387.Can SAS Predict Railway Signal Failures Before Disaster Strikes?

Can SAS Predict Railway Signal Failures Before Disaster Strikes?

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —
DATA STEP | PROC SQL |  PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | APPEND | PROC DATASETS DELETE | DATA FUNCTIONS

INTRODUCTION

Railway signals are one of the most critical safety systems in any railway network.
Every green or red signal decides whether a train moves or stops. A single wrong signal can lead to massive delays, accidents, or even loss of lives.

But in real life, railway departments manage thousands of signal systems across multiple routes. It is impossible to manually monitor:

·       Which signals fail frequently

·       Which routes are risky

·       Which systems cost too much but fail too often

·       And which maintenance records look suspicious

So the real question becomes:

Can data analytics help railway authorities predict failures, improve safety, and even detect fraud?

This project answers that question using SAS analytics.
We create a realistic railway signal dataset and apply:

·       Statistical analysis

·       SQL reporting

·       Visual dashboards

·       Automation macros

·       Fraud detection logic

BUSINESS CONTEXT

Railway signaling systems are the backbone of railway safety.
A single signal failure can cause:

• Train delays
• Collisions
• Passenger safety risk
• Huge financial losses

Railway authorities maintain thousands of signal systems across routes.
They track:

• How often signals fail
• How long repairs take
• Which routes are most risky
• Which systems show suspicious patterns (possible fraud / negligence)

This project simulates how a Railway Operations Analytics Team uses SAS to:

1. Monitor system reliability
2. Identify high-risk signals
3. Detect abnormal maintenance behavior
4. Generate safety dashboards

TABLE OF CONTENTS

1. Dataset Creation
2. Data Engineering (dates, functions)
3. PROC SQL Analysis
4. PROC MEANS
5. PROC UNIVARIATE
6. PROC FREQ
7. PROC CORR
8. PROC SGPLOT
9. Macros
10. Fraud Detection Logic
11. SET / MERGE / APPEND / TRANSPOSE
12. Character & Numeric Functions
13. PROC DATASETS DELETE

1. DATASET CREATION

data railway_signals;

    input Signal_ID $ Route:$15. Failure_Count Downtime_Minutes 

          Safety_Impact $ Maintenance_Cost Reliability_Index 

          Install_Date : date9. Last_Service_Date : date9.;

    format Install_Date Last_Service_Date date9.;

datalines;

SIG001 SouthLine 5 180 High 12000 82 01JAN2020 10DEC2023

SIG002 EastLine 2 60 Low 4000 95 15MAR2019 05NOV2023

SIG003 NorthLine 9 300 Critical 20000 65 10FEB2018 20OCT2023

SIG004 WestLine 1 20 Low 2000 98 05MAY2021 01JAN2024

SIG005 SouthLine 7 240 High 15000 70 12JUL2017 15SEP2023

SIG006 EastLine 4 120 Medium 8000 85 09AUG2019 30NOV2023

SIG007 NorthLine 10 360 Critical 25000 60 01JAN2016 05OCT2023

SIG008 WestLine 3 90 Medium 6000 88 18DEC2020 12DEC2023

SIG009 CentralLine 6 210 High 13000 75 02FEB2018 22NOV2023

SIG010 CentralLine 1 30 Low 3000 97 15JUN2022 05JAN2024

SIG011 MetroLine 8 260 High 17000 68 20MAR2017 18SEP2023

SIG012 MetroLine 2 70 Medium 5000 90 10JAN2021 02DEC2023

SIG013 FreightLine 11 400 Critical 30000 55 01JAN2015 15OCT2023

SIG014 FreightLine 3 100 Medium 7000 86 05MAY2020 01DEC2023

SIG015 ExpressLine 4 110 Medium 9000 83 12APR2019 28NOV2023

;

run;

proc print data=railway_signals;

run;

OUTPUT:

