Optimizing Origin-Destination (O-D) Estimation with respect to Redundant Information and Route Choice

Friedrich, Bernhard; Wang, Yun-Pang

doi:10.3182/20060829-3-nl-2908.00109

Cited by 2 publications

(6 citation statements)

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“…However, completely redundant information should still be avoided. According to Wang (2008) and Friedrich/Wang (2006, completely redundant information arises for example from a link that splits up into several turning links at a node. If the information on the first link is available and used as constraint, as well as the information on several or all of the turning links, the latter are completely redundant and thus the information has a higher weight during the estimation process compared to other non-redundant information, which is not justified and leads to a reduced quality of the estimation result.…”

Section: Elimination Of Redundant Informationmentioning

confidence: 99%

“…The approaching link only contains sort of accumulated information. Therefore, several rules have been defined by Wang (2008) and Friedrich/Wang (2006 in order to eliminate completely redundant information while maintaining the most precise information possible. These rules consider different types and combinations of nodes with a varying number and arrangement of connected links (see figure 6 with examples for the most relevant considered structures).…”

Section: Elimination Of Redundant Informationmentioning

confidence: 99%

“…The second module estimates the aforementioned OD matrices and traffic volumes based on the forecasted counts. It is based on the research by Wang (2008) and Friedrich/Wang (2006 respectively who base themselves on van Zuylen/Willumsen (1980). Their method has been adopted, examined and enhanced with regard to further modification for online control strategies.…”

Section: Introductionmentioning

confidence: 99%

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A combined method to forecast and estimate traffic demand in urban networks

Pohlmann

Friedrich

2013

Transportation Research Part C: Emerging Technologies

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This paper presents a combined method for short-term forecasting of detector counts in urban networks and subsequent traffic demand estimation using the forecasted counts as constraints to estimate OD flows, route and link volumes. The method is intended to be used in the framework of an adaptive traffic control strategy with consecutive optimization intervals of 15 minutes. The method continuously estimates the forthcoming traffic demand that can be used as input data for the optimization. The forecasting uses current and reference spacetime-patterns of detector counts. The reference patterns are derived from data collected in the past. The current pattern comprises all detector counts of the last four time intervals. A simple but effective pattern matching is used for forecasting. The subsequent demand estimation is based on the information minimization model that has been integrated into an iterative procedure with repeated traffic assignment and matrix estimation until a stable solution is found. Some enhancements including the improvement of constraints, redundancy elimination of these constraints and a travel time estimation based on a macroscopic simulation using the Cell Transmission Model have been implemented. The overall method, its modules and its performance, which has been assessed using artificially created data for a real sub-network in Hannover, Germany, by means of a microsimulation with Aimsun NG, are presented in this paper.

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Section: Elimination Of Redundant Informationmentioning

confidence: 99%

Section: Elimination Of Redundant Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A combined method to forecast and estimate traffic demand in urban networks

Pohlmann

Friedrich

2013

Transportation Research Part C: Emerging Technologies

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“…An iterative procedure of traffic assignment and OD matrix estimation is used that is inspired by [5], [6]. It is based on the information minimization model [24].…”

Section: A Traffic Demand Forecasting and Estimationmentioning

confidence: 99%

“…This paper describes the prototype of a newly developed ATCS. The development has been motivated by the promising results of two previous projects conducted at the authors' former research institute, the first dealing with traffic demand estimation in urban networks [5], [6], and the second with offline optimization of signal plan offsets (i.e. the shift of signal plans to enable a good coordination of adjacent intersections) in signalized networks based on the Cell Transmission Model (CTM) and Genetic Algorithms (GA) [7].…”

Section: Introductionmentioning

confidence: 99%

Online control of signalized networks using the Cell Transmission Model

Pohlmann

Friedrich

2010

13th International IEEE Conference on Intelligent Transportation Systems

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Adaptive Traffic Control Systems (ATCS) aim at the improvement of traffic flow in terms of delay, number of stops, travel time etc. in urban networks under the regime of traffic signal control. Well known ATCS are SCOOT, SCATS, BALANCE and MOTION to name only a few. Lately the Cell Transmission Model (CTM) attracted some attention in the context of traffic signal control. It can be used to model the effects of different signal settings. Based on the CTM a prototype of a new ATCS has been developed. It can handle urban sub-networks with several interconnected signalized intersections. Every 15 minutes it adapts and optimizes signal plans and coordination patterns to the currently estimated traffic demand in the network. In a first step the upcoming traffic demand is forecasted and estimated. Based on this demand cycle length and green splits are adjusted. Finally, offsets are optimized by using the CTM in combination with Genetic Algorithms and a second alternative approach named Sequential Enumeration. The emphasis of this paper is on the offset optimization and the performance of the new ATCS which has been investigated in a microsimulation study.

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Optimizing Origin-Destination (O-D) Estimation with respect to Redundant Information and Route Choice

Cited by 2 publications

References 0 publications

A combined method to forecast and estimate traffic demand in urban networks

A combined method to forecast and estimate traffic demand in urban networks

Online control of signalized networks using the Cell Transmission Model

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