Traditionally, travel behavior modeling has been based on the axioms of expected utility (Bernoulli, 1738; Von-Neumann & Morgenstern, 1944; Luce & Raiffa, 1957). Random utility based discrete-choice models or RUM provide an econometric interpretation of expected utility theory. This approach is still regarded as the official workhorse for most travel related behavioral modeling. RUM have been developed considerably in the past three decades and specifically for route-choice modeling. Chronologically speaking, the classic Multinomial Logit Model (Daganzo & Sheffi, 1977) was the first to be applied followed by the C-logit model (Cascetta et al., 1996), the IAP logit (Cascetta & Papola, 1998) and Path-Size Logit (Ben-Akiva & Bierlaire, 1999). Following the presentation of the General Extreme Value theorem (McFadden, 1978) more flexible modeling structures were developed including: Nested Logit (Ben Akiva & Lerman, 1985); Cross-Nested Logit (Vovsha, 1997); General- Nested Logit (Wen & Koppelman, 2001) and Paired-Combinatorial Logit (Chu, 1989; Koppelman & Wen, 2000). These GEV-based models were applied to route-choice models with an adaptation of GNL and PCL (Prashker & Bekhor, 1998; Gliebe et al., 1999; Bekhor & Prashker, 2001) and a GNL based Link Nested Logit (Vovsha & Bekhor, 1998). In the last decade, a major breakthrough in modeling capabilities was achieved by the introduction of the Mixed Logit or Logit Kernel model (Ben Akiva & Bolduc, 1996; Bhat, 1998; Bhat, 2000; McFadden & Train, 2000). Several studies have recently adapted Mixed Logit in the context of route-choice modeling: (Bekhor et al., 2002; Srinivasan & Mahamassani, 2003; Jou et al., 2007). A detailed review of such RUM-basedroute-choice models is provided by (Prashker & Bekhor, 2004).
In this navigation system, identification of clusters of vehicles based on their location and direction in which they are moving is performed. Speed information from the cluster is used to set path costs for every road. Based on path costs, routes to the specified destination are ranked and presented to a particular user based on his location. Section 2 contains description of various components of the system and information flow within the system. Section 3 contains a graphical model for roads, where each path has certain cost. An algorithm for identification of clusters is described in Section 4 of this paper. Section 5 defines two important quantities the system uses to assign costs to each path. Section 6 describes an equation to compute costs for paths. Section 7 presents an algorithm called Route Select which selects less congested routes to reach a specified destination for a particular vehicle.
Smart Parking is a parking reservation system that can be described as it consists of mainly three entities: the user, the parking facility agent and the parking management agent (Figure 1.2). The user entity is connected to the system via a device able to communicate (GPRS-3G) and to track position (GPS/GNSS/Galileo). The parking facility entity that provide services (parking spaces) and information to users. The third entity is a control agent that gathers information from the user and the parking entities as well as from various other sources (traffic counts, road sensors) in realtime and combines all pieces of information into a suggestion for reserving a specific parking space (Jonkers et al., 2011). The conceptual design of the system informs the driver about the closest - to the destination - available parking spots
The aim of this paper is to ascertain whether and to what extent the way data are collected and used can affect the interpretation of experimental results obtained by stated preference (SP) surveys based on the use of travel simulators and applied through repeated choices. The final goal here is to assess the impacts of Advanced Traveller Information Systems (ATIS) on both travellers’ compliance with information and routechoice. In recent decades ATIS applications have been a popular research topic for transportation analysts and many models have been proposed and discussed due to the widespread need to solve traffic oversaturation problems with ever-diminishing infrastruc- tural investments. Moreover, from a commercial point of view, traffic information is also a valuable content for modern applica- tions in the field of telecommunications. Indeed, some studies have shown that travellers exhibit a considerable willingness to pay for reliable advanced traffic information . ATIS are intrinsically integrated with advanced communication platforms and devices, and aim to enhance (or integrate) the information level on network conditions that most travellers already have from their own traffic estimation process (experience). Information contents are gathered, elaborated and delivered by traffic control centres which are able to increase the reliability and effectiveness of (real-time) monitored traffic data.
