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Content Provider | IET Digital Library |
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Author | Gan, Hongcheng |
Abstract | Travellers' mode switch behaviour with the presence of high-quality Smartphone delivered multimodal information (SMMI) seems to have rarely been addressed. This study investigated commuters’ en-trip mode decision about switching from ‘auto’ to ‘park-and-ride’ (P + R) under high-quality SMMI that provides travel time for both modes, delay for auto, cause of delay, P + R cost and comfort level of rail transit. It is based on a stated preference survey of Shanghai travellers. A binary logit model was developed to identify contributing factors that affect mode switching decisions. Results showed that SMMI can significantly influence mode choice and its impacts depend on traveller attributes, driver's previous experience, and level of service attributes. Statistically significant explanatory variables in the model are delay for auto, comfort level of rail transit, gender, education level, income, driving experience, driving frequency, main criterion of mode choice, owning an easy public transportation ride card, previous use of P + R, perceived value of existing real-time traveller information and frequency of using real-time traveller information. This study also developed a practical logit model that encompasses policy related explanatory variables to obtain policy implications for real application of SMMI services in Shanghai. |
Starting Page | 382 |
Ending Page | 390 |
Page Count | 9 |
ISSN | 1751956X |
Volume Number | 9 |
e-ISSN | 17519578 |
Issue Number | Issue 4, May (2015) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/9/4 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2014.0150 |
Journal | IET Intelligent Transport Systems |
Publisher Date | 2014-10-01 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Auto Delay Binary Logit Model Commuters En-trip Mode Decision Drivers Previous Experience Driving Experience Driving Frequency Easy Public Transportation Ride Card Education Level Gender Income Main Mode Choice Criterion Mobile Computing Mode Switching Decisions P + R Park-and-ride Pervasive Computing Rail Traffic Rail Transit Comfort Level Real-time Traveller Information Perceived Value Road Traffic Service Attribute Level Shanghai Travellers Smart Phones Smartphone Delivered Multimodal Information SMMI Traffic Engineering Computing Traffic Information System Traveller Attributes Travellers Mode Switch Behaviour Ubiquitous |
Content Type | Text |
Resource Type | Article |
Subject | Law Transportation Environmental Science Mechanical Engineering |
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