Lee Engineering. Routes selection began with 14 significant routes

identified by MAGTPO for inclusion in the 1993 travel speed study. The remaining routes were

routes were randomly selected in such a manner to balance the number of roadway miles for

each roadway functional classification of each area type. This was accomplished by developing a



data entering program that allowed a route to be entered in to a data base table in a link-by-link

fashion with MAG functional class. Links on a route were identified using the reference street

scheme, in the form of on-street, from-street and to-street. A data base program was developed

that output a matrix of roadway miles for each functional classification for each area type. Using

this matrix, routes were selected in an iterative manner to obtain a balance of roadway miles

between each cross-classification in the matrix. The route selected includes over 1228 km (800

mi) of arterial road ways and over 161 km (100 mi) of freeway and high occupancy vehicle

(HOV) lanes. A total of 61 routes were selected. Each link on the selected routes was then

assigned unique link identification (ID). Other attributes such as the MAG area type, MAG

functional class, speed limit, number of lanes, and average daily traffic (ADV), were collected

and keyed into the table. There are total of 1790 links for the 61 routes selected, each

representing a road way segment for a given travel time.

4.4) GPS DATA COLLECTION DESIGN

Generally , all studies have in common that the GPS

tracking is only one part of the overall survey structure. GPS monitored and therefore passively

collected travel behavior data needs to be framed by socio-demographic attributes of the

travelers but especially by further information on trip purposes and the size of the company.

Some methodological approaches to detect trip purposes – based on recent studies. Future GPS

applications will be possibly combined with more interactive techniques (such as the usage

of PDAs to state trip purposes and car occupancies) to obtain a broader picture of the

observed trips or travel patterns (Doherty, Nöel, Lee Gosselin, Sirois, Ueno and Theberge,

1999). The level of user interaction is believed to be an important issue for the development

of future survey design incorporating GPS data collection elements.



USING TEST VEHICLE

Travel speed data on the selected routes were collected for

representative weekday conditions (Monday through Friday) for three time periods; Morning

peak (7:00 AM to 9:00 AM), off peak (10:00 AM to 2:00 PM), and evening peak (4:00 PM to

6:00 PM). Travel speed data were collected for all 61 routes during the evening peak. In

addition, 18 routes had morning peak data collected, and 19 routes had off-peak data collected.

The data collection vehicles or test vehicles were equipped with GPS

units that were set to record the data, time, vehicle position, and velocity of the vehicle on a 2-

second sampling interval. Each vehicle also was equipped with a portable computer that was

used for collecting other data such as identifying the time and type of delays encountered. The

data collectors (drivers) were instructed to start each route at a particular location and time. The

data collectors were also given a starting direction. Travel speed data for a route were collected

for a minimum of three runs for each period. To avoid bias in the data, for any given route during

a period, the starting points and the direction of the travel were varied. The data collectors were

instructed to record any event that caused a stop delay other than general intersection delay on a

portable computer. A stop delay is defined as the time during which the vehicle is traveling at a

speed less than or equal to 8 km/hr (5mph). Such events included traffic School zones; begin/end

construction zones, pedestrians, emergency vehicles, and trains. Careful consideration was given

to categorizing the type of delay so that remembering the classifications and corresponding keys

would not be difficult. The GPS data collection is collected from the selected route section as

shown in the fig-2.



Fig.2:- Collection of Information from the Selected Route

4.5) AUTOMATED DATA COLLECTION

Collecting long-term mobility data on the person and household

level remains a challenge for travel behavior research. Despite the Mobil- drive success in

data collection, it is questionable if for bigger samples the same extent of intensive support

of the respondents could be guaranteed. The Potential drawbacks of travel surveys based

on self-administered paper based survey designs are well known, and are especially worth

considering for the observation of long-term mobility patterns. These include among others

The limited pool of respondents for long-term travel behavior studies

Item and unit non-response

reporting errors / inaccuracy (especially times/durations and distances)

Fatigue effects in longitudinal surveys

Besides, most travel survey data lack information for the route choice decisions of

motorized individual transport which is a substantial pillar of travel demand modeling.



This has raised the interest in longitudinal data bases – eventually

covering even longer periods of time than in Mob drive – without the high expenses for data-

collection, though. Possible approaches to meet this requirement are

a) To reduce the respondent’s burden in ordinary paper based instruments by frequent activities

elements. Or

b) The use of Computer Assisted Data Collection (CADAC) methods which has been

promoted extensively in the last years.

GPS provides specially coded satellite signals that can be processed in a GPS

receiver, enabling the receiver to compute location, speed and time. Trip data generated by

GPS is getting especially appealing for travel analysis if it is connected with GIS applications

which offer digital mapping (e.g. including detailed land use information). The

combination of the two methodologies promises to accurately track movements of

vehicles or individuals which may supplement or even substitute ordinary travel diary

survey designs. The technique offers data accuracy and comprehensiveness which would never

be reached by ordinary paper surveys, especially in the detection of micro space-time details

(short trips, speeds etc.), obtaining route choice behavior information and the extension of

the observation period (Lee-Gosselin, 2002). Normally, the positioning data is accurate to

only few meters

Figure 3 shows an example of a technical implementation of

automated GPS data collection. Similar settings have been applied elsewhere. Here, the

mobile data collection system consisted of a GPS receiver with RDS/FM correction, a data

storage device / micro-computer (called GEOMATE) and mobile power supply. The

equipment used in this study was portable, and had the total size of a video-camera, whereas in

several other studies the instruments were installed permanently in cars. For each trip

(irrespective of made by car, foot or public transport), the respondents switched on the

system independently which started data transmission to the computer(GEOMATE) in intervals of

4 to 10 seconds. After data collection, the data was transferred from GEOMATE to a

conventional PC for processing.



Figure.3:- Automated GPS Data Collection

Additional useful data sources could be explored to support the

identification of further attributes such as trip purposes and route choice. Table 1 gives an

overview over the data available.



Table. 1: Available data: GPS data and additionally collected information

Category DetailsTrips (GPS)(Source: Rätt Fart project team)

Vehicle number, trip number, start time of trip, end time of trip, totalength of trip, total duration of trip, total duration of trip withimonitoring area

Logs

(Source: Rätt Fart project team)

Vehicle number, trip number, sequential number of log, log date and time, used road network link at time of logging,direction of travelling on link , exact position on vehicle (given as sector of the used link), speed at time of logging, speed limitat position of log, acceleration at time of log

Vehicle data

(Source: Rätt Fart project team)

Vehicle number, type of vehicle, traffic grouptype of owner, type of ISA device, ISA device activation date

GIS Road network

(Source: City of Borlänge / Swedish National Road Administration)

Link number, length of link, permanent speed limits on link(for both directions), temporary speed limit(s) on link and details, log intensity (every second/every tenth second), type of warning at speed limit exceeding, permissible turnings, direction of flows

Other data

(Different sources)

Selected socio-demographic characteristics of the test drivers

(Type of driver (actual test driver or additional driver of the car), sexage, home address, kilometrage of thelast year, number of cars in household, main obligatory(work or education) and leisure activities: Type, time of day, weekly frequency and locality, main shopping locations, share of non-catravel (public transport and bicycle), access to bicycle, most used mode of gettingto: Work/school, groceries, leisure activity locations, ownership of season ticket / public transport discount card, places the test drivers would never go by car)

Digital land use data in GIS; scale of base maps: 1:1000: Selection

of land use layers, including residential andcommercial areas, leisure and cultural sites, nature andagricultural areas

Swedish national travel survey data 1994-2001 (RIKS RVU& RES)



4.6) ACCURACY AND VALIDITY OF FIELD GPS DATA

The positional accuracy that can be achieved with GPS

ranges from 100m to millimeters, depending on the type of receiver used and the data collected

are differentially corrected. To obtain the higher level of accuracy for any receiver requires

differential correction. The process of placing a receiver on a known location, called a base

station, and using the collected satellite data to adjust GPS position to compute by other receivers

at unknown location during the same period. The differential correction can be done in real time,

if the receiver is equipped for real-time correction and a correction signal being received, or in a

postprocessor fashion. Post-differential correction was used for the travel speed to improve the

accuracy from 100 m to 2 to 5 m. Base station data for post-differential processing was provided

by Maricopa Country.

The majority of the travel time data collection effort was

completed in October and November of 1993. Additional runs were made for those routes

requiring more data runs to meet the designated travel time tolerance. By April of 1994, a total of

416 runs had been collected, of which 392 had at least partial useful GPS data. The run without

useful GPS data were mainly attributed to drivers’ failure the set up the GPS unit correctly on

wire due to unavailability of the base station data for correction. Total loss of data could be

reduced or eliminated through better training of the data-collecting personnel and the use of real-

time differential GPS. Partial GPS data loss occurrence because portion of the data were missing

or not correctable against the base station because of the unfavorable satellite configurations.