I would like to take this opportunity to thank all those who have contributed to this thesis. First of all, I would like to thank my supervisors from the University of Twente: Msc. Jaap Vreeswijk, Dr. Ir. Luc Wismans and Prof. Dr. Eric van Berkum. They helped crystallize the research and their time and expertise provided me with valuable insights. In addition, I am very thankful to my supervisor at the Virginia Tech Transportation institute, Prof. Dr. Hesham Rakha, for inviting me over and giving me the opportunity to do my thesis and the experiment at VTTI. His advice and suggestions during the research process were very valuable to me. I am also thankful to Dr. Ihab El-Shawarby for his guidance during the experiment and introduction process. Furthermore, I would like to thank Roeland Ottens for designing and preparing the experiment in a way that was easy to pick up and continue with, and Jinghui Wang, Arash Jahangiri, Karim Fadhloun, Andy Edwardes, John Sangster, Mohammed Elhenawy and Dalia Rakha for giving up some of their spare time to perform driving sessions, process the data, handle the cash, refuel the cars, call participants and have good times together 1 . I am also very thankful for the support of family and friends who believed in me, especially during hard times. Lastly, I’ve got to thank you, the reader, for taking the time to read my thesis. I hope you find it interesting and gain new insights which might be useful to you in the future.
Although Discrete choice models provide parsimony and an elegant econometric generalization of drivers’ behavior, they do not include an explicit behavioral abstraction of the effect of the real-timeinformation provided by ATIS. Capturing this effect on drivers within the framework of discrete choice models requires additional behavioral assumptions. Previous attempts to address the effect of partial information on drivers’ behavior focus on three main approaches. Under one approach, the information affects the error term in the routes’ utilities functions thus providing information implied less error in the system (e.g. Watling & Van Vuren, 1993 and see review by Bonsall, 2000). Another approach is to reduce system error by gaining more knowledge through reinforced learning. For example, Horowitz (1984) described a simple routechoicemodel over time whereby decisions are based on the weighted average of previous travel decisions’ utilities.
intersections, which can be either signalized or unsignalized. A VMS displays travel-times for alternative routes, which are updated at each time-step. The travel-time to the destination following each of the downstream links is predicted based on the estimated traffic in the links connecting a decision point. The estimated traffic state using CTM-EKF model provides an update of the traffic state at each time-step, and this estimate is based on measurements from sensors, which are located so as to detect any unexpected changes in road capacity or traffic demand. The estimated traffic state at time-step k, [ x(k+1|k)] , is provided to the CTM prediction model, which then predicts the future propagation of traffic flows and predicts the travel time that will be experienced by a vehicle entering the link at time-step k. In our case, we consider a simple network in which the links emanating from a decision point constitute the complete route to the destination, and so predicting the link travel times means predicting the complete travel time to the destination. The CTM model for predicting travel-times for vehicles that enter a link at time-step k is simulated up to time-step k+ n, where n is the maximum number of time-steps required to traverse the link by the last vehicle entering the link at time-step k. This predicted travel time is communicated to travellers using a VMS, placed before the decision point at time-step k+ 1.
mechanisms of behavior including beliefs, motivation and intentions , individual differences , social in- fluence , and environment and demographics [19– 21]. A substantive body of research has identified the ef- fectiveness of theory-based interventions targeting change in modifiable determinants or mechanisms [22– 24]. For example, syntheses of evidence have indicated that interventions targeting change in social cognitive beliefs and motivation [25–31], social support and norms [32, 33], and planning  to be effective in pro- moting behavior change in randomized controlled trials. Similarly, interventions based on health-risk communi- cations have been successful in promoting behavior change , with graphic images on tobacco products a prominent example [35–38]. Research targeting change in determinants derived from social-ecological theories, encompassing environmental, community, and policy factors, have also been shown to be effective [21, 32, 39, 40]. Interventions based on choice architecture, some- times referred to as ‘nudging’ , have demonstrated effect- iveness in changing behavior in laboratory and field settings [41–46]. In addition, interventions adopting spe- cific strategies such as self-monitoring [47, 48], prompt- ing social support , planning , behavioral skills , and affective appeals  have been found to be particularly effective. Taken together, primary studies and research syntheses indicate that theory-based inter- ventions are effective in changing behavior in laboratory and ‘real world’ contexts .
