402.Can SAS Identify the Most Efficient Waste Collection Routes in a City?

Can SAS Identify the Most Efficient Waste Collection Routes in a City?

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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 | SET | PROC TRANSPOSE | PROC DATASETS DELETE | DATA FUNCTIONS

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INTRODUCTION

Urban waste collection is one of the most critical municipal services. Cities spend millions of rupees every year on fuel, manpower, vehicles, and maintenance for waste collection.

However, inefficient routing, fuel misuse, time delays, and fraud (fake fuel claims, inflated tonnage reporting) can lead to huge losses.

This project demonstrates how a SAS Programmer / Data Analyst can:

·       Build a clean analytical dataset

·       Measure route efficiency

·       Detect operational red flags (fraud-like patterns)

·       Analyze fuel usage, time, distance, and waste

·       Generate statistical and visual insights

TABLE OF CONTENTS

1.     Business Context

2.     Dataset Design

3.     Raw Data Creation

4.     Date Handling (MDY, INTCK, INTNX)

5.     Data Cleaning & Standardization

6.     Derived Metrics & Efficiency Score

7.     Macro: Utilization Classification

8.     Macro: Fraud Detection Logic

9.     PROC SQL Analytics

10.  PROC FREQ

11.  PROC MEANS

12.  PROC UNIVARIATE

13.  PROC CORR

14.  PROC SGPLOT

15.  TRANSPOSE, SET

16.  Character & Numeric Functions

17.  PROC DATASETS DELETE

18.  5 Key Points About This Project

19.  Who Can Use and Read This Project?

20.  Conclusion

1. BUSINESS CONTEXT

Municipal corporations want answers to questions like:

·       Which routes are efficient or inefficient?

·       Are drivers over-reporting fuel usage?

·       Is waste collected proportional to distance?

·       Which cities perform better?

·       How does time affect fuel and waste volume?

This SAS project answers all of these.

2. DATASET DESIGN

Variables Used

Variable	Description
Route_ID	Unique route identifier
City	City name
Route_Date	Collection date
Distance_km	Distance covered
Waste_Collected_Tons	Waste collected
Fuel_Used_Liters	Fuel used
Time_Taken_Hours	Time taken
Vehicle_Type	Truck type
Driver_Name	Driver
Efficiency_Score	Calculated %
Utilization_Class	High/Medium/Low
Fraud_Flag	Y / N

 

3. RAW DATA CREATION (DATA STEP)

data waste_routes_raw;

    input Route_ID $ City:$12. Route_Date : date9. Distance_km Waste_Collected_Tons

           Fuel_Used_Liters Time_Taken_Hours Vehicle_Type:$14. Driver_Name $;

    format Route_Date date9.;

datalines;

R001 Hyderabad 01JAN2026 25 12.5 18 4 Compactor Ravi

R002 Hyderabad 02JAN2026 30 14.0 22 5 Tipper Kumar

R003 Chennai   01JAN2026 20 10.2 15 3 Compactor Arjun

R004 Chennai   03JAN2026 28 16.8 26 6 Tipper Suresh

R005 Mumbai    01JAN2026 35 20.0 30 7 Compactor Ramesh

R006 Mumbai    02JAN2026 40 21.5 36 8 Tipper Mahesh

R007 Pune      01JAN2026 18 8.0 14 3 MiniTruck Ajay

R008 Pune      02JAN2026 22 9.5 16 4 MiniTruck Vijay

R009 Delhi     01JAN2026 45 24.0 40 9 Compactor Deepak

R010 Delhi     03JAN2026 50 26.5 45 10 Tipper Aman

R011 Jaipur    01JAN2026 20 11.0 17 4 MiniTruck Rohit

R012 Jaipur    02JAN2026 24 12.2 18 5 MiniTruck Mohan

R013 Kochi     01JAN2026 15 7.8 10 3 Compactor Sajan

R014 Kochi     02JAN2026 17 8.5 12 3 Compactor Anil

R015 Indore    03JAN2026 26 13.5 21 5 Tipper Sanjay

;

run;

proc print data=waste_routes_raw;

run;

OUTPUT:

