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Nowadays in mobile and wireless networks environment, Mobile IP is the preferred standard in supporting IP mobility among several standards. However, several problems still need to be solved. One of them is the triangle routing problem. Although drafts have been proposed by the IETF (Internet Engineering Task Force) for solving this problem, the proposed solution can not be achieved unless the draft of the Mobile IP route optimization method becomes a typical standard of the Mobile IP. In this paper, we present an extended routing agent architecture to solve this problem. The Middle Mobility Agent (MMA) in the proposed architecture can intercept datagrams earlier and determine to tunnel the incoming packet or not than the MH¡¦s original home agent. This architecture can solve the triangle routing problem by reducing packet¡¦s routing length. An analytical model and a simulation environment were set up to evaluate and measure the packet¡¦s routing length and delay time of proposed architecture. The evaluation and simulation results demonstrate that the proposed method can reduce the routing length, delay time and solve the triangle routing problem.

Due to recent pricing studies that have revealed substantial variability in values of time among decision makers with the same socioeconomic characteristics, there is substantial interest in modeling the observed heterogeneity. This study addresses this problem by revealing a previously overlooked connection between choice set and choice behavior. This study estimates a discrete choice model for mode plus route plus time choice, subdivides the population according to empirically formed choice sets, and finds systematic variations among four choice set groups in user preferences for price managed lanes. Rather than assume the same values of the coefficients for all users, the model is separately estimated for each choice set group, and the null hypothesis of no taste variations among them is rejected, suggesting that choice set is an indicator for choice behavior. In the State Route 91 study corridor, the price-managed lanes compete with at least two other congestion-avoiding alternatives. The principal hypothesis is that a person’s willingness to pay depends on whether or not he perceives as personally feasible the option to bypass some congestion in a traditional carpool lane or by traveling outside the peak period. The procedure for estimating the choice sets empirically is predicated on the notion that individuals operate within a wide array of unobservable constraints that can establish the infeasibility of either alternative. The universal choice set includes eight combinations of mode and time and route, wherein there are exactly two alternatives for each. Choice sets are formed from an assumed minimum set, which is expanded to one of three others whenever a non-zero choice probability for either ridesharing, or shoulder period travel, or both is revealed in a person’s history of choice behavior. Based on the test of taste variations, this author finds different values of time across the four choice set groups in the study sample. If these relationships can be validated in other locations, this would make a strong case for modeling choice behavior in value pricing as a function of choice set.

<p>This thesis consists of two parts. The first part analyzesthe accessibility, generation and license holding effects incar ownership models. The second part develops a route choicemodeling framework with an attempt to address the differencesin drivers' route choice behavior. These two parts of work areboth based on the discrete choice theory - the car ownershipmodels are built up on the standard logit model, whereas theroute choice models are formulated in a mixed logit form.</p><p>The study result of the first part shows that measuring theaccessibility by the monetary inclusive value reasonably wellcaptures the mechanism of the accessibility impact. Otheraccessibility proxies such as the parking costs, parking typeand house type are correlated with the accessibility but not toa great extent. Both young and old households are less likelyto have a car. The reduction of the propensity to own a car issignificant for households with average birth year before 1920,whereas this reduction is moderate for households with birthyear between 1920 and 1945. It is also demonstrated thatdriving license holding choice is conditional on the carownership level choice, and that these two choices need to bemodeled in a dynamic framework.</p><p>The second part of the work investigates the performance ofthe mixed logit model using both simulated data and empiricalroute switching data. The empirical study mainly focused on theimpacts of information and incident related factors on drivers'route switching behavior.</p><p>The result shows that using mixed logit gives a significantimprovement in model performance as well as a more sensitiveexplanation of drivers' decision-making behavior. For apopulation with greatly varying tastes, simply using thestandard logit model to analyze its behavior can yield veryunrealistic results. However, care must be taken when settingthe number of random draws for simulating the choiceprobability of the mixed logit model in order to get reliableestimates.</p><p>The empirical results demonstrate that incident relatedfactors such as delay and information reliability havesignificant impacts on drivers' route switching, where themagnitude of the response to the change in the delay is shownto vary significantly between individuals. Other factors, suchas confidence in the estimated delay, gender, frequency of cardriving and attitude towards congestion, also make majorcontributions. In addition, it is found that individual's routeswitching behavior may differ depending on the purpose of thetrip and when the choice is made, i.e. pre-trip oren-route.</p><p><b>Keywords</b>: car ownership, accessibility, logit model,route choice, heterogeneity, mixed logit model</p>

Title from PDF of title page. / Document formatted into pages; contains 92 pages. / Thesis (M.S.I.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: In this research a heuristic algorithm is developed for searching and identifying preferred actions as applied to the bus route design problem. The search routine evaluates each subsequent segment added to the route in the context of the value of that segment and also the value of future decisions and opportunities for subsequent segments. The total overall maximum accessibility of the system is calculated using a minimum path network between each node pair and adding the accessibility of all route segments. This is equivalent to assuming that there was a direct shortest path route between every two destinations in the network. The quality of the designed network is obtained by comparing the share of the total benefits obtained from the heuristic with the share of the costs incurred with respect to a minimum path network. Several test cases and network scenarios are studied to evaluate the analytical tool developed. / ABSTRACT: In addition, different performance measures are used to identify the connecting routes that increase the accessibility of the system. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Reproduction de : Thèse de doctorat : Histoire : Lyon 2 : 2002. / Titre provenant de l'écran-titre. Bibliogr.

