Cost-effectiveness of cordon studies for trip matrix estimation from traffic counts

Cordon studies can substantially improve estimation of origin-destination (O/D) trip matrices from link traffic counts. This paper proposes a method for integrating cordon survey results in O/D matrix estimation. This method is applied to 5 inter-urban networks in the Valencia Region, focusing on the specific formulation of the O/D matrix estimation technique, the assignment model to be implemented, the selection of traffic count locations in the cordoned area and, especially, the selection of cordon survey stations and data collection. Cost-effectiveness of cordon survey selection is addressed. The results show that cost-effectiveness ratios tend to decrease with the number of surveyed cordons in the 5 networks analyzed. This is due to non-linear reduction of origin-destination estimation errors and economies of scale on conducting cordon surveys. The results of this study can be useful to assess decision-making when conducting cordon surveys in interurban networks. Once such decisions have been taken, the method presented in this paper can be applied to the whole O/D matrix estimation procedure.Torres Martínez, AJ. (2012). Cost-effectiveness of cordon studies for trip matrix estimation from traffic counts. http://hdl.handle.net/10251/1496

A Fuzzy-Logical Approach for Integrating Multi-Agent Estimators

This paper proposes a novel approach for integrating estimations from multiple agents. The approach is based on the fuzzy set theory. However, compared to existing fuzzy logical methods that use fuzzy if-then rules, this method is based on solving an over-determined fuzzy equation system. The result is either a global inconsistency message or the consistent core of the equation system. We demonstrate the approach with data from an actual case study undertaken by a German automotive manufacturer

Prying open the black box of causality: a causal mediation analysis test of procedural justice policing

Objectives: Review causal mediation analysis as a method for estimating and assessing direct and indirect effects. Re-examine a field experiment with an apparent implementation failure. Test procedural justice theory by examining to which extent procedural justice mediates the impact of contact with the police on police legitimacy and social identity. Methods: Data from a block-randomised controlled trial of procedural justice policing (the Scottish Community Engagement Trial) were analysed. All constructs were measured using surveys distributed during roadside police checks. Treatment implementation was assessed by analysing the treatment effect’s consistency and heterogeneity. Causal mediation analysis, which can derive the indirect effect even in the presence of a treatment–mediator interaction, was used as a versatile technique of effect decomposition. Sensitivity analysis was carried out to assess the robustness of the mediating role of procedural justice. Results: First, the treatment effect was fairly consistent and homogeneous, indicating that the treatment’s effect is attributable to the design. Second, there is evidence that procedural justice channels the treatment’s effect towards normative alignment (NIE = − 0.207), duty to obey (NIE = − 0.153), and social identity (NIE = − 0.052), all of which are moderately robust to unmeasured confounding (ρ = 0.3–0.6, LOVE = 0.5–0.7). Conclusions: The effect’s consistency and homogeneity should be examined in future block-randomised designs. Causal mediation analysis is a versatile tool that can salvage experiments with systematic yet ambiguous treatment effects by allowing researchers to “pry open” the black box of causality. The theoretical propositions of procedural justice policing were supported. Future studies are needed with more discernible causal mediation effects

Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs

Estimation of origin-destination matrix from traffic counts: the state of the art

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix

Estimation of origin-destination matrix from traffic counts: the state of the art

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix

Event-Driven Network Programming

Software-defined networking (SDN) programs must simultaneously describe static forwarding behavior and dynamic updates in response to events. Event-driven updates are critical to get right, but difficult to implement correctly due to the high degree of concurrency in networks. Existing SDN platforms offer weak guarantees that can break application invariants, leading to problems such as dropped packets, degraded performance, security violations, etc. This paper introduces EVENT-DRIVEN CONSISTENT UPDATES that are guaranteed to preserve well-defined behaviors when transitioning between configurations in response to events. We propose NETWORK EVENT STRUCTURES (NESs) to model constraints on updates, such as which events can be enabled simultaneously and causal dependencies between events. We define an extension of the NetKAT language with mutable state, give semantics to stateful programs using NESs, and discuss provably-correct strategies for implementing NESs in SDNs. Finally, we evaluate our approach empirically, demonstrating that it gives well-defined consistency guarantees while avoiding expensive synchronization and packet buffering

The operational utility of the Walton-McKersie attitudinal structuring model in collective bargaining