Obs	Route_ID	City	Route_Date	Distance_km	Waste_Collected_Tons	Fuel_Used_Liters	Time_Taken_Hours	Vehicle_Type	Driver_Name
1	R001	Hyderabad	01JAN2026	25	12.5	18	4	Compactor	Ravi
2	R002	Hyderabad	02JAN2026	30	14.0	22	5	Tipper	Kumar
3	R003	Chennai	01JAN2026	20	10.2	15	3	Compactor	Arjun
4	R004	Chennai	03JAN2026	28	16.8	26	6	Tipper	Suresh
5	R005	Mumbai	01JAN2026	35	20.0	30	7	Compactor	Ramesh
6	R006	Mumbai	02JAN2026	40	21.5	36	8	Tipper	Mahesh
7	R007	Pune	01JAN2026	18	8.0	14	3	MiniTruck	Ajay
8	R008	Pune	02JAN2026	22	9.5	16	4	MiniTruck	Vijay
9	R009	Delhi	01JAN2026	45	24.0	40	9	Compactor	Deepak
10	R010	Delhi	03JAN2026	50	26.5	45	10	Tipper	Aman
11	R011	Jaipur	01JAN2026	20	11.0	17	4	MiniTruck	Rohit
12	R012	Jaipur	02JAN2026	24	12.2	18	5	MiniTruck	Mohan
13	R013	Kochi	01JAN2026	15	7.8	10	3	Compactor	Sajan
14	R014	Kochi	02JAN2026	17	8.5	12	3	Compactor	Anil
15	R015	Indore	03JAN2026	26	13.5	21	5	Tipper	Sanjay

·  Same as real municipal data

·  Includes dates, numbers, and character variables

4. DATE FUNCTIONS (MDY, INTCK, INTNX)

data waste_routes_dates;

    set waste_routes_raw;

    Month_Start = intnx('month', Route_Date, 0, 'b');

    Days_From_Start = intck('day', '01JAN2026'd, Route_Date);

    format Month_Start date9.;

drop Distance_km Waste_Collected_Tons Fuel_Used_Liters Time_Taken_Hours Vehicle_Type;

run;

proc print data=waste_routes_dates;

run;

OUTPUT:

Obs	Route_ID	City	Route_Date	Driver_Name	Month_Start	Days_From_Start
1	R001	Hyderabad	01JAN2026	Ravi	01JAN2026	0
2	R002	Hyderabad	02JAN2026	Kumar	01JAN2026	1
3	R003	Chennai	01JAN2026	Arjun	01JAN2026	0
4	R004	Chennai	03JAN2026	Suresh	01JAN2026	2
5	R005	Mumbai	01JAN2026	Ramesh	01JAN2026	0
6	R006	Mumbai	02JAN2026	Mahesh	01JAN2026	1
7	R007	Pune	01JAN2026	Ajay	01JAN2026	0
8	R008	Pune	02JAN2026	Vijay	01JAN2026	1
9	R009	Delhi	01JAN2026	Deepak	01JAN2026	0
10	R010	Delhi	03JAN2026	Aman	01JAN2026	2
11	R011	Jaipur	01JAN2026	Rohit	01JAN2026	0
12	R012	Jaipur	02JAN2026	Mohan	01JAN2026	1
13	R013	Kochi	01JAN2026	Sajan	01JAN2026	0
14	R014	Kochi	02JAN2026	Anil	01JAN2026	1
15	R015	Indore	03JAN2026	Sanjay	01JAN2026	2

Why this is used

·  INTNX: aligns reporting to month start

·  INTCK: measures time gaps

·  Used heavily in operational reporting

5. DATA CLEANING & CHARACTER FUNCTIONS

data waste_routes_clean;

    set waste_routes_raw;

    City = propcase(strip(City));

    Driver_Name = upcase(trim(Driver_Name));

    Vehicle_Type = lowcase(Vehicle_Type);

    Route_Key = catx('_', City, Route_ID);

keep Route_ID City Vehicle_Type Driver_Name;

run;

proc print data=waste_routes_clean;

run;

OUTPUT:

Obs	Route_ID	City	Vehicle_Type	Driver_Name
1	R001	Hyderabad	compactor	RAVI
2	R002	Hyderabad	tipper	KUMAR
3	R003	Chennai	compactor	ARJUN
4	R004	Chennai	tipper	SURESH
5	R005	Mumbai	compactor	RAMESH
6	R006	Mumbai	tipper	MAHESH
7	R007	Pune	minitruck	AJAY
8	R008	Pune	minitruck	VIJAY
9	R009	Delhi	compactor	DEEPAK
10	R010	Delhi	tipper	AMAN
11	R011	Jaipur	minitruck	ROHIT
12	R012	Jaipur	minitruck	MOHAN
13	R013	Kochi	compactor	SAJAN
14	R014	Kochi	compactor	ANIL
15	R015	Indore	tipper	SANJAY

Functions explained

·  STRIP / TRIM → remove spaces

·  PROPCASE → city formatting

·  UPCASE / LOWCASE

·  CATX → create composite keys

6. EFFICIENCY SCORE CALCULATION

data waste_routes_metrics;

    set waste_routes_raw;

    Efficiency_Score = (Waste_Collected_Tons / Fuel_Used_Liters) * 100;

    format Efficiency_Score 6.2;

run;

proc print data=waste_routes_metrics;

run;

OUTPUT:

Obs	Route_ID	City	Route_Date	Distance_km	Waste_Collected_Tons	Fuel_Used_Liters	Time_Taken_Hours	Vehicle_Type	Driver_Name	Efficiency_Score
1	R001	Hyderabad	01JAN2026	25	12.5	18	4	Compactor	Ravi	69.44
2	R002	Hyderabad	02JAN2026	30	14.0	22	5	Tipper	Kumar	63.64
3	R003	Chennai	01JAN2026	20	10.2	15	3	Compactor	Arjun	68.00
4	R004	Chennai	03JAN2026	28	16.8	26	6	Tipper	Suresh	64.62
5	R005	Mumbai	01JAN2026	35	20.0	30	7	Compactor	Ramesh	66.67
6	R006	Mumbai	02JAN2026	40	21.5	36	8	Tipper	Mahesh	59.72
7	R007	Pune	01JAN2026	18	8.0	14	3	MiniTruck	Ajay	57.14
8	R008	Pune	02JAN2026	22	9.5	16	4	MiniTruck	Vijay	59.38
9	R009	Delhi	01JAN2026	45	24.0	40	9	Compactor	Deepak	60.00
10	R010	Delhi	03JAN2026	50	26.5	45	10	Tipper	Aman	58.89
11	R011	Jaipur	01JAN2026	20	11.0	17	4	MiniTruck	Rohit	64.71
12	R012	Jaipur	02JAN2026	24	12.2	18	5	MiniTruck	Mohan	67.78
13	R013	Kochi	01JAN2026	15	7.8	10	3	Compactor	Sajan	78.00
14	R014	Kochi	02JAN2026	17	8.5	12	3	Compactor	Anil	70.83
15	R015	Indore	03JAN2026	26	13.5	21	5	Tipper	Sanjay	64.29

·  Shows waste per fuel

·  Higher = better operational efficiency

7. MACRO: UTILIZATION CLASSIFICATION

%macro utilization;

data waste_routes_util;

    set waste_routes_metrics;

length Utilization_Class $8.;

    if Efficiency_Score >= 70 then Utilization_Class = 'HIGH';

    else if Efficiency_Score >= 60 then Utilization_Class = 'MEDIUM';

    else Utilization_Class = 'LOW';

run;

proc print data=waste_routes_util;

run;

%mend;

%utilization;

OUTPUT:

