{"id":63322,"date":"2022-04-15T17:50:52","date_gmt":"2022-04-15T21:50:52","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=63322"},"modified":"2022-09-12T16:23:49","modified_gmt":"2022-09-12T20:23:49","slug":"rochester-transit-service","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/rochester-transit-service\/","title":{"rendered":"Rochester Transit Service"},"content":{"rendered":"\n

Team<\/h2>\n\n\n\n

Yihe Chen<\/p>\n\n\n\n

Harry Huang<\/p>\n\n\n\n

Junting Chen<\/p>\n\n\n\n

Kehan Yu<\/p>\n\n\n\n

Mentor<\/h2>\n\n\n\n

Cantay Caliskan<\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

Predictive Analytics for Demand Responsive Para- transportation<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Vision & Goal<\/strong><\/p>\n\n\n\n

\u25cf Create a productive schedule for Demand Responsive Para-transportation by predicting the customers’ cancellation. <\/p>\n\n\n\n

\u25cf Provide executable Python code and classification model.<\/p>\n\n\n\n

\u25cf Discover best performance metrics.<\/p>\n\n\n\n

\u25cf Generate well-organized supporting<\/p>\n\n\n\n

Data Overview<\/h2>\n\n\n\n

Internal data<\/p>\n\n\n\n

\u25cf  We acquired the internal data from our sponsor<\/p>\n\n\n\n

\u25cf  Our original dataset contains 102754 observations, and 21 explanatory variables from May 17th, 2021 to December 5th, 2021.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

External Data<\/p>\n\n\n\n

\u25cf  We acquired the From NOAA (National Oceanic and Atmospheric Administration)<\/p>\n\n\n\n

\u25cf<\/strong>  Acquired daily weather information<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Data Visualization<\/h2>\n\n\n\n
\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n<\/figure>\n\n\n\n

Feature engineering<\/strong><\/p>\n\n\n\n

\u25cf  Created a label for the cancellation (1 for canceled, 0 for performed)<\/p>\n\n\n\n

\u25cf  Transformed ‘date’ variables into informative variables (e.g.. month, day, weekday)<\/p>\n\n\n\n

\u25cf Encoded categorical variables<\/p>\n\n\n\n

\u25cf Aggregated passengers by type (with children, need lift)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Modeling<\/h2>\n\n\n\n

Handle the Class Imbalance<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Random Forest with SMOTE<\/strong><\/p>\n\n\n\n

Accuracy: 81.5% -> 84.8%<\/strong><\/p>\n\n\n\n

Precision: 32.2% -> 62.1%<\/strong><\/p>\n\n\n\n

Precision: 53.9 -> 57.7%<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Precision is significantly improved by 92.8%, while Recall and Accuracy are slightly improved<\/p>\n\n\n\n

Weighted Random Forest Classifier<\/strong><\/h3>\n\n\n\n

Compared to the Random Forest Classifier, Weighted Random Forest Classifier penalizes the misclassification of minority class more<\/p>\n\n\n\n

Confusion matrix<\/strong><\/p>\n\n\n\n

<\/td>Actually Canceled<\/td>Actually Uncanceled<\/td><\/tr>
Predicted Canceled<\/td>TP = 1808<\/td>FP = 598<\/td><\/tr>
Predicted Uncanceled<\/td>FN = 2207<\/td>TN = 15938<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

XGBoost Classifier<\/strong><\/p>\n\n\n\n

<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Accuracy: 81.5% -><\/strong> 86.1%<\/strong><\/p>\n\n\n\n

Precision: 32.2% -><\/strong> 65.2%<\/strong><\/p>\n\n\n\n

Recall: 53.9% -><\/strong> 62.3%<\/strong><\/p>\n\n\n\n

Confusion matrix<\/strong><\/p>\n\n\n\n

<\/td>Actually Canceled<\/td>Actually Uncanceled<\/td><\/tr>
Predicted Canceled<\/td>TP = 2503<\/td>FP = 1336<\/td><\/tr>
Predicted Uncanceled<\/td>FN = 1512<\/td>TN = 15200<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Key Insights<\/h2>\n\n\n\n

\u25cf Our sponsor(RTS) has an extra bus on standby to cover any missing cases. <\/p>\n\n\n\n

\u25cf During busy hours (from 8 am<\/strong> to 3 pm):<\/strong><\/p>\n\n\n\n

\u25cb Excessively running the extra bus is costly when the prediction is not precise \u25cb It\u2019s better to use the Weighted Random Forest Classifier<\/strong>, which gives the highest precision <\/p>\n\n\n\n

\u25cf During other times<\/strong>:<\/p>\n\n\n\n

\u25cb It\u2019s less costly <\/strong>for running extra buses since fewer clients use the service
\u25cb It\u2019s better to use XGBoost Classifier<\/strong>, which balances recall (covering more<\/p>\n\n\n\n

canceled trip) and precision (making fewer errors when predicting the cancellation)<\/p>\n","protected":false},"excerpt":{"rendered":"

Team Yihe Chen Harry Huang Junting Chen Kehan Yu Mentor Cantay Caliskan Abstract Predictive Analytics for Demand Responsive Para- transportation Vision & Goal \u25cf Create a productive schedule for Demand…<\/p>\n","protected":false},"author":6242,"featured_media":39542,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[106,3006],"tags":[],"coauthors":[8612],"class_list":["post-63322","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dsc-archive","category-machine-learning-archive"],"acf":[],"yoast_head":"\nRochester Transit Service - Senior Design Day<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/rochester-transit-service\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Rochester Transit Service - 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