This document summarizes a student project analyzing bike rental data to build predictive models for casual and registered bike users. The students created separate linear regression models for casual and registered users. For casual users, they found the original model violated assumptions, so they took the response variable to the power of 0.4 and saw improved linearity and constant variance. Their best casual user model included variables for weather, season, month, and temperature. For registered users, they applied a mean shift which showed unbiasedness. The students' predictive models were better at predicting bike use away from holidays and extreme weather.
Bounce Scooter is a Mysore-based startup that provides an urban mobility solution called Bounce bikes, also known as Metro bikes. It allows users to pick up a scooter from anywhere and ride it to their destination, then drop it off at any location. Bounce competes with other scooter sharing services like Onn bikes, Wheelstreet, Vogo, Drivezy, Roadpanda, and Rentomojo. Bounce targets students and working professionals between 18-35 years old in middle-income brackets living in metro and urban areas near metro stations. It positions itself as allowing users to "Pick and drop anywhere" for convenient, affordable mobility around town.
The document discusses different types of bike sharing systems, including traditional rental shops, large-scale public systems with thousands of bikes and hundreds of stations like in San Francisco and Paris, standalone rental units, peer-to-peer rental like Spinlister, delivery services like Minaport, and considerations for sharing like convenience, trust, information, technology, and user service.
Disruptive Trends That Will Transform The Automotive IndustryStradablog
Technology-driven trends will revolutionize how industry players respond to changing consumer behavior, develop partnerships, and drive transformational change.
This document discusses smart cities and KT Corporation's smart city strategy. It begins with definitions of traditional urban ICT, U-City, and smart city concepts. It then outlines KT's vision for smart cities and its partnership with Cisco to provide total ICT services through all phases of smart space development. KT aims to export its smart city expertise and has established a public-private company called Incheon U-City to implement its first smart city project in South Korea.
India is definitely developing at a rapid pace. Being native Bengalurians, we've seen the city grow from being called the Garden City of India to the Startup Capital of the world.
With advancement comes it's own set of challenges, and one that we face here is that of traffic management. While we weren't able to bring the solution to life, happy to share our deck and hope we can collaborate with someone who'd like to take this forward. Do reach out to me at me@suhasmotwani.com - Happy to forward our research!
Do let me know if I could help you on your Product and Growth journey > www.suhasmotwani.com
Bounce Scooter is a Mysore-based startup that provides an urban mobility solution called Bounce bikes, also known as Metro bikes. It allows users to pick up a scooter from anywhere and ride it to their destination, then drop it off at any location. Bounce competes with other scooter sharing services like Onn bikes, Wheelstreet, Vogo, Drivezy, Roadpanda, and Rentomojo. Bounce targets students and working professionals between 18-35 years old in middle-income brackets living in metro and urban areas near metro stations. It positions itself as allowing users to "Pick and drop anywhere" for convenient, affordable mobility around town.
The document discusses different types of bike sharing systems, including traditional rental shops, large-scale public systems with thousands of bikes and hundreds of stations like in San Francisco and Paris, standalone rental units, peer-to-peer rental like Spinlister, delivery services like Minaport, and considerations for sharing like convenience, trust, information, technology, and user service.
Disruptive Trends That Will Transform The Automotive IndustryStradablog
Technology-driven trends will revolutionize how industry players respond to changing consumer behavior, develop partnerships, and drive transformational change.
This document discusses smart cities and KT Corporation's smart city strategy. It begins with definitions of traditional urban ICT, U-City, and smart city concepts. It then outlines KT's vision for smart cities and its partnership with Cisco to provide total ICT services through all phases of smart space development. KT aims to export its smart city expertise and has established a public-private company called Incheon U-City to implement its first smart city project in South Korea.
India is definitely developing at a rapid pace. Being native Bengalurians, we've seen the city grow from being called the Garden City of India to the Startup Capital of the world.
With advancement comes it's own set of challenges, and one that we face here is that of traffic management. While we weren't able to bring the solution to life, happy to share our deck and hope we can collaborate with someone who'd like to take this forward. Do reach out to me at me@suhasmotwani.com - Happy to forward our research!
Do let me know if I could help you on your Product and Growth journey > www.suhasmotwani.com
Everyday Democracy Evaluation Guide Toolkit with Ripple MappingEveryday Democracy
This document provides tools and guidance for evaluating community engagement efforts, including a self-assessment of evaluation capacity, a sample logic model, and templates for data collection planning and mapping ripple effects. Key tools include an evaluation capacity self-assessment matrix to rate resources, knowledge, data availability, and practices; a logic model template to outline inputs, activities, outputs, outcomes and goals; and templates for planning data collection strategies and mapping impacts. The overall document aims to initiate discussion around readiness to evaluate and provide templates to facilitate the evaluation process.
A starter motor in a car is a cranking contrivance used to crank the internal combustion engine to bring the vehicle into the state of operation. It allows the engine to start by spinning over the turning crankshaft. The failure of a starter motor can be annoying. Sometimes, it burns up due to manufacturing defects, and sometimes, the cause can be a solenoidal issue or a user error. Incorrect starter fluid also puts a lot of stress on the starter. Some problems are virtually impossible to avoid. To avoid hefty repair bill on repair, you have to keep up with the recommend replacement interval and keep an eye on the vehicle’s abnormality related to the performance.