Obs	Signal_ID	Route	Failure_Count	Downtime_Minutes	Safety_Impact	Maintenance_Cost	Reliability_Index	Install_Date	Last_Service_Date
1	SIG001	SouthLine	5	180	High	12000	82	01JAN2020	10DEC2023
2	SIG002	EastLine	2	60	Low	4000	95	15MAR2019	05NOV2023
3	SIG003	NorthLine	9	300	Critical	20000	65	10FEB2018	20OCT2023
4	SIG004	WestLine	1	20	Low	2000	98	05MAY2021	01JAN2024
5	SIG005	SouthLine	7	240	High	15000	70	12JUL2017	15SEP2023
6	SIG006	EastLine	4	120	Medium	8000	85	09AUG2019	30NOV2023
7	SIG007	NorthLine	10	360	Critical	25000	60	01JAN2016	05OCT2023
8	SIG008	WestLine	3	90	Medium	6000	88	18DEC2020	12DEC2023
9	SIG009	CentralLine	6	210	High	13000	75	02FEB2018	22NOV2023
10	SIG010	CentralLine	1	30	Low	3000	97	15JUN2022	05JAN2024
11	SIG011	MetroLine	8	260	High	17000	68	20MAR2017	18SEP2023
12	SIG012	MetroLine	2	70	Medium	5000	90	10JAN2021	02DEC2023
13	SIG013	FreightLine	11	400	Critical	30000	55	01JAN2015	15OCT2023
14	SIG014	FreightLine	3	100	Medium	7000	86	05MAY2020	01DEC2023
15	SIG015	ExpressLine	4	110	Medium	9000	83	12APR2019	28NOV2023

Used for raw data ingestion from operational systems.

2. DATE ENGINEERING (MDY, INTCK, INTNX)

data railway_dates;

    set railway_signals;

    Years_In_Service = intck('year', Install_Date, today());

    Next_Service_Date = intnx('month', Last_Service_Date, 6);

run;

proc print data=railway_dates;

 var Signal_ID Route Install_Date Last_Service_Date Years_In_Service Next_Service_Date;

run;

OUTPUT:

Obs	Signal_ID	Route	Install_Date	Last_Service_Date	Years_In_Service	Next_Service_Date
1	SIG001	SouthLine	01JAN2020	10DEC2023	6	23528
2	SIG002	EastLine	15MAR2019	05NOV2023	7	23497
3	SIG003	NorthLine	10FEB2018	20OCT2023	8	23467
4	SIG004	WestLine	05MAY2021	01JAN2024	5	23558
5	SIG005	SouthLine	12JUL2017	15SEP2023	9	23436
6	SIG006	EastLine	09AUG2019	30NOV2023	7	23497
7	SIG007	NorthLine	01JAN2016	05OCT2023	10	23467
8	SIG008	WestLine	18DEC2020	12DEC2023	6	23528
9	SIG009	CentralLine	02FEB2018	22NOV2023	8	23497
10	SIG010	CentralLine	15JUN2022	05JAN2024	4	23558
11	SIG011	MetroLine	20MAR2017	18SEP2023	9	23436
12	SIG012	MetroLine	10JAN2021	02DEC2023	5	23528
13	SIG013	FreightLine	01JAN2015	15OCT2023	11	23467
14	SIG014	FreightLine	05MAY2020	01DEC2023	6	23528
15	SIG015	ExpressLine	12APR2019	28NOV2023	7	23497

Used in real projects to:

·       Calculate service life

·       Predict next maintenance

·       Build SLA reports

3. PROC SQL

proc sql;

    create table route_summary as

    select Route,

           count(Signal_ID) as Total_Signals,

           avg(Failure_Count) as Avg_Failures,

           sum(Maintenance_Cost) as Total_Cost

    from railway_dates

    group by Route;

quit;

proc print data=route_summary;

 var Route Total_Signals Avg_Failures Total_Cost;

run;

OUTPUT:

Obs	Route	Total_Signals	Avg_Failures	Total_Cost
1	CentralLine	2	3.5	16000
2	EastLine	2	3.0	12000
3	ExpressLine	1	4.0	9000
4	FreightLine	2	7.0	37000
5	MetroLine	2	5.0	22000
6	NorthLine	2	9.5	45000
7	SouthLine	2	6.0	27000
8	WestLine	2	2.0	8000

