D.C. Metro Transportation
By Aric Fellers, Yale Williams, and David Zubenko Geography 463 Term Project Final Report December 15th, 2014
Executive Summary
Our team’s goal was to design a database that would make it easier to find tourist
attractions around the Washington D.C. Metropolitan area. This database will be easy for anyone to use, but our main target client is tourists and new residents to the Washington D.C. area. This database will help to promote restaurants and tourist attractions, which can also lead to increased business for other businesses in the area and an increase in economic activity overall.
We focused on designing a user friendly database that would make traveling on the
Washington D.C. Metro System less confusing and tourist friendly. The database includes information on the stations and lines that make up the Metro System. Within the stations and lines we included the total time of travel, station names, parking costs, the number of stations, and the states that lines start and end in. The main focus of our database was tourist attractions and restaurants, so we included information on restaurants, tours, and entertainment attractions that are close to each station along the Metro System. Within the information on tourist attractions and restaurants we included the type of entertainment, the cost, and the intended age group. Finally we included information on major transportation other than the Metro System and the closest station to the different types of major transportation. The different types of transportation include airports, bus terminals, and Amtrak stations. The major problem we encountered was deciding which information would be useful to our target client and determining what information to include within each major attribute. Overall, once we decided on the most useful information to include in our database, the project came together very well.
Introduction: Like many cities Washington, D.C. has an expansive metro system (Metro) to help people navigate around the city and the Greater D.C. area. Locals that have lived in the region for an extended period of time are probably familiar with the intricacies of the Metro, however, these nuances may be difficult for new residents and tourists to pick up on. To aid these people we have developed a Metro database with information about the lines, stations, and attractions at each station. This database will also help the attractions themselves by providing a platform for them to market their products and services. It was our intention when we started the project to populate the database to make it fully functional, and this is something we were able to achieve.
Procedures: We began this project by acquiring background information about the Metro that we used to develop a list of data elements (entities). We then determined the appropriate attributes for these entities and the relationships between the entities. This information was visually represented in an entity-‐relationship (E-‐R) diagram. The diagram contains 6 entities (Stations, Lines, Entertainment/Attractions, Restaurants, Tours, and Major Transportation), and the relationships between them. Most of these relationships are “Many to 1” or M:1 relationships. For instance, each station may have many restaurants, but each restaurant has only one station. The other relationship we encountered was an M:N relationship, where each entity has many, but not necessarily the same amount, of the other. For instance, each station has multiple lines and each line has multiple stations, but these numbers are not exactly the same. Each of the entities also has various attributes that are used to provide additional detail.
For instance, “Station Name” is an attribute of Entertainment/Attractions, Restaurants, and Tours while “Color” is an attribute of Lines and “Type” is an attribute of Major Transportation. Some of these attributes also serve as a primary key that helps link the entities together. This process is better understood in the tables that were the next step of the project.
Type(
M(
Cost(
Cost(
Length( (Distance)(
Length((Time)(
Tour(
Tours(
Sta$on(Name( Type(
1(
Start_Sta$on( Total_Time(
Start_State( End_State( Close;to(
M(
Color(
Type(
Cost(
Age( Group(
Closest(Sta$on( Name(
Distance(from( Sta$on(
Major(Transporta$on(
Type(
Sta$on(Name(
Entertainment/A3rac$ons(
Entertainment_ A3rac$on(
M(
M(
Name(
Close;to(
#_of_Sta$ons(
End_Sta$on(
Lines( M( Have(
1( Close;to(
Transporta$on(to(&( from(Metro(Sta$on(
N( 1(
Bus_Ride(
Close;to(
1(
Restaurants(
Name(
Sta$on(Name( Restaurant(
Below_Ground(
Long(
Lat(
Sta$ons(
State(
City(
21+(
Line*(
Parking(
Parking_Costs(
Entertainment(
After completing the E-‐R diagram we broke the information down into tables (below) that were used to better understand the relationships between the entities and attributes. Each individual table is representative of an entity and the attributes that define it. Each attribute is identified as being either “string” or “integer”. There are eight tables in all, six for the six entities, one for the M:N relationship between Stations and Lines, and one for the multi-‐valued attribute of Transportation to and from Metro Station.
