scholarly journals Travel Mode Detection with Varying Smartphone Data Collection Frequencies

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 716 ◽  
Author(s):  
Muhammad Shafique ◽  
Eiji Hato
Keyword(s):  
Data Collection ◽  
Travel Mode ◽  
Mode Detection

scholarly journals Travel Mode Detection Exploiting Cellular Network Data

2016 ◽  
Vol 81 ◽  
pp. 03008 ◽  
Author(s):  
Arash Kalatian ◽  
Yousef Shafahi
Keyword(s):  
Cellular Network ◽  
Network Data ◽  
Travel Mode ◽  
Mode Detection

scholarly journals Travel mode detection based on GPS raw data collected by smartphones: a systematic review of the existing methodologies

2016 ◽  
Author(s):  
Linlin Wu ◽  
Biao Yang ◽  
Peng Jing
Keyword(s):  
Systematic Review ◽  
Large Scale ◽  
Great Chance ◽  
Raw Data ◽  
Travel Mode ◽  
Review Section ◽  
Travel Survey ◽  
Large Scale Data ◽  
Mode Detection ◽  
Gps Devices

Over the past couple of decades, Global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., need much money to buy devices, forget to take devices to collect data, limit the simple size because of the number of devices, etc.), and the phenomenon that the smartphone is becoming one of necessities of life, there is a great chance for the smartphone to replace dedicated GPS devices. Although, several general reviews have been done about smartphone-based GPS travel survey in the literature review section in some articles, a systematic review from smartphone-based GPS data collection to travel mode detection has none. The included studies were searched from six databases. The purpose of this review is to critically assess the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to the systematic comparison among different methods from data-preprocessing to travel mode detection, this paper could carefully provide the Strengths and Weaknesses of existing methods. Furthermore, it is the crucial step to develop the methodologies and applications of GPS raw data collected by smartphones.

scholarly journals Using genetic programming on GPS trajectories for travel mode detection

2021 ◽  
Author(s):  
Farnoosh Namdarpour ◽  
Mahmoud Mesbah ◽  
Amir H. Gandomi ◽  
Behrang Assemi
Keyword(s):  
Genetic Programming ◽  
Travel Mode ◽  
Mode Detection ◽  
Gps Trajectories

Effects of accessibility to the transit stations on intercity travel mode choices in contexts of high speed rail in the Windsor–Quebec corridor in Canada

2015 ◽  
Vol 42 (11) ◽  
pp. 930-939 ◽  
Author(s):  
Billy Wong ◽  
Khandker M. Nurul Habib
Keyword(s):  
Social Media ◽  
Data Collection ◽  
High Speed ◽  
Choice Model ◽  
High Speed Rail ◽  
Travel Mode ◽  
Web Based ◽  
Intercity Travel ◽  
Transit Station ◽  
Data Collection Approach

Main objective of this paper is investigating the role of transit station accessibility on intercity travel mode choices in contexts of a proposed High Speed Rail. The study area is the Quebec–Windsor corridor, which is the most important corridor in Canada and one of the most important corridors in North America. A web-based joint revealed preference – stated preference survey is used to collect data for empirical investigation. To contribute further to travel survey methods, an innovative social media based data collection approach is taken. As opposed to explicit sample frame-based sample selection approach, it applies a reverse procedure of open sample frame-based data collection. The web-based survey is spread through social media groups (that are open in sense that information of all individuals are not known explicitly) and the collected responses are screened to match with population distributions. Results prove the potential of such data collection approach in extracting representative samples of the population of concern. The collected dataset, which has close representation of the population, is used to estimate discrete mode choice model (Nested Logit model) of intercity mode choices. Empirical model reveals that intercity travellers are more concerned about access to and egress from transit stations than the main in-vehicle travel while selecting intercity travel modes. The result of this investigate imply that transit station accessibility should be given careful consideration for the success of any innovative travel mode, e.g., high speed rail.

scholarly journals Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization

Information ◽  
2015 ◽  
Vol 6 (3) ◽  
pp. 522-535 ◽  
Author(s):  
Guangnian Xiao ◽  
Zhicai Juan ◽  
Jingxin Gao
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Travel Mode ◽  
Mode Detection

scholarly journals Travel mode detection method based on big smartphone global positioning system tracking data

2017 ◽  
Vol 9 (6) ◽  
pp. 168781401770813 ◽  
Author(s):  
Chaoran Zhou ◽  
Hongfei Jia ◽  
Jingxin Gao ◽  
Lili Yang ◽  
Yixiong Feng ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Detection Method ◽  
Positioning System ◽  
Tracking Data ◽  
Travel Mode ◽  
Mode Detection ◽  
Global Positioning ◽  
Global Positioning System Tracking

Travel mode detection based on GPS track data and Bayesian networks

2015 ◽  
Vol 54 ◽  
pp. 14-22 ◽  
Author(s):  
Guangnian Xiao ◽  
Zhicai Juan ◽  
Chunqin Zhang
Keyword(s):  
Bayesian Networks ◽  
Travel Mode ◽  
Mode Detection
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