Partial GPS data loss also occurred because of obstruction of GPS signals from tall buildings,

bridges, tunnels and so on. A link without complete GPS data in a run was combined with its

adjacent links from a longer link for travel speed data analysis whenever possible.

5) Advantages and Disadvantages of GPS Data Collection



The ADVANTAGES of data collection by GPS are

The reduction (or even elimination) of respondents’ burden

The availability of path choice information

The high level of spatial accuracy

The fact that data is generated in digital format which allows direct analysis.

The DISADVANTAGE of the data collection by GPS are

The possibility of sporadic or even systematic technical problems of transmission,

eventually leading to total loss of information (e.g. certain warm-up times before receiving

signals)

Costly post-processing of the GPS data, i.e. trip end, trip purpose and street address detection

Still relatively high equipment costs

Unlike most ordinary paper-pen surveys, no motives for travel are queried.

6) POST-PROCESSING SYSTEM DESIGN



The field data collection produced more than 40 megabytes of the

raw GPS data event data. Successful reduction and analysis data in such volume can only be

achieved through a careful designed data analysis produced that takes full advantage of the

integrated GIS, data base, and statistical analysis packages of different platforms to maximize

efficiency and minimize errors. Most of the data conversion and manipulation were performed

though programming in FoxPro on the PC platform with a PC SAS interface for statistical

analysis and an ARC/INFO interface for spatial analysis and graphics reporting. The integrated

data analysis system shown in FIG.4. There are three major components in the system, the

functions of which are discussed in the following sections.

FIG-4:- Data Processing Flow Chat



6.1) FIELD DATA CONVERSION AND VALIDATION

This module prepares the field-collected GPS data and portable

computer data for analysis. First, the field-collected GPS and portable computer data are

downloaded to the PC. The GPS data are then corrected using base station data to increase

accuracy. After correction the GPS data are exported to an ASCII files using NAD 27 and

standard coordinate systems, a system that the GIS base map uses. A set of data base programs

(ASC2BDF) merge the GPS ASCII data with field event data to create a table in the dBase

format. The events are put in a temporal order, and the coordinates of the events are determined

by interpolating in the newly created dBase file.

Validation of the GPS data is an important part of the module. The

ASC2DBF program ASCII GPS and event data files with coordinate information, which serves

as input files to an ARC/INFO Macro Language (AML) program. This AML program generates

two-point coverage, one for the GPS points and the other for the events points for a given run.

Once the coverage’s generated, a menu-driven AML program overlays the GPS points and event

points on the GIS street network, a process used for the checking the validity of the runs. Figure-

5 shows an ARC/INFO screen in which GPS run was overlaid or, the street network. If the trace

of a GPS run showed a different or incomplete path of the of the designated route for run, causes

for such abnormality, such driver negligence, bad GPS signal reception, or improper route

definition, were identified, and appropriate measures were taken. Expect for the part for GPS

validity checking, which requires human interaction and judgment, the process is designed for

batch processing with log files for error reporting.



Fig-5:-A GPS run was overlaid on the street base map for validity checking

6.2) LINK TOPOLOGY DEFINITION

The population of link travel time is usually defined by link travel

times of all vehicles traveling a link during a time interval. For a typical link, there are nine

distinct subpopulations associated with each combination of access and departure movements

(left turning, right turning and through) and these subpopulations have different travel time

characteristics. Further, the population also can be divided by intersection delay (some

vehicles will experience the intersection delay at downstream intersection of the link, while

others will depart without intersection delay) and the two subpopulations also have significant

different travel time. Therefore, the population of link travel time has large variance and it is

difficult to reliably estimate the population mean from the sample mean when sample size is

small.

There are several notations to define a link on a route. The

reference street mutation (in the form of on-street, from-street, and to-street) is easy for human

comprehension. However, computers prefer the segment-node notation (on-segment, fro-node to-

node, or on-segment from-XY to-XY) or the route-measure notation (on-route, from-measure,

and to-measure). The task of this program module is to convert links already defined in the

reference street from into the segment-node notation and to the route-measure notation.



Segment-node notation is needed to structure the GPS data. Which are continuous throughout a

run, on link-by-link basis using the coordinates of the control nodes of the links? The route-

measure notation is needed to map the link attributes on to the GIS coverage, in addition to

obtaining the length of each individual link.

Link topology was defined by extracting information from the

base map; therefore, an accurate base map is the key to the success of the process. The base map

used for the study was digitized from stereo aerial photography in 1991 at a scale of 1:24,000 by

the Maricopa Country Development of Transportation in cooperation with the Arizona

Department of Transportation. The relative accuracy of the map is 7.5 m (25 ft), which was

deemed adequate for the study.