This work is an heuristic inquiry into behavioral change theory designed for application to labor/management interaction in collective bargaining. The theory itself was postulated by Richard E. Walton and Robert B. McKersie in their book A Behavioral Theory Of Labor Negotiations. The principles of their theory are highly axiomatic and their importance and validity can only be recognized through applied empirical analyses that demonstrate or refute its concept. The aspect of the theory which is the focal point of this research pertains to the structuring and restructuring of attitudes and attendant relationships resulting from the collective bargaining process. The objective of this work is two-fold. First, the analytical utility of the theory is examined by applying its tenets to an analysis of the behavioral strategies and tactics used by the respective labor and management operatives in the 1981 Professional Air Traffic Controllers Organization strike. Second, the consistency of the theory and model with current knowledge and research in the field is examined; also how that knowledge and research enhances the Walton-McKersie analysis is discussed. Case study methodology is used to illustrate the thesis concept because any empirical study that examines the validity and practicality of a theory has added value when it is done within the realm of that given discipline. Also, absolute studies beat illustrate the trends by which researchers and practitioners approach problems in their fields and help to possibly clarify those approaches. From this study it is concluded that the Walton-McKersie attitudinal structuring model offers the most elucidative classification of relationships and behaviors descriptive of the negotiating process of all materials researched. It can be applied in collective bargaining interactions to reduce behavioral uncertainties. However,, to improve the model\u27s operational utility as a motivational, predictive, and informational tool, additional research and study is required in the following areas. First, as shown in the case study example, humans do not always use probability information effectively; sometimes they ignore it. The probability of a confrontation and the attendant consequences were made clear to all operatives in the Professional Air Traffic Controllers Organization (PATCO) strike, however, shattering consequences for both sides were not avoided. Additional study and research on how collective bargaining processes are affected by varying political, economic, intra-organizational and inter-organizational policies, and social climates will enhance the operational utility of the Walton-McKersie model. Second, the implication interpretable from the above probability of occurrence example is that people, and the organizations that they comprise, estimate the probability of single occurrences more adequately than aggregate probabilities of occurrence, and that the strategies often adopted as a result, are not optimal. Research on how objective probabilities and payoff values (based on past bargaining profiles and current information) can be applied to the Walton-McKersie concepts will allow for simulation and decision theory type analysis of negotiating processes, thus improving the model\u27s predictive utility. Finally, it is suggested in the thesis that goals rather than attitudes be the focal point of behavioral change models related to negotiations. Additional research on the motivational qualities of goal setting in bargaining activities will help in the understanding of how goals and behaviors are linked. The developmental implication of all the above is to move towards a more useful and analytical model of collective bargaining processes

Towards a general temporal theory

The research work presented herein addresses time representation and temporal reasoning in the domain of artificial intelligence. A general temporal theory, as an extension of Alien and Hayes', Gallon's and Vilain's theories, is proposed which treats both time intervals and time points on an equal footing; that is, both intervals and points are taken as primitive time elements in the theory. This means that neither do intervals have to be constructed out of points, nor do points have to be created as some limiting construction of intervals. This approach is different from that of Ladkin, of Van Beek, of Dechter, Meiri and Pearl, and of Maiocchi, which is either to construct intervals out of points, or to treat points and intervals separately. The theory is presented in terms of a series of axioms which characterise a single temporal relation, "meets", over time elements. The axiomatisation allows non-linear time structures such as branching time and parallel time, and additional axioms specifying the linearity and density of time are specially presented. A formal characterisation for the open and closed nature of primitive intervals, which has been a problematic question of time representation in artificial intelligence, is provided in terms of the "meets" relation. It is shown to be consistent with the conventional definitions of open/closed intervals which are constructed out of points. It is also shown that this general theory is powerful enough to subsume some representative temporal theories, such as Alien and Hayes's interval based theory, Bruce's and McDermott's point based theories, and the interval and point based theory of Vilain, and of Gallon. A finite time network based on the theory is specially addressed, where a consistency checker in two different forms is provided for cases with, and without, duration reasoning, respectively. Utilising the time axiomatisation, the syntax and semantics of a temporal logic for reasoning about propositions whose truth values are associated with particular intervals/points are explicitly defined. It is shown that the logic is more expressive than that of some existing systems, such as Alien's interval-based logic, the revised theory proposed by Gallon, Shoham's point-based interval logic, and Haugh's MTA based logic; and the corresponding problems with these systems are satisfactorily solved. Finally, as an application of the temporal theory, a new architecture for a temporal database system which allows the expression of relative temporal knowledge of data transaction and data validity times is proposed. A general retrieval mechanism is presented for a database with a purely qualitative temporal component which allows queries with temporal constraints in terms of any logical combination of Alien's temporal relations. To reduce the computational complexity of the consistency checking algorithm when quantitative time duration knowledge is added, a class of databases, termed time-limited databases, is introduced. This class allows absolute-time-stamped and relative time information in a form which is suitable for many practical applications, where qualitative temporal information is only occasionally needed, and the efficient retrieval mechanisms for absolute-time-stamped databases may be adopted