StaFons*
*S*=*string,***I*=*integer*
Restaurants*
A"ribute:**Restaurant***StaFon_Name****Type****Entertainment****21+****Cost* * Data*Type:*****S****************************S*******************S*****************S*******************S**********I*
A"ribute:***Name**Line***City**State**Lat**Long**Parking**Parking_Costs**Bus*&*Ride**Above/Below*Ground*** * Data*Type:**S*************S********S********S*********I******I**********S*******************I*************************S*****************************S** ******
Tours*
Lines*
A"ribute:**Tour****StaFon_Name***Type****Distance***Time***Cost* * Data*Type:***S*********************S*****************S*************I****************I**********I*
StaFons*have*Lines* A"ribute:****Name*******Color* * Data*Type:*******S****************S**
A"ribute:***Color***#_of_StaFons***Start_StaFon**End_StaFon**Start_State**End_State*Total*Time**Start_End_Cost* * Data*Type:******S*************I****************************S************************S*********************S*******************S****************I********************I*
Major*TransportaFon*MulFUValue*
Major*TransportaFon*
Entertainment/A"racFons*
A"ribute:****TransportaFon_Name*****TransportaFon*Type* * Data*Type:********************S******************************************S*******
A"ribute:**Entertainment_A"racFon***StaFon_Name****Type***Cost***Age*Group* * Data*Type:*******************S****************************************S*******************S*********I**************I**
A"ribute:*TransportaFon_Name***Type****Closest_StaFon***Distance_from_StaFon**TransportaFon_To_From* * Data*Type:*************S******************************S***********************S******************************I*****************************************S*
Once we understood the relationships between the entities and attributes we created and populated the tables in Microsoft Excel before importing them into Microsoft Access. Once in Access each field name, or attribute, was defined as a certain data type such as short text, long text, yes/no, number, or currency. All of us collaborated on each aspect of the project.
Description of the Data Model: Sample Queries (in plain English): -‐ -‐ -‐ -‐ -‐
What stops are on a particular line? What entertainment establishments are near a particular stop? What stop is Restaurant X closest to? What other forms of transportation are available at a particular stop? Where does a line begin and end?
List of Conceptual Elements -‐
-‐ -‐
-‐ -‐ -‐
Stations – We included the stations on the Metro because they are an integral part of the system we are trying to document. They are arguably the most important element. Lines – We included the 6 Metro lines because they connect the stations in the system Entertainment/Attractions – The database is supposed to help new residences and tourists navigate their way to local entertainment/attractions. Entertainment/Attractions includes museums, sports stadiums, music establishments, and parks. Note that restaurants is a separate element. Restaurants – The database is supposed to help new residents and tourists navigate their way to local restaurants. Note that this is a separate element from Entertainment/Attractions Tours – The database provides information about tours of the region to help new residents and tourists become more familiar with the area. Information regarding the length of the tour (distance and time) and cost is included. Major Transportation – The database provides information about other forms of major transportation in the region (trains, buses, airports) to further help users of the database navigate their way around the region.
Sample SQL Queries
-‐ Stations on the Blue Line:
-‐
-‐
-‐
SELECT Station_Name, Color_Line, City, State FROM Stations WHERE (Color_Line ='B') or (Color_Line ='S, B') or (Color_Line ='O, S, B') or (Color_Line ='B') or (Color_Line ='R, O, S, B') or (Color_Line ='B, Y') or (Color_Line ='O, S, B, Y, G'); Free Attractions at Gallery Place-‐Chinatown Stop: SELECT Entertainment_Attraction, Type, Cost_of_Admission, Website FROM Entertainment_Attractions WHERE (Entertainment_Attractions.Station_Name = 'Gallery Place-‐Chinatown') AND (Cost_of_Admission = 'Free'); Orange Line End Points: SELECT End_Station1, ES1_State, End_Station2, ES2_State, Frequency FROM Lines WHERE (Color = 'Orange'); Smithsonian Stop Attractions: SELECT Entertainment_Attraction, Type, Cost_of_Admission, Website FROM Entertainment_Attractions WHERE (Entertainment_Attractions.Station_Name = 'Smithsonian');
Description of Implementation: We populated the tables in Excel before successfully bringing them into Access. In Access we were able to define the relationships between the tables, thus resulting in a functional database. We tested the database to verify this. The data we used came from various online sources. We did not encounter any significant problems.
Summary and Conclusions: We feel that we were able to successfully complete the project within the given time limit. We followed a structured approach where we identified a list of data elements and then refined it into an E-‐R diagram and tables that were the basis of our database. By completing this
database we are able to provide the tourists and residents of Washington, D.C. with a valuable tool that will improve the navigability of the city.
References Washington Metropolitan Area Transportation Authority -‐ http://www.wmata.com Smithsonian -‐ http://www.si.edu/Museums Google Maps -‐ https://www.google.com/maps
Appendix Tables in Access. Note that the Stations table has 91 entries. Not all are shown here.