DEVELOPMENT OF SPATIOTEMPORAL CONGESTION PATTERN OBSERVATION MODEL USING HISTORICAL AND NEAR REAL TIME DATA

Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was used as a case study. Real time traffic data was acquired from the Georgia Department of Transportation (GDOT) Navigator system. For a qualitative analysis, speed data was categorized into three levels: low, median, and high. Cluster analysis was performed with respect to the categorized speed data in the spatiotemporal domain to identify where and when congestion has occurred and for how long, which indicate the severity of congestion. This qualitative analysis was performed by day of week to identify potential variation in congestion over weekdays and weekend. For a quantitative analysis, actual speed data was used to construct daily spatiotemporal maps to reveal congestion patterns at a more granular level, where congestion is represented as “cloud” in the spatiotemporal domain. Future work will be focusing on deep learning of congestion patterns using Convolutional Long Short Term Memory (ConvLSTM) networks

Traffic flow prediction model based on neighbouring roads using neural network and multiple regression

Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays and weekends to achieve more accurate results. We exploit similarity of traffic flow pattern on weekdays and weekends by adding time cluster in our proposed model. Thus, our neural network model proposed high correlation road, time and day clusters as input factors in neural network model prediction. Our initial phase of the methodology involves investigation on correlation between neighbouring roads. This paper discusses the results of experiments we have conducted to determine relationship between roads in a neighbouring area and to determine input factors for our neural network traffic flow prediction model. To choose a particular road as a predicting factor, we calculated the distance between roads in neighbouring area to identify the nearest road. Then, we calculated correlation based on traffic condition (congestion) between roads in neighbouring area. The results were then used as input factors for prediction of traffic flow. We compared the results of the experiment using neural network without cluster parameters and multiple regression methods. We observed that neural network with time cluster parameter produced better results compared to neural network without parameter and multiple regression method in predicting average speed of vehicles on neighbouring roads

The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

It's difficult to deal with the dynamic nature of VANETs and WSNs in a way that makes sense. Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. Several techniques for WSN and VANETs based on ML are covered in this study, which provides a fast overview of the main ML ideas. Open difficulties and challenges in quickly changing networks, as well as diverse algorithms in relation to ML models and methodologies, are also covered in the following sections. We've provided a list of some of the most popular machine learning (ML) approaches for you to consider. As a starting point for further research, this article provides an overview of the various ML techniques and their difficulties. This paper's comparative examination of current state-of-the-art ML applications in WSN and VANETs is outstanding

Three Studies Of Stakeholder Influence In The Formation And Management Of Tax Policies

This dissertation consists of three separate but interrelated studies examining the formation and management of tax policies. The first study uses stakeholder theory (ST) to investigate the strategic management practices of the Transport for London (TfL) during discrete stages in the adoption, implementation, and amendments of the tax policy reform known as the London Congestion Charge (LCC). Results indicate that TfL has utilized power, legitimacy, and urgency as its main policy management tactics with a significant emphasis on legitimatizing the LCC and its subsequent policy amendments. The second study draws on social exchange theory (SET) to reexamine the relationship between corporations and legislators during tax policy processes. Data for the study come from publicly available political action committee (PAC) contribution activities surrounding the Energy Independence and Security Act of 2007 (EISA07). By examining the endogeneity between legislators’ voting patterns and PAC contributions by corporations, this study aims to refine empirical work on corporate political strategy, especially as it relates to crucial tax provisions embedded within an intensely debated policy proposal. Using simultaneous equations modeling (SEM), results are consistent with SET showing that an implicit and reciprocal relationship exists between corporations and legislators. This relationship affects the interdependence of how legislators vote for public policies and the amount of corporations’ financial contributions to legislators. The third study investigates and aims to validate the empirical applicability of Dahan’s (2005) typology of political resources in explicating the political interactions between stakeholder groups and legislators in the development of EISA07. I discuss how and why the mode of operations and various political resources employed by stakeholder groups affected the iii final EISA07 language concerning domestic production deduction tax credits for the oil and gas industry. Publicly available data show that both supporting and opposing stakeholder groups employ tactics consistent with Dahan’s (2005) typology. However, both stakeholder groups tend to use an interactive or positive political approach to gain access and favor of legislators instead of an adversarial approach. Ultimately, the tax credits were preserved. Taken as a whole, the three studies advance the tax and public policy research literature in accounting by studying how and why relevant stakeholders affect the formation and ongoing management of public and tax policie

The Development of a Common Investment Appraisal for Urban Transport Projects.

