Evaluation of Coordinated Ramp Metering (CRM) Implemented By Caltrans

Coordinated ramp metering (CRM) is a critical component of smart freeway corridors that rely on real-time traffic data from ramps and freeway mainline to improve decision-making by the motorists and Traffic Management Center (TMC) personnel. CRM uses an algorithm that considers real-time traffic volumes on freeway mainline and ramps and then adjusts the metering rates on the ramps accordingly for optimal flow along the entire corridor. Improving capacity through smart corridors is less costly and easier to deploy than freeway widening due to high costs associated with right-of-way acquisition and construction. Nevertheless, conversion to smart corridors still represents a sizable investment for public agencies. However, in the U.S. there have been limited evaluations of smart corridors in general, and CRM in particular, based on real operational data. This project examined the recent Smart Corridor implementation on Interstate 80 (I-80) in the Bay Area and State Route 99 (SR-99, SR99) in Sacramento based on travel time reliability measures, efficiency measures, and before-and-after safety evaluation using the Empirical Bayes (EB) approach. As such, this evaluation represents the most complete before-and-after evaluation of such systems. The reliability measures include buffer index, planning time, and measures from the literature that account for both the skew and width of the travel time distribution. For efficiency, the study estimates the ratio of vehicle miles traveled vs. vehicle hour traveled. The research contextualizes before-and-after comparisons for efficiency and reliability measures through similar measures from another corridor (i.e., the control corridor of I-280 in District 4 and I-5 in District 3) from the same region, which did not have CRM implemented. The results show there has been an improvement in freeway operation based on efficiency data. Post-CRM implementation, travel time reliability measures do not show a similar improvement. The report also provides a counterfactual estimate of expected crashes in the post-implementation period, which can be compared with the actual number of crashes in the “after” period to evaluate effectiveness

Rapid Recovery for Systems with Scarce Faults

Our goal is to achieve a high degree of fault tolerance through the control of a safety critical systems. This reduces to solving a game between a malicious environment that injects failures and a controller who tries to establish a correct behavior. We suggest a new control objective for such systems that offers a better balance between complexity and precision: we seek systems that are k-resilient. In order to be k-resilient, a system needs to be able to rapidly recover from a small number, up to k, of local faults infinitely many times, provided that blocks of up to k faults are separated by short recovery periods in which no fault occurs. k-resilience is a simple but powerful abstraction from the precise distribution of local faults, but much more refined than the traditional objective to maximize the number of local faults. We argue why we believe this to be the right level of abstraction for safety critical systems when local faults are few and far between. We show that the computational complexity of constructing optimal control with respect to resilience is low and demonstrate the feasibility through an implementation and experimental results.Comment: In Proceedings GandALF 2012, arXiv:1210.202

A systematic literature review of the use of social media for business process management

In today’s expansion of new technologies, innovation is found necessary for organizations to be up to date with the latest management trends. Although organizations are increasingly using new technologies, opportunities still exist to achieve the nowadays essential omnichannel management strategy. More precisely, social media are opening a path for benefiting more from an organization’s process orientation. However, social media strategies are still an under-investigated field, especially when it comes to the research of social media use for the management and improvement of business processes or the internal way of working in organizations. By classifying a variety of articles, this study explores the evolution of social media implementation within the BPM discipline. We also provide avenues for future research and strategic implications for practitioners to use social media more comprehensively

Revisiting the Use of Customer Information for CRM

For the past decade, customer relationship management (CRM) has been one of the priorities in marketing research and practice. However, many of the CRM systems did not perform as the companies expected. As such shortcoming could be due to inappropriate data input, this study provides a comprehensive overview of the empirical CRM literature. Along the phases of the CRM process, the authors show which kind of data has successfully proven to achieve the CRM objectives. The study provides researchers with a review of the empirical research on CRM and allows practitioners insights on the usability of customer data for CRM. --Customer Relationship Management (CRM),Customer Data

Strategic Investments In The Right CRM Technologies, In The Right Amount, and In The Right Order

Although many companies have spent a great deal of money to adopt CRM (Customer Relationship Management) technologies, many have not seen satisfactory returns on their CRM installations. One of the reasons for such dissatisfaction and low ROI may be the lack of a comprehensive approach to evaluating the impact of CRM technologies, which are very different from traditional cost-cutting and quality-improving IT. To bridge the gap between the existing research stream on IT investment and firms’ dissatisfaction with returns on CRM technologies, we aim to analyze the optimal CRM implementation strategy and the impact of CRM investments on a firm’s profitability. For our analysis, we classify CRM technology into two broad categories, targeting-related CRM technology and support-related CRM technology. We find that the two types of CRM technologies are substitutive in generating firms’ revenue rather than complementary. We also find that firms’ investments in both targeting-related CRM and support- related CRM can decrease consumer welfare under certain conditions. We develop a model that not only considers different factors across industries and environments, but is also helpful in determining the right CRM technology, in the right amount, and in the right order

ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input

A Framework for Key Account Management and Revenue Management Integration

This is an Open Access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license.Key Account Management (KAM) and Revenue Management (RevM) have been widely practiced in the service industries for more than three decades, but the effects of RevM on KAM remain largely unknown. This paper addresses this neglected area of study in the marketing field by presenting a framework for KAM and RevM integration that aligns the potentially conflicting management priorities of the two. The study uses an international hotel company as a research context to investigate, first, how a long-term relational approach to KAM may have been affected by RevM short-term revenue maximization goals, and, second, how KAM could be facilitated by RevM through an integrated approach to yield optimization from perishable products and from key accounts. The proposed framework is the first attempt of its kind to amalgamate KAM and RevM, involving critical analysis to assess comprehensively the revenue and the relationship value of a key accountPeer reviewedFinal Published versio