Used for:

·       Aggregations

·       Reporting dashboards

·       KPI summaries

4. PROC MEANS

proc means data=railway_dates mean min max;

    var Failure_Count Downtime_Minutes Maintenance_Cost Reliability_Index;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Failure_Count Downtime_Minutes Maintenance_Cost Reliability_Index	5.0666667 170.0000000 11733.33 79.8000000	1.0000000 20.0000000 2000.00 55.0000000	11.0000000 400.0000000 30000.00 98.0000000

Used by managers to:

·       Understand average failures

·       Identify worst-performing systems

5. PROC UNIVARIATE

proc univariate data=railway_dates;

    var Downtime_Minutes;

    histogram Downtime_Minutes;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Downtime_Minutes

Moments
N	15	Sum Weights	15
Mean	170	Sum Observations	2550
Std Deviation	120.178439	Variance	14442.8571
Skewness	0.60370171	Kurtosis	-0.7662038
Uncorrected SS	635700	Corrected SS	202200
Coeff Variation	70.6931993	Std Error Mean	31.0299395

Basic Statistical Measures
Location		Variability
Mean	170.0000	Std Deviation	120.17844
Median	120.0000	Variance	14443
Mode	.	Range	380.00000
		Interquartile Range	190.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	5.47858	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	400
99%	400
95%	400
90%	360
75% Q3	260
50% Median	120
25% Q1	70
10%	30
5%	20
1%	20
0% Min	20

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
20	4	240	5
30	10	260	11
60	2	300	3
70	12	360	7
90	8	400	13

The UNIVARIATE Procedure

Used by QA teams to:

·       Detect extreme downtime

·       Identify outliers

6. PROC FREQ

proc freq data=railway_dates;

    tables Safety_Impact Route;

run;

OUTPUT:

The FREQ Procedure

Safety_Impact	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Critical	3	20.00	3	20.00
High	4	26.67	7	46.67
Low	3	20.00	10	66.67
Medium	5	33.33	15	100.00

Route	Frequency	Percent	Cumulative Frequency	Cumulative Percent
CentralLine	2	13.33	2	13.33
EastLine	2	13.33	4	26.67
ExpressLine	1	6.67	5	33.33
FreightLine	2	13.33	7	46.67
MetroLine	2	13.33	9	60.00
NorthLine	2	13.33	11	73.33
SouthLine	2	13.33	13	86.67
WestLine	2	13.33	15	100.00

Used to:

·       Count safety risks

·       Compare routes

7. PROC CORR

proc corr data=railway_dates;

    var Failure_Count Downtime_Minutes Maintenance_Cost Reliability_Index;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Failure_Count Downtime_Minutes Maintenance_Cost Reliability_Index

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Failure_Count	15	5.06667	3.28344	76.00000	1.00000	11.00000
Downtime_Minutes	15	170.00000	120.17844	2550	20.00000	400.00000
Maintenance_Cost	15	11733	8328	176000	2000	30000
Reliability_Index	15	79.80000	13.61302	1197	55.00000	98.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Failure_Count	Downtime_Minutes	Maintenance_Cost	Reliability_Index
Failure_Count	1.00000	0.99559 <.0001	0.98551 <.0001	-0.99366 <.0001
Downtime_Minutes	0.99559 <.0001	1.00000	0.99204 <.0001	-0.99023 <.0001
Maintenance_Cost	0.98551 <.0001	0.99204 <.0001	1.00000	-0.98026 <.0001
Reliability_Index	-0.99366 <.0001	-0.99023 <.0001	-0.98026 <.0001	1.00000

Used to:

·       Check if more failures → lower reliability

·       Predict future risk

8. PROC SGPLOT

proc sgplot data=railway_dates;

    scatter x=Failure_Count y=Reliability_Index;

run;

OUTPUT:

Used for:

·       Executive dashboards

·       Visual inspection

9. UTILIZATION MACRO

%macro classify;

data utilization;

    set railway_dates;

    if Failure_Count > 7 then Utilization = "Overloaded";

    else if 4 < Failure_Count < 7 then Utilization = "Moderate";

    else Utilization = "Stable";

run;

proc print data=utilization;

run;