Triangle Bike Share - Pitch PresentationJosh Bielick
The document proposes a public bike share system for the Triangle region of North Carolina. It summarizes the benefits of bike sharing for transportation, health, environment, social and economic development. It then provides an overview of existing bike share systems in other cities and describes how a potential Triangle system would work, including use of GPS and RFID technology. Finally, it outlines plans for operating a bike share start-up, including target markets, membership types, pricing, costs and funding sources.
Waze, the mobile location landscape; avichai bakstMatthew Robinson
This document discusses location-based marketing and Waze's role in the mobile location landscape. It notes that location-based marketing spending is projected to grow significantly by 2019. Waze is described as a real-time crowdsourced traffic and navigation app with over 50 million global monthly active users. The document outlines Waze Ads which allows targeted mobile advertising on the app based on location and route details. It provides examples of ad effectiveness studies showing increased brand recall and navigation engagement from Waze ads.
The slide gives overview of slolving current heavy traffic situation by changing minimal infrastructure anfd investing less cost..
This slide is a presentation slide of an international conference named - ICIEV, 2016. And the paper is published in IEEE xplore. The full paper can be found here: http://ieeexplore.ieee.org/abstract/document/7760025/
This document summarizes a project that aims to develop a software system using computer vision and machine learning to detect whether motorcycle riders in Bangladesh are wearing helmets. It will use a camera to take photos of riders, apply object detection models like YOLO to identify bikes and people, and check if the riders are wearing helmets. If not, it will record the bike's license plate number. The document reviews similar existing works and compares the parameters of this project. It outlines that the project will be implemented in Python using YOLO and OpenCV for real-time object detection and helmet detection from images to help enforce road safety in Bangladesh.
1. What is a Smart city?
2. Criteria for a Smart city.
3. Timeline of smart city project.
4. Smart city projects in India.
5. Smart city elements.
6. Special Purpose Vehicle (SPV)
The document discusses a proposed new bike rebalancing program for Divvy bike share that incorporates user assistance. It summarizes that current rebalancing is ineffective, leading to stations running low on bikes. The recommendation is to incentivize users traveling to overpopulated stations to change destinations to nearby in-demand stations through trip credit rewards. This would help rebalance bikes while ensuring user safety by alerting them about crime hotspots. Benefits include improved system efficiency and encouraging subscribers. Future steps involve further pricing and notification strategies.
MARKETING PLAN FOR AN ANDROID APP: LIFTd ( THE CARPOOLING APP)Anjali Setiya
LIFTd is a carpooling app that aims to address issues like high traffic, pollution, and driving hours. The app allows drivers to offer available seats and passengers to book seats. Users can choose drivers or passengers based on preferences like gender or occupation. The goal is to make rides cheaper and more sustainable by sharing. The marketing plan discusses launching the app in cities and towns across India, with targets of 500,000 users within two fiscal years. It outlines strategies for user acquisition, partnerships, pricing, and building a brand positioned as "sharing the planet." Tactics include cashbacks, filters, GPS safety, and building an online community.
Real time detection system of driver distraction.pdfReena_Jadhav
There is accumulating evidence that driver distrac- tion is a leading cause of vehicle crashes and incidents. In par- ticular, increased use of so-called in-vehicle information systems (IVIS) have raised important and growing safety concerns. Thus, detecting the driver’s state is of paramount importance, to adapt IVIS, therefore avoiding or mitigating their possible negative effects. The purpose of this presentation is to show a method for the nonintrusive and real- time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a BSN has outperformed all the other ML methods, providing the highest classification rate for most of the subjects.
Index Terms—Accident prevention, artificial intelligence and machine learning (ML), driver distraction and inattention, intel- ligent supporting systems.
This document provides information about NOAH Conference Berlin 2019 and the company NOAH. Some key points:
- NOAH offers a mobility solution called VIRTUO that provides car rentals from 1-30+ days through an app-based experience with a fleet of premium vehicles.
- Their customers include young urban residents and those taking weekend, holiday, or business trips who are moving away from car ownership.
- NOAH sees an opportunity in the €450 billion long-distance mobility market in Europe, which is currently 75% served by cars.
- NOAH aims to create a unique car rental experience to make owning or renting elsewhere irrelevant, providing proximity, comfort, and accessibility advantages without
Carpooling App India - UX Desin - Avjit ChinaraAvijit Chinara
In these slides I have explained the UX design process used during carpooling App. These slides have been sanitized to make shareable. Hope this will give a clear idea about the UX design process.
Ride sharing app (Taxi Booking App) - This is an app similar to Uber or any other cab booking applications. It provides a secure and hassle-free platform for Passengers to find & book their rides. Drivers can use this app for finding nearby trips and communicating with passengers. Interested to develop a similar Ride sharing app like Uber? Drop an email to us at business@techcronus.com
This document provides instructions for a listening activity where the participant will hear responses to sentence pairs that are either option a or b. The responses will use the same words but with different intonation, either going up or down, and the participant must draw a line in the corresponding box to indicate the intonation and choose if it matches option a or b. An example is provided to demonstrate.
Este documento describe una escuela de tiempo completo centrada en mejorar los aprendizajes de los estudiantes. Explica que la escuela funciona de lunes a viernes de 8 am a 4 pm, ofreciendo clases académicas, actividades extracurriculares y apoyo socioemocional. El objetivo es brindar a los estudiantes un ambiente integral que promueva su desarrollo académico y personal.