Obs	Route_ID	City	Route_Date	Distance_km	Waste_Collected_Tons	Fuel_Used_Liters	Time_Taken_Hours	Vehicle_Type	Driver_Name	Efficiency_Score	Utilization_Class
1	R001	Hyderabad	01JAN2026	25	12.5	18	4	Compactor	Ravi	69.44	MEDIUM
2	R002	Hyderabad	02JAN2026	30	14.0	22	5	Tipper	Kumar	63.64	MEDIUM
3	R003	Chennai	01JAN2026	20	10.2	15	3	Compactor	Arjun	68.00	MEDIUM
4	R004	Chennai	03JAN2026	28	16.8	26	6	Tipper	Suresh	64.62	MEDIUM
5	R005	Mumbai	01JAN2026	35	20.0	30	7	Compactor	Ramesh	66.67	MEDIUM
6	R006	Mumbai	02JAN2026	40	21.5	36	8	Tipper	Mahesh	59.72	LOW
7	R007	Pune	01JAN2026	18	8.0	14	3	MiniTruck	Ajay	57.14	LOW
8	R008	Pune	02JAN2026	22	9.5	16	4	MiniTruck	Vijay	59.38	LOW
9	R009	Delhi	01JAN2026	45	24.0	40	9	Compactor	Deepak	60.00	MEDIUM
10	R010	Delhi	03JAN2026	50	26.5	45	10	Tipper	Aman	58.89	LOW
11	R011	Jaipur	01JAN2026	20	11.0	17	4	MiniTruck	Rohit	64.71	MEDIUM
12	R012	Jaipur	02JAN2026	24	12.2	18	5	MiniTruck	Mohan	67.78	MEDIUM
13	R013	Kochi	01JAN2026	15	7.8	10	3	Compactor	Sajan	78.00	HIGH
14	R014	Kochi	02JAN2026	17	8.5	12	3	Compactor	Anil	70.83	HIGH
15	R015	Indore	03JAN2026	26	13.5	21	5	Tipper	Sanjay	64.29	MEDIUM

Business Value

·  Helps management rank routes

·  Used in dashboards & KPIs

8. MACRO: FRAUD DETECTION LOGIC

%macro fraud_check;

data waste_routes_fraud;

    set waste_routes_util;

    if Fuel_Used_Liters > (Distance_km * 0.8) then Fraud_Flag = 'Y';

    else Fraud_Flag = 'N';

run;

proc print data=waste_routes_fraud;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Route_ID	City	Route_Date	Distance_km	Waste_Collected_Tons	Fuel_Used_Liters	Time_Taken_Hours	Vehicle_Type	Driver_Name	Efficiency_Score	Utilization_Class	Fraud_Flag
1	R001	Hyderabad	01JAN2026	25	12.5	18	4	Compactor	Ravi	69.44	MEDIUM	N
2	R002	Hyderabad	02JAN2026	30	14.0	22	5	Tipper	Kumar	63.64	MEDIUM	N
3	R003	Chennai	01JAN2026	20	10.2	15	3	Compactor	Arjun	68.00	MEDIUM	N
4	R004	Chennai	03JAN2026	28	16.8	26	6	Tipper	Suresh	64.62	MEDIUM	Y
5	R005	Mumbai	01JAN2026	35	20.0	30	7	Compactor	Ramesh	66.67	MEDIUM	Y
6	R006	Mumbai	02JAN2026	40	21.5	36	8	Tipper	Mahesh	59.72	LOW	Y
7	R007	Pune	01JAN2026	18	8.0	14	3	MiniTruck	Ajay	57.14	LOW	N
8	R008	Pune	02JAN2026	22	9.5	16	4	MiniTruck	Vijay	59.38	LOW	N
9	R009	Delhi	01JAN2026	45	24.0	40	9	Compactor	Deepak	60.00	MEDIUM	Y
10	R010	Delhi	03JAN2026	50	26.5	45	10	Tipper	Aman	58.89	LOW	Y
11	R011	Jaipur	01JAN2026	20	11.0	17	4	MiniTruck	Rohit	64.71	MEDIUM	Y
12	R012	Jaipur	02JAN2026	24	12.2	18	5	MiniTruck	Mohan	67.78	MEDIUM	N
13	R013	Kochi	01JAN2026	15	7.8	10	3	Compactor	Sajan	78.00	HIGH	N
14	R014	Kochi	02JAN2026	17	8.5	12	3	Compactor	Anil	70.83	HIGH	N
15	R015	Indore	03JAN2026	26	13.5	21	5	Tipper	Sanjay	64.29	MEDIUM	Y

