Public transit agencies often struggle with service reliability issues; when a bus or train does not arrive on time, passengers become frustrated and may be less likely to choose transit for future trips. To address reliability problems, transit authorities increasingly provide real-time vehicle location and arrival information to riders via web-enabled and mobile devices. Although prior studies have found several benefits of offering this information to passengers, researchers have had difficulty determining if real-time information affects ridership levels. Therefore, the objective of this dissertation is to quantify the impact of real-time information on public transit ridership. Statistical and econometric methods were used to analyze passenger behavior in three American cities that share a common real-time information platform: New York City, Tampa, and Atlanta. New York City was the setting for a natural experiment in which real-time bus information was gradually launched on a borough-by-borough basis over a three year period. Panel regression techniques were used to evaluate route-level bus ridership while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. In Tampa, a behavioral experiment was performed with a before-after control group design in which access to real-time bus information was the treatment variable and web-based surveys measured behavior changes over a three month period. In Atlanta, a methodology to combine smart card fare collection data with web-based survey responses was developed to quantify changes in transit travel of individual riders in a before-after study. In summary, each study utilized different data sources and quantitative methods to assess changes in transit ridership. The results varied between cities and suggest that the impact of real-time information on transit travel is greatest in locations that have high levels of transit service. These findings have immediate implications for decision-makers at transit agencies, who often face pressure to increase ridership with limited resources.

No / In many production processes real time information may be obtained from process control computers and other monitoring systems, but most existing scheduling models are unable to use this information to effectively influence scheduling decisions in real time. In this paper we develop a general framework for using real time information to improve scheduling decisions, which allows us to trade off the quality of the revised schedule against the production disturbance which results from changing the planned schedule. We illustrate how our framework can be used to select a strategy for using real time information for a single machine scheduling model and discuss how it may be used to incorporate real time information into scheduling the complex production processes of steel continuous caster planning.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Real-time monitoring of temperature is a critical factor in ensuring the integrity of food during the cold chain. In this work, we compare techniques related to real-time data visualization to contribute to more efficient monitoring of the cold chain. Three real-time data display attributes were evaluated, and we constructed a dataset based on the Frisbee database (CDD). In this paper, we proposed graphics containing different line and area techniques to be evaluated for a specialist. The proposed graphs contained the line and area techniques that, when performing the experiment, obtained a higher success rate compared to the auto-charting technique. However, it was evidenced that elements such as color facilitate the detection of anomalies and trends in temperature change due to its high percentage of effectiveness in the results. / Revisión por pares

This research studies travelers' route choice behavior in a driving simulator with real-time information en-route. We investigate whether travelers plan strategically for real-time information en-route or simply select a fixed path from origin to destination at the beginning of a trip, and whether network complexity and a parallel driving task affect subjects' strategic thinking ability. In this study, strategic thinking refers to a traveler's route choice decision taking into account future diversion possibilities downstream enabled by information at the diversion node. All of the subjects in this study participated in driving-simulator-based tests while half of the subjects participated in additional PC-based tests. Three types of maps were used. The first type required a one-time choice at the beginning of a trip to test the traveler's risk attitude. The other two types offered route choices both at the beginning of and during a trip to test the traveler's strategic thinking. The study shows that a significant portion of route choice decisions are strategic in a realistic driving simulator environment. Furthermore, different network complexities impose different cognitive demands on a subject and affect his/her strategic thinking ability. A subject tends to be more strategic in a simple network. Lastly, a parallel driving task does not significantly affect a subject's strategic thinking ability. This seemingly counterintuitive conclusion might be caused by the simplicity of the tested network.

