The impact of travel information’s accuracy on route-choice

2013 ◽  
Vol 26 ◽  
pp. 146-159 ◽  
Author(s):  
Eran Ben-Elia ◽  
Roberta Di Pace ◽  
Gennaro N. Bifulco ◽  
Yoram Shiftan
Keyword(s):  
Route Choice ◽  
The Impact

Using Bluetooth to Estimate the Impact of Congestion on Pedestrian Route Choice at Train Stations

2014 ◽  
pp. 73-82 ◽  
Author(s):  
Jeroen van den Heuvel ◽  
Aral Voskamp ◽  
Winnie Daamen ◽  
Serge P. Hoogendoorn
Keyword(s):  
Route Choice ◽  
The Impact

The Impact of Route Choice Modeling on Dynamic OD Estimation

2015 ◽  
Author(s):  
Ernesto Cipriani ◽  
Andrea del Giudice ◽  
Nigro Marialisa ◽  
Francesco Viti ◽  
Guido Cantelmo
Keyword(s):  
Route Choice ◽  
Choice Modeling ◽  
Od Estimation ◽  
The Impact

scholarly journals Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows

2019 ◽  
Vol 16 (12) ◽  
pp. 2194 ◽  
Author(s):  
Xinhua Mao ◽  
Changwei Yuan ◽  
Jiahua Gan ◽  
Jibiao Zhou
Keyword(s):  
Ant Colony Algorithm ◽  
Route Choice ◽  
Traffic Flows ◽  
Dynamic Background ◽  
Evacuation Time ◽  
Parking Lots ◽  
Evacuation Strategy ◽  
Background Traffic ◽  
Parking Lot ◽  
The Impact

An optimal evacuation strategy for parking lots can shorten evacuation times and reduce casualties and economic loss. However, the impact of dynamic background traffic flows in a road network on the evacuation plan is rarely taken into account in existing approaches. This research develops an optimal evacuation model with total evacuation time minimization by dividing the evacuation process in a parking lot into two periods. In the first period, a queuing theory is used to estimate the queuing time, and in the second period, a traffic flow equilibrium model and an intersection delay model are employed to simulate vehicles’ route choice. To deal with these models, a modified ant colony algorithm is developed. The results of a numerical example prove that the proposed method has an advantage in improving evacuation efficiency. The results also show that background traffic flows affect not only vehicles’ average queuing time in parking lots but also optimal evacuation route choice. Additionally, a sensitivity analysis indicates that the minimum threshold of headway time that allows vehicles out of a parking lot to merge into the background traffic flows on the roads connecting the exits has a great impact on average queuing time, average travel time, and total evacuation time.

scholarly journals Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jiangfeng Wang ◽  
Jiarun Lv ◽  
Chao Wang ◽  
Zhiqi Zhang
Keyword(s):  
Prediction Model ◽  
Information Acquisition ◽  
Route Choice ◽  
Route Planning ◽  
Calibration Model ◽  
Good Prediction ◽  
Connected Vehicle ◽  
Vehicle Guidance ◽  
Simulation Scenario ◽  
The Impact

A route choice prediction model is proposed considering the connected vehicle guidance characteristics. This model is proposed to prevent the delay in the release of guidance information and route planning due to inaccurate timing predictions of the traditional guidance systems. Based on the analysis of the impact of different connected vehicle (CV) guidance strategies on traffic flow, an indexes system for CV guidance characteristics is presented. Selecting five characteristic indexes, a route choice prediction model is designed using the logistic model. A simulation scenario is established by programming different agents for controlling the flow of vehicles and for information acquisition and transmission. The prediction model is validated using the simulation scenario, and the simulation results indicate that the characteristic indexes have a significant influence on the probability of choosing a particular route. The average root mean square error (RMSE) of the prediction model is 3.19%, which indicates that the calibration model shows a good prediction performance. In the implementation of CV guidance, the penetration rate can be considered an optional index in the adjustment of the guidance effect.

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.

Research on Vehicles’ Detouring Route Choice Behavior under Traffic Accident Conditions

2012 ◽  
Vol 209-211 ◽  
pp. 874-878
Author(s):  
Jian Jun Wang ◽  
A Jin Ma ◽  
Fa Yao Xu ◽  
Hao Qi Yin
Keyword(s):  
Prospect Theory ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Traffic Assignment ◽  
Route Choice ◽  
Route Choice Behavior ◽  
Impact Area ◽  
Accident Conditions ◽  
Two Stages ◽  
The Impact

In order to determine the impact area and impact degree of traffic accidents, we made this research. We divided vehicles’ detouring behavior under traffic accident conditions into two stages and studied them respectively. The two stages are the initial detouring route choice and the route choice after vehicles reach the normal network. For the first stage, we firstly analyzed the particularity of vehicles’ detouring behavior, on the basis of which we determined the detouring process. Then, combing with an example, we calculated the “utilities” of alternative paths using the “prospect theory”, and used the result to determine vehicles’ initial detouring route. For the second stage, we suggested using the traditional traffic assignment methods, with reasons given. In this paper, a combination of the “prospect theory” and the traditional traffic assignment methods is used. The result helps to study vehicles’ detouring behavior and traffic accidents’ impact.

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.

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