Parking Prediction based on Mobile application
$begingroup$
I am quite new to predictive modelling but have knowledge of GIS, R, python, SQL, etc.
I am currently doing a project in work trying to predict when parking spaces will be available based on data received from a mobile phone application.
I have 2 sql tables
ParkingTickets
- ParkingTicketId (Integer)
- ParkingAreaId (Integer)
- Latitude (Integer)
- Longitude (Integer)
- Location (Text)
- ParkingDate (DateTime)
- DurationInMinutes (Integer)
- ExpiryDate (DateTime)
- Day (Text)
ParkingAreas:
- ParkingAreaId (Integer)
- MaxSpaces (Integer)
Main assumption is that only app users can park in the 18 parking lots (both on-street and off-street lots (no multi store or underground)). I do not take in to account user preference, weather, events, etc., etc. This is purely presence and absence based on data at hand. I have researched techniques and was looking into using a birth/death model but struggling to find examples of it in use.
Any help or pointers on models to use would be brilliant!
sample data:
ParkingTicketId ParkingAreaId Latitude Longitude Date DurationInMinutes ExpiryDate Day
60465 302 42.56246869 -70.91313754 2014/03/07 16:36 5 2014/03/07 16:41 Friday
60466 302 42.57139883 -70.91906364 2014/03/07 16:36 23 2014/03/07 16:59 Friday
60467 302 42.54419925 -70.9417496 2014/03/07 16:36 24 2014/03/07 17:00 Friday
60472 302 42.57576595 -70.92876607 2014/03/07 16:36 16 2014/03/07 16:52 Friday
60477 302 42.55573294 -70.92912634 2014/03/07 16:36 9 2014/03/07 16:45 Friday
60479 302 42.55711998 -70.91200458 2014/03/07 16:36 19 2014/03/07 16:55 Friday
60480 302 42.58008043 -70.91559081 2014/03/07 16:37 5 2014/03/07 16:42 Friday
60485 302 42.55161223 -70.9240808 2014/03/07 16:37 21 2014/03/07 16:58 Friday
60492 302 42.58437849 -70.92764527 2014/03/07 16:37 6 2014/03/07 16:43 Friday
ParkingAreaId MaxSpaces
302 8
304 50
306 95
308 30
machine-learning python r predictive-modeling
asked Jul 1 '16 at 18:52
RobRob
211
$endgroup$
add a comment |
$begingroup$
I am quite new to predictive modelling but have knowledge of GIS, R, python, SQL, etc.
I am currently doing a project in work trying to predict when parking spaces will be available based on data received from a mobile phone application.
I have 2 sql tables
ParkingTickets
- ParkingTicketId (Integer)
- ParkingAreaId (Integer)
- Latitude (Integer)
- Longitude (Integer)
- Location (Text)
- ParkingDate (DateTime)
- DurationInMinutes (Integer)
- ExpiryDate (DateTime)
- Day (Text)
ParkingAreas:
- ParkingAreaId (Integer)
- MaxSpaces (Integer)
Main assumption is that only app users can park in the 18 parking lots (both on-street and off-street lots (no multi store or underground)). I do not take in to account user preference, weather, events, etc., etc. This is purely presence and absence based on data at hand. I have researched techniques and was looking into using a birth/death model but struggling to find examples of it in use.
Any help or pointers on models to use would be brilliant!
sample data:
ParkingTicketId ParkingAreaId Latitude Longitude Date DurationInMinutes ExpiryDate Day
60465 302 42.56246869 -70.91313754 2014/03/07 16:36 5 2014/03/07 16:41 Friday
60466 302 42.57139883 -70.91906364 2014/03/07 16:36 23 2014/03/07 16:59 Friday
60467 302 42.54419925 -70.9417496 2014/03/07 16:36 24 2014/03/07 17:00 Friday
60472 302 42.57576595 -70.92876607 2014/03/07 16:36 16 2014/03/07 16:52 Friday
60477 302 42.55573294 -70.92912634 2014/03/07 16:36 9 2014/03/07 16:45 Friday
60479 302 42.55711998 -70.91200458 2014/03/07 16:36 19 2014/03/07 16:55 Friday
60480 302 42.58008043 -70.91559081 2014/03/07 16:37 5 2014/03/07 16:42 Friday
60485 302 42.55161223 -70.9240808 2014/03/07 16:37 21 2014/03/07 16:58 Friday
60492 302 42.58437849 -70.92764527 2014/03/07 16:37 6 2014/03/07 16:43 Friday
ParkingAreaId MaxSpaces
302 8
304 50
306 95
308 30
machine-learning python r predictive-modeling
asked Jul 1 '16 at 18:52
RobRob
211
$endgroup$
add a comment |
$begingroup$
I am quite new to predictive modelling but have knowledge of GIS, R, python, SQL, etc.
I am currently doing a project in work trying to predict when parking spaces will be available based on data received from a mobile phone application.
