154.​UNVEILING URBAN TRAFFIC DYNAMICS: A COMPREHENSIVE ANALYSIS USING SAS PROCEDURES | PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC FORMAT | PROC REPORT | PROC SGPLOT | PROC SQL | PROC UNIVARIATE | PROC CORR​

​UNVEILING URBAN TRAFFIC DYNAMICS: A COMPREHENSIVE ANALYSIS USING SAS PROCEDURES | PROC CONTENTS | PROC PRINT | PROC MEANS | PROC FREQ | PROC FORMAT | PROC REPORT | PROC SGPLOT | PROC SQL | PROC UNIVARIATE | PROC CORR​

 /*Create a unique dataset centered around urban traffic patterns and apply various SAS procedures to analyze it.*/

Dataset Overview: Urban Traffic Patterns

Dataset Name: urban_traffic_data

Variables:

Intersection_ID (Character): Unique identifier for each intersection (e.g., "INT001").

Date (Date): Date of observation.

Time (Time): Time of observation.

Vehicle_Count (Numeric): Number of vehicles passing through the intersection during the observed time interval.

Average_Speed (Numeric): Average speed of vehicles in km/h.

Accident_Occurred (Character): Indicates if an accident occurred ("Yes"/"No").

Weather_Condition (Character): Weather during observation ("Clear", "Rainy", "Foggy", etc.).

Traffic_Signal_Status (Character): Status of the traffic signal ("Operational", "Flashing", "Out of Order").

Creating the Dataset in SAS

data urban_traffic_data;

    format Date date9. Time time5.;

    input Intersection_ID $ Date :date9. Time :time5. Vehicle_Count Average_Speed Accident_Occurred $ Weather_Condition $ Traffic_Signal_Status $;

    datalines;

INT001 01JAN2025 08:00 120 45 No Clear Operational

INT002 01JAN2025 08:00 95 50 No Rainy Operational

INT003 01JAN2025 08:00 110 40 Yes Foggy Flashing

INT001 01JAN2025 09:00 130 47 No Clear Operational

INT002 01JAN2025 09:00 100 52 No Rainy Operational

INT003 01JAN2025 09:00 115 42 No Foggy Flashing

;

run;

proc print data=urban_traffic_data (obs=10);

    title "Sample Observations from Urban Traffic Data";

run;

Output:

Sample Observations from Urban Traffic Data

Obs	Date	Time	Intersection_ID	Vehicle_Count	Average_Speed	Accident_Occurred	Weather_Condition	Traffic_Signal_Status
1	01JAN2025	8:00	INT001	120	45	No	Clear	Operational
2	01JAN2025	8:00	INT002	95	50	No	Rainy	Operational
3	01JAN2025	8:00	INT003	110	40	Yes	Foggy	Flashing
4	01JAN2025	9:00	INT001	130	47	No	Clear	Operational
5	01JAN2025	9:00	INT002	100	52	No	Rainy	Operational
6	01JAN2025	9:00	INT003	115	42	No	Foggy	Flashing

Applying SAS Procedures

1. PROC CONTENTS: Understanding the Dataset Structure

proc contents data=urban_traffic_data;

run;

Output:

                                                                The CONTENTS Procedure

Data Set Name	WORK.URBAN_TRAFFIC_DATA	Observations	6
Member Type	DATA	Variables	8
Engine	V9	Indexes	0
Created	14/09/2015 00:37:18	Observation Length	72
Last Modified	14/09/2015 00:37:18	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	WINDOWS_64
Encoding	wlatin1 Western (Windows)

Engine/Host Dependent Information
Data Set Page Size	65536
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	908
Obs in First Data Page	6
Number of Data Set Repairs	0
ExtendObsCounter	YES
Filename	C:\Users\Lenovo\AppData\Local\Temp\SAS Temporary Files\_TD11628_DESKTOP-QFAA4KV_\urban_traffic_data.sas7bdat
Release Created	9.0401M2
Host Created	X64_8HOME

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
6	Accident_Occurred	Char	8
5	Average_Speed	Num	8
1	Date	Num	8	DATE9.
3	Intersection_ID	Char	8
2	Time	Num	8	TIME5.
8	Traffic_Signal_Status	Char	15
4	Vehicle_Count	Num	8
7	Weather_Condition	Char	8

/*This procedure provides metadata about the dataset, including variable types, lengths, and formats.*/

2. PROC MEANS: Descriptive Statistics

proc means data=urban_traffic_data n mean std min max;

    var Vehicle_Count Average_Speed;

run;

Output:

                                                             The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Vehicle_Count Average_Speed	6 6	111.6666667 46.0000000	12.9099445 4.6043458	95.0000000 40.0000000	130.0000000 52.0000000

/*Calculates basic statistics for Vehicle_Count and Average_Speed.*/

3. PROC FREQ: Frequency Analysis

proc freq data=urban_traffic_data;

    tables Accident_Occurred Weather_Condition Traffic_Signal_Status;

run;

