A STOCHASTIC NETWORK EQUILIBRIUM MODEL CONSIDERING TRAVEL TIME UNCERTAINTY

2004 ◽  
pp. 67-77 ◽  
Author(s):  
Shoichiro NAKAYAMA ◽  
Jun-ichi TAKAYAMA ◽  
Kazuki NAGAO ◽  
Takahiro KASASHIMA
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Network Equilibrium ◽  
Stochastic Network ◽  
Time Uncertainty ◽  
Travel Time Uncertainty

scholarly journals The Effect of the Integrated Service Mode and Travel Time Uncertainty on Taxis Network Equilibrium

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Jian Wang ◽  
Zhu Bai ◽  
Xiaowei Hu
Keyword(s):  
Travel Time ◽  
Utility Maximization ◽  
Customer Behavior ◽  
Nested Logit ◽  
Network Equilibrium ◽  
Time Uncertainty ◽  
Mode Split ◽  
Service Mode ◽  
Travel Time Uncertainty ◽  
Integrated Service

This paper aims to discuss the trip mode choice problem by using cumulative prospect theory (CPT) rather than utility maximization from the network uncertainty perspective and evaluates the effect of the integrated service mode on taxi network equilibrium. The integrated service mode means taxis either are actively moving through traffic zones to pick up customers (cruising mode) or are queued at the center of a zone waiting for customers (dispatch mode). Based on this, CPT models are adopted to analyze the choice of customers’ trip mode. The travel time uncertainty of the network and the applicability of CPT are considered first, and the Nested Logit model was used to complete the trip mode split problem. Further, several relevant relationships including supply-demand equilibrium, network conditions, taxi behavior, and customer behavior perspectives were analyzed with respect to the integrated mode. Moreover, a network equilibrium model was established and its algorithm was designed. Finally, this paper presented a numerical example and discussed the taxi network equilibrium’s characteristic after introducing the integrated service mode.

scholarly journals Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Vol 178 (2) ◽  
pp. 313-339
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Effects ◽  
Mean Squared Error ◽  
Random Effects Model ◽  
Location Error ◽  
Crust And Upper Mantle ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Event Location ◽  
Travel Time Uncertainty

AbstractThe regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

Robustifying humanitarian relief systems against travel time uncertainty

2021 ◽  
Author(s):  
Jianghua Zhang ◽  
Yang Liu ◽  
Guodong Yu ◽  
Zuo‐Jun (Max) Shen
Keyword(s):  
Travel Time ◽  
Humanitarian Relief ◽  
Time Uncertainty ◽  
Travel Time Uncertainty

scholarly journals Correction to: Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Time Uncertainty ◽  
Travel Time Uncertainty ◽  
Path Dependent

scholarly journals A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chenming Jiang ◽  
Linjun Lu ◽  
Junliang He ◽  
Caimao Tan
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Route Choice ◽  
Weather Condition ◽  
Weather Conditions ◽  
User Equilibrium ◽  
Network Equilibrium ◽  
Adverse Weather Conditions ◽  
Adverse Weather Condition ◽  
Adverse Weather

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

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