Real time information about traffic conditions is becoming widely available through various media, and the focus on Advanced Traveler Information Systems (ATIS) is gaining importance rapidly. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes along their travel in order to optimize their own objective of travel, which can be characterized by disutility functions. The focus of this research is to study the behavior of travelers with multiple trip objectives, when provided with real time information. A web based experiment is carried out to simulate a traffic network with information provision and different travel objectives. The decision strategies of participants are analyzed and compared to the optimal policy, along with few other possible decision rules and a general model is calibrated to describe the travelers' decision strategy. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes. / text

As a result of a growing population, the city of Stockholm is facing many challenges. Getting more people to travel by public transportation is a key factor in coping with this increased urbanization. In the strive for increased ridership, it is the Stockholm Public Transport Administration’s (SL) job to make sure that the services provided are of high quality. One of these services is the real time bus departure predictions provided to the travellers through digital signs or by web and mobile applications. Due to a lack of proper tools, SL has unfortunately not yet been able to establish a systematic assessment of the quality of these bus prediction. The goal of this study was to help SL find such tools and solutions for assessing the quality of bus predictions. More specifically, the purpose of the study was to investigate the concept of prediction quality and identify suitable statistical tools for measuring quality. In order to do this a comprehensive literature study has been conducted. The findings of the literature study were then tested in practice in order to answer how such quality measurements should be made in the context of SL’s ITinfrastructure. This was answered by carrying out a pilot study in which the prediction quality was assessed on data from one week for a specific bus line. From the initial literature study, it was concluded that there are many dimensions that potentially affect the traveller’s perception of bus prediction quality. However, it was also concluded that a quality assessment plausibly should start with an evaluation of the precision. In order to assess the precision, several types of descriptive measures and analytical perspectives were proposed. As of how these findings should be made in the context of SL’s IT-systems, a method for creating observations from the available prediction data was presented. It was also concluded that in order to mirror the travellers experience, the prediction data should be collected “late” in the process of bus prediction generation.

When faced with an uncertain network, travelers adjust departure time as well as route choices in response to real-time traveler information. Previous studies on algorithm design focus on adaptive route choices and cannot model adaptive departure time choices (DTC). In this thesis, the optimal adaptive departure time and route choice problem in a stochastic time-dependent network is studied. Travelers are assumed to minimize expected generalized cost which is the sum of expected travel cost and arrival delay costs. The uncertain network is modeled by jointly distributed random travel time variables for all links at all time periods. Real-time traveler information reveals realized link travel times and thus reduces uncertainties in the network. The adaptive departure time and route choice process is conceptualized as a routing policy, defined as a decision rule that specifies what node to take next at each decision node based on realized link travel times and the current time. Waiting at origin nodes is allowed to model DTCs that are dependent on traveler information. Departure time is a random variable rather than fixed as in previous studies. A new concept of action time is introduced, which is the time-of-day when a traveler starts the DTC decision process. Because of the efforts involved in processing information and making decisions, a cost could be associated with a departure made after the action time. An algorithm is designed to compute the minimum expected generalized cost routing policy and the corresponding optimal action time, from all origins to a destination for a given desired arrival time window. Computational tests are carried out on a hypothetical network and randomly generated networks. It is shown that adaptive DTCs lead to less expected generalized cost than fixed DTCs do. The benefit of adaptive DTC is larger when the variance of the travel time increases. The departure time distribution is more concentrated with a larger unit cost of departure delay. A wider arrival time window leads to a more dispersed departure time distribution, when there is no departure penalty.

