A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel
Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts and transportation facilities are the factors with the greatest influence on intercity online car-hailing travel. These results can provide practical help to city managers improve the management of intercity traffic and develop better transportation policies.
Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel
Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts and transportation facilities are the factors with the greatest influence on intercity online car-hailing travel. These results can provide practical help to city managers improve the management of intercity traffic and develop better transportation policies.
Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel
Jincheng Wang (author) / Qunqi Wu (author) / Feng Mao (author) / Yilong Ren (author) / Zilin Chen (author) / Yaqun Gao (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Demand Forecasting for Long-haul Intercity Rail Travel: A Case Study
| British Library Online Contents | 2006
Logit Models for Forecasting Nationwide Intercity Travel Demand in the United States
| British Library Online Contents | 2007
The Trouble with Intercity Travel Demand Models
| British Library Online Contents | 2004
Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
| DOAJ | 2022
Intercity travel characteristics
| Engineering Index Backfile | 1965