Ports’ congestion factors: Applying risk analysis as a problem identification tool to figure out the interrelated complex factors that contribute to the problem by assigning weights and probabilities to each factor

Ports’ congestion is a recurring problem that is caused by several factors. There are several past attempts to resolve ports’ congestion by applying governing and constructional reforms. Due to divergence and instability of congestion causal factors, the available studies and solutions are specific to individual ports. The main objective of this master thesis is to apply risk analysis as a problem identifier to figure out the interrelated complex factors that contribute to the congestion problem by assigning weights and probabilities to each factor. The research is based on qualitative data from secondary sources to gather all available information about the causal factors for ports’ congestion. A structured questionnaire was carried out and sent to various ports’ managers to figure out the most effective causal factors globally, as a means of validation for the secondary data and to ensure that the data reflect the current congestions causing factors from the port’s users themselves. Congestion’s factors can be human, technical, or organizational with different magnitudes based on the port’s features and capabilities. They are vulnerable to sudden and quick changes due to their interrelated and complex structure. Bayesian network (BN) is a risk analysis tool that fits the complex and changing scenarios of the congestion problem. It can incorporate the newly received information into the pre-established network of causal factors for port congestion. BN managed to reflect the cause-and-effect relationship between the causal factors and by means of appropriate software, the effect of any new event on congestion occurrence is visualized. Furthermore, the application of BN needs to be integrated into the port information management system as a permanent warning system that predicts the congestion and virtually shows the results of applying suggested solutions before applying it. Keywords: port congestion, congestion factors, Bayesian network, port productivit

Analyzing the Real Time Factors: Which Causing the Traffic Congestions and Proposing the Solution for Pakistani City

AbstractVehicle ownerships integral part of modern life and traffic congestion an unavoidable inconvenience. The Western countries have a far better control on the pace of number of vehicles on a road matched with supporting infrastructure. In contrast, cash strapped underdeveloped countries have a poorly built and scarce number of main roads with problems compounded by soft car loans, leases and other discounts. As a result several developing countries have been inundated with peripheral complications such as pollution and congestion undermining their economy with enormous energy bills negatively impacting respective economy. Case in point is Pakistan, where depilating infrastructure or absence outright thereof and ever more number of vehicles on the road presents a unique and highly complicated problem. One can term traffic in Sub-Continent as controlled chaos and we plan to develop an organized solution from the chaos. This presents a unique challenge in traffic management. We have developed a smart phone application when the phone is placed in vehicles, provides data for the origin and destination routes. Taking 6 parameters, which we believe mostly impacts the destination arrival time for the driver in Pakistan we propose to develop a model supported by empirical data that will enable driver to select weather they are interested in economy of fuel or economy of time in reaching their destination. We propose to plot time it takes to reach destination versus the 6 factors that determines destination arrival time. The curve will be generated for each route and from the graph median time, standard deviation as well as confidence interval will be computed. Large data will be collected and statistical analysis will be performed to verify the integrity of the model

Analysis and operational challenges of dynamic ride sharing demand responsive transportation models

There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version

Exploring the Effects of Cooperative Adaptive Cruise Control in Mitigating Traffic Congestion

The aim of this research is to examine the impact of CACC (Cooperative Adaptive Cruise Control) equipped vehicles on traffic-flow characteristics of a multilane highway system. The research identifies how CACC vehicles affect the dynamics of traffic flow on a road network and demonstrates the potential benefits of reducing traffic congestion due to stop-and-go traffic conditions. An agent-based traffic simulation model is developed specifically to examine the effect of these intelligent vehicles on the traffic flow dynamics. Traffic performance metrics characterizing the evolution of traffic congestion throughout the road network, are analyzed. Different CACC penetration levels are studied. The positive impact of the CACC technology is demonstrated and shown that it has an impact of increasing the highway capacity and mitigating traffic congestions. This effect is sensitive to the market penetration and the traffic arrival rate. In addition, a progressive deployment strategy for CACC is proposed and validated

Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF