<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3ASyg" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Fuzzy Decision Systems for Sustainable Transport: Mapping the Future</title><link>http://pubsindex.trb.org/view/2111879</link><description><![CDATA[The transportation sector has extensive environmental, social, and economic impacts on society. Given such impacts, it is necessary for this sector to rely on sustainable development. A growing number of investigations explore fuzzy decision systems for sustainable transport each year, addressing a multitude of topics in this area of research. The purpose of this study is to investigate the current status and trends of studies exploring “fuzzy decision systems for sustainable transport.” To accomplish this, the study relies on the scientometric analysis and uses the bibliographic information stored on Web of Science Core Collection. The study, more specifically, seeks to focus on three central purposes: to identify the specifications of publications investigating fuzzy decision systems for sustainable transport; to explain how research studies in this field have taken shape; and to specify hot topics and frontiers of research exploration in this area. The findings of this study offer new insights into research on fuzzy decision systems for sustainable transport, from various perspectives. Another contribution of this study is that it helps researchers to readily observe the status of various topics in this area and gain knowledge of the frontiers of research.]]></description><pubDate>Mon, 06 Feb 2023 15:32:50 GMT</pubDate><guid>http://pubsindex.trb.org/view/2111879</guid></item><item><title>Multi-stage emergency decision-making method based on cumulative prospect theory and intuitionistic fuzzy number</title><link>http://pubsindex.trb.org/view/1759695</link><description><![CDATA[This paper has been withdrawn at the request of the author(s).]]></description><pubDate>Thu, 04 Feb 2021 10:57:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/1759695</guid></item><item><title>Combining Machine Learning and Fuzzy Rule-Based System in Automating Signal Timing Experts’ Decisions during Non-Recurrent Congestion</title><link>http://pubsindex.trb.org/view/1707342</link><description><![CDATA[Events such as surges in demand or lane blockages can create queue spillbacks even during off-peak periods, resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change signal timings in real time based on traffic signal engineers’ observations of incident and traffic conditions at the intersections upstream and downstream of the congested locations. Decisions to change the signal timing are governed by many factors, such as queue length, conditions of the main and side streets, potential of traffic spilling back to upstream intersections, the importance of upstream cross streets, and the potential of the queue backing up to a freeway ramp. This paper investigates and assesses automating the process of updating the signal timing plans during non-recurrent conditions by capturing the history of the responses of the traffic signal engineers to non-recurrent conditions and utilizing this experience to train a machine learning model. A combination of recursive partitioning and regression decision tree (RPART) and fuzzy rule-based system (FRBS) is utilized in this study to deal with the vagueness and uncertainty of human decisions. Comparing the decisions made based on the resulting fuzzy rules from applying the methodology with previously recorded expert decisions for a project case study indicates accurate recommendations for shifts in the green phases of traffic signals. The simulation results indicate that changing the green times based on the output of the fuzzy rules decreased delays caused by lane blockages or demand surge.]]></description><pubDate>Thu, 21 May 2020 17:37:39 GMT</pubDate><guid>http://pubsindex.trb.org/view/1707342</guid></item><item><title>Modeling of Vehicles Merging Movement at Unsignalized Intersections Considering Drivers’ Risk Perception</title><link>http://pubsindex.trb.org/view/1437354</link><description><![CDATA[In the dynamic interaction of a driver-vehicle-environment system, risk perception of drivers changes dynamically, having significant impacts on driving behavior and vehicles movement. In China, because there is less construction of stop signs, as well as limited regulation of driving courtesy, traffic operation and safety issues at unsignalized intersections require harder concern. Thus, in this study, focus was on risk perception of drivers at unsignalized intersections in China and then analysis of vehicles movement with consideration of drivers’ risk perception. A total of 150 typical merging cases were selected at an unsignalized intersection in Kunming City. On the basis of cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established. Drivers’ acceptable risk perception levels were identified by using a self-developed data analysis method. On the basis of game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed; then the vehicles merging movement model was established. Finally, the vehicles merging movement model was validated by using data collected from real-world vehicle movements and driver decisions. Results showed that the developed vehicles movement model had both high accuracy and good applicability. This study could provide theoretical and algorithmic references for the microscopic simulation and vehicle active safety control system.]]