%mend;

%classify;

OUTPUT:

Obs	Signal_ID	Route	Failure_Count	Downtime_Minutes	Safety_Impact	Maintenance_Cost	Reliability_Index	Install_Date	Last_Service_Date	Years_In_Service	Next_Service_Date	Utilization
1	SIG001	SouthLine	5	180	High	12000	82	01JAN2020	10DEC2023	6	23528	Moderate
2	SIG002	EastLine	2	60	Low	4000	95	15MAR2019	05NOV2023	7	23497	Stable
3	SIG003	NorthLine	9	300	Critical	20000	65	10FEB2018	20OCT2023	8	23467	Overloaded
4	SIG004	WestLine	1	20	Low	2000	98	05MAY2021	01JAN2024	5	23558	Stable
5	SIG005	SouthLine	7	240	High	15000	70	12JUL2017	15SEP2023	9	23436	Stable
6	SIG006	EastLine	4	120	Medium	8000	85	09AUG2019	30NOV2023	7	23497	Stable
7	SIG007	NorthLine	10	360	Critical	25000	60	01JAN2016	05OCT2023	10	23467	Overloaded
8	SIG008	WestLine	3	90	Medium	6000	88	18DEC2020	12DEC2023	6	23528	Stable
9	SIG009	CentralLine	6	210	High	13000	75	02FEB2018	22NOV2023	8	23497	Moderate
10	SIG010	CentralLine	1	30	Low	3000	97	15JUN2022	05JAN2024	4	23558	Stable
11	SIG011	MetroLine	8	260	High	17000	68	20MAR2017	18SEP2023	9	23436	Overloaded
12	SIG012	MetroLine	2	70	Medium	5000	90	10JAN2021	02DEC2023	5	23528	Stable
13	SIG013	FreightLine	11	400	Critical	30000	55	01JAN2015	15OCT2023	11	23467	Overloaded
14	SIG014	FreightLine	3	100	Medium	7000	86	05MAY2020	01DEC2023	6	23528	Stable
15	SIG015	ExpressLine	4	110	Medium	9000	83	12APR2019	28NOV2023	7	23497	Stable

Used in industry for:

·       Reusable logic

·       Automation

·       Batch processing

10. FRAUD DETECTION MACRO

%macro fraud;

data fraud_flags;

    set utilization;

    if Maintenance_Cost > 20000 and Failure_Count < 3 then Fraud_Flag = "Yes";

    else Fraud_Flag = "No";

run;

proc print data=fraud_flags;

run;

%mend;

%fraud;

OUTPUT:

Obs	Signal_ID	Route	Failure_Count	Downtime_Minutes	Safety_Impact	Maintenance_Cost	Reliability_Index	Install_Date	Last_Service_Date	Years_In_Service	Next_Service_Date	Utilization	Fraud_Flag
1	SIG001	SouthLine	5	180	High	12000	82	01JAN2020	10DEC2023	6	23528	Moderate	No
2	SIG002	EastLine	2	60	Low	4000	95	15MAR2019	05NOV2023	7	23497	Stable	No
3	SIG003	NorthLine	9	300	Critical	20000	65	10FEB2018	20OCT2023	8	23467	Overloaded	No
4	SIG004	WestLine	1	20	Low	2000	98	05MAY2021	01JAN2024	5	23558	Stable	No
5	SIG005	SouthLine	7	240	High	15000	70	12JUL2017	15SEP2023	9	23436	Stable	No
6	SIG006	EastLine	4	120	Medium	8000	85	09AUG2019	30NOV2023	7	23497	Stable	No
7	SIG007	NorthLine	10	360	Critical	25000	60	01JAN2016	05OCT2023	10	23467	Overloaded	No
8	SIG008	WestLine	3	90	Medium	6000	88	18DEC2020	12DEC2023	6	23528	Stable	No
9	SIG009	CentralLine	6	210	High	13000	75	02FEB2018	22NOV2023	8	23497	Moderate	No
10	SIG010	CentralLine	1	30	Low	3000	97	15JUN2022	05JAN2024	4	23558	Stable	No
11	SIG011	MetroLine	8	260	High	17000	68	20MAR2017	18SEP2023	9	23436	Overloaded	No
12	SIG012	MetroLine	2	70	Medium	5000	90	10JAN2021	02DEC2023	5	23528	Stable	No
13	SIG013	FreightLine	11	400	Critical	30000	55	01JAN2015	15OCT2023	11	23467	Overloaded	No
14	SIG014	FreightLine	3	100	Medium	7000	86	05MAY2020	01DEC2023	6	23528	Stable	No
15	SIG015	ExpressLine	4	110	Medium	9000	83	12APR2019	28NOV2023	7	23497	Stable	No

