scholarly journals A traffic demand estimation model based on observed link volumes through the idea of shadow cost.

1989 ◽  
pp. 41-50
Author(s):  
Hiroshi INOUYE
Keyword(s):  
Demand Estimation ◽  
Estimation Model ◽  
Traffic Demand ◽  
Model Based ◽  
Shadow Cost

A Study of Traffic Demand Estimation Model by Link Flows Using Dial's Probabilistic Assignment

1984 ◽  
Vol 19 (0) ◽  
pp. 13-18
Author(s):  
Yasunori Iida ◽  
Junichi Takayama ◽  
Ichiji Kanai ◽  
Reiji Mizuguchi
Keyword(s):  
Demand Estimation ◽  
Estimation Model ◽  
Traffic Demand ◽  
Probabilistic Assignment

Dynamic Origin-Destination Demand Estimation with Multiday Link Traffic Counts for Planning Applications

2003 ◽  
Vol 1831 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Xuesong Zhou ◽  
Xiao Qin ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
Demand Estimation ◽  
Test Bed ◽  
Estimation Model ◽  
Interactive Approach ◽  
Traffic Demand ◽  
Level Problem ◽  
Upper Level ◽  
Demand Patterns

A dynamic origin–destination demand estimation model for planning applications with real-time link counts from multiple days is presented. Based on an iterative bilevel estimation framework, the upper-level problem is to minimize both the deviation between estimated link flows and real-time link counts and the deviation between estimated time-dependent demand and given historical static demand. These two types of deviations are combined into a weighted objective function, where the weighting value is determined by an interactive approach to obtain the best compromise solution. The single-day formulation is further extended to use link counts from multiple days to estimate the variation in traffic demand over multiple days. A case study based on the Irvine test bed network is conducted to illustrate the methodology and estimate day-to-day demand patterns. The application illustrates considerable benefits in analyzing the demand dynamics with multiday data.

scholarly journals Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

2021 ◽  
Vol 13 (23) ◽  
pp. 13057
Author(s):  
Hui Chen ◽  
Zhaoming Chu ◽  
Chao Sun
Keyword(s):  
Maximum Likelihood ◽  
Input Parameter ◽  
Weighted Average ◽  
Demand Estimation ◽  
User Equilibrium ◽  
Sensor Deployment ◽  
Stochastic User Equilibrium ◽  
Estimation Model ◽  
Traffic Demand ◽  
Level Model

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

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