scholarly journals When is big data big enough? Implications of using GPS-based surveys for travel demand analysis

2015 ◽  
Vol 56 ◽  
pp. 446-462 ◽  
Author(s):  
Akshay Vij ◽  
K. Shankari
2013 ◽  
Vol 361-363 ◽  
pp. 2122-2126
Author(s):  
Jun Chen ◽  
Xiao Hua Li ◽  
Lan Ma

Traditional transit travel information is acquired by Trip Sample Survey which has some disadvantages including high cost and short data lifecycle. This paper researched transit travel demand analysis method using Advanced Public Transportation Systems (APTS) data. The study collected APTS data of Nanning City in China and established APTS multi-source data analysis platform applying data warehouse technology. Based on key problems research, the paper presented the analysis procedure and content. Then, this study proposed the core algorithms of the method which are determinations of boarding bus stops, alighting bus stops and transfer bus stops of smart card passengers. Finally, these algorithms programs are experimented using large scale practical APTS data. The results show that this analysis method is low cost, operability and high accuracy.


Author(s):  
Vincent L. Bernardin ◽  
Nazneen Ferdous ◽  
Hadi Sadrsadat ◽  
Steven Trevino ◽  
Chin-Cheng Chen

The Tennessee Department of Transportation replaced the quick-response-based long-distance component in its statewide model by integrating the new national long-distance passenger travel demand model in a new statewide model and calibrating it to long-distance trips observed in cell phone origin–destination data. The national long-distance model is a tour-based simulation model developed from FHWA research on long-distance travel behavior and patterns. The tool allows the evaluation of many policy scenarios, including fare or service changes for various modes, such as commercial air, intercity bus, Amtrak rail, and highway travel. The availability of this tool presents an opportunity for state departments of transportation in developing statewide models. Commercial big data from cell phones for long-distance trips also pre-sents an opportunity and a new data source for long-distance travel patterns, which previously have been the subject of limited data collection, in the form of surveys. This project is the first to seize on both of these opportunities by integrating the national long-distance model with the new Tennessee statewide model and by processing big data for use as a calibration target for long-distance travel in a statewide model. The paper demonstrates the feasibility of integrating the national model with statewide models, the ability of the national model to be calibrated to new data sources, the ability to combine multiple big data sources, and the value of big data on long-distance travel, as well as important lessons on its expansion.


2018 ◽  
Vol 47 (1) ◽  
pp. 75-108
Author(s):  
Mark Wardman ◽  
Jeremy Toner

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