Congestion is a world-wide problem in transportation. One major reason is random interruptions. The traffic network is inherently stochastic, and strong dependencies exist among traffic quantities, e.g., travel time, traffic speed, link volume. Information in stochastic networks can help with adaptive routing in terms of minimizing expected travel time or disutility. Routing in such networks is different from that in deterministic networks or when stochastic dependencies are not taken into account. This dissertation addresses the optimal routing problems, including the optimal a priori path problem and the optimal adaptive routing problem with different information scenarios, in stochastic and time-dependent networks with explicit consideration of the correlations between link travel time random variables. There are a number of studies in the literature addressing the optimal routing problems, but most of them ignore the correlations between link travel times. The consideration of the correlations makes the problem studied in this dissertation difficult, both conceptually and computationally. The optimal path finding problem in such networks is different from that in stochastic and time-dependent networks with no consideration of the correlations. This dissertation firstly provides an empirical study of the correlations between random link travel times and also verifies the importance of the consideration of the spatial and temporal correlations in estimating trip travel time and its reliability. It then shows that Bellman's principle of optimality or non-dominance is not valid due to the time-dependency and the correlations. A new property termed purity is introduced and an exact label-correcting algorithm is designed to solve the problem. With the fast advance of telecommunication technologies, real-time traffic information will soon become an integral part of travelers' route choice decision making. The study of optimal adaptive routing problems is thus timely and of great value. This dissertation studies the problems with a wide variety of information scenarios, including delayed global information, real-time local information, pre-trip global information, no online information, and trajectory information. It is shown that, for the first four partial information scenarios, Bellman's principle of optimality does not hold. A heuristic algorithm is developed and employed based on a set of necessary conditions for optimality. The same algorithm is showed to be exact for the perfect online information scenario. For optimal adaptive routing problem with trajectory information, this dissertation proves that, if the routing policy is defined in a similar way to other four information scenarios, i.e., the trajectory information is included in the state variable, Bellman's principle of optimality is valid. However, this definition results in a prohibitively large number of the states and the computation can hardly be carried out. The dissertation provides a recursive definition for the trajectory-adaptive routing policy, for which the information is not included in the state variable. In this way, the number of states is small, but Bellman's principle of optimality or non-dominance is invalid for a similar reason as in the optimal path problem. Again purity is introduced to the trajectory-adaptive routing policy and an exact algorithm is designed based on the concept of decreasing order of time.

Increasing size and prevalence of real time information have become important characteristics of databases found on the internet. Due to changing information, the relevancy ranking of the search results also changes. Current methods in information retrieval, which are based on offline indexing, are not efficient in such dynamic search spaces and cannot quickly provide the most current results. Due to the explosive growth of the internet, stove-piped approaches for dealing with dynamism by simply employing large computational resources are ultimately not scalable. A new processing methodology that incorporates intelligent resource allocation strategies is required. Also, modeling the dynamism in the search space in real time is essential for effective resource allocation. In order to support multi-grained dynamic resource allocation, we propose to use a partial processing approach that uses anytime algorithms to process the documents in multiple steps. At each successive step, a more accurate approximation of the final similarity values of the documents is produced. Resource allocation algorithm use these partial results to select documents for processing, decide on the number of processing steps and the computation time allocated for each step. We validate the processing paradigm by demonstrating its viability with image documents. We design an anytime image algorithm that uses a combination of wavelet transforms and machine learning techniques to map low level visual features to higher level concepts. Experimental validation is done by implementing the image algorithm within an established multiagent information retrieval framework called I-FGM. We also formulate a multiagent resource allocation framework for design and performance analysis of resource allocation with partial processing. A key aspect of the framework is modeling changes in the search space as external and internal dynamism using a grid-based search space model. The search space model divides the documents or candidates into groups based on its partial-value and portion processed. Hence the changes in the search space can be effectively represented in the search space model as flow of agents and candidates between the grids. Using comparative experimental studies and detailed statistical analysis we validate the search space model and demonstrate the effectiveness of the resource allocation framework. / Ph. D.

Our cities have been continually growing at an uncontrolled rate leading to the problem of trafficcongestion, which has discernable effects on all the aspects of sustainability, be it social,environmental or economical. This continual shift of increasing size of centre and decreasingsize of periphery poses huge sustainability challenge of meeting the consumption demands. Wepresently face the most unprecedented times in terms of the pace at which our natural resourcesare getting consumed. It is clear that replenishing some of these resources is totally out ofquestion. On the other side of the coin, the advances of human technology have provided itsgreatest gift of information & communication technology (ICT). Today we have access to datafrom any point of the world to anywhere. There is a growing need to use this data andinformation with a holistic view to build more Intelligent Transport Systems. In our paper wediscuss how the advent of ICT can have an impact on bringing a sustainable transportationsystem. The work is divided in two folds, by first understanding the direct role of ICT intransport sustainability and then observing the direct correlation between usage of ICT andtravel demand. The problems of traffic congestion and its solutions like congestion pricing haveexisted in practice since ages; the perspective which we add to it is the role of ICT in making itbetter. The greater perspective that is being researched here is at an absolute fundamental leveland takes us to the question if and how ICT can work on root level challenges, like findingmethods to have a better traceability without compromising on privacy, changing driverbehaviour patterns and stopping the expansion of centre & contraction of periphery.