O documento descreve os componentes básicos de um computador, incluindo o processador, memória, barramento e periféricos. Explica que o processador processa informações armazenadas na memória, a memória armazena programas e dados, e os periféricos permitem a entrada e saída de dados. O barramento conecta esses componentes para permitir a comunicação dentro do sistema.
Cheryl Thurwanger is an experienced professional buyer seeking a buyer/seller position that offers progression. She has over 10 years of experience in procurement, negotiations, and contract administration for aerospace and defense programs. Her skills include customer service, teamwork, Microsoft Office, and APICS certification. Her work history includes positions as a buyer for an aerospace supplier, food service manager for schools, and material coordinator and data entry roles for defense contractors. She has a background in supply chain, FAR/DFAR, and program negotiations.
Rassegna stampa dei dati di Bilancio 1° semestre 2016, del gruppo di Gregorio Fogliani, con previsioni di fatturato sopra i 650 milioni del 2015 e un miglioramento della redditività del 20%.
El documento describe un estudio para generar curvas de intensidad-duración-frecuencia (IDF) para 52 cuencas hidrográficas en Panamá utilizando registros de precipitación de estaciones meteorológicas. El estudio analiza series de precipitación máxima anual para calcular intensidades y aplica modelos estadísticos como el de Chow para estimar intensidades de precipitación para diferentes períodos de retorno. El objetivo es actualizar las curvas IDF existentes con nuevos datos para mejorar el diseño de infraestructura hidráulica en Panam
Everyday Democracy Evaluation Guide Toolkit with Ripple MappingEveryday Democracy
This document provides tools and guidance for evaluating community engagement efforts, including a self-assessment of evaluation capacity, a sample logic model, and templates for data collection planning and mapping ripple effects. Key tools include an evaluation capacity self-assessment matrix to rate resources, knowledge, data availability, and practices; a logic model template to outline inputs, activities, outputs, outcomes and goals; and templates for planning data collection strategies and mapping impacts. The overall document aims to initiate discussion around readiness to evaluate and provide templates to facilitate the evaluation process.
A starter motor in a car is a cranking contrivance used to crank the internal combustion engine to bring the vehicle into the state of operation. It allows the engine to start by spinning over the turning crankshaft. The failure of a starter motor can be annoying. Sometimes, it burns up due to manufacturing defects, and sometimes, the cause can be a solenoidal issue or a user error. Incorrect starter fluid also puts a lot of stress on the starter. Some problems are virtually impossible to avoid. To avoid hefty repair bill on repair, you have to keep up with the recommend replacement interval and keep an eye on the vehicle’s abnormality related to the performance.
Triangle Bike Share - Pitch PresentationJosh Bielick
The document proposes a public bike share system for the Triangle region of North Carolina. It summarizes the benefits of bike sharing for transportation, health, environment, social and economic development. It then provides an overview of existing bike share systems in other cities and describes how a potential Triangle system would work, including use of GPS and RFID technology. Finally, it outlines plans for operating a bike share start-up, including target markets, membership types, pricing, costs and funding sources.
Waze, the mobile location landscape; avichai bakstMatthew Robinson
This document discusses location-based marketing and Waze's role in the mobile location landscape. It notes that location-based marketing spending is projected to grow significantly by 2019. Waze is described as a real-time crowdsourced traffic and navigation app with over 50 million global monthly active users. The document outlines Waze Ads which allows targeted mobile advertising on the app based on location and route details. It provides examples of ad effectiveness studies showing increased brand recall and navigation engagement from Waze ads.
The slide gives overview of slolving current heavy traffic situation by changing minimal infrastructure anfd investing less cost..
This slide is a presentation slide of an international conference named - ICIEV, 2016. And the paper is published in IEEE xplore. The full paper can be found here: http://ieeexplore.ieee.org/abstract/document/7760025/
This document summarizes a project that aims to develop a software system using computer vision and machine learning to detect whether motorcycle riders in Bangladesh are wearing helmets. It will use a camera to take photos of riders, apply object detection models like YOLO to identify bikes and people, and check if the riders are wearing helmets. If not, it will record the bike's license plate number. The document reviews similar existing works and compares the parameters of this project. It outlines that the project will be implemented in Python using YOLO and OpenCV for real-time object detection and helmet detection from images to help enforce road safety in Bangladesh.
1. What is a Smart city?
2. Criteria for a Smart city.
3. Timeline of smart city project.
4. Smart city projects in India.
5. Smart city elements.
6. Special Purpose Vehicle (SPV)
The document discusses a proposed new bike rebalancing program for Divvy bike share that incorporates user assistance. It summarizes that current rebalancing is ineffective, leading to stations running low on bikes. The recommendation is to incentivize users traveling to overpopulated stations to change destinations to nearby in-demand stations through trip credit rewards. This would help rebalance bikes while ensuring user safety by alerting them about crime hotspots. Benefits include improved system efficiency and encouraging subscribers. Future steps involve further pricing and notification strategies.
MARKETING PLAN FOR AN ANDROID APP: LIFTd ( THE CARPOOLING APP)Anjali Setiya
LIFTd is a carpooling app that aims to address issues like high traffic, pollution, and driving hours. The app allows drivers to offer available seats and passengers to book seats. Users can choose drivers or passengers based on preferences like gender or occupation. The goal is to make rides cheaper and more sustainable by sharing. The marketing plan discusses launching the app in cities and towns across India, with targets of 500,000 users within two fiscal years. It outlines strategies for user acquisition, partnerships, pricing, and building a brand positioned as "sharing the planet." Tactics include cashbacks, filters, GPS safety, and building an online community.