The first step in the module is to build a route feature that contains

the travel route and link for the study. Special attention was given to ensure that each route was

continuous and without any unwanted offshoot. Programs were written to detect discontinuity

between adjacent arcs on each route in the base map so such gaps could be corrected. Once the

route feature was built, various attribute tables relating to the study routes and sections/arcs were

exported to PC, where the data base programs convert the already defined reference street link

definition into segment-node definitions by making to these reference tables. The resulting link

control nodes, in X, Y coordinate from, were exported to ARC/INFO to check their validity.

Once the control nodes passed the validation, their measures or linear positions on the roués were

generated using Dynamic Segmentation commands in ARC/INFO. These were then used to

calculate the from-measure and to-measure and the length of the links. The LIKTOPO table

resulting from the procedure contains the tree different notation of route-link definition.



6.3) DATA ANALYSIS AND REPORT MODULE

These are two major procedures involved in the Data Analysis and

Reporting module. The ADDLINK procedure structures the continuous GPS data on a link-by-

link basis. It inserts the control node in the GPS data and labels each with a link ID. The time the

vehicle passes through the control node of a links interpolated based on the time for the two

adjacent GPS points and the distance of the control node to the two adjacent GPS points. This

procedure generates the second version of RETURN.DBF.

The ANALINK procedure then calculate travel time, running time, and

delays (including different types of stop delay) on each link for each individual run and generates

a report of mean travel time, mean running time, mean delay, and type of delays for each travel

link. Stop delay is defined as the time that the vehicle is traveling at a speed less than or equal to

8 km/hr (5 mph). The program first scans for delay groups on a given link. A delay group is the

time span during which the vehicle is traveling at a speed less than or equal to 8 km/hr (5 mph).

it was arbitrarily decided that if the time gap of adjacent delay groups is within 10 sec, these

delay groups are combined as one group. The ANALINK procedure then tries to associate a

delay group with any event recorded within the time span of 20 sec before the delay group or 5

sec after the delay group. The 20-secand and 5-sec time spans for event association were also

arbitrarily decided.

Embedded in these procedures is Structured Query Language (SQL)

commands that produce output files for interface with SAS and ARC/INFO. After checking the

variability of the average travel speed and average running speed, the SAS analysis procedures

calculate the mean travel speed, mean recurring and nonrecurring stop delay, and mean free flow

speed for different combinations of area type, number of lanes, and functional classification

during a given period. The SAS procedures were also used to calculate miles of roads surveyed

by functional class. The resulting data were then used to estimate travel speeds for the link that

were not surveyed but shared the same combination of the above mentioned attributes. The

tables containing such link-based information as, mean travel sped, mean running speed, mean

recurring, and nonrecurring stop delay are then passed on to ARC/INFO to conduct network

analysis and to generate graphics reports. The preliminary network analysis produced the travel



time contour maps and the minimum paths to and from major activity centers during all three

periods. A left-turn delay of 51 sec and a right-turn delay of 37 sec resulting from the

intersection delay study conducted as part of the project were used as turn impedance for the

network analysis. Figure 6 shows travel time contour from Sky Harbor International Airport

during typical weekday pm peak hours.

Figure-6:- PM travel time from airport



8). CONCLUSION

GPS technology provides a new way to collecting network travel

speed data for processing in the context of GIS. GPS has its advantages in areas of GIS

integration, data collection effort, and data validation. GPS units are expensive to purchase or to

rent. The post processing can be very complex and requires a good electronic base map. If real

time differential GPS is not available, post-differential correction adds another burden to the

post-processing. A number of factors can affect good GPS reception, many of which are

difficult to prevent or even to predict. The success of an integrated GPS/GIS system in network

travel time study relies on good GPS reception, accuracy base map, a well designed post-

processing system plus a good planning and quality control for field data collection.

Th e description of the data processing steps has already

shown that the usage of the GPS data set for travel time analysis is connected with a

range of inherent data difficulties Summarizing the challenges, it can be noted that

There exists only limited digital land-use information and data on the socio-demographics of

the test drivers is restricted

The selective inaccuracy of the available GPS data requires additional data processing work

The supplemental follow-up answers of the test drivers in order to clear ambiguous

findings and mismatches.

The perspective for the future data collection and availability may be

described by less burden for the respondents, higher accuracy of several data elements

and the addition of new travel data attributes such as route choice and speed behavior.



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http://gtresearchnews.gatech.edu/reshor/rhwin02/speed.html,Georgia



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Gps Design for Network Travel Time Study