Output:

                                                                    The FREQ Procedure

Accident_Occurred	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	5	83.33	5	83.33
Yes	1	16.67	6	100.00

Weather_Condition	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Clear	2	33.33	2	33.33
Foggy	2	33.33	4	66.67
Rainy	2	33.33	6	100.00

Traffic_Signal_Status	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Flashing	2	33.33	2	33.33
Operational	4	66.67	6	100.00

/*Analyzes the frequency of accidents, weather conditions, and signal statuses.*/

4. PROC FORMAT: Creating Custom Formats

proc format;

    value $accident_fmt

        'Yes' = 'Accident'

        'No'  = 'No Accident';

    value $weather_fmt

        'Clear' = 'Clear Weather'

        'Rainy' = 'Rainy Weather'

        'Foggy' = 'Foggy Conditions';

run;

/*Defines custom formats for better readability.*/

5. PROC REPORT: Customized Reporting

proc report data=urban_traffic_data nowd;

    columns Intersection_ID Date Vehicle_Count Average_Speed Accident_Occurred;

    define Intersection_ID / group;

    define Date / group;

    define Vehicle_Count / analysis sum;

    define Average_Speed / analysis mean;

    define Accident_Occurred / display format=$accident_fmt.;

    title "Traffic Summary by Intersection and Date";

run;

Output:

Traffic Summary by Intersection and Date

Intersection_ID	Date	Vehicle_Count	Average_Speed	Accident_Occurred
INT001	01JAN2025	120	45	No Accident
		130	47	No Accident
INT002	01JAN2025	95	50	No Accident
		100	52	No Accident
INT003	01JAN2025	110	40	Accident
		115	42	No Accident

/*Generates a report summarizing traffic data by intersection and date.*/

6. PROC SGPLOT: Visualizing Data

proc sgplot data=urban_traffic_data;

    series x=Time y=Vehicle_Count / group=Intersection_ID;

    title "Vehicle Count Over Time by Intersection";

run;

/*Creates a line plot showing vehicle counts over time for each intersection.*/

7. PROC SQL: Advanced Data Queries

proc sql;

    create table high_traffic as

    select Intersection_ID, Date, sum(Vehicle_Count) as Total_Vehicles

    from urban_traffic_data

    group by Intersection_ID, Date

    having Total_Vehicles > 1000;

quit;

proc print;run;

Log:

NOTE: Table WORK.HIGH_TRAFFIC created, with 0 rows and 3 columns.

/*Identifies intersections with high traffic volumes on specific dates.*/

8. PROC UNIVARIATE: Detailed Statistical Analysis

proc univariate data=urban_traffic_data;

    var Average_Speed;

    histogram Average_Speed / normal;

    inset mean std / position=ne;

    title "Distribution of Average Speeds";

run;

Output:

Distribution of Average Speeds

                                                        The UNIVARIATE Procedure

                                              Fitted Normal Distribution for Average_Speed

Parameters for Normal Distribution
Parameter	Symbol	Estimate
Mean	Mu	46
Std Dev	Sigma	4.604346

Goodness-of-Fit Tests for Normal Distribution
Test	Statistic		p Value
Kolmogorov-Smirnov	D	0.14083988	Pr > D	>0.150
Cramer-von Mises	W-Sq	0.02085136	Pr > W-Sq	>0.250
Anderson-Darling	A-Sq	0.15729075	Pr > A-Sq	>0.250

Quantiles for Normal Distribution
Percent	Quantile
	Observed	Estimated
1.0	40.0000	35.2887
5.0	40.0000	38.4265
10.0	40.0000	40.0993
25.0	42.0000	42.8944
50.0	46.0000	46.0000
75.0	50.0000	49.1056
90.0	52.0000	51.9007
95.0	52.0000	53.5735
99.0	52.0000	56.7113

/*Analyzes the distribution of average speeds, including a histogram and statistical measures.*/

9. PROC CORR: Correlation Analysis

proc corr data=urban_traffic_data;

    var Vehicle_Count Average_Speed;

    title "Correlation Between Vehicle Count and Average Speed";

run;

Output:

Correlation Between Vehicle Count and Average Speed

                                                            The CORR Procedure

2 Variables:	Vehicle_Count Average_Speed

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Vehicle_Count	6	111.66667	12.90994	670.00000	95.00000	130.00000
Average_Speed	6	46.00000	4.60435	276.00000	40.00000	52.00000

Pearson Correlation Coefficients, N = 6 Prob > |r| under H0: Rho=0
	Vehicle_Count	Average_Speed
Vehicle_Count	1.00000	-0.43740 0.3857
Average_Speed	-0.43740 0.3857	1.00000

/*Examines the relationship between vehicle count and average speed.*/

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