One major disadvantage of our approach is that we compare the results of our **route** **choice** variants with only one dataset. We use the data of Enschede to measure the reality of our variants, but it is possible that we get other results if we use data of other cities. In Enschede, congestions do not occur often, therefore we do not know if the same results hold for a congestion situation. Further, we test our **route** **choice** variants for short trips, but it is generally agreed that **route** decisions for long trips are different from **route** decisions for short trips. Therefore our results also do not have to hold for long trips. We get a more general result at the last test, by testing on multiple OD’s, but we still have to deal with the fact that the travelers in Enschede could make other **route** choices than travelers in other regions. Another disadvantage of our research approach is that our measure, reality, is based on an outcome of more processes than the **route** **choice** step. The **route** choices resulting from a transport model are a result of the network in the model, the travel time functions, the origin destination matrix estimation, the loading method, and the **route** **choice** model. Errors in other steps than the **route** **choice** **modeling** influence the result of our tests. Therefore we do not know if our results also hold for other loading methods, or for instance in other networks. Of course, we cannot test with all possible loading methods to investigate all possible influences of the loading methods, but we use different loading methods to get a good sense of the possible influences of the loading method. In the Enschede tests we assign all vehicles in a 15 minutes interval according to the same probability distribution. We choose to aggregate the vehicles in a 15 minute interval, because there is not much traffic, and it is hard to assign 1 vehicle for 30% to one **route** and for 70% to another **route**. The aggregation of the vehicles influences the results of the tests, we expect that the shortest **route** principle is more accurate if the shortest **route** is calculated in smaller time intervals. In Chapter 3 we mentioned two other shortcomings of our research approach. We do not investigate other utility functions than the travel time, predicted travel time and combinations of distance and travel time. Also we do not investigate **choice** set generation methods, although we know that the **choice** set and the utility function strongly influence the **route** **choice** results. An investigation of these two elements is beyond the scope of this master thesis project.

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For the sake of tractability, mathematical programming approaches in passenger-oriented public transportation problems usually use strongly simplified demand models which avoid such bilevel **modeling**. Often, passenger routes are fixed a priori, i.e., they are assumed to be independent of the implemented transportation system. Models which do not presume fixed passenger routes normally operate under the implicit assumption that passengers can be assigned to routes by the operators. By design, (meta-)heuristic frameworks like iterative or genetic algorithms which split solution generation and solution evaluation, can handle more **realistic** demand models. However, these approaches can get stuck in suboptimal solutions.

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The first sub question is answered by using the information gathered in the literature study whereby section 5.1, 5.3 and 5.4 are especially important. The used model, the **choice** model and the **modeling** assumptions of the HOV-scanner must be in line with the current knowledge on transportation **modeling**. The HOV- scanner is modeled according to the four-stage model. This model is still used despite the main limitation namely the lack of the availability to implement congestion effects. Fortunately this is not an obstruction for the HOV-scanner. The four-stage model as used in the HOV-scanner is therefore suitable and in line with current knowledge on transport **modeling**. As a **choice** model the HOV-scanner makes use of the MNL model. Although MNL is still widely used a shift towards ML can been seen in the recent literature. This implies that the **choice** model is not state of the art, but is still a good option because it matches the available data and the intended use. Regarding **modeling** assumptions the HOV-scanner makes use of one **route** for PT, car and the bicycle trips. To model the utility of a trip the parameter time is used. Whereby the PT trips are distinguished in IVT, access and egress time, waiting time and transfer time. The used trip cost calculation is not in line with the current knowledge of transportation **modeling**, because the monetary cost are excluded. It is generally accepted that cost, time and effort should be included. Furthermore trip purpose and a population segmentation could be included. This is done in the NRM/LMS model. This can improve accuracy but also increases the data need. The use of only one PT **route** for PT trips is in line with other models like the NRM/LMS model, but it is considered as an oversimplification of the model because the focus of the HOV-scanner is on the **modeling** of changes in the PT network.

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Pattern formation is possible if and only if a short-ranging self- enhancing reaction is antagonized by an inhibitory reaction of longer range (Gierer and Meinhardt, 1972, Gierer, 1977; Meinhardt, 1982; 2008); (see also Gierer, this volume). These two components together allow for a situation in which a homogeneous distribution of both substances is instable. A straightforward realization of our general scheme consists of an activator whose autocatalytic acti- vation is antagonized by a long-ranging inhibitor that is produced under activator control. Even random fluctuations are sufficient to initiate pattern formation (Fig. 1). A minute increase of the activator will increase further due to the self-enhancement. The concomitantly elevated inhibitor production cannot compensate this increase due to its rapid diffusion, causing a de-activation in the surroundings of the incipient maximum. A patterned stable steady state will be reached when the activation in an emerging maximum is balanced by the surrounding cloud of inhibition (Fig. 1). The mechanism is compatible with that proposed by Alan Turing in his pioneering paper, which demonstrated that reaction-diffusion systems are capable of generating concentration patterns (Turing, 1952). However, the crucial conditions mentioned above, local self-enhancement and long-range inhibition, are not inherent in his paper. By knowing this condition we were able to recognize pattern-forming capabilities in more complex interactions and to introduce non-linear interactions that are indispensable to formulate molecularly **realistic** interactions leading to stable, reproducible patterns. As discussed elsewhere (Meinhardt, 1982), pattern formation in the inorganic world such as formation of sand dunes, patterns of erosion, lightning and many pattern in social interactions are based on the same principle. Pattern regulation and regeneration