In freight transport there is a variety of significant influencing factors that have a major impact on transport processes. Travel times, costs for loading and unloading, transport cost as well as handling cost (all distinguishing different modes but also for different commodity types) are some examples. Enhanced by costs for warehousing etc., these costs constitute total logistics cost and can be integrated in modelling via specific logistics cost functions. However, there are supplementary factors that are not commonly integrated in demand models. Reliability and delay, for example, have received considerable attention in last years. There are still few studies on the valuation of reliability (see e.g. Halse et al., 2010; Significance et al., 2013) but the integration of proper cost functions considering reliability should be among the research objectives in the near future. A similar case may be made for including factors like flexibility of transports as well as damages possibly occurring during the transport process. There is still not enough information on that and, therefore, cost functions used in practice are a rather incomplete representation of the actual factors influencing decision-making. The same applies to technological change like the utilization of information and communication technologies (ICT), which is an important topic in freight transport. ICT refers to all actors and its utilisation can vary significantly (see e.g. Ruijgrok, 2008). However, the effects of ICT are poorly investigated and, therefore, not integrated properly in most models. All the mentioned influencing factors can affect the models considerably. Their integration would increase the explanatory power and enable new scenario calculations etc. However, different and detailed cost functions are needed to integrate the different factors in modelling adequately.
For the following forecasting time step the RIBS model is run with the new parameter set, though starting from a basin state obtained with the parent parameter set. Since moisture profiles correspond to different parameter values from those applied in the new simulation, a large change of parameter values may lead to instabilities in the resolution of differ- ential equations of the RIBS model, and therefore σ values were limited to 0.1. Moreover, given that values higher than 0.1 lead to very high dispersion, influence of σ equal to 0.05 and 0.075 in forecast results was tested. The sensitivity anal- ysis entailed running the forecast model with data assimila- tion, applying the mutation process to only one of the model parameters, fixing σ for this model parameter and setting σ equal to zero for the other two parameters. The flood event that occurred in February 2004 was selected to conduct the sensitivity analysis. The results are shown in Table 2 and Fig. 4.
The strive for economic development must focus on community inclusiveness, which must begin with the role education in transforming the mindsets of the youth to take their productive roles in ensuring sustainability. Thus, there is a need to understand what determines the behavioral intent to participate in community service. Based on the extended theory of planned behavior (TPB) and the educational theories of constructivism and social learning, this study develops a research model to identify how curriculum connections and attitude can influence undergraduates’ behavioral intent to serve the community today and in the future, with the hope that they become empowered to take control of their communities and transformatively advance sustainability in their lifetimes. Based on a survey of 256 undergraduates who participated in service learning projects, this study uses structural equation modeling (SEM) approach to investigate the research model. The results indicate that curriculum connections and attitude significantly influenced their behavioral intent to serve the communities in future. Finally, this study discusses the implications of these findings and offer directions for future research.
The theoretical tests in a static assignment show promising results for both the C-logit and the game theory variants. The C-logit variants show that they are able to score better on reality than the shortest route principle. The choice of the commonalty factor function has not much influence on the route choices in this theoretical network. The θ value, the θ value has a strong influence on the route choices. The routechoice probability of route 3 decreases if the θ value increases. The β value has also a strong decreasing influence on the routechoice probability of route 3. The utility expression travel time+ shows promising results, it scores the best on realistic routing for the most but not all parameter settings. Further research towards the possibilities of this utility expression in a dynamic equilibrium assignment is strongly recommended. We will use the utility expression travel time in the remaining of this research project because we did not investigate the possibilities of the travel time+ usage in a dynamic equilibrium assignment. Another variant we do not test in the dynamic assignment although it shows promising results is the road category bias. This variant suffers too much of computational difficulties in realistic networks and difficult calibrating abilities. In contrary to the road cate- gory bias, the penalty function does not suffer from computational difficulties. The penalty function variant shows a strong decreasing effect for increasing penalty values.
of the routing protocol in MANETs  is widely based on two methodologies: Qualitative approach and Quantitative approach. The Qualitative approach primarily incorporates the following measurements – Loop Free Network: In wireless network where the data transfer capacity (bandwidth) is constrained the interference from the neighbouring nodes will prompt the collision of the transmitted packets. Furthermore, in this way the packet is re-transmitted until it is not received by the destination which will leading to the formation of a loop. In this way avoid these loops for the effective bandwidth utilization and time processing is required.