Real time detection system of driver distraction.pdfReena_Jadhav
There is accumulating evidence that driver distrac- tion is a leading cause of vehicle crashes and incidents. In par- ticular, increased use of so-called in-vehicle information systems (IVIS) have raised important and growing safety concerns. Thus, detecting the driver’s state is of paramount importance, to adapt IVIS, therefore avoiding or mitigating their possible negative effects. The purpose of this presentation is to show a method for the nonintrusive and real- time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a BSN has outperformed all the other ML methods, providing the highest classification rate for most of the subjects.
Index Terms—Accident prevention, artificial intelligence and machine learning (ML), driver distraction and inattention, intel- ligent supporting systems.
This document provides information about NOAH Conference Berlin 2019 and the company NOAH. Some key points:
- NOAH offers a mobility solution called VIRTUO that provides car rentals from 1-30+ days through an app-based experience with a fleet of premium vehicles.
- Their customers include young urban residents and those taking weekend, holiday, or business trips who are moving away from car ownership.
- NOAH sees an opportunity in the €450 billion long-distance mobility market in Europe, which is currently 75% served by cars.
- NOAH aims to create a unique car rental experience to make owning or renting elsewhere irrelevant, providing proximity, comfort, and accessibility advantages without
Carpooling App India - UX Desin - Avjit ChinaraAvijit Chinara
In these slides I have explained the UX design process used during carpooling App. These slides have been sanitized to make shareable. Hope this will give a clear idea about the UX design process.
Ride sharing app (Taxi Booking App) - This is an app similar to Uber or any other cab booking applications. It provides a secure and hassle-free platform for Passengers to find & book their rides. Drivers can use this app for finding nearby trips and communicating with passengers. Interested to develop a similar Ride sharing app like Uber? Drop an email to us at business@techcronus.com
This document provides instructions for a listening activity where the participant will hear responses to sentence pairs that are either option a or b. The responses will use the same words but with different intonation, either going up or down, and the participant must draw a line in the corresponding box to indicate the intonation and choose if it matches option a or b. An example is provided to demonstrate.
Este documento describe una escuela de tiempo completo centrada en mejorar los aprendizajes de los estudiantes. Explica que la escuela funciona de lunes a viernes de 8 am a 4 pm, ofreciendo clases académicas, actividades extracurriculares y apoyo socioemocional. El objetivo es brindar a los estudiantes un ambiente integral que promueva su desarrollo académico y personal.
O documento descreve os componentes básicos de um computador, incluindo o processador, memória, barramento e periféricos. Explica que o processador processa informações armazenadas na memória, a memória armazena programas e dados, e os periféricos permitem a entrada e saída de dados. O barramento conecta esses componentes para permitir a comunicação dentro do sistema.
Cheryl Thurwanger is an experienced professional buyer seeking a buyer/seller position that offers progression. She has over 10 years of experience in procurement, negotiations, and contract administration for aerospace and defense programs. Her skills include customer service, teamwork, Microsoft Office, and APICS certification. Her work history includes positions as a buyer for an aerospace supplier, food service manager for schools, and material coordinator and data entry roles for defense contractors. She has a background in supply chain, FAR/DFAR, and program negotiations.
Rassegna stampa dei dati di Bilancio 1° semestre 2016, del gruppo di Gregorio Fogliani, con previsioni di fatturato sopra i 650 milioni del 2015 e un miglioramento della redditività del 20%.
El documento describe un estudio para generar curvas de intensidad-duración-frecuencia (IDF) para 52 cuencas hidrográficas en Panamá utilizando registros de precipitación de estaciones meteorológicas. El estudio analiza series de precipitación máxima anual para calcular intensidades y aplica modelos estadísticos como el de Chow para estimar intensidades de precipitación para diferentes períodos de retorno. El objetivo es actualizar las curvas IDF existentes con nuevos datos para mejorar el diseño de infraestructura hidráulica en Panam
Using Platelet Rich Plasma for Orthopedic Conditionsregenmedsr
Platelet Rich Plasma is an excellent option, often with far better results than traditional methods, for musculoskeletal problems involving joint, tendons, and ligaments.
El documento habla sobre minerales y sus propiedades. Describe que los minerales son sólidos inorgánicos de origen natural con una composición química y estructura definidas. Explica algunas de las propiedades más importantes de los minerales como su color, brillo, transparencia y dureza, y cómo estas dependen de la composición y estructura del mineral. También menciona los diferentes tipos de morfología cristalina y los sistemas cristalinos en que se pueden clasificar los minerales.
Diabetic cardiomyopathy refers to myocardial disease in diabetic subjects that cannot be ascribed to hypertension, coronary artery disease (CAD), or any other known cardiac disease. Key aspects discussed include the high prevalence of heart failure in diabetic patients, pathophysiological changes such as hypertrophy and fibrosis, and risk factors like hyperglycemia and hypertension. Management involves tight control of blood pressure and blood glucose, as well as medications like angiotensin-converting enzyme inhibitors, beta blockers, and aldosterone receptor antagonists which have been shown to improve outcomes. Aggressive modification of cardiovascular risk factors is important in the management of diabetic cardiomyopathy.