I have 2 sql tables
ParkingTickets
- ParkingTicketId (Integer)
- ParkingAreaId (Integer)
- Latitude (Integer)
- Longitude (Integer)
- Location (Text)
- ParkingDate (DateTime)
- DurationInMinutes (Integer)
- ExpiryDate (DateTime)
- Day (Text)
ParkingAreas:
- ParkingAreaId (Integer)
- MaxSpaces (Integer)
Main assumption is that only app users can park in the 18 parking lots (both on-street and off-street lots (no multi store or underground)). I do not take in to account user preference, weather, events, etc., etc. This is purely presence and absence based on data at hand. I have researched techniques and was looking into using a birth/death model but struggling to find examples of it in use.
Any help or pointers on models to use would be brilliant!
sample data:
ParkingTicketId ParkingAreaId Latitude Longitude Date DurationInMinutes ExpiryDate Day
60465 302 42.56246869 -70.91313754 2014/03/07 16:36 5 2014/03/07 16:41 Friday
60466 302 42.57139883 -70.91906364 2014/03/07 16:36 23 2014/03/07 16:59 Friday
60467 302 42.54419925 -70.9417496 2014/03/07 16:36 24 2014/03/07 17:00 Friday
60472 302 42.57576595 -70.92876607 2014/03/07 16:36 16 2014/03/07 16:52 Friday
60477 302 42.55573294 -70.92912634 2014/03/07 16:36 9 2014/03/07 16:45 Friday
60479 302 42.55711998 -70.91200458 2014/03/07 16:36 19 2014/03/07 16:55 Friday
60480 302 42.58008043 -70.91559081 2014/03/07 16:37 5 2014/03/07 16:42 Friday
60485 302 42.55161223 -70.9240808 2014/03/07 16:37 21 2014/03/07 16:58 Friday
60492 302 42.58437849 -70.92764527 2014/03/07 16:37 6 2014/03/07 16:43 Friday
ParkingAreaId MaxSpaces
302 8
304 50
306 95
308 30
machine-learning python r predictive-modeling
asked Jul 1 '16 at 18:52
RobRob
211
$endgroup$
I am quite new to predictive modelling but have knowledge of GIS, R, python, SQL, etc.
I am currently doing a project in work trying to predict when parking spaces will be available based on data received from a mobile phone application.
I have 2 sql tables
ParkingTickets
- ParkingTicketId (Integer)
- ParkingAreaId (Integer)
- Latitude (Integer)
- Longitude (Integer)
- Location (Text)
- ParkingDate (DateTime)
- DurationInMinutes (Integer)
- ExpiryDate (DateTime)
- Day (Text)
ParkingAreas:
- ParkingAreaId (Integer)
- MaxSpaces (Integer)
Main assumption is that only app users can park in the 18 parking lots (both on-street and off-street lots (no multi store or underground)). I do not take in to account user preference, weather, events, etc., etc. This is purely presence and absence based on data at hand. I have researched techniques and was looking into using a birth/death model but struggling to find examples of it in use.
Any help or pointers on models to use would be brilliant!
sample data:
ParkingTicketId ParkingAreaId Latitude Longitude Date DurationInMinutes ExpiryDate Day
60465 302 42.56246869 -70.91313754 2014/03/07 16:36 5 2014/03/07 16:41 Friday
60466 302 42.57139883 -70.91906364 2014/03/07 16:36 23 2014/03/07 16:59 Friday
60467 302 42.54419925 -70.9417496 2014/03/07 16:36 24 2014/03/07 17:00 Friday
60472 302 42.57576595 -70.92876607 2014/03/07 16:36 16 2014/03/07 16:52 Friday
60477 302 42.55573294 -70.92912634 2014/03/07 16:36 9 2014/03/07 16:45 Friday
60479 302 42.55711998 -70.91200458 2014/03/07 16:36 19 2014/03/07 16:55 Friday
60480 302 42.58008043 -70.91559081 2014/03/07 16:37 5 2014/03/07 16:42 Friday
60485 302 42.55161223 -70.9240808 2014/03/07 16:37 21 2014/03/07 16:58 Friday
60492 302 42.58437849 -70.92764527 2014/03/07 16:37 6 2014/03/07 16:43 Friday
ParkingAreaId MaxSpaces
302 8
304 50
306 95
308 30
machine-learning python r predictive-modeling
machine-learning python r predictive-modeling
asked Jul 1 '16 at 18:52
RobRob
211
asked Jul 1 '16 at 18:52
RobRob
211
asked Jul 1 '16 at 18:52
RobRob
211
asked Jul 1 '16 at 18:52
RobRob
211
asked Jul 1 '16 at 18:52
RobRob
211
211
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
If this data is coming in real-time then you don't need a model -- simply check how many spots have an ExpiryDate greater than now (i.e. when you need to provide a prediction) and subtract this from the total capacity of spots.
If the data is not coming in real-time, then you could use time of day and day of week as predictors. You might even want to make them into interaction terms. You would also need to decide how often you want to call your model and group your data so that each row represents how many tickets were active during that timeframe; this will define your target variable (what you're trying to predict).