·  Fuel should be proportional to distance

·  Flags possible misuse or data issues

9. PROC SQL – ANALYTICS

proc sql;

    create table city_summary as

    select City,

           count(*) as Routes,

           avg(Efficiency_Score) as Avg_Efficiency,

           sum(Waste_Collected_Tons) as Total_Waste

    from waste_routes_fraud

    group by City;

quit;

proc print data=city_summary;

run;

OUTPUT:

Obs	City	Routes	Avg_Efficiency	Total_Waste
1	Chennai	2	66.3077	27.0
2	Delhi	2	59.4444	50.5
3	Hyderabad	2	66.5404	26.5
4	Indore	1	64.2857	13.5
5	Jaipur	2	66.2418	23.2
6	Kochi	2	74.4167	16.3
7	Mumbai	2	63.1944	41.5
8	Pune	2	58.2589	17.5

10. PROC FREQ

proc freq data=waste_routes_fraud;

    tables City*Fraud_Flag / nocol nopercent;

run;

OUTPUT:

The FREQ Procedure

Frequency Row Pct

Table of City by Fraud_Flag City Fraud_Flag N Y Total Chennai 1 50.00 1 50.00 2   Delhi 0 0.00 2 100.00 2   Hyderabad 2 100.00 0 0.00 2   Indore 0 0.00 1 100.00 1   Jaipur 1 50.00 1 50.00 2   Kochi 2 100.00 0 0.00 2   Mumbai 0 0.00 2 100.00 2   Pune 2 100.00 0 0.00 2   Total 8 7 15

11. PROC MEANS

proc means data=waste_routes_fraud mean min max;

    var Distance_km Fuel_Used_Liters Waste_Collected_Tons Efficiency_Score;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Distance_km Fuel_Used_Liters Waste_Collected_Tons Efficiency_Score	27.6666667 22.6666667 14.4000000 64.8729690	15.0000000 10.0000000 7.8000000 57.1428571	50.0000000 45.0000000 26.5000000 78.0000000

12. PROC UNIVARIATE

proc univariate data=waste_routes_fraud;

    var Efficiency_Score;

    histogram Efficiency_Score;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Efficiency_Score

Moments
N	15	Sum Weights	15
Mean	64.872969	Sum Observations	973.094535
Std Deviation	5.53051739	Variance	30.5866226
Skewness	0.74705715	Kurtosis	0.76556131
Uncorrected SS	63555.7444	Corrected SS	428.212717
Coeff Variation	8.52514919	Std Error Mean	1.42797345

Basic Statistical Measures
Location		Variability
Mean	64.87297	Std Deviation	5.53052
Median	64.61538	Variance	30.58662
Mode	.	Range	20.85714
		Interquartile Range	8.27778

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	45.43009	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	78.0000
99%	78.0000
95%	78.0000
90%	70.8333
75% Q3	68.0000
50% Median	64.6154
25% Q1	59.7222
10%	58.8889
5%	57.1429
1%	57.1429
0% Min	57.1429

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
57.1429	7	67.7778	12
58.8889	10	68.0000	3
59.3750	8	69.4444	1
59.7222	6	70.8333	14
60.0000	9	78.0000	13

The UNIVARIATE Procedure

13. PROC CORR

proc corr data=waste_routes_fraud;

    var Distance_km Fuel_Used_Liters Waste_Collected_Tons Time_Taken_Hours;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Distance_km Fuel_Used_Liters Waste_Collected_Tons Time_Taken_Hours

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Distance_km	15	27.66667	10.52661	415.00000	15.00000	50.00000
Fuel_Used_Liters	15	22.66667	10.60099	340.00000	10.00000	45.00000
Waste_Collected_Tons	15	14.40000	6.02412	216.00000	7.80000	26.50000
Time_Taken_Hours	15	5.26667	2.28244	79.00000	3.00000	10.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Distance_km	Fuel_Used_Liters	Waste_Collected_Tons	Time_Taken_Hours
Distance_km	1.00000	0.99042 <.0001	0.98469 <.0001	0.98206 <.0001
Fuel_Used_Liters	0.99042 <.0001	1.00000	0.99255 <.0001	0.98993 <.0001
Waste_Collected_Tons	0.98469 <.0001	0.99255 <.0001	1.00000	0.98808 <.0001
Time_Taken_Hours	0.98206 <.0001	0.98993 <.0001	0.98808 <.0001	1.00000