Predictive Analysis of Bike Sharing System Using Machine Learning Algorithmssushantparte
Provided business solutions based on the ethical aspects of data collection and shortcomings of business by visualizing data and forecasting the demand using Ensemble Learning Technique (Random Forest) with an RMSE of 89.09%.
This document discusses bike sharing prediction and analyzing hourly bike rental data. It provides background on bike sharing systems and the problem of predicting hourly bike rental counts ("cnt") using weather and seasonal data. Several models are tested on the data including SGDRegressor, Lasso, ElasticNet, Ridge, SVR, BaggingRegressor, NuSVR, and RandomForestRegressor. The RandomForestRegressor performed best with a validation mean squared error of 1817.10 and score of 0.94. Metrics for the random forest model on training and validation data are displayed in a table.
This document is a final project report for a data analysis project predicting bike demand in the Capital Bikeshare program. The report describes preprocessing the dataset, visualizing trends in bike usage, selecting important features, and comparing models including decision trees, conditional inference trees, and random forests. The best model was found to be a generalized additive model with interaction terms, achieving lower error than other methods in predicting hourly bike counts.
This document describes a project analyzing bike sharing demand data using the SEMMA process. It explores and modifies the data, builds multiple regression, decision tree, boosted tree, bootstrap forest, and neural network models, and assesses the models. Key steps included adding new variables, removing outliers, splitting the data into training, validation, and test sets, and modeling with different algorithms to predict bike rent count and compare model performance. The best predictors of demand were found to be time of day, day of week, season, temperature, and humidity.
This document summarizes a study analyzing how weather impacts Uber ridership in New York City boroughs. The authors used six months of Uber ride data from NYC along with corresponding weather data. They found that rain and snow increased rides the most in Manhattan. Fog most impacted rides in the Bronx. ARIMA time series models were used to forecast future rides by borough with weather factors like temperature and visibility. The models showed weather influences Uber demand, especially on transition days from normal to rainy or snowy conditions. Wednesdays had the highest ridership in Manhattan while Sundays were busiest in other boroughs.
Project template for projects looks like thiskaniuppu
This capstone project aims to address the challenge of predicting bike rental demand at different hours using machine learning. The proposed solution involves collecting historical rental data, preprocessing the data, and using a time-series forecasting model like ARIMA or LSTM to predict bike counts based on patterns in the data. The model will be trained on historical rental figures and external factors like weather and events. Its performance will be evaluated using metrics like MAE and RMSE. Accurate demand forecasting will help ensure a stable supply of rental bikes in urban areas.
The document describes a project to predict bike rental counts in Washington DC using a C4.5 decision tree model. The key steps included: (1) understanding the training data which contained bike rental information from 2011-2012, (2) using C4.5 to build a decision tree model to predict rental counts based on attributes like date, weather, and temperature, (3) evaluating the model on a validation set to check accuracy, and (4) deploying the model to predict rental counts for given dates and times which could help bike companies plan revenues.
Rides Request Demand Forecast- OLA BikeIRJET Journal
The document presents a study that develops a model to forecast demand for Ola bike rides in Bangalore, India using ride request data from Ola. The study uses clustering and machine learning techniques like XGBoost to predict demand for rides by time period and location. This will help Ola better understand demand patterns and maximize the efficiency of their bike fleet to meet rider needs. The model is trained on attributes from ride requests including booking time, pickup and drop off locations.
The document describes a project applying machine learning techniques to forecast bike rental demand using the Capital Bikeshare program in Washington D.C. Multiple techniques are evaluated including linear regression, lasso regression, elastic net, ensemble learning, neural networks and local linear regression. Ensemble learning with regularized bagging had the best performance with a root mean squared logarithmic error of 0.63302 on validation data. Further tuning of methods and additional analysis of features could potentially improve predictions.
Forecasting Municipal Solid Waste Generation Using a Multiple Linear Regressi...IRJET Journal
- The document describes developing a multiple linear regression model to forecast municipal solid waste generation based on factors like population, population density, education levels, access to services, and income levels.
- The model was developed using data from various municipalities in Italy. Exploratory data analysis was conducted to determine linear relationships between waste generation and predictors.
- The linear regression model achieved a high R-squared value of 91.81%, indicating a close fit to the data. Various error metrics like MAE, MSE and RMSE were calculated to evaluate model performance.
- The regression model provides a simple yet accurate means of predicting municipal solid waste that requires minimal data and can be generalized to other locations.
Accident Prediction System Using Machine LearningIRJET Journal
This document describes a machine learning model to predict road accident hotspots in Bangalore, India. The researchers collected accident data from government websites and other sources. They used K-means clustering to group similar data points and label them as high or low risk zones. The dataset was preprocessed and split into training and testing sets. A K-means clustering algorithm was trained on the larger training set to create clusters of accident-prone areas based on factors like weather, road conditions, etc. The model can then predict whether new locations belong to a high or low risk cluster. The user interface allows emergency responders and city planners to input a location and get a prediction to help prevent future accidents.
Modelling Mobile payment services revenue using Artificial Neural Network Kyalo Richard
This presentation elaborate application of Neural Network in modelling mobile payment services in kenya.The policy implication of this study is that ANN can be used to model revenue from mobile payments services, which is certainly useful for various financial players such as government and policy makers of the country.