By the way, I think you're referring to a survival model (https://en.wikipedia.org/wiki/Proportional_hazards_model). I would recommend gradient boosting instead; it's much more powerful. Gradient boosting models (GBM) are part of caret in R and scikit-learn in Python by the way.
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
$endgroup$
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
add a comment |
$begingroup$
I am also working on a si.ilar problem. I was wondering if you were able to develop a working model to predict parking spot availability with date and time?
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
If this data is coming in real-time then you don't need a model -- simply check how many spots have an ExpiryDate greater than now (i.e. when you need to provide a prediction) and subtract this from the total capacity of spots.
If the data is not coming in real-time, then you could use time of day and day of week as predictors. You might even want to make them into interaction terms. You would also need to decide how often you want to call your model and group your data so that each row represents how many tickets were active during that timeframe; this will define your target variable (what you're trying to predict).
By the way, I think you're referring to a survival model (https://en.wikipedia.org/wiki/Proportional_hazards_model). I would recommend gradient boosting instead; it's much more powerful. Gradient boosting models (GBM) are part of caret in R and scikit-learn in Python by the way.
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
$endgroup$
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
add a comment |
$begingroup$
If this data is coming in real-time then you don't need a model -- simply check how many spots have an ExpiryDate greater than now (i.e. when you need to provide a prediction) and subtract this from the total capacity of spots.
If the data is not coming in real-time, then you could use time of day and day of week as predictors. You might even want to make them into interaction terms. You would also need to decide how often you want to call your model and group your data so that each row represents how many tickets were active during that timeframe; this will define your target variable (what you're trying to predict).
By the way, I think you're referring to a survival model (https://en.wikipedia.org/wiki/Proportional_hazards_model). I would recommend gradient boosting instead; it's much more powerful. Gradient boosting models (GBM) are part of caret in R and scikit-learn in Python by the way.
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
$endgroup$
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
add a comment |
$begingroup$
If this data is coming in real-time then you don't need a model -- simply check how many spots have an ExpiryDate greater than now (i.e. when you need to provide a prediction) and subtract this from the total capacity of spots.
If the data is not coming in real-time, then you could use time of day and day of week as predictors. You might even want to make them into interaction terms. You would also need to decide how often you want to call your model and group your data so that each row represents how many tickets were active during that timeframe; this will define your target variable (what you're trying to predict).
By the way, I think you're referring to a survival model (https://en.wikipedia.org/wiki/Proportional_hazards_model). I would recommend gradient boosting instead; it's much more powerful. Gradient boosting models (GBM) are part of caret in R and scikit-learn in Python by the way.
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
$endgroup$
If this data is coming in real-time then you don't need a model -- simply check how many spots have an ExpiryDate greater than now (i.e. when you need to provide a prediction) and subtract this from the total capacity of spots.
If the data is not coming in real-time, then you could use time of day and day of week as predictors. You might even want to make them into interaction terms. You would also need to decide how often you want to call your model and group your data so that each row represents how many tickets were active during that timeframe; this will define your target variable (what you're trying to predict).
By the way, I think you're referring to a survival model (https://en.wikipedia.org/wiki/Proportional_hazards_model). I would recommend gradient boosting instead; it's much more powerful. Gradient boosting models (GBM) are part of caret in R and scikit-learn in Python by the way.
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
answered Jul 1 '16 at 21:23
Ryan ZottiRyan Zotti
2,57431227
2,57431227
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
add a comment |
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Thank you @RyanZotti for your reply. I am using data sourced from monthly backups as I currently do not have access to the Live database (as it sourced externally) so it is not in real-time data. If I was to use time of day/ day of week as predictor would I need to have the data in a single table or could I use it as is and link to the "ParkingArea" table to get the MaxSpaces for each area? Thank you for GBM seems like it will do the job!
$endgroup$
– Rob
Jul 2 '16 at 15:06
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Caret and scikit-learn require your dataset comes in a single dataframe. So yes, one way would be to make your dataset into a temporary table and then use something like Pandas (Python) or RMySQL (R) to import and convert the temporary table into a dataframe. ... Either way you're going to have to do some SQL transformations because the data is not structured in such a way that your model would be able to use it as is (you'll need to aggregate by time)
$endgroup$
– Ryan Zotti
Jul 2 '16 at 15:23
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
$begingroup$
Thank you very Ryan. You have been very helpful. I may be back in contact if I get stuck but should be able to progress.
$endgroup$
– Rob
Jul 4 '16 at 8:22
add a comment |
$begingroup$
I am also working on a si.ilar problem. I was wondering if you were able to develop a working model to predict parking spot availability with date and time?
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am also working on a si.ilar problem. I was wondering if you were able to develop a working model to predict parking spot availability with date and time?
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am also working on a si.ilar problem. I was wondering if you were able to develop a working model to predict parking spot availability with date and time?
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am also working on a si.ilar problem. I was wondering if you were able to develop a working model to predict parking spot availability with date and time?
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
answered 13 mins ago
Farrukh SohailFarrukh Sohail
1
1
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Farrukh Sohail is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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