This paper presents a methodology using GIS to assess the reliability of links in a transportation network based on congestion components in Charlotte, North Carolina. It emphasizes the impact of both recurring and nonrecurring congestion on travel reliability and analyzes data from 1,053.2 miles of road. Key findings indicate that 75% of congestion during peak periods is due to traffic volume, while crashes also significantly affect travel delays, particularly during off-peak times.

1. GUJARAT TECHNOLOGICAL
UNIVERSITY
BIRLA VISHWAKARMA
MAHAVIDHYALAYA
PAPER REVIEWED
1. Assessment of Link Reliability as Function of Congestion
components.
KUSHALKUMAR G PATEL
140080713009

2. Assessment of Link Reliability as a Function of
Congestion Components
This paper is written by Srinivas S. Pulugurtha,
Associate Professor, Civil and Environmental Engineering
dept., Univ. of North Carolina at Charlotte. & Nagaswetha
Pasupuleti Graduate Student, Civil and Environmental
Engineering, Univ. of North Carolina at Charlotte.
This paper is part of the Journal of Transportation
Engineering, Vol. 136, No. 10, October 1, 2010.

3. INTRODUCTION
Increasing travel congestion has been a growing concern to
engineers and planners of the states’ DOT, responsible local
agencies, the general public and elected officials due to its
impact on mobility and economy.
Congestion, in general, reduces the capacity of the roadway
and makes the traffic condition unstable.
As congestion increases, reliability of travel becomes an
increasingly important attribute for users of transportation
networks

4. ABSTRACT
The focus of this paper is to develop and illustrate the
working of a geographic information systems GIS based
methodology to estimate congestion and assess reliability of
links on a road network considering both recurring and
nonrecurring congestion components by time period of the
day.
The estimated reliability can be used to identify optimal travel
paths and make better routing decisions
For this research data collected was for the city of Charlotte
in Mecklenburg County, North Carolina which are used to
demonstrate the methodology.

5. LITERATURE REVIEW
This paper is an extension of earlier efforts by the writers
Pulugurtha and Pasupuleti in 2008, to address the limitations
of past research by defining and estimating reliability of each
link in the transportation network as a function of travel time
variation and travel delay index due to crashes.
Past research does not consider the effect of factors such as
crashes related to nonrecurring congestion component
along with recurring congestion component in estimating
congestion and/or reliability for all the links on major roads in
the transportation network.

6. METHODOLOGY
The purposed methodology used to determine the travel time
and travel delay due to crash recurring and non- recurring
congestion include the following steps:
◦ Data collection.
◦ Estimate travel time and its variation under recurring congestion
conditions.
◦ Estimate travel delay due to crashes under nonrecurring
congestion conditions.
◦ Integrate congestion components to compute congestion
score and reliability.

7. METHODOLOGY- Step: 1 Data collection
Four different time periods were considered for research
purpose in this paper:
◦ AM Peak period - 6.30am to 9.30am
◦ MIDDAY Off-peak period - 9.30am to 3.30am
◦ PM Peak period - 3.30pm to 6.30pm
◦ NIGHT Off-peak period – 6.30pm to 6.30am
For calculating recurring congestions (RC) travel time and its
variation data for each link requires link capacity, travel
speeds, and traffic volumes for each time period.
While for Non-recurring congestions (NRC) past crash data,
temporary changes in networks and the delays related to it
will be needed such historical data information will be
available from local agencies.
The Data was collected for total of 1053.2 miles in
Charlotte city

8. METHODOLOGY- Step: 2 Estimate travel time
and its variation under recurring congestion
conditions.
For calculating travel time equation by fundamental bureau of
public roads was used.
Once the travel time is known RC will also be determined
from it by using the below equation.

