Personalized route recommendation through historical travel behavior analysis

2021 ◽  
Author(s):  
Rodrigo Augusto de Oliveira e Silva ◽  
Ge Cui ◽  
Seyyed Mohammadreza Rahimi ◽  
Xin Wang
Keyword(s):  
Behavior Analysis ◽  
Travel Behavior ◽  
Route Recommendation

Clustering of Fuzzy Cognitive Maps for Travel Behavior Analysis

2012 ◽  
pp. 57-66 ◽  
Author(s):  
Lusine Mkrtchyan ◽  
Maikel León ◽  
Benoît Depaire ◽  
Da Ruan ◽  
Koen Vanhoof
Keyword(s):  
Behavior Analysis ◽  
Travel Behavior ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

scholarly journals Travel Behavior Analysis of the Licensed and the Unlicensed by Life Cycle Stages in Households

1991 ◽  
Vol 22 (1) ◽  
pp. 151-165 ◽  
Author(s):  
Naojiro AOSHIMA ◽  
Tomohiko ISOBE ◽  
Keiko TAKAHASHI
Keyword(s):  
Life Cycle ◽  
Behavior Analysis ◽  
Travel Behavior ◽  
Life Cycle Stages

scholarly journals Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ge Gao ◽  
Zhen Wang ◽  
Xinmin Liu ◽  
Qing Li ◽  
Wei Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Field Study ◽  
Behavior Analysis ◽  
Travel Time ◽  
Travel Behavior ◽  
High Accuracy ◽  
Travel Distance ◽  
Distance Analysis ◽  
Trip Time ◽  
Time Information

Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.

scholarly journals Need-based travel behavior analysis: new potential for mobility survey

2018 ◽  
Vol 32 ◽  
pp. 110-118 ◽  
Author(s):  
Floridea Di Ciommo ◽  
Francesca Pagliara ◽  
Maurizio De Crescenzo
Keyword(s):  
Behavior Analysis ◽  
Travel Behavior

Box-Cox Mixed Logit Model for Travel Behavior Analysis

2010 ◽  
Author(s):  
Alfonso Orro ◽  
Margarita Novales ◽  
Francisco G. Benitez ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
...  
Keyword(s):  
Behavior Analysis ◽  
Travel Behavior ◽  
Logit Model ◽  
Mixed Logit ◽  
Mixed Logit Model
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