This document summarizes a research paper that aims to predict delays in bus travel times in Dublin, Ireland using machine learning models. The researchers collected over 22 million records of real-time bus location and schedule data. They cleaned and preprocessed the data, engineered features, and applied support vector regression, XGBoost regression, and random forest regression models. Feature engineering improved the prediction accuracy of the models, with XGBoost achieving the best results at 69.25% accuracy. The researchers concluded that feature engineering and XGBoost are effective for predicting bus delays using transit data.
The document summarizes an analysis of bike rental demand data from the Capital Bike Sharing system in Washington D.C. over a two-year period. Key factors like temperature, humidity, season, and day of week were found to influence rental patterns. Separate linear regression models were developed to forecast demand for casual users and registered users. The proposed ensemble model was found to increase projected profits by 26.4% over the current model when tested on 6 months of out-of-sample data.
This document discusses modeling revenue collected from mobile payments in Kenya using artificial neural networks. It presents the background and motivation for the study, which is that mobile payments generate significant revenue for the Kenyan government but there is no existing model for predicting these tax revenues. The document describes the methodology, which uses a neural network with one hidden node to model mobile payment revenue data. It finds the number of transactions is a significant predictor of revenues. The neural network model achieves good prediction accuracy on out-of-sample testing data.
IRJET- Facial Age Estimation with Age DifferenceIRJET Journal
This document presents a method for facial age estimation that uses age difference information. The method trains a convolutional neural network on a dataset containing over 200,000 face images labeled with timestamps. It uses entropy loss, cross entropy loss, and K-L divergence loss functions to estimate the age difference between image pairs and improve accuracy. Testing on a year-labeled dataset containing 122,986 image pairs achieved a mean absolute error of 1.74 years for age difference estimation, outperforming previous methods. The approach estimates age without explicit age labels by leveraging information from the difference in ages of the same subjects in images taken years apart.
This project analyzes employee commute data to predict car usage. Exploratory data analysis identifies outliers and correlations. Logistic regression finds age, distance, and license significantly predict car use. Naive Bayes on SMOTE-boosted data best predicts car use with 97% accuracy, showing SMOTE and variables like age and distance critically impact predictions. Bagging also performs well at 96% accuracy, and both SMOTE and bagging increase model sensitivity and specificity.
This document summarizes and compares different methods for modeling traffic demand, including the traditional four-step model, activity-based models, and microsimulation/agent-based modeling. The four-step model is described as having shortcomings like focusing on aggregate behavior rather than individuals. Activity-based models provide more nuanced modeling by using "tours" rather than trips as the basic unit and considering factors like household interactions. Microsimulation and agent-based modeling simulate individual movements but may not accurately model an entire region. The document examines issues with predicting traffic from new developments and argues newer methods can better account for factors like internal capture rates and parking costs.
Data Driven Energy Economy Prediction for Electric City Buses Using Machine L...Shakas Technologies
Data Driven Energy Economy Prediction for Electric City Buses Using Machine Learning.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
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Analysis on Bike Rental Data to Predict Future Use
1. MA 575
Analysis on Bike Rental Data to
Predict Future Use
By: Miles Avila, Kevin Choi, JungTak Joo, Kimberly
Nguyen, Tianyuan Zhou
12/9/2014
Casual Model Building: J.J., T.Z. Registered Model Building: K.C., J.J. K.N. Introduction & Background:
M.A. Modeling and Analysis: K.N. Prediction & Discussion: T.Z. Proofread & formatting: M.A., K.C., K.N
2. Analysis on Bike Rental Data to Predict Future Use
Abstract
The goal of this analysis is to predict the number of bike users on any given day in a year
using linear model techniques. Due to the increasing popularity of bike sharing and the amount of
available data, predictive models and analysis are seemingly more important to better understand
bike users and programs. Our analysis begins with exploratory data analysis techniques including
scatterplots of the original data. The exploratory analysis provided preliminary insight about our
dataset, which helped us create our early models. We proceeded to improve our models using
variable selection, transformation, comparison, and testing for non-constant variance. Our final
predictive model is divided into two separate models: casual and registered bike users. The final
casual model includes bias from the bike user population, due mostly to increases in bike users in
2012, and the registered model, after using the mean shift, shows unbiasedness and large variance.
Our predictive models suggests that the worst predictions for both models occurred around holidays
and during extreme weather conditions.
Introduction
Bike sharing is an innovative transportation program, ideal for short distance point-to-point
trips providing users the ability to pick up a bicycle at any self-serve bike-station and return it to any
other bike-station located within the system's service area. These systems have become popular in
major metropolitan areas around the world. Currently, there are over 500 bike-sharing programs
worldwide, which is composed of over 500 thousand bicycles. Today, there exists great interest in
these systems due to their important role in traffic, environmental, and health issues. The way in
which these bikes may be rented is automated, which, when coupled with other sensor data such as
temperature and weather characteristics, facilitates the process of predicting use of the bikes in the
future. From the perspective of the companies that own these systems, it is of interest to create
accurate models in order to predict bike use on any given day. In contrast to other methods of
transportation, such as bus or subway, the duration of travel, departure and arrival positions are
explicitly recorded in these systems. This is a unique feature that lets the bike sharing system act as a
virtual sensor network that can be utilized as a tool for sensing mobility in the city. It may be
possible, even, to detect which events are most important in a city by monitoring these data.
