scholarly journals A Simplified Network Model for Travel Time Reliability Analysis in a Road Network

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Kenetsu Uchida ◽  
Teppei Kato
Keyword(s):  
Travel Time ◽  
Network Model ◽  
Road Network ◽  
Choice Behavior ◽  
Risk Averse ◽  
Travel Time Reliability ◽  
Time Variance ◽  
Proposed Model ◽  
Risk Neutral ◽  
The Mean

This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows). In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.

scholarly journals User Equilibrium Analysis Considering Travelers’ Context-Dependent Route Choice Behavior on the Risky Traffic Network

2020 ◽  
Vol 12 (17) ◽  
pp. 6706
Author(s):  
Qinghui Xu ◽  
Xiangfeng Ji
Keyword(s):  
High Probability ◽  
Choice Behavior ◽  
Route Choice ◽  
User Equilibrium ◽  
Equilibrium Analysis ◽  
Risk Averse ◽  
Risk Seeking ◽  
Route Choice Behavior ◽  
Risk Neutral ◽  
Context Dependent

This paper studies travelers’ context-dependent route choice behavior in a risky trafficnetwork from a long-term perspective, focusing on the effect of travelers’ salience characteristics. In particular, a flow-dependent salience theory is proposed for this analysis, where the flow denotes the traffic flow on the risky route. In the proposed model, travelers’ attention is drawn to the salient travel utility, and the objective probabilities of the state of the world are replaced by the decision weights distorted in favor of this salient travel utility. A long-run user equilibrium will be achieved when no traveler can improve his or her salient travel utility by unilaterally changing routes, termed salient user equilibrium, which extends the scope of the Wardropian user equilibrium. Furthermore, we prove the existence and uniqueness of this salient user equilibrium. Finally, numerical studies demonstrate our theoretical findings. The equilibrium results show non-intuitive insights into travelers’ route choice behavior. (1) Travelers can be risk-seeking (the travel utility of a risky route is small with a relatively high probability), risk-neutral (in special situations), or risk-averse (the travel utility of a risky route is large with a relatively high probability), which depends on the salient state. (2) The extent of travelers’ risk-seeking or risk-averse behavior depends on their extent of salience bias, while the risk-neutral behavior is irrelative to this salience bias.

scholarly journals Vehicle Routing for Dynamic Road Network Based on Travel Time Reliability

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 190596-190604
Author(s):  
Luping Zhi ◽  
Xizhao Zhou ◽  
Jing Zhao
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Road Network ◽  
Travel Time Reliability

Comprehending the roles of traveler perception of travel time reliability on route choice behavior

2019 ◽  
Vol 16 ◽  
pp. 13-22 ◽  
Author(s):  
Zohreh Rashidi Moghaddam ◽  
Mansoureh Jeihani ◽  
Srinivas Peeta ◽  
Snehanshu Banerjee
Keyword(s):  
Travel Time ◽  
Choice Behavior ◽  
Route Choice ◽  
Travel Time Reliability ◽  
Route Choice Behavior

Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior

2012 ◽  
Vol 2275 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Yanshuo Sun ◽  
Ruihua Xu
Keyword(s):  
Travel Time ◽  
Choice Behavior ◽  
Route Choice ◽  
Service Reliability ◽  
Travel Time Reliability ◽  
Elapsed Time ◽  
Route Choice Behavior ◽  
Collection Data ◽  
Passenger Flow Assignment ◽  
Transfer Stations

Applications of automatic fare collection data were investigated, with a focus on analysis of travel time reliability and estimation of passenger route choice behavior. Beijing Metro was used as a case study. A rail journey was decomposed, and each component was studied with regard to the uncertainties involved. Methods were then designed and validated to infer platform elapsed time (PET) for through stations and platform elapsed time–transfer (PET-Trans) for transfer stations by using smart card transactional data, train schedules, and complementary manual surveys. With this information, the journey time distribution of any path can be established, and methods were proposed for inferring route choice proportions. After data preparation, the methods were applied to two typical origins and destinations from the Beijing Metro. Key values concerning travel time reliability, such as PET, PET-Trans, travelers left behind (unable to board), and path coefficients, were obtained and interpreted in detail. The outcome of this research could facilitate analysis of transit service reliability and passenger flow assignment in daily operations.

Stochastic Traffic Assignment Model Based on Travel Time Reliability

2011 ◽  
Vol 130-134 ◽  
pp. 3716-3720
Author(s):  
Yi Ran Cheng ◽  
Yin Han ◽  
Xin Kai Jiang ◽  
Jia Lei Gu
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Choice Behavior ◽  
Transportation Networks ◽  
Route Choice ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Stochastic User Equilibrium ◽  
Model Based ◽  
Route Choice Behavior

Considering the un-deterministic transportation networks, the paper proposes the change of the route choice decisions under the stochastic transportation networks. The route choice behavior is described as a choice for a time shortest route which is subject to a time-reliability level. The paper also considered this new route choice behavior in the stochastic user equilibrium model, and proposed stochastic user equilibrium model based on the optimized reliability travel time route choice behavior in the stochastic networks. The equivalence and uniqueness of the solution of the model are demonstrated. Numerical results of a small network show that the proposed model can reflect the real traveler’s route choice behavior in stochastic transportation networks.

scholarly journals A Reliability-Based Network Equilibrium Model with Adaptive Risk-Averse Travelers

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhao ◽  
Hongzhi Guan ◽  
Junze Zhu ◽  
Yunfeng Wei
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Choice Behavior ◽  
Route Choice ◽  
Great Influence ◽  
User Equilibrium ◽  
Probability Density Distribution ◽  
Risk Averse ◽  
Route Choice Behavior ◽  
Travel Time Distribution

In this paper, route free-flow travel time is taken as the lower bound of route travel time to examine its impacts on budget time and reliability for degradable transportation networks. A truncated probability density distribution with respect to route travel time is proposed and the corresponding travel time budget (TTB) model is derived. The budget time and reliability are compared between TTB models with and without truncated travel time distribution. Under truncated travel time distribution, the risk-averse levels of travelers are adaptive, which are affected by the characteristics of the used routes besides the confidence level of travelers. Then, a TTB-based stochastic user equilibrium (SUE) is developed to model travelers’ route choice behavior. Moreover, its equivalent variational inequality (VI) problem is formulated and a route-based algorithm is used to solve the proposed model. Numerical results indicate that route travel time boundary produces a great influence on decision cost and route choice behavior of travelers.

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