14. PROC SGPLOT

proc sgplot data=waste_routes_fraud;

    scatter x=Distance_km y=Fuel_Used_Liters;

    reg x=Distance_km y=Fuel_Used_Liters;

run;

OUTPUT:

15. TRANSPOSE,SET

proc transpose data=city_summary out=city_trans;

    by City NotSorted;

run;

proc print data=city_trans;

run;

OUTPUT:

Obs	City	_NAME_	COL1
1	Chennai	Routes	2.0000
2	Chennai	Avg_Efficiency	66.3077
3	Chennai	Total_Waste	27.0000
4	Delhi	Routes	2.0000
5	Delhi	Avg_Efficiency	59.4444
6	Delhi	Total_Waste	50.5000
7	Hyderabad	Routes	2.0000
8	Hyderabad	Avg_Efficiency	66.5404
9	Hyderabad	Total_Waste	26.5000
10	Indore	Routes	1.0000
11	Indore	Avg_Efficiency	64.2857
12	Indore	Total_Waste	13.5000
13	Jaipur	Routes	2.0000
14	Jaipur	Avg_Efficiency	66.2418
15	Jaipur	Total_Waste	23.2000
16	Kochi	Routes	2.0000
17	Kochi	Avg_Efficiency	74.4167
18	Kochi	Total_Waste	16.3000
19	Mumbai	Routes	2.0000
20	Mumbai	Avg_Efficiency	63.1944
21	Mumbai	Total_Waste	41.5000
22	Pune	Routes	2.0000
23	Pune	Avg_Efficiency	58.2589
24	Pune	Total_Waste	17.5000

data all_routes;

    set waste_routes_raw 

        waste_routes_fraud;

run;

proc print data=all_routes;

run;

OUTPUT:

Obs	Route_ID	City	Route_Date	Distance_km	Waste_Collected_Tons	Fuel_Used_Liters	Time_Taken_Hours	Vehicle_Type	Driver_Name	Efficiency_Score	Utilization_Class	Fraud_Flag
1	R001	Hyderabad	01JAN2026	25	12.5	18	4	Compactor	Ravi	.
2	R002	Hyderabad	02JAN2026	30	14.0	22	5	Tipper	Kumar	.
3	R003	Chennai	01JAN2026	20	10.2	15	3	Compactor	Arjun	.
4	R004	Chennai	03JAN2026	28	16.8	26	6	Tipper	Suresh	.
5	R005	Mumbai	01JAN2026	35	20.0	30	7	Compactor	Ramesh	.
6	R006	Mumbai	02JAN2026	40	21.5	36	8	Tipper	Mahesh	.
7	R007	Pune	01JAN2026	18	8.0	14	3	MiniTruck	Ajay	.
8	R008	Pune	02JAN2026	22	9.5	16	4	MiniTruck	Vijay	.
9	R009	Delhi	01JAN2026	45	24.0	40	9	Compactor	Deepak	.
10	R010	Delhi	03JAN2026	50	26.5	45	10	Tipper	Aman	.
11	R011	Jaipur	01JAN2026	20	11.0	17	4	MiniTruck	Rohit	.
12	R012	Jaipur	02JAN2026	24	12.2	18	5	MiniTruck	Mohan	.
13	R013	Kochi	01JAN2026	15	7.8	10	3	Compactor	Sajan	.
14	R014	Kochi	02JAN2026	17	8.5	12	3	Compactor	Anil	.
15	R015	Indore	03JAN2026	26	13.5	21	5	Tipper	Sanjay	.
16	R001	Hyderabad	01JAN2026	25	12.5	18	4	Compactor	Ravi	69.44	MEDIUM	N
17	R002	Hyderabad	02JAN2026	30	14.0	22	5	Tipper	Kumar	63.64	MEDIUM	N
18	R003	Chennai	01JAN2026	20	10.2	15	3	Compactor	Arjun	68.00	MEDIUM	N
19	R004	Chennai	03JAN2026	28	16.8	26	6	Tipper	Suresh	64.62	MEDIUM	Y
20	R005	Mumbai	01JAN2026	35	20.0	30	7	Compactor	Ramesh	66.67	MEDIUM	Y
21	R006	Mumbai	02JAN2026	40	21.5	36	8	Tipper	Mahesh	59.72	LOW	Y
22	R007	Pune	01JAN2026	18	8.0	14	3	MiniTruck	Ajay	57.14	LOW	N
23	R008	Pune	02JAN2026	22	9.5	16	4	MiniTruck	Vijay	59.38	LOW	N
24	R009	Delhi	01JAN2026	45	24.0	40	9	Compactor	Deepak	60.00	MEDIUM	Y
25	R010	Delhi	03JAN2026	50	26.5	45	10	Tipper	Aman	58.89	LOW	Y
26	R011	Jaipur	01JAN2026	20	11.0	17	4	MiniTruck	Rohit	64.71	MEDIUM	Y
27	R012	Jaipur	02JAN2026	24	12.2	18	5	MiniTruck	Mohan	67.78	MEDIUM	N
28	R013	Kochi	01JAN2026	15	7.8	10	3	Compactor	Sajan	78.00	HIGH	N
29	R014	Kochi	02JAN2026	17	8.5	12	3	Compactor	Anil	70.83	HIGH	N
30	R015	Indore	03JAN2026	26	13.5	21	5	Tipper	Sanjay	64.29	MEDIUM	Y