Background
In this study, we are creating a model that predicts the number of bike-sharing users on any
given day in a particular year to the same day in a different year. In general, predictions are difficult
because there are many variables that are unaccounted for in our dataset. These include, but are not
limited to, business affairs among bike-sharing companies, an increase in popularity among the
services (i.e. has bike-sharing become a societal trend), and especially cost fluctuations of the
services.
The data here are a mix of numerical and categorical variables. These include the count of
users on any given day, split by casual and registered users, along with the state of the weather
(measured by temperature, actual temperature (feeling temperature), humidity, wind speed, and
weather sit), and finally in conjunction with categorical variables describing what kind of day it was
(weekday, holiday, season, and month). The data set is collected from the years 2011 and 2012 in
Washington, D.C.
3. The core data set is related to the two-year historical log corresponding to years 2011 and
2012 from Capital Bikeshare system, Washington D.C., USA which is publicly available in
http://capitalbikeshare.com/system-data. UCI Machine Learning Repository aggregated the data into
two hourly and daily basis datasets, and added the corresponding weather and seasonal information.
Weather information are extracted from http://www.freemeteo.com.
The essential goal of this study was to create a linear model that predicts the amount of bike
users on a given day with constant variance and minimal residual values.
Modeling & Analysis
The first step we took in this process was to examine a scatterplot matrix in order to
understand the correlations among the variables (A1).
…………………………………………………………………………………………………………
….
From here, we created an initial model with Count (cnt) as the predictor and we included all
the variables in the dataset as the regressors (A2). To assess our model, we first tested whether or not
our model violates the assumption of constant variance (A3). At a significance level of .05, we can
barely conclude that this model has constant variance. The non-constant variance test shows our p-
value is 0.05701636. Nonetheless, from the residual plot we can conclude that this model is linear
(A4).
4. Next, we chose to transform the response variable with a logarithmic transformation, by
convention (A5). We tested once more for constant variance, and contrary to our expectations, this
model was far from having constant variance (A6). We also found that this model is not linear in
nature, based on the residual plot (A7).
Understanding that neither of these are the best model, we chose to utilize the AIC tool to
determine which variables should be included in order to obtain the best model. We conducted AIC
in the backward directions (A8). Running a linear model on this data we obtain the following model
(A9):
cnt=1975.08+424.48*season2+850.09*season3+1151.59*season4+185.36*month2+354.96*
month3+897.26*month4+1637.06*month5+1337.41*month6+573.99*month7+699.64*month8+112
5.10*month9+960.06*month10+552.16*month11+495.29*month12-386.34*holiday+3084.11*temp-
1330.70*humidity-2015.69*windspeed-280.06*weathersit2-1596.43*weathersit3
We also test for constant variance, and the p-value is large enough to fail to reject the null hypothesis
at .05 (A10). Having met the assumptions of constant variance and normality, we decided to use the
preceding model to predict the 2012 bicycle data.
We found that on average, our predictions were lower than the actual value of the cnt of users
in 2012 on any given day (A11).
In an attempt to explain this result, we hypothesized that this may be due to the different
behaviors that casual and registered users display towards the bike sharing service, given the
different factors. For example, on an extremely cold day, a casual user may decide to take their car
rather than use the bike share service, where a registered user may decide to use the bike sharing
service despite the bad weather, because they have already paid for their account. Also, we thought
advertisement would have different impact on casual and registered users. This led us to the decision
of creating separate models for casual and registered users in an attempt to obtain smaller residuals
when predicting 2012 data.
5. We started by creating a model for just casual users. Having run a backward selection on all
our variables, we obtained the following model from our backward selection (A12):
casual= 1975.0791+ 185.3567*month2 +354.9600*month3+897.2602*month4+
1637.0600*month5+1337.4082*month6+573.9875*month7+699.6399*month8+1125.0984*
month9+960.0629*month10+552.1595*month11+495.2866*month12-
280.0560*weathersit2 -
1596.4329*weathersit3+424.4753*season2+850.0882*season3+1151.5869*season4 -
1330.7019*humidity-2015.6888 *windspeed-386.3378*holiday +3084.1052*temp
However, the backward selection model violates the assumptions of constant variance (A13)
and linearity (A14). In order to fix these violations and improve the linearity of the model, we ran a
Box-Cox method and chose to transform the response variable to the power of .4 (A15). The chosen
power transformation makes sense because the inverse response plot showed a slight square root
relation between number of casual users and the chosen regressors.
The model for casual users after the power transformation is (A16):
casual0.4
= 1975.0791+ 185.3567*month2 +354.9600*month3+897.2602*month4+
1637.0600*month5+1337.4082*month6+573.9875*month7+699.6399*month8+1125.0984*
month9+960.0629*month10+552.1595*month11+495.2866*month12-
280.0560*weathersit2 -
1596.4329*weathersit3+424.4753*season2+850.0882*season3+1151.5869*season4 -
1330.7019*humidity-2015.6888 *windspeed-386.3378*holiday +3084.1052*temp
Furthermore, we checked for linearity (A17) and non-constant variance (A18) for the above model.
Our tests yielded the following results:
6. In comparison to the original backward selected model for causal users (A14), our model
with the Box-Cox method shows more linearity. In addition, the p-value from the non-constant
variance test in the transformed model, in comparison to the original backward selected model,
shows more constant variance. The p-value went from 3.42573E-05 (A13) in the original model to
0.006777438 in the transformed model (A18). Clearly, the transformed model using the Box-Cox
method is better for casual users.