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Moreover, the **modeling** of **route** **choice** behavior using revealed **choice** data normally have to deal with generating a reasonable **choice** set. There are many well-used methods to identify the alternative routes which are feasible, such as K-shortest path [16], link elimination and penalty [17], branch and bound, simulation and labeling approach [18]. These methods consider more about the **route** character itself, but less about the city built environment besides the **route**. However, for pedestrians on streets, especially in the big event, **route** **choice** maybe influenced by the buildings, shops, activities and others in the city environment. As so far, there is no any perfect method to study the relationship between built environment and pedestrian **route** **choice**. So we can’t calculate the alternative routes according to any methods and models.

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Advanced Travel Information Systems (ATIS) are designed to provide real time information enabling drivers to choose efficiently among routes and save travel time. Psychological research suggests that **route**-**choice** models can be improved by adding **realistic** behavioral assumptions. However, different generalizations imply deviations in different directions. Specifically, different choices arise when decisions are taken on the basis of information compared to those taken on the basis of personal experience. An experimental study of **route** choices investigates the combined effects of information and experience on **route** **choice** decisions in a simulated environment whereby the participants can rely on a description of travel time variability and at the same time can rely also on personal experience through feedback. The experiment consisted of a simple two **route** network, one **route** on average faster than the other with three traffic scenarios representing different travel time ranges. Respondents were divided to two groups: with real-time information and without. Both groups received feedback information of their actual travel time. During the experiment, participants chose repeatedly between the routes and across

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The evaluation of micro‐spatial attributes along the routes particularly for purposeful walking in TODs are crucial for designing proactive policies and guidelines to improve the quality of the walking environment to inspire individuals to leave their personal vehicles and walk to transit stations and commute by public transport system. To assess the pedestrian environment, the majorities of current methods use neighborhoods or inhabitants as their unit of analysis and use self‐reported data or secondary data in their analyses. This research uses routes as the unit of analysis, determination **route** **choice** as the method, and **route** level features along the routes as data. To analyze data, conditional logistic model (discrete **choice** model) is used in order to assign and explain the **choice** for each individual participants based on his **choice** set, and identifying the trade‐off between statistically significant variables with **route** distance. The outcomes of the study demonstrate that minimizing the **route** length is a salient parameter for selecting the **route**. To choose a **route** in a set of **choice** identified by ‐ shortest path algorithm, transit users do consider a variety of micro‐spatial attributes as well, and make a trade between distance and features. The results of **route** **choice** **modeling** indicate that transit users prefer to walk on a shorter **route**, flat **route**, shaded **route**, and safer **route** provided by having a traffic light, stop sign, crosswalk, pedestrian signal, median/traffic island, and curb extension.

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i)For a network of a **realistic** size, the model proposed will put a great demand on computational power. In the dynamic case, such as in Model F, the most demanding stage will be the determination of a minimum cost departure time and **route** over a network with dynamic link costs, which must be carried out separately for each individual on each simulated day. A natural first stage would be to attempt to express this as a shortest **route** problem with static link costs over an extended network. Since for each individual, the interest will only be in `feasible' routes for a single origin-destination movement, intuition suggests that it should be possible to restrict such an extended network to a manageable size. Existing algorithms for determining dynamic minimum time routes are also worthy of investigation. Approximate methods for determining a near-optimal solution should also be investigated, possibly by restricting the range of **choice** open to the user relative to the previous day's **choice**. If one of these latter approaches led to a significant saving in computer processing time, then they should be preferred, even if the solution is not exact. In any case, if a computer has difficulty finding an optimal-**choice** with a purpose-built algorithm, then an individual will surely have similar difficulty. If the approximation methods used in the model in any way reflect human heuristics, it could be claimed that the model is then more **realistic**.

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national freight models of Norway and Sweden). However, using statistical values (e.g. averages) does scarcely reflect reality. In contrast to that, in stochastic models relations are determined statistically, as representing imperfect information, for instance by adding a random utility component. This enables the models to capture intrinsic variability, which underlie most transport processes. Nevertheless, both model types can assume cost minimisation behaviour on the side of agents. Furthermore, a probabilistic model (e.g. of the logit family) can be estimated on micro-data and may probably lead to smoother response functions. The **choice** of the proper model type should, therefore, be made thoroughly and with the purpose of the model in mind. Work on probabilistic models of mode and shipment size **choice** is currently going in Sweden, Norway and for the new European transport model Transtools3.