></description><pubDate>Tue, 24 Jan 2017 12:03:12 GMT</pubDate><guid>http://pubsindex.trb.org/view/1437354</guid></item><item><title>Fuzzy and Random Reliability of Traffic Operations on Urban Interchanges-A Pilot Study</title><link>http://pubsindex.trb.org/view/1392415</link><description><![CDATA[Interchanges are the key component of urban road system, its quality of service determines the efficiency of urban road system. At present, the main evaluating method is capacity or level of service, and the subject is the merge segment, weaving segment, diverge segment and ramp. The defect of these methods is to evaluate the traffic operation effect independently, and not considering the fact that interchanges is composed of mainlines, ramps, and junctions, actually, it is a small and complex road net. It is should be evaluated by systematic method, but the existing method is unfit for it. This paper intended to present a systematic analysis method about the interchange system based on the theory of reliability. First, regarding the driving time on every segment as random variable, at the same time, setting up a parameter of multiples of free flow driving time which reflects the degree of actual driving time more than the driving time in the condition of free flow, and analyzing the property of this parameter, and building a reliability model using actual driving time divided by driving time in the condition of free flow. After that, considering that drivers’ perception is fuzzy to the driving time fluctuation, setting up a membership grade function model from the drivers’ perspective and giving the calibrating method of this model based on the field data. At last, building a fuzzy and random reliability model, and proposing an algorithm to calculate the reliability of interchanges based on the series and parallel principle.]]></description><pubDate>Thu, 18 Feb 2016 16:57:43 GMT</pubDate><guid>http://pubsindex.trb.org/view/1392415</guid></item><item><title>Potential Changes to Travel Behaviors &amp; Patterns: A Fuzzy Cognitive Map Modeling Approach</title><link>http://pubsindex.trb.org/view/1338774</link><description><![CDATA[The future of travel will be affected by a number of disruptive changes, including advancements in vehicle technology, such as automated vehicles, changes in population demographics and the economy, and lifestyle changes.  It is difficult to say just how much each change will affect the amount and type of travel in the future, especially given the amount of uncertainty there is regarding the trajectory of these changes and their effects. The authors examined changes that are likely to affect transportation behaviors in the future, developed a “fuzzy cognitive map” (FCM) of the relationships, and used the FCM model to investigate the effects of those relationships.  The results of the study show that FCM models offer a promising method for transportation planners to enhance their ability to reason about system effects when quantitative information is limited and uncertain.  More specifically, the results provide some initial guidance on the potential impacts of disruptive changes on future travel, which may help in targeting limited research funds on the most consequential potential changes.]]></description><pubDate>Tue, 31 Mar 2015 08:51:29 GMT</pubDate><guid>http://pubsindex.trb.org/view/1338774</guid></item><item><title>Maintenance Decision Indicators for Treating Squats in Railway Infrastructures</title><link>http://pubsindex.trb.org/view/1337196</link><description><![CDATA[In this study, the authors use a defect prediction-based methodology to support maintenance decisions for railway infrastructure that are related to surface defects known as squats. The performance and cost-effectiveness of possible squat maintenance countermeasures are assessed by analysing scenarios for the evolution of detected squats. Thus, indicators are identified that can enable an infrastructure manager to determine which sections of the track are healthy and which sections require grinding or replacement. To support the decision-making process, a fuzzy expert system is developed to determine the health condition of the tracks and cluster of squats, to facilitate corrective maintenance planning. The benefits of the developed approach are demonstrated by considering a section of the Groningen-Assen track of the Dutch railway network.]]></description><pubDate>Thu, 12 Mar 2015 07:53:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/1337196</guid></item><item><title>Modeling Uncertainty in Households’ Activity Engagement Decisions</title><link>http://pubsindex.trb.org/view/1241712</link><description><![CDATA[Studying travel behavior and activity engagement in an activity-based framework has been a focus of research for nearly half a century. A number of elegant and comprehensive models have been developed to address questions pertaining to activity participation, agenda formation, scheduling, and travel behavior of individuals. Despite the progress made in activity-based models, there is still a significant need for model improvements in the sense of modeling activity selection procedure and scheduling. In this paper, the authors propose a comprehensive model, which is the integration of discrete choice models, fuzzy concepts and Household Activity Pattern Problem (HAPP) to forecast household activity pattern based on socio-demographic characteristics. By using the values of probabilities obtained from a multivariate probit model applied to clustered households and mapping them to a set of fuzzy graphs, the authors compute the possibility of inclusion of an activity in the agenda. Activity scheduling and selection is then modeled as the outcome of a mixed integer optimization problem, in which the objective function is maximizing the expected desirability gained from activities and total saved time, subject to network connectivity, time windows, time budget and cost budget constraints.]]