Very common in:

·       Finance

·       Maintenance

·       Insurance analytics

Flags cases like:

Low failures but very high cost → possible fake billing.

11. SET / APPEND

data backup;

    set fraud_flags;

run;

proc print data=backup;

run;

OUTPUT:

Obs	Signal_ID	Route	Failure_Count	Downtime_Minutes	Safety_Impact	Maintenance_Cost	Reliability_Index	Install_Date	Last_Service_Date	Years_In_Service	Next_Service_Date	Utilization	Fraud_Flag
1	SIG001	SouthLine	5	180	High	12000	82	01JAN2020	10DEC2023	6	23528	Moderate	No
2	SIG002	EastLine	2	60	Low	4000	95	15MAR2019	05NOV2023	7	23497	Stable	No
3	SIG003	NorthLine	9	300	Critical	20000	65	10FEB2018	20OCT2023	8	23467	Overloaded	No
4	SIG004	WestLine	1	20	Low	2000	98	05MAY2021	01JAN2024	5	23558	Stable	No
5	SIG005	SouthLine	7	240	High	15000	70	12JUL2017	15SEP2023	9	23436	Stable	No
6	SIG006	EastLine	4	120	Medium	8000	85	09AUG2019	30NOV2023	7	23497	Stable	No
7	SIG007	NorthLine	10	360	Critical	25000	60	01JAN2016	05OCT2023	10	23467	Overloaded	No
8	SIG008	WestLine	3	90	Medium	6000	88	18DEC2020	12DEC2023	6	23528	Stable	No
9	SIG009	CentralLine	6	210	High	13000	75	02FEB2018	22NOV2023	8	23497	Moderate	No
10	SIG010	CentralLine	1	30	Low	3000	97	15JUN2022	05JAN2024	4	23558	Stable	No
11	SIG011	MetroLine	8	260	High	17000	68	20MAR2017	18SEP2023	9	23436	Overloaded	No
12	SIG012	MetroLine	2	70	Medium	5000	90	10JAN2021	02DEC2023	5	23528	Stable	No
13	SIG013	FreightLine	11	400	Critical	30000	55	01JAN2015	15OCT2023	11	23467	Overloaded	No
14	SIG014	FreightLine	3	100	Medium	7000	86	05MAY2020	01DEC2023	6	23528	Stable	No
15	SIG015	ExpressLine	4	110	Medium	9000	83	12APR2019	28NOV2023	7	23497	Stable	No

proc append base=railway_dates 

            data=backup force;

run;

proc print data=railway_dates;

run;

OUTPUT:

Obs	Signal_ID	Route	Failure_Count	Downtime_Minutes	Safety_Impact	Maintenance_Cost	Reliability_Index	Install_Date	Last_Service_Date	Years_In_Service	Next_Service_Date
1	SIG001	SouthLine	5	180	High	12000	82	01JAN2020	10DEC2023	6	23528
2	SIG002	EastLine	2	60	Low	4000	95	15MAR2019	05NOV2023	7	23497
3	SIG003	NorthLine	9	300	Critical	20000	65	10FEB2018	20OCT2023	8	23467
4	SIG004	WestLine	1	20	Low	2000	98	05MAY2021	01JAN2024	5	23558
5	SIG005	SouthLine	7	240	High	15000	70	12JUL2017	15SEP2023	9	23436
6	SIG006	EastLine	4	120	Medium	8000	85	09AUG2019	30NOV2023	7	23497
7	SIG007	NorthLine	10	360	Critical	25000	60	01JAN2016	05OCT2023	10	23467
8	SIG008	WestLine	3	90	Medium	6000	88	18DEC2020	12DEC2023	6	23528
9	SIG009	CentralLine	6	210	High	13000	75	02FEB2018	22NOV2023	8	23497
10	SIG010	CentralLine	1	30	Low	3000	97	15JUN2022	05JAN2024	4	23558
11	SIG011	MetroLine	8	260	High	17000	68	20MAR2017	18SEP2023	9	23436
12	SIG012	MetroLine	2	70	Medium	5000	90	10JAN2021	02DEC2023	5	23528
13	SIG013	FreightLine	11	400	Critical	30000	55	01JAN2015	15OCT2023	11	23467
14	SIG014	FreightLine	3	100	Medium	7000	86	05MAY2020	01DEC2023	6	23528
15	SIG015	ExpressLine	4	110	Medium	9000	83	12APR2019	28NOV2023	7	23497
16	SIG001	SouthLine	5	180	High	12000	82	01JAN2020	10DEC2023	6	23528
17	SIG002	EastLine	2	60	Low	4000	95	15MAR2019	05NOV2023	7	23497
18	SIG003	NorthLine	9	300	Critical	20000	65	10FEB2018	20OCT2023	8	23467
19	SIG004	WestLine	1	20	Low	2000	98	05MAY2021	01JAN2024	5	23558
20	SIG005	SouthLine	7	240	High	15000	70	12JUL2017	15SEP2023	9	23436
21	SIG006	EastLine	4	120	Medium	8000	85	09AUG2019	30NOV2023	7	23497
22	SIG007	NorthLine	10	360	Critical	25000	60	01JAN2016	05OCT2023	10	23467
23	SIG008	WestLine	3	90	Medium	6000	88	18DEC2020	12DEC2023	6	23528
24	SIG009	CentralLine	6	210	High	13000	75	02FEB2018	22NOV2023	8	23497
25	SIG010	CentralLine	1	30	Low	3000	97	15JUN2022	05JAN2024	4	23558
26	SIG011	MetroLine	8	260	High	17000	68	20MAR2017	18SEP2023	9	23436
27	SIG012	MetroLine	2	70	Medium	5000	90	10JAN2021	02DEC2023	5	23528
28	SIG013	FreightLine	11	400	Critical	30000	55	01JAN2015	15OCT2023	11	23467
29	SIG014	FreightLine	3	100	Medium	7000	86	05MAY2020	01DEC2023	6	23528
30	SIG015	ExpressLine	4	110	Medium	9000	83	12APR2019	28NOV2023	7	23497

Used for:

·       Combining months

·       Merging systems

·       Creating history tables

12. TRANSPOSE

proc transpose data=route_summary out=transposed;

    by Route;

run;

proc print data=transposed;

run;

OUTPUT:

Obs	Route	_NAME_	COL1
1	CentralLine	Total_Signals	2.0
2	CentralLine	Avg_Failures	3.5
3	CentralLine	Total_Cost	16000.0
4	EastLine	Total_Signals	2.0
5	EastLine	Avg_Failures	3.0
6	EastLine	Total_Cost	12000.0
7	ExpressLine	Total_Signals	1.0
8	ExpressLine	Avg_Failures	4.0
9	ExpressLine	Total_Cost	9000.0
10	FreightLine	Total_Signals	2.0
11	FreightLine	Avg_Failures	7.0
12	FreightLine	Total_Cost	37000.0
13	MetroLine	Total_Signals	2.0
14	MetroLine	Avg_Failures	5.0
15	MetroLine	Total_Cost	22000.0
16	NorthLine	Total_Signals	2.0
17	NorthLine	Avg_Failures	9.5
18	NorthLine	Total_Cost	45000.0
19	SouthLine	Total_Signals	2.0
20	SouthLine	Avg_Failures	6.0
21	SouthLine	Total_Cost	27000.0
22	WestLine	Total_Signals	2.0
23	WestLine	Avg_Failures	2.0
24	WestLine	Total_Cost	8000.0

Used for:

·       Pivot reports

·       Excel-style layouts

13. CHARACTER FUNCTIONS

data char_demo;

    set railway_dates;

    Route_Clean = propcase(strip(Route));

    Signal_Upper = upcase(Signal_ID);

    Safety_Lower = lowcase(Safety_Impact);

    Full_Tag = catx("-", Signal_ID, Route);

run;

proc print data=char_demo;

 var Signal_ID Route Route_Clean Signal_Upper Safety_Lower Safety_Impact Full_Tag;

run;

OUTPUT:

Obs	Signal_ID	Route	Route_Clean	Signal_Upper	Safety_Lower	Safety_Impact	Full_Tag
1	SIG001	SouthLine	Southline	SIG001	high	High	SIG001-SouthLine
2	SIG002	EastLine	Eastline	SIG002	low	Low	SIG002-EastLine
3	SIG003	NorthLine	Northline	SIG003	critical	Critical	SIG003-NorthLine
4	SIG004	WestLine	Westline	SIG004	low	Low	SIG004-WestLine
5	SIG005	SouthLine	Southline	SIG005	high	High	SIG005-SouthLine
6	SIG006	EastLine	Eastline	SIG006	medium	Medium	SIG006-EastLine
7	SIG007	NorthLine	Northline	SIG007	critical	Critical	SIG007-NorthLine
8	SIG008	WestLine	Westline	SIG008	medium	Medium	SIG008-WestLine
9	SIG009	CentralLine	Centralline	SIG009	high	High	SIG009-CentralLine
10	SIG010	CentralLine	Centralline	SIG010	low	Low	SIG010-CentralLine
11	SIG011	MetroLine	Metroline	SIG011	high	High	SIG011-MetroLine
12	SIG012	MetroLine	Metroline	SIG012	medium	Medium	SIG012-MetroLine
13	SIG013	FreightLine	Freightline	SIG013	critical	Critical	SIG013-FreightLine
14	SIG014	FreightLine	Freightline	SIG014	medium	Medium	SIG014-FreightLine
15	SIG015	ExpressLine	Expressline	SIG015	medium	Medium	SIG015-ExpressLine
16	SIG001	SouthLine	Southline	SIG001	high	High	SIG001-SouthLine
17	SIG002	EastLine	Eastline	SIG002	low	Low	SIG002-EastLine
18	SIG003	NorthLine	Northline	SIG003	critical	Critical	SIG003-NorthLine
19	SIG004	WestLine	Westline	SIG004	low	Low	SIG004-WestLine
20	SIG005	SouthLine	Southline	SIG005	high	High	SIG005-SouthLine
21	SIG006	EastLine	Eastline	SIG006	medium	Medium	SIG006-EastLine
22	SIG007	NorthLine	Northline	SIG007	critical	Critical	SIG007-NorthLine
23	SIG008	WestLine	Westline	SIG008	medium	Medium	SIG008-WestLine
24	SIG009	CentralLine	Centralline	SIG009	high	High	SIG009-CentralLine
25	SIG010	CentralLine	Centralline	SIG010	low	Low	SIG010-CentralLine
26	SIG011	MetroLine	Metroline	SIG011	high	High	SIG011-MetroLine
27	SIG012	MetroLine	Metroline	SIG012	medium	Medium	SIG012-MetroLine
28	SIG013	FreightLine	Freightline	SIG013	critical	Critical	SIG013-FreightLine
29	SIG014	FreightLine	Freightline	SIG014	medium	Medium	SIG014-FreightLine
30	SIG015	ExpressLine	Expressline	SIG015	medium	Medium	SIG015-ExpressLine

Used for:

·       Cleaning messy data

·       Standardizing names

·       Generating IDs

14. NUMERIC FUNCTIONS

data numeric_demo;

    set railway_dates;

    Cost_Rounded = round(Maintenance_Cost,1000);

    Downtime_Hours = Downtime_Minutes/60;

