Dynamic Route Choice Behavior Analysis considering En Route Learning and Choices

2013 ◽  
Vol 2383 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Dawei Li ◽  
Tomio Miwa ◽  
Takayuki Morikawa
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Route Choice ◽  
Route Learning ◽  
Route Choice Behavior ◽  
Dynamic Route

Experimental analysis of dynamic route choice behavior

1992 ◽  
Vol 26 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Yasunori Iida ◽  
Takamasa Akiyama ◽  
Takashi Uchida
Keyword(s):  
Experimental Analysis ◽  
Choice Behavior ◽  
Route Choice ◽  
Route Choice Behavior ◽  
Dynamic Route

scholarly journals Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yajuan Deng ◽  
Meiye Li ◽  
Qing Tang ◽  
Renjie He ◽  
Xianbiao Hu
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Stated Preference ◽  
Route Choice ◽  
Small Scale ◽  
Trajectory Data ◽  
Underlying Assumption ◽  
Route Choice Behavior ◽  
Mnl Model ◽  
Taxi Trajectory

Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters’ route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi’an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters’ route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters’ route choice behaviors, when compared with the traditional MNL model.

Drivers' route choice behavior analysis under ATIS

2016 ◽  
Vol 33 (2) ◽  
pp. 164 ◽  
Author(s):  
Chunyan Li ◽  
Jun Chen ◽  
Zheng'an Sun ◽  
Xiaofei Ye
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Route Choice ◽  
Route Choice Behavior

Taxi Driver's Route Choice Behavior Analysis Based on Floating Car Data

2013 ◽  
Vol 361-363 ◽  
pp. 2036-2039 ◽  
Author(s):  
En Jian Yao ◽  
Long Pan ◽  
Yang Yang ◽  
Yong Sheng Zhang
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Road Traffic ◽  
Choice Model ◽  
Route Choice ◽  
Map Matching ◽  
Taxi Drivers ◽  
Floating Car Data ◽  
Route Choice Behavior ◽  
Minor Road

Taxi drivers are viewed having more driving experience, being more familiar with road traffic condition, and in turn having more rational route choice behaviors than ordinary drivers. Using floating car data (FCD) of Beijing taxi in 2010, this study discusses the influence of road network conditions and traffic status to taxi drivers route choice behaviors. First, trip information is extracted from FCD using trip-identification method; Second, map matching and K-shortest paths are used to construct the trajectories and the sets of alternate routes, and route similarity evaluation is conducted to build the sample data of route choice behavior analysis; Finally, route choice model for taxi drivers based on Multinomial Logit (MNL) Model is estimated. The result shows that taxi drivers tend to choose the route which has faster driving speed, less frequency of left turns, more proportion of express way and less proportion of minor road, and increasing a left-turn or decreasing travel speed by 2.12km/h has the same effect on route choice utility. This study is expected to be helpful to establish map-matching algorithm of FCD, route guidance scheme and traffic assignment model.

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