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By identifying all routes in order of increasing length, this method will eventually find the one that was used. The k-shortest algorithm which is based on the double-sweep method and the FORTRAN code on which our implementation of the algorithm is based is described by Phillips and Garcia-Diaz (1981). The algorithm identifies all paths up to the k-shortest including those with loops, in other words routes in which at least one node is reached more than once. We do not desire to have a **route** **choice** set containing such routes. They can easily be excluded in general but this results in fewer than k routes without loops being generated. The FORTRAN code uses integers for the lengths and so there is the possibility of more than one **route** with the same integer length. This motivates the use of k as the number of distinct **route** lengths and “**route**” as the number of routes. We chose a value of 50 as the maximum number of routes to generate.

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Such incident information does in fact affect the individual behavior pattern, where more participants tend to choose the stochastic branch at the origin node and the stochastic shortcut[r]

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Due to the ongoing miniaturization of digital camera sensors and the steady increase of the “number of megapixels”, individual sensor elements of the camera become more sensitive to noise, even deteriorating the ﬁnal image quality. To go around this problem, sophisticated processing algorithms in the devices, can help to maximally exploit the knowledge on the sensor characteristics (e.g., in terms of noise), and oﬀer a better image reconstruction. Although a lot of research focuses on rather simplistic noise models, such as stationary additive white Gaussian noise, only limited attention has gone to more **realistic** digital camera noise models. In this article, we ﬁrst present a digital camera noise model that takes several processing steps in the camera into account, such as sensor signal ampliﬁcation, clipping, post-processing, ... We then apply this noise model to the reconstruction problem of high dynamic range (HDR) images from a small set of low dynamic range (LDR) exposures of a static scene. In literature, HDR reconstruction is mostly performed by computing a weighted average, in which the weights are directly related to the observer pixel intensities of the LDR image. In this work, we derive a Bayesian probabilistic formulation of a weighting function that is near-optimal in the MSE sense (or SNR sense) of the reconstructed HDR image, by assuming exponentially distributed irradiance values. We deﬁne the weighting function as the probability that the observed pixel intensity is

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I n the urban route choice experiment; five respondents; £ran a total of 77; supplied time based rankiqs whilst the correspding figures for such rankings based on total cost and The rank[r]

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More detailed ccmparisons of the RP and SP approaches are considered below; involving the analysis of variations in the value of time and using values of time derived at the individual l[r]

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(21) Using this definition the power gain depends on the power distribution among the elements of the excitation vector b S 0 ,S. Note, that the power fed into the transmit antennas is n[r]

Though the final application of the model is different than what it was applied for in all these related literature, the behavior of the cloth would not be different. But previous research all have been on **modeling** the fabric based on assumed approximate stiffness parameters for the material under consideration. The actual property when considered was not verified to be accurate in different scenarios of loading. The present research aims to consider and address these in a more effective way.

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Data collection took place in the more suburban parts of the Netherlands. Children who travel in more urban or more rural areas may use different routes to school. Although a similar analysis for rural routes would be less interesting because of the lack of alternative routes, it would be interesting to see if active transportation routes in the bigger cities are similar. Furthermore, most of the data collection took place around spring and the beginning of summer. This could have influenced trans- portation behavior of the children. It is well known that seasonal changes, e.g., hours of daylight, weather, can have a large impact on daily physical activity levels and mode of transportation [40]. In the winter when it is still dark during morning trips, for example street lighting may play a more significant role in **route** **choice** for walk- ing and/or cycling. Thus, some of the results of the current study may be the consequence of methodological choices and challenges, e.g., cross-sectional study, size of the buffers, aligning GPS signals with the pedestrian street network, multi-destination trips.

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Choice modeling contains three sub sets, each with different number of multiple questions. Analysis of respondents’ sensitivity depending on the number of choice sets.[r]

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The problem with such simplifications, as well as with purely statistical models that ignore entirely issues of how agents form expectations and form decision rules, is that they can run a-foul of the “Lucas critique of econometric policy evaluation,” due to Marschak (1952) and Lucas (1976). To understand the Lucas critique, consider the fol- lowing simple example. Suppose after a few years of shopping I realize that my favorite supermarket always cuts the price of my favorite detergent, say Era, from $4.00 to $3.50 per 32oz during one week of every month. Most other shoppers at the store realize this too. So those who like Era adopt a decision rule that says: “Only buy Era if the price is $3.50, and then buy enough so that my inventory will last at least 7 weeks (the longest possible time until the next sale).” There are some random deviations from this rule (e.g., sometimes people may have big positive usage shocks that cause them to stock out and deviate from the rule), but it is the dominant pattern of behavior. If one were to estimate a MNL model for detergent **choice** in the store, the price elasticity of demand for Era would appear to be enormous.

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Purpose: There are several routes between some OD pairs in the urban rail transit network. In order to carry out the fare allocating, operators use some models to estimate which **route** the passengers choose, but there are some errors between estimation results and actual choices results. The aim of this study is analyzing the passenger **route** **choice** behavior in detail based on passenger classification and improving the models to make the results more in line with the actual situations.

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