    Safe_Reliability = coalesce(Reliability_Index, 0);

run;

proc print data=numeric_demo;

 var Signal_ID Route Maintenance_Cost Cost_Rounded Downtime_Minutes Downtime_Hours 

     Reliability_Index Safe_Reliability;

run;

OUTPUT:

Obs	Signal_ID	Route	Maintenance_Cost	Cost_Rounded	Downtime_Minutes	Downtime_Hours	Reliability_Index	Safe_Reliability
1	SIG001	SouthLine	12000	12000	180	3.00000	82	82
2	SIG002	EastLine	4000	4000	60	1.00000	95	95
3	SIG003	NorthLine	20000	20000	300	5.00000	65	65
4	SIG004	WestLine	2000	2000	20	0.33333	98	98
5	SIG005	SouthLine	15000	15000	240	4.00000	70	70
6	SIG006	EastLine	8000	8000	120	2.00000	85	85
7	SIG007	NorthLine	25000	25000	360	6.00000	60	60
8	SIG008	WestLine	6000	6000	90	1.50000	88	88
9	SIG009	CentralLine	13000	13000	210	3.50000	75	75
10	SIG010	CentralLine	3000	3000	30	0.50000	97	97
11	SIG011	MetroLine	17000	17000	260	4.33333	68	68
12	SIG012	MetroLine	5000	5000	70	1.16667	90	90
13	SIG013	FreightLine	30000	30000	400	6.66667	55	55
14	SIG014	FreightLine	7000	7000	100	1.66667	86	86
15	SIG015	ExpressLine	9000	9000	110	1.83333	83	83
16	SIG001	SouthLine	12000	12000	180	3.00000	82	82
17	SIG002	EastLine	4000	4000	60	1.00000	95	95
18	SIG003	NorthLine	20000	20000	300	5.00000	65	65
19	SIG004	WestLine	2000	2000	20	0.33333	98	98
20	SIG005	SouthLine	15000	15000	240	4.00000	70	70
21	SIG006	EastLine	8000	8000	120	2.00000	85	85
22	SIG007	NorthLine	25000	25000	360	6.00000	60	60
23	SIG008	WestLine	6000	6000	90	1.50000	88	88
24	SIG009	CentralLine	13000	13000	210	3.50000	75	75
25	SIG010	CentralLine	3000	3000	30	0.50000	97	97
26	SIG011	MetroLine	17000	17000	260	4.33333	68	68
27	SIG012	MetroLine	5000	5000	70	1.16667	90	90
28	SIG013	FreightLine	30000	30000	400	6.66667	55	55
29	SIG014	FreightLine	7000	7000	100	1.66667	86	86
30	SIG015	ExpressLine	9000	9000	110	1.83333	83	83

15. PROC DATASETS DELETE

proc datasets library=work;

    delete backup;

quit;

LOG:

NOTE: Deleting WORK.BACKUP (memtype=DATA).

Used to:

·       Free memory

·       Avoid confusion

·       Clean temporary tables

WHY THIS PROJECT IS VERY STRONG

This single project demonstrates:

·       Data creation

·       Business thinking

·       Statistical analysis

·       Automation

·       Fraud detection

·       Visualization

CONCLUSION

Through this project, we proved that railway signal data is not just numbers — it is directly connected to human safety and operational efficiency.

Using SAS, we were able to:

·       Identify high-risk signal systems

·       Measure reliability across routes

·       Visualize downtime and failure patterns

·       Automatically classify utilization levels

·       Detect suspicious maintenance behavior (fraud logic)

Instead of reacting after accidents happen, this kind of analytics allows organizations to:

Predict problems before they become disasters.

The biggest learning from this project is:

Data does not just support decisions — it can prevent real-world failures.

INTERVIEW QUESTIONS FOR YOU

 1.What happens during the compilation and execution phase of a DATA step?

 2.What is the difference between SET and MERGE?

 3. What is HAVING clause?

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About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent Railway Signals data.

Our Mission:

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This project is suitable for:

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·  Bloggers writing about analytics and smart cities

·  EV and energy industry professionals

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