></description><pubDate>Mon, 15 Apr 2013 13:14:12 GMT</pubDate><guid>http://pubsindex.trb.org/view/1241712</guid></item><item><title>A Multiobjective, Stochastic, and Capacity-Constrained Static Location Model for Ambulances</title><link>http://pubsindex.trb.org/view/1242908</link><description><![CDATA[In this study, a new static ambulance location model is presented. To develop this model, the authors first introduce measures that an ideal ambulance location model is expected to include. Seven measures are defined for the ideal model. Then previous models are adapted to these seven measures to see which model is logically the best among models presented up until now. The analysis shows that at best only five out of these seven measures have been addressed by the current models. The authors propose a new ambulance location model which can cover all the seven measures. This model is a multi-objective, probabilistic, and capacity-constrained location model (MPCLM). The MPCLM is the first model that can determine simultaneously location of ambulances and their under-coverage regions. To show its applicability in real world, the MPCLM is run for a large city of approximately 3 million people. The Fuzzy Goal Programming (FGP), as an approach to solve multi-objective problems, is employed. Sensitivity analysis of its variables shows that the model is flexible and works very well in different situations.]]></description><pubDate>Thu, 28 Feb 2013 16:32:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/1242908</guid></item><item><title>Design and Construction of Transportation Infrastructure</title><link>http://pubsindex.trb.org/view/1225250</link><description><![CDATA[Any decision regarding the design and construction of transportation infrastructure should be prompt and linked to the entire transportation system infrastructure. Transportation networks typically span a large geographical area and have a modular structure that consists of many subsystems with actors and sensors, while involving both continuous and discrete dynamics that evolve over different time scales. The difficulty in such a case arises from the fact that most often the available information is quite intensive; in a network-level thinking, a large amount of transportation assets should be taken into consideration that, many times, may impose conflicting design and construction goals. In transportation infrastructure design and construction problems there is the need to jointly consider information that may be measured and quantified and other information that is subjective and should be qualitatively assessed. Artificial intelligence (AI) provides methodologies for developing flexible multivariate models for solving difficult approximation and optimization problems. The flexibility of AI methods [e.g., knowledge-based systems, expert systems, pattern recognition, machine learning, neural networks, genetic algorithms (GAs) and evolutionary computation, fuzzy systems, etc.], as well as their performance in various interdisciplinary applications are well suited for the complexity and variety of transportation systems. AI has been applied to various fields of transportation engineering. AI has also played a critical role in solving many infrastructure problems related to design and construction (including maintenance). Some of the AI techniques that are most popular in infrastructure design and construction are GAs, simulated annealing, ant algorithms, and fuzzy logic.]]></description><pubDate>Tue, 11 Dec 2012 10:35:48 GMT</pubDate><guid>http://pubsindex.trb.org/view/1225250</guid></item><item><title>Application of Soft Computing Techniques in Modeling Train Delays</title><link>http://pubsindex.trb.org/view/1128731</link><description><![CDATA[Train delays are the time between scheduled and actual arrival of the train. They have a great influence on the timetable and technological processes related to the train traffic. A model for calculating train delays can be used in the process of railway operations and timetable planning, and operational management. The model for train delays is based on the soft computing techniques. Neural Networks model and Adaptive Network-based Fuzzy Inference System  model are trained and verified by the data collected from train dispatcher’s and infrastructure manager’s database. Model is tested on Rakovica station in Serbian Railways.]]></description><pubDate>Wed, 25 Apr 2012 08:00:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/1128731</guid></item><item><title>Route Priority Analysis Method for Intersection Group</title><link>http://pubsindex.trb.org/view/1129797</link><description><![CDATA[A route priority analysis method for intersection group is presented in this paper to explore the internal correlated characteristics of the routes. Considering the traffic movement along with the critical route are significant, a wavelet transform method is utilized to extract the short time varying features of traffic flow series through the process of decomposition, de-noise and reconstruction. Spectrum analysis is applied to obtain the relevance of every two movements on a specific route. Based on the analysis of coherence and phase of the cross spectrum, the route characteristics of the intersection group could be acquired. Finally, fuzzy clustering method was used to divide the routes with different priorities for traffic control and management purpose. Simulated data from an intersection group around Wutaishan Stadium, Nanjing, China, was selected to test the proposed method. The results indicated that the method presented in this paper could classify the routes into several categories with different priorities for traffic control and management purpose at intersections group.]]></description><pubDate>Thu, 29 Mar 2012 07:14:51 GMT</pubDate><guid>http://pubsindex.trb.org/view/1129797</guid></item><item><title>Learning User Preferences of Route Choice Using Fuzzy Decision Tree Induction</title><link>http://pubsindex.trb.org/view/1093154</link><description><![CDATA[Decision tree induction, one of machine learning techniques, can be used to build a route choice model by finding patterns observed in repeated route choice behaviour. Although a decision tree successfully accommodates route choice preferences, the use of “sharp cut-off points” to discretise the domain of a continuous-valued attribute makes a decision tree too sensitive, leading to misclassifications. This paper introduces fuzzy decision tree induction to relax the sensitivity of classical decision trees. Transforming crisp discretisation in a classical decision tree into soft discretisation using fuzzy representation, fuzzy decision tree induction increases flexibility in classifying a new instance. The experiment results indicate that fuzzy route choice decision trees outperform non-fuzzy decision trees in terms of predictive accuracy and that the fuzzy models are more effectively applicable in practice.]]></description><pubDate>Wed, 18 May 2011 10:51:48 GMT</pubDate><guid>http://pubsindex.trb.org/view/1093154</guid></item><item><title>Locomotive Routing and Scheduling Problem with Fuzzy Time Windows</title><link>http://pubsindex.trb.org/view/910620</link><description><![CDATA[The problem of assigning locomotives to a set of pre-planned trains is very important for railway companies, in view of high cost of operating locomotives. The model considered in this paper is to assign a set of homogeneous locomotives locating in some geographically dispersed depots to a set of pre-schedules trains with different degrees of priorities in order to provide sufficient power to pull the trains  from their origins to their destinations at minimum costs. This model is presented by the use of the  vehicle routing and scheduling problem where  trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows.  A cluster-first, route-second approach is used to inform the multi-depot locomotive assignment to a set of single depot problems and after that each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm with various heuristics in the evolutionary search, part of initial population is initialized using Push Forward Insertion Heuristic (PFIH) and part is initialized randomly. A interchange mechanism interchanges customers between routes and generates neighborhood solution. The suggestive algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover,  some of the results are compared with of those  solutions produced by Branch &amp; Bound technique to determine validity and quality of the model. Results show that suggestive approach is rather  effective in respect of quality and time.]]></description><pubDate>Mon, 24 May 2010 14:08:38 GMT</pubDate><guid>http://pubsindex.trb.org/view/910620</guid></item><item><title>Using a Fuzzy Approach for Evaluating Sustainability of Transportation System Pollution-Reducing Policies: A Case Study</title><link>http://pubsindex.trb.org/view/910379</link><description><![CDATA[In recent years a wide debate between stakeholders and experts from different fields has developed about the concept of sustainability and sustainable development. In transportation systems analysis the idea of sustainable mobility has received increasing attention, finding a reference point in the concept of the “three pillars of sustainability”, which considers the idea of sustainability from a three-dimensional perspective (social, economic and environmental). This paper presents an innovative Fuzzy Multi-Level (FML) model, which formalizes and implements this conceptual approach, using the theory of fuzzy systems, useful as a tool for evaluating sustainability of transportation policies.  The model works on three different levels, starting from a set of indicators used as input variables, and gives a fuzzy indicator of the durability and sustainability offered by the alternative policies analyzed; furthermore, intermediate fuzzy inference systems, based on rules formulated by experts, provide information about combined dimensions of sustainability (equity, viability and bearableness).  A comparative analysis of the FML model and the Analytic Hierarchy Process (AHP) method, applied to the evaluation of different pollution-reducing policies, has been developed. The interpretation of results yields information about the use of the FML model as an alternative to traditional methods of sustainability evaluation and suggests further developments of research to improve its effectiveness.]]></description><pubDate>Wed, 21 Apr 2010 08:09:02 GMT</pubDate><guid>http://pubsindex.trb.org/view/910379</guid></item></channel></rss>