We tried to further improve our variance for the casual model by removing outliers. Utilizing
the outlier test, we removed two potential outliers. We re-ran the transformed backward selected
model but it did not improve the constancy of our variance. Therefore, we reverted back to the
transformed causal model above (A16) to predict the 2012 bicycle dataset.
The mean of the residuals of the actual number of casual users is approximately
300 However, the mean of the residuals of our 2012 data using the transformed model is
approximately 1.92 (A19). Although the prediction results are not ideal, we decided we’ll leave the
model for now and go on to the registered users and see if we’ll get better behavior from that group
and then possibly (figure out) why our predictions have large residuals.
In addition to the causal model, we also created a model for registered users. Initially, we put all the
variables into a backward selection algorithm in order to decide which variables are most significant
(A20). Running a linear model on the significant variables yields the following model (A21):
registered =
1071.5075+380.9067*season2+736.0770*season3+1135.6424*season4+131.9103*month2 +1
29.0006*month3+534.9357*month4+1220.9336*month5+1121.6707*month6+404.6830*month7+6
11.2622*month8+891.2946*month9+563.7859*month10+342.5887*month11+457.4749*month12-
853.9828*holiday+714.6518*weekday1+816.7486*weekday2+803.8267*weekday3+771.9176*wee
kday4+ 728.6853*weekday5+119.0951*weekday6 -207.3430*weathersit2-1335.0019*weathersit3
+1952.3536*atemp-906.3608*hum-961.8719*windspeed
The test of nonconstant variance yielded a p-value of 0.7760796, leading us to conclude that our
model has constant variance(A22). In addition, the model fulfills the linear assumption (A23):
7. I
The mean of the residuals from this model is 1765 (A24). Like the casual model, the registered
model is also underestimating. Before considering any transformations to fix the underestimations in
our models, we decided to take a second look at our data to figure out if there was another cause. We
noticed that the numbers of both registered and casual users in 2012 seem to be much larger than
those numbers in 2011, so we calculated average numbers of registered and casual users in both years.
We found that on average, there is a mean increase of 342 casual users and a mean increase of 1859
registered users in 2012 from 2011 (A25). At the same time, temperatures, humidity, and weather
situations overall didn’t change significantly (month, week of days, and holidays don’t change either,
obviously). Therefore, we have strong evidence to believe that these increases are not due to any of
the variables that are available to us in the dataset, but due to other factors that we do not have
information about such as increasing popularity of the system or advertisement. In order to capture
these increases, we applied a mean shift to the model for the registered users. In other words, our
model for the registered users have now become (A24):
registered =
1071.5075+380.9067*season2+736.0770*season3+1135.6424*season4+131.9103*month2 +1
29.0006*month3+534.9357*month4+1220.9336*month5+1121.6707*month6+404.6830*month7+6
11.2622*month8+891.2946*month9+563.7859*month10+342.5887*month11+457.4749*month12-
853.9828*holiday+714.6518*weekday1+816.7486*weekday2+803.8267*weekday3+771.9176*wee
kday4+ 728.6853*weekday5+119.0951*weekday6 -207.3430*weathersit2-1335.0019*weathersit3
+1952.3536*atemp-906.3608*hum-961.8719*windspeed + 1764.549*year
In the model above, we added a “year” variable, and we obtained the coefficient of this variable from
the mean residuals of our predicted values of 2012 data. However, we decided against applying a
similar mean shift to the casual data because our casual users model has a transformed response
variable. The transformed response variable affects the mean shift and hinders its predictability and
interpretability.
Prediction
From our constructed model using 2011 data, we were able to explain a fair amount of
variability in both registered and casual users of capital bikeshare system in 2012. (R2
of around .66
8. in both cases) . The casual user model has a bias due to the underestimated amount of users in 2012.
The underestimated amount of users could account for many different factors including bicycle
trends and advertisement, but these factors are not included in our dataset. However, the variance of
the casual user model is rather small, with a MSE of 8.78 . Our registered user model is unbiased
after the mean shift where the mean residual is basically zero. However, due to large amount of
registered users, (and thus large fluctuations of data) our estimation of registered users in 2012 have
large variance, with an MSE of 754653. Overall, the worst predictions for both models occurred
around holidays where there were either a lot of people or very little people using bikes, and in
extreme weather conditions (such as when hurricane Sandy hit in October 2012) where very few, if
any users were using the bike system. Nonetheless, our model predicted well (A26).
Discussion
One should note that the mean shifts we applied to our registered model is a special case to
this project. In this project, we had the luxury of observing the 2012 data and knowing about this
average increase and therefore able to make the proper adjustment for our model. However, in most
real life situation, we would be using the data we have to create a model that predicts future
outcomes, in these situations we would not know the future value of response variables ahead of time.
Therefore, we need to be especially careful when we build these models. We need to gather as much
information as possible to maximize our chance to capture all the predicting variables. Furthermore,
for the dataset that are likely to see an increase in values (both predictor and response) we should
monitor the data closely and update it frequently and quickly after we’ve received new information
regarding the data. Finally, for data that shows a strong and clear trend or pattern related to time,
other statistical technique such as time series modeling would be more appropriate to use and results
in better prediction of the data.