A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions

2011 ◽  
Vol 19 (6) ◽  
pp. 1215-1224 ◽  
Author(s):  
Camille N. Kamga ◽  
Kyriacos C. Mouskos ◽  
Robert E. Paaswell
Keyword(s):  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Estimate Travel Time

scholarly journals A Dynamic Traffic Assignment Model for the Sustainability of Pavement Performance

2018 ◽  
Vol 11 (1) ◽  
pp. 170 ◽  
Author(s):  
Xinhua Mao ◽  
Jianwei Wang ◽  
Changwei Yuan ◽  
Wei Yu ◽  
Jiahua Gan
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Heavy Traffic ◽  
Pollutant Emissions ◽  
Pavement Performance ◽  
Dynamic Traffic ◽  
Teaching Learning ◽  
Pavement Damage

Existing Dynamic Traffic Assignment (DTA) models assign traffic flow with the principle of travel time, which are easy to distribute most of the traffic flows on the shortest path. A serious unbalance of traffic flow in the network can speed up pavement deterioration of highways with heavy traffic, which influences the sustainability of pavement performance and increases maintenance expenditures. The purpose of this research is to obtain a more optimized traffic assignment for pavement damage reduction by establishing a multi-objective DTA model with the objectives of not only minimum travel time but minimum decline of Present Serviceability Index (PSI) for pavements. Then, teaching-learning-based optimization (TLBO) algorithm is utilized to solve the proposed model. Results of a case study indicate that a more balanced traffic flow assignment can be realized by the model, which can effectively reduce average PSI loss, save maintenance expenditures, extend pavement service life span, save fuel consumption and reduce pollutant emissions in spite of a little increase of average travel time. Additionally, sensitivity of weight factor for the two objective functions is analyzed. This research provides some insights on methods on sustainable pavement performance.

Energy and Mobility Impacts of System Optimal Dynamic Traffic Assignment for a Mixed Traffic of Legacy and Automated Vehicles

2019 ◽  
Vol 2673 (9) ◽  
pp. 389-406
Author(s):  
H. M. Abdul Aziz
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Transportation Networks ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Dynamic Traffic ◽  
System Optimal ◽  
Optimal Dynamic

This research develops a system optimal dynamic traffic assignment (DTA) model for mixed traffic of human drivers and automated vehicles (AVs) and investigates network level mobility and energy impacts for different market shares of AVs. A methodology based on vehicle-specific-energy is proposed to estimate the energy consumption from the embedded spatial-queuing traffic flow model within the DTA formulation. Results with a test network indicate that potential travel time and energy consumption reductions are possible with increased AV market share in transportation networks. Results also report a decrease in travel time as high as 49% and energy consumption as high as 28% at the system level. The developed DTA model will be able to assist in transportation planning and the investment decision process by estimating the mobility and energy impacts in future transportation networks with mixed traffic of human drivers and AVs.

Formulation and Solution Algorithm for Fuzzy Dynamic Traffic Assignment Model

2003 ◽  
Vol 1854 (1) ◽  
pp. 114-123 ◽  
Author(s):  
Henry X. Liu ◽  
Xuegang Ban ◽  
Bin Ran ◽  
Pitu Mirchandani
Keyword(s):  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Route Choice ◽  
Travel Times ◽  
Dynamic Traffic ◽  
Choice Process ◽  
Fuzzy Variables ◽  
Assignment Model ◽  
Proposed Model

An issue that is always important in the development of traffic assignment models is how travelers' perceptions of travel time should be modeled. Because travelers rarely have perfect knowledge of the road network or of the travel conditions, they choose routes on the basis of their perceived travel times. Traditionally, travelers' perceived travel times are treated as random variables, leading to the stochastic traffic assignment problem. However, uncertain factors are also observed in the subjective recognition of travel times by travelers, and these can be illustrated as fuzzy variables. Therefore, a fuzzy dynamic traffic assignment model that takes into account the imprecision and the uncertainties in the route choice process is proposed. By modeling the expressions of perceived travel times as fuzzy variables, this model makes possible the description of a traveler's process of choosing a route that is more accurate and realistic than those from its deterministic or stochastic counter parts. The fuzzy perceived link travel time and fuzzy perceived path travel time are defined, and a fuzzy shortest path algorithm is used to find the group of fuzzy shortest paths and to assign traffic to each of them by using the so-called C-logit method. The results of the proposed model are also compared with those from the stochastic dynamic traffic assignment model, and it is demonstrated that the impact of advanced traveler information systems on the traveler's route choice process can be readily incorporated into the proposed model.

Multiclass, Multicriteria Dynamic Traffic Assignment with Path-Dependent Link Cost and Entropy-Based Risk Preference

2017 ◽  
Vol 2667 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Jiangbo Gabriel Yu ◽  
R. Jayakrishnan
Keyword(s):  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Time Uncertainty ◽  
Link Cost ◽  
Travel Time Uncertainty ◽  
Path Dependent ◽  
Dependent Link

Challenges arise in dynamic traffic assignment (DTA) when heterogeneous users evaluate choices on the basis of multiple interrelated criteria such as travel time and travel time uncertainty. This paper proposes a density-based formulation along with a stochastic quasigradient projection (SQGP) solution scheme with the aid of a traffic simulator. Path-dependent link cost is proposed to allow for the objective function formulation and more tractable analysis. The criteria in the discussion and the case study are travel time (link-additive), monetary cost (non additive), and travel time uncertainty (path-dependent link-additive). An information entropy-based uncertainty measure is proposed because of concerns about using conventional measures such as variability and reliability. The case study shows stochastic and efficient convergence, demonstrates the ability of SQGP to bypass local optima, and exemplifies the significant effect of using path-independent and path-dependent link costs to forecast traffic pattern and toll revenue. The results also suggest that a pricing strategy aimed at optimizing travel time and reliability for different user classes should consider travel time correlations between toll segments and the adjacent no-toll segments if enumerating paths is practically infeasible.

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