16. PROC DATASETS DELETE

proc datasets library=work;

    delete waste_routes_dates waste_routes_clean;

quit;

LOG:

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

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

17. 5 Key Points About This Project

1.     This project shows how waste collection route data can be created, cleaned, and analyzed using SAS in a real-world style.

2.     It explains how to measure route efficiency using distance, fuel usage, time taken, and waste collected.

3.     It demonstrates the use of macros for utilization classification and basic fraud detection logic.

4.     It covers important SAS procedures like PROC SQL, PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC CORR, and PROC SGPLOT.

5.     The project is written in a simple, step-by-step manner, making it easy to understand even for beginners.

18. Who Can Use and Read This Project?

·       SAS Beginners who want hands-on practice with real-life data

·       SAS Programmers preparing for interviews

·       Data Analysts working in government or urban planning domains

·       Students learning data analytics or statistics

·       Municipal or operations teams interested in route efficiency analysis

19. Conclusion

This Waste Collection Routes Analytics project explains how data can be used to improve daily municipal operations in a simple and practical way. Waste collection is an important activity for every city, and managing it efficiently helps save fuel, time, and money. Through this project, we created a realistic dataset and analyzed it using SAS to understand how distance, fuel usage, time taken, and waste collected are connected.

The project shows how efficiency can be calculated and how routes can be classified as high, medium, or low utilization. It also introduces a basic fraud detection idea by checking unusual fuel usage patterns. All steps are explained clearly, using simple logic and commonly used SAS procedures, so even beginners can follow along without confusion.

This project is useful not only for learning SAS but also for understanding how data supports decision-making in real life. It helps identify inefficient routes, possible data issues, and areas where operations can be improved. Overall, this project builds strong confidence in using SAS for practical analytics and prepares learners for interviews, real projects, and professional data analysis work in an easy and understandable way.

INTERVIEW QUESTIONS FOR YOU

1.What is the difference between PROC SQL and the DATA step?

2.What is the difference between INTCK and INTNX, and how are they useful in SAS date processing?

3.What is the difference between CALL SYMPUT and CALL SYMPUTX?

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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 WASTE COLLECTION data.

Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

·  Bloggers writing about analytics and smart cities

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To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1. Are We Losing Wildlife Faster Than We Think? – Animal Conservation Analysis Using SAS

2.Can Modern Art Data Explain What Actually Sells? – A Real-World SAS Analytics Project

3.Which Vehicles Truly Perform Better on Indian Roads? – A Real-World SAS Analytics Project

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