Staging magnetic resonance imaging scan of the spine, performed just before thyroidectomy, showed osteoblastic lesions in lumbar vertebral bodies and sacrum. A computed tomography (CT) scan performed 1 month later detected left pulmonary hilum and lower lobe metastases with massive mediastinal involvement. In particular, an infiltration of the superior lobar artery and the inferior pulmonary vein, as well as a compression of both principal and apical bronchi were observed. The upper-left segment was significantly impaired by lymphangitic carcinomatosis, and the lower lobe was partially atelectatic. Keeping in view the advanced stage of the disease and the clinical implications associated with thoracic involve- ment, radiometabolic treatment with iodine (I-131) was omitted to give priority to the management of lung lesions. The paper was published after obtaining the patient’s written consent.
Digital computers are Simulating the code in the machine. Simulation generally is known as the imitation of the reality. Alan Turing uses the word simulation to describe the action of any discrete-state machine when running. These machines have changed our lives because they interact with the outside world. Object oriented design is a successful methodology perhaps partially because it better imitates the reality. As we can see here, from the machine perspective it is hard to draw a distinction between real and virtual objects. Put it in other words, as far as you behave like a character in a computer game, you can be substituted in that role. This concept is utilized in Virtual Reality, with many applications for example in flight simulators , bio- informatics , realtime weather forecasting, business  and robotics [4, 5, 6, 7, 8]. The simulation and the models being simulated together are called modeling and simulation (M&S). Modeling and simulation can be used as the feedback of the testing phase during system development.
If the increase in demand brought about though the introduction of a QBP is sufficient to trigger new entry into the market, then we need a methodological framework to be used to assess competition. The most pragmatic way forward is to specify a series of plausible competitive scenarios rather than define a set of supply side algorithms that lead model convergence at an equilibrium. The competitive strategies available to each agent include those based on: pricing, quantity, service quality and cost reduction. The costs and benefits associated with each scenario are then compared with base statistics for operator profitability, consumer surplus and overall economic welfare. The following sections detail possible strategies available to the Entrant and Incumbent, though the model is capable of assessing scenarios with many more operators.
The activities that occur during driving include idling, accelerating, cruising, and decelerating. These activities as judged by a driver who may exhibit behaviours such as aggression, defensive, mildness, etc and make different degrees of input in the overall emission output. Again, it is well known that driving patterns such as speed profile; acceleration and choice of gears greatly influence fuel consumption and vehicle exhaust emissions (Vicente et al., 2013). Driving disorder, such as having difficulty in staying in the required lane, abrupt lane changes and driving on the shoulder are grouped under the heading "lateral discipline of driving" and are typical consequences of many dangerous driving situations. Two categories of cost characterize the cost implications in urban air quality management: 1. that originating from elevation of air pollution and 2. those emanating due to implementation of an abatement program. Real world driving has frequent speed fluctuations as well as sharp acceleration and deceleration. Sharp acceleration could increase emission rates by increasing the air/fuel ratio (Bokare and Maurya, 2013).
Bierlaire, M., 2003. BIOGEME: a free package for the estimation of discrete choice models, Proceedings of the 3rd Swiss Transportation Research Conference, Ascona, Switzerland . Bonsall, P., 2008. Information Systems and Other Intelligent Transport System Innovations. In: Hensher, D. A., Button, K. J. (Eds), 2 Edition: Emerald Group Pub Ltd, pp. 559-574.
In this paper, take QQ network for instance, we proposed and built the model of the online real-time informa- tion transmission network, namely ORITN, based on the connection properties between users of network, and combined with the evolving characteristics in social network - and local world network , then the statistical topological properties of the network is obtained by numerical simulation. Furthermore, we simulated the process of information transmission on the network, and put the average entropy of the real-timeinformation, which is received by the network nodes, as a time series. Through statistical analysis, we got the fluctuation scaling of the real-timeinformation transmission on the network.
and learn to predict an ordering for new sets of so- lutions. This setup is related to previous studies on information ordering where the aim is to learn statistical patterns of document structure which can be then used to order new sentences or paragraphs in a coherent manner. Some approaches approxi- mate the structure of a document via topic and entity sequences using local dependencies such as condi- tional probabilities (Lapata, 2003; Barzilay and La- pata, 2008) or Hidden Markov Models (Barzilay and Lee, 2004). More recently, global approaches which directly model the permutations of topics in the doc- ument have been proposed (Chen et al., 2009b). Fol- lowing this line of work, one of our models uses the Generalized Mallows Model (Fligner and Ver- ducci, 1986) in its generative process which allows to model permutations of complexity levels in the training data.