In December 1990 we were invited by Birmingham City Council and Centro to submit a proposal for an introductory study of the development of a common investment appraisal for urban transport projects. Many of the issues had arisen during the Birmingham Integrated Transport Study (BITS) in which we were involved, and in the subsequent assessment of light rail schemes of which we have considerable experience. In subsequent discussion, the objectives were identified as being:- (i) to identify, briefly, the weaknesses with existing appraisal techniques; (ii) to develop proposals for common methods for the social cost-benefit appraisal of both urban road and rail schemes which overcome these weaknesses; (iii) to develop complementary and consistent proposals for common methods of financial appraisal of such projects; (iv) to develop proposals for variants of the methods in (ii) and (iii) which are appropriate to schemes of differing complexity and cost; (v) to consider briefly methods of treating externalities, and performance against other public sector goals, which are consistent with those developed under (ii) to (iv) above; (vi) to recommend work to be done in the second phase of the study (beyond March 1991) on the provision of input to such evaluation methods from strategic and mode-specific models, and on the testing of the proposed evaluation methods. Such issues are particularly topical at present, and we have been able to draw, in our study, on experience of:- (i) evaluation methods developed for BITS and subsequent integrated transport studies (MVA) (ii) evaluation of individual light rail and heavy rail investment projects (ITS,MVA); (iii) the recommendations of AMA in "Changing Gear" (iv) advice to IPPR on appraisal methodology (ITS); (v) submissions to the House of Commons enquiry into "Roads for the Future" (ITS); (vi) advice to the National Audit Office (ITS) (vii) involvement in the SACTRA study of urban road appraisal (MVA, ITS

A study of flight cancellation and delays in the UK

Flight delays and flight cancellations have always been a problem for the aviation industry. However, the different nature of both phenomena has made research focus almost solely on studying and predicting delays. This is due to the fact that, ultimately, it is the airline who decides whether a flight gets cancelled, whereas delays are an involuntary result of a vast array of different causes, many times due to bad management practices by airports and airlines. The literature has studied delays from a wide range of perspectives, taking into consideration several factors that influence them. Some studies have predicted delays from a machine learning perspective, while others have taken into consideration the importance of the time series component of the data. However, research shows that it is actually flight cancellations that is the most important determinant for consumer dissatisfaction and complaints, being detrimental for airlines' reputation and resulting in passengers switching carriers. Therefore, a more careful study and comprehension of what drives and affects flight cancellations is needed. Analyzing the research that has focused on understanding the underlying patterns of cancellations, what can mostly be found are theoretical and machine learning approaches. Some findings have been made in determining what further increases or helps reduce the number of cancellations, like the importance of a well-managed airport capacity to improve service quality in terms of cancellations \citep{mead2000flight}. As mentioned, there is also behavioral research on the consequences that cancellations have on airlines (Yanying et al., 2019), pointing towards an increased dissatisfaction and distrust from customers, resulting in serious damages for the airline's corporate reputation and passengers' loyalty. Nevertheless, there are components of the understanding of cancellations that remained unclear. On the one hand, a thorough time series analysis of cancellations needs to be done. In fact, as Lemke et al. (p. 85, 2009) point out, the diverse characteristics and underlying data generation processes of time series has resulted in the fact that "it seems as if no method has ever proven successful across various studies and time series". On the other hand, delays and cancellations are two phenomena that cannot be completely understood independently and, although there is a vast number of studies analyzing delay propagation, there are no conclusive results on the impact of delays on cancellations. Therefore, research must determine whether taking delays into account when analyzing cancellations improves the accuracy of cancellations forecasts and the relation among these parameters. Lastly, as they cannot only be studied alone, a more thorough study of the capacity factors that influence the number of cancellations also needs to be done. Moreover, the outbreak of the COVID-19 in the midst of the research process made the accuracy of the forecasts deviate. Delays and cancellations have evolved dramatically differently over the first months of 2020. Hence, there is a need for taking a new parameter into account that would help make sense of the abnormal cancellations in 2020 and improve forecasts accuracies. For this, the behavioral changes of the population have been taking into consideration, which has been done with Google Trends. Also, it opened a door for understanding the passengers' behavioral reaction towards air travel under these circumstances, taking into consideration both local and global factors. Therefore, this study is divided into three sections. The first one studies the relationship between delays and cancellations from a time series perspective, and it is found that taking delays into account as a parameter in the study of cancellations improves the accuracy of time series forecasts at different levels of aggregation. The second one focuses on studying the relevance of competition and network factors in the distribution of cancellations. Flights arriving or departing from a hub airport are found to be less likely to be cancelled, pointing towards the relevance of maintaining networks for airlines, thus strengthening passenger reliability and trust. However, it was found that route and airport competition, while confirming the nature of the impact, was not statistically significant in predicting flight cancellations. Finally, it was found that public concern in the context of a global pandemic varies according to local circumstances, and that shortly after the first and most shocking news, both concern and a positive consumer attitude decrease to a stabilized level, which indicating double-edged passive behavior, in which both concern and willingness to purchase flight or event tickets (i.e., requiring travel or social gatherings) are reduced to similarly low levels for at least one month after the initial mayhe