Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets
Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/10/1305 |
| _version_ | 1827679639708893184 |
|---|---|
| author | Shenghan Zhou Bang Chen Houxiang Liu Xinpeng Ji Chaofan Wei Wenbing Chang Yiyong Xiao |
| author_facet | Shenghan Zhou Bang Chen Houxiang Liu Xinpeng Ji Chaofan Wei Wenbing Chang Yiyong Xiao |
| author_sort | Shenghan Zhou |
| collection | DOAJ |
| description | Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets. |
| first_indexed | 2024-03-10T06:35:08Z |
| format | Article |
| id | doaj.art-e1af6153402f4d76aaa21230a8c05fb8 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T06:35:08Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e1af6153402f4d76aaa21230a8c05fb82023-11-22T18:10:59ZengMDPI AGEntropy1099-43002021-10-012310130510.3390/e23101305Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data SetsShenghan Zhou0Bang Chen1Houxiang Liu2Xinpeng Ji3Chaofan Wei4Wenbing Chang5Yiyong Xiao6School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSmart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.https://www.mdpi.com/1099-4300/23/10/1305online car-hailingtravel characteristics analysistraffic prediction modelingmultivariate variables time serieshybrid model |
| spellingShingle | Shenghan Zhou Bang Chen Houxiang Liu Xinpeng Ji Chaofan Wei Wenbing Chang Yiyong Xiao Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets Entropy online car-hailing travel characteristics analysis traffic prediction modeling multivariate variables time series hybrid model |
| title | Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets |
| title_full | Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets |
| title_fullStr | Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets |
| title_full_unstemmed | Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets |
| title_short | Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets |
| title_sort | travel characteristics analysis and traffic prediction modeling based on online car hailing operational data sets |
| topic | online car-hailing travel characteristics analysis traffic prediction modeling multivariate variables time series hybrid model |
| url | https://www.mdpi.com/1099-4300/23/10/1305 |
| work_keys_str_mv | AT shenghanzhou travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets AT bangchen travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets AT houxiangliu travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets AT xinpengji travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets AT chaofanwei travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets AT wenbingchang travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets AT yiyongxiao travelcharacteristicsanalysisandtrafficpredictionmodelingbasedononlinecarhailingoperationaldatasets |