9. METHODOLOGY- Step: 3 Estimate travel delay
due to crashes under nonrecurring congestion
conditions.
NRC occurred in a particular link can be calculated by using
the following equation:
TYPE OF CRASH CRASH SEVERITY VALUE
A FATAL 8
B SEVERE 6
C LESS SEVERE 3
O(PDO) PROPERTY DAMAGE
ONLY
1

10. METHODOLOGY- Step: 4 To compute
congestion score and reliability.
Congestion score can be calculated by using the following
series of equations:

11. METHODOLOGY- Step: 4 To compute
congestion score and reliability.
In the above equations we need to note that Maximum
possible congestion for RC and NRC for any link is 100
And the term WR & WNR are the weight(Importance) for the
link has to be logically decided by looking over the link
capacity and volume.

12. RESULT AND ANALYSIS
The data collected from the study areas in the city of
charlottes was of 1053.2 miles and the recurring
congestions, non recurring congestion and congestions
score were found out and plotted on map using GIS module.
The least the congestion score of the link the more the
reliable will be the link.

13. TRAVEL TIME OF EACH LINK
Travel time value per unit distance mile is calculated for each
link on selected major roads in the city of Charlotte, N.C.
The links are classified into the following five groups based
on their travel time per mile:
0.70 (speed=85 mi / h) to 1.00 min (speed=60 mi / h)
1.00 (speed=60 mi / h) to 1.33 min (speed=45 mi / h)
1.33 (speed=45 mi / h) to 1.71 min (speed=35 mi / h)
1.71 (speed=35 mi / h) to 2.40 min (speed=25 mi / h) and
2.40 min (speed=25 mi / h)

14. Group(Min) a.m. Midday p.m. Night
Total Travel Time
0.70 and =
1.00
95.7 121.3 91.4 123.5
1.00 and =
1.33
348.4 444.1 298.8 608.7
1.33 and =
1.71
304 273 285.3 228.1
1.71 and =
2.40
195.6 150.2 226.8 68.7
2.4 109.4 64.5 151 24.1
Total
1,053.2
0
1,053.2
0
1,053.2
0
1,053.2
0

15. Group(Min) a.m. Midday p.m. Night
Variation in travel time
= 0 4.3 4.1 3.2
1,053.2
0
5 and = 15 456.9 680.2 386.3 0
15 and = 25 260.9 237.2 210.9 0
25 and = 50 218.4 96.8 236.2 0
50 112.7 34.9 216.6 0
Total
1,053.2
0
1,053.2
0
1,053.2
0
1,053.2
0

16. TRAVEL DELAY INDEX DUE TO CRASH PER
MILE
Crash data were used to compute the travel delay index
due to crashes per mile. A total of 18,782crashes (47
fatal crashes, 6,202 injury crashes) and remaining are
PDO crashes were reported during 2006 on the
considered 1,053.2 center-lane miles.
The number of crashes per mile per year is
approximately 17.8
The links in the road network were classified based on
the travel delay index due to crashes per mile during a
time period on a day into the following five groups:
◦ 0.000 no crashes
◦ 0.000 to 0.003 one crash during a day;
◦ 0.003 to 0.008 two or three crashes during a day
◦ 0.008 to 0.016 four to six crashes during day
◦ 0.016 more than six crashes during a day

17. Group a.m. Midday p.m. Night
Travel delay index due to crashes per mile
0 571.5 480.6 499.1 487.3
0.000 and =
0.003
42.7 52.7 53.4 42.4
0.003 and =
0.008
139.6 133.3 145.4 148.7
0.008 and =
0.016
131.5 116.6 117 140.7
0.016 168 270 238.2 234.1
Total 1,053.20 1,053.20 1,053.20 1,053.20

18. Group a.m. Midday p.m. Night
Congestion score
0 4.1 4.1 3.2 487.3
0 and = 5 370.5 561.9 309.5 548.8
5 and = 15 263.9 293.6 202.8 16.2
15 and = 25 174.5 106.3 172.5 0.9
25 240.2 87.2 365.2 0
Total 1,053.20 1,053.20 1,053.20 1,053.20

19. CONCLUSION
From the results obtained, it can be concluded that 75% of
total congestion during a.m. and p.m. peak periods is due
to traffic volume on roads.
On the other hand, crashes and their severity contribute as
much as traffic volume to travel delays during off-peak
periods. On an average, reliability of links is lowest during
p.m. peak period and highest during night hours.