Huiling He
Master's of City Planning | Smart Cities Concentration University of Pennsylvania
Content 01 Bike Share Rebalancing in Chicago
With 1 partner | Instructor: Ken Steif
02 Risk Terrain Model for Crime Prediction
Individual | Instructor: Ken Steif
03 Sensing the City - Conversational Bin
With 2 partners | Instructor: Allison Lassiter
01 Bike Share Rebalancing in Chicago With 1 partner | Instructor: Ken Steif
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BIKE SHARE REBALANCING IN CHICAGO
Bike Share Rebalancing in Chicago
Divvy is operating 580 bike stations in Chicago. However, we discover that this bike share system needs improvement. There are too many stations either with not enough bikes or with riders waiting for an empty slot to park their bike. The existing Divvy app is sometimes inconvenient for users since there is a lag between the reality and the information shown on the app. Our goal is to create a new app that will provide more user-friendly and time efficient divvy trips for all the bike users, and therefore to promote the usage of this bike share system.
OUR NEW APP
Predicted departure < (Predicted Arrival + Predicted departure < Bikes Predicted departure Bikes)< (Predicted Arrival +
THE EXISTING APP
Divvy can only show one Predicted departure < Bikes Bikes) status of bike stations, while our Divvy Plus will Predicted departure > (Predicted Arrival + Bike = 0 Bikes)of classify different types bike stations by different colors. Predicted departure > (Predicted Arrival +
What can the existing Divvy app tells you? • Location of divvy station • Bikes available
Bikes)
Predicted departure > (Predicted Arrival + Bikes)
• Docks available • Where to stop the bike
Predicted departure < (Predicted Arrival + Bikes)
Predicted departure < Bikes
Predicted departure < Bikes
Predicted departure < (Predicted Arrival + Bikes)
Predicted departure > (Predicted Arrival + Bikes)
Bike = 0
Bike = 0
Bike = 0
What can't the existing Divvy app tells you? • Will there be bikes when arriving • How long to wait in no-bike stations • Will dock still be available when trip ends
Bike Balance Forecast Youtube video for more information: https://www.youtube.com/watch?v=j_K0VTCtEyg 4
Wait Time Calculation
Trips Comparison
BIKE SHARE REBALANCING IN CHICAGO
PREDICTION MODEL
REGRESSION RESULT Accuracy: 0.69
Variables • Demographic characteristics • Economic characteristics • Spatial characteristics • Day and hour • Weather • Historical departures and arrivals Training Set • 2015 Aug 1st to Aug 7th • 24192 samples Test Set
Our result shows that the accuracy of o u r m o d e l i s a ro u n d 0 . 6 9 and the mean absolute error is 1.72, which means that for each departure we predict, the average error is around 2 departures. From the test set we predicted on August 8th and 10th, the result shows that the percent of error is larger at night for both the weekday and the weekend, but it is much lower and more stable during the day time, which means that our model is doing well at hours with more departures. This is the outcome we want, since typically, more people use bikes in daytime, and fewer people use bikes at midnight.
• 2015 Aug 8th & Aug 10th Most Significant Variables • Departures a week ago • Departures an hour ago • Arrivals a week ago • Rain intensity • Number of the day Selected Divvy Stations Youtube video for more information: https://www.youtube.com/watch?v=j_K0VTCtEyg 5
02 Risk Terrain Model (RTM) for Crime Prediction Individual | Instructor: Ken Steif
02
Risk Terrain Model (RTM) for Crime Prediction KERNEL DENSITY MAPS
In this project, I am doing the crime analysis using the Chicago assault data in 2014, and do this analysis using two different methods. The first method, which is commonly used in many crime analysis cases, is generating the hot spot of crimes using kernel density in ArcMap. The second method is using the Risk Terrain Model for crime prediction. In this model, I choose 12 statistically significant factors and build a poission regression. Then I compare the result of these two methods and discover that the RTM is doing better at predicting the count of crime than the traditional kernal density map. This discovery may improve the accuracy of the crime prediction and the efficiency of the police resource allocation.
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RISK TERRAIN MODEL FOR CRIME PREDICTION
RMT MODEL - STATISTICALLY SIGNIFICANT FACTORS
BIKE SHARE REBALANCING IN CHICAGO
RMT PREDICTION AND GOODNESS OF FIT When testing the goodness of fit, I am comparing the result of the RMT model and four kernel density maps with four different search radius. Notice that for kernel density map, as the search radius increase, the percent of correctly predicted cases on higher risk level increase as well, but compared to RMT model, it is still very low. In the real world, we want our model to do well in predicting areas with higher risk level, and from the bar chart we can see that when predicting for areas with higher risk level, RMT model works much better than kernel density model. That is what we want.
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03 Sensing the City - Conversational Bin With 2 partners | Instructor: Allison Lassiter
03
SENSING THE CITY - CONVERSATIONAL BIN
Sensing the City - Conversational Bin
IN PHILADELPHIA, HANDLING TRASH OVERFLOW ON STREETS IS EXPENSIVE ISSUE 1: Is
$3,700 per BigBelly worth it?
ISSUE 2: The alert system wihch cost
annually usually DOES NOT WORK
$132,000
â&#x20AC;&#x153;The trash compactors broke down and the city needed a five-person squad dedicated to fixing that, an unforeseen expense.â&#x20AC;? Source: philadelphiacontroller.org
ISSUE 3: The super expensive BigBellys are mainly
installed in
CENTER CITY
The high expense of bigbellys makes the use of them limited to center city, while the need of trash management along other retail corridors that serve communities more locally is usually neglected.
Source: philadelphiacontroller.org
Source: billypenn.com
The Bigbelly app only opens to the internal uses for the streets department
Source: PERSPECTIVES
See our blog for more information: http://www.sensingthecity.com/conversational-bin/ 10
We realized that the current approach of dealing with trash overflow fails to achieve the goal of city-wide street maintenance. It is solely dependent on the manager side - the streets department, while users are missing from the maintenance process. They could generate positive impacts when provided proper directions about trash throwing.
SENSING THE CITY - CONVERSATIONAL BIN
LOW-COST CONVERSATIONAL BINS THAT ENGAGE USERS IN MAINTENANCE
SCENARIO ONE No one is going to use the trash bin.
SCENARIO TWO If it is not full, light up and Say Hi to somebody who wants to use the bin.
SCENARIO THREE
Location: 52nd Chestnut Street
See our blog for more information: http://www.sensingthecity.com/conversational-bin/ 11
If it is already full, point the arrow to another usable bin and light up.
SENSING THE CITY - CONVERSATIONAL BIN
PROTOTYPE USING SENSORS
How to protect our electronic parts from trashes?
DOUBLE LAYER TRASH BIN
Hardware
3 Arduino Boards
3 Bread Boards
3 Batteries
What are we sensing? Approaching a person person Approachingof of a
Full levelofof a trash Full level a trash cancan
PIR Sensor $9.95 PIR Motion Motion Sensor $9.95
IRIRBreak BeamSensor Sensor $1.95 Break Beam $1.95
How to respond?
Scenario One
Communicate with other trash cans Communicate with other
Direct to the nearest available trash can Direct to the nearest
Bluetooth $11.49 Bluetooth $11.49
Servo $5.95 Servo $5.95
trash cans
available trash can
Scenario Two
Indicator of the full level & Indicator approaching of people of the full level & approaching of people
LED $3.95 LED $3.95
* See our blog for a more interactive video showing how our prototype works
See our blog for more information: http://www.sensingthecity.com/conversational-bin/ 12
Scenario Three