Latent Class Choice Model of Heterogeneous Drivers' Route Choice Behavior Based on Learning in a Real-World Experiment

2013 ◽  
Vol 2334 (1) ◽  
pp. 84-94 ◽  
Author(s):  
Aly M. Tawfik ◽  
Hesham A. Rakha
Keyword(s):  
Real World ◽  
Choice Behavior ◽  
Latent Class ◽  
Choice Model ◽  
Route Choice ◽  
Route Choice Behavior ◽  
Behavior Based

Modeling of Bicycle Route and Destination Choice Behavior for Bicycle Road Network Plan

2000 ◽  
Vol 1705 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Tetsuro Hyodo ◽  
Norikazu Suzuki ◽  
Katsumi Takahashi
Keyword(s):  
Road Network ◽  
Choice Behavior ◽  
Planning Process ◽  
Choice Model ◽  
Route Choice ◽  
Single Mode ◽  
Destination Choice ◽  
Route Choice Behavior ◽  
Road Width ◽  
The Relationship

A new modeling method that describes bicycle route or destination choice behavior is presented. Although there are numerous bicycle users in Japan, the urban transportation planning process often treats bicycles and pedestrians as a single mode. Therefore, a methodology by which to evaluate and analyze bicycle demand needs to be developed. A bicycle route choice model that describes the relationship between route choice behavior and facility characteristics (e.g., road width or sidewalk) has been proposed. This model can be applied to the planning of bicycle road networks. The data from a bicycle trip survey conducted in Japan are used to study the characteristics of the model. The model is applied to study access railway station choice (destination choice). The model can produce a better fit than can a conventional model.

Effect of Route Choice Models on Estimating Network Capacity Reliability

2000 ◽  
Vol 1733 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Anthony Chen ◽  
Maya Tatineni ◽  
Der-Horng Lee ◽  
Hai Yang
Keyword(s):  
Network Performance ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Network Capacity ◽  
Service Level ◽  
Choice Models ◽  
Transportation Systems ◽  
Traffic Demand ◽  
Route Choice Behavior

The issue of planning for adequate capacity in transportation systems to accommodate growing traffic demand is becoming a serious problem. Recent research has introduced "capacity reliability" as a new network performance index. Capacity reliability is defined as the probability that a network can accommodate a certain volume of traffic demand at a required service level given variable arc capacities, while accounting for drivers' route choice behavior. Previous papers on this topic provide a comprehensive methodology for assessing capacity reliability along with extensive simulation results. However, an important issue that remains is what type of route choice model should be used to model driver behavior in estimating network capacity reliability. Three different route choice models (one deterministic and two stochastic models) are compared, and the effect of using each of these models on estimating network capacity reliability is examined.

The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

2011 ◽  
Vol 97-98 ◽  
pp. 925-930
Author(s):  
Shi Xu Liu ◽  
Hong Zhi Guan
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Statistical Characteristics ◽  
Traffic Information ◽  
Route Choice Behavior ◽  
The Impact

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

Route-Choice Modeling for Pedestrian Evacuation Based on Infrastructure Knowledge and Personal Preferences

2017 ◽  
Vol 2623 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Martin Stubenschrott ◽  
Thomas Matyus ◽  
Helmut Schrom-Feiertag ◽  
Christian Kogler ◽  
Stefan Seer
Keyword(s):  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Behavioral Model ◽  
Emergency Evacuation ◽  
Global Knowledge ◽  
Evacuation Simulation ◽  
Route Choice Behavior ◽  
Crowd Density ◽  
The Impact

In recent years, pedestrian simulation has been a valuable tool for the quantitative assessment of egress performance in various environments during emergency evacuation. For a high level of realism, an evacuation simulation requires a behavioral model that takes into account behavioral aspects of real pedestrians. In many studies, however, it is assumed that simulated pedestrians have a global knowledge of the infrastructure and choose either a predefined or the shortest route. It is questionable whether this simplification provides realistic results. This study addresses the problem of human-like route-choice behavior for microscopic pedestrian simulations. A route-choice model is presented that considers two concepts: first, the modeling of infrastructure knowledge to represent the variations in the decision-making processes of pedestrians with different degrees of familiarity with the infrastructure (e.g., regular commuters versus first-time visitors). Second, for each pedestrian the internal preference for selecting a certain path can be calibrated to allow the choice for the fastest routes or the ones that are most convenient for the agent (e.g., by avoiding stairs). The approach here uses a hybrid route-choice behavior model composed of a graph-based macrolevel representation of the environment, which is augmented with local information to avoid obstacles and dense crowds in the vicinity. This method was applied with different parameter sets in an evacuation study of a multilevel subway station. The results show the impact of these parameters on evacuation times, use of infrastructure elements, and crowd density at specific locations.

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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