Der Radweg Berlin–Dresden ist ein Routenvorschlag für Fahrradtouren zwischen Berlin und Dresden, der seit 2012 von Mitgliedern des Allgemeinen Deutschen Fahrrad-Clubs (ADFC) ausgearbeitet wird. Die Route streckt sich über 251km zwischen der Frauenkirche in Dresden und dem Brandenburger Tor in Berlin. Eine Beschilderung der Route wird langfristig angestrebt. Obwohl die Strecke heute nur „auf dem Papier“ existiert, ist sie schon gut befahrbar: Alle Wege (bis auf kleine unvermeidbare Lücken) sind asphaltiert oder gut verdichtet und frei oder wenig belastet vom motorisierten Verkehr. Auf www.radweg-berlin-dresden.de finden Sie aktuelle Informationen zur Route und es steht eine GPX-Datei für GPS-Geräte kostenlos zur Verfügung.

Researchers in multiple areas have shown that characterizing and accounting for the uncertainty inherent in decision support models is critical for developing more efficient planning and operational strategies. This is particularly applicable for the transportation engineering domain as most strategic decisions involve a significant investment of money and resources across multiple stakeholders and has a considerable impact on the society. Moreover, most inputs to transportation models such as travel demand depend on a number of social, economic and political factors and cannot be predicted with certainty. Therefore, in recent times there has been an increasing emphasis being placed on identifying and quantifying this uncertainty and developing models which account for the same. This dissertation contributes to the growing body of literature in tackling uncertainty in transportation models by developing methodologies which address the uncertainty in input parameters in traffic assignment models. One of the primary sources of uncertainty in traffic assignment models is uncertainty in origin destination demand. This uncertainty can be classified into long term and short term demand uncertainty. Accounting for long term demand uncertainty is vital when traffic assignment models are used to make planning decisions like where to add capacity. This dissertation quantifies the impact of long term demand uncertainty by assigning multi-variate probability distributions to the demand. In order to arrive at accurate estimates of the expected future system performance, several statistical sampling techniques are then compared through extensive numerical testing to determine the most "efficient" sampling techniques for network assignment models. Two applications of assignment models, network design and network pricing are studied to illustrate the importance of considering long term demand uncertainty in transportation networks. Short term demand uncertainty such as the day-to-day variation in demand affect traffic assignment models when used to make operational decisions like tolling. This dissertation presents a novel new definition of equilibrium when the short term demand is assumed to follow a probability distribution. Various properties of the equilibrium such as existence, uniqueness and presence of a mathematical programming formulation are investigated. Apart from demand uncertainty, operating capacity in real world networks can also vary from day to day depending on various factors like weather conditions and incidents. With increasing deployment of Intelligent Transportation Systems, users get information about the impact of capacity or the state of the roads through various dissemination devices like dynamic message signs. This dissertation presents a new equilibrium formulation termed user equilibrium with recourse to model information provision and capacity uncertainty, where users learn the state or capacity of the link when they arrive at the upstream node of that link. Depending on the information received about the state of the upstream links, users make different route choice decisions. In this work, the capacity of the links in the network is assumed to follow a discrete probability distribution. A mathematical programming formulation of the user equilibrium with recourse model is presented along with solution algorithm. This model can be extended to analytically model network flows under information provision where the arcs have different cost functional form depending on the state of the arc. The corresponding system optimal with recourse model is also presented where the objective is minimize the total system cost. The network design problem where users are routed according to the user equilibrium with recourse principle is studied. The focus of this study is to show that planning decisions for networks users have access to information is significantly different from the no-information scenario. / text

Commuter rail systems, operating on unused or under-used railroad rights-of-way, are being introduced into many urban transportation systems. Since locations of available rail rights-of-way were typically chosen long ago to serve the needs of rail freight customers, these locations are not optimal for commuter rail users. The majority of commuter rail users do not live or work within walking distance of potential commuter rail stations, so provision of quick, convenient access to and from stations is a critical part of overall commuter decisions to use commuter rail. Minimizing access time to rail stations and final destinations is crucial if commuter rail is to be a viable option for commuters. Well-designed feeder routes or circulator systems are regarded as potential solutions to provide train station to ultimate destination access. Transit planning for main line or feeder routes relies upon static demand estimates describing a typical day. Daily and peak-hour demands change in response to the state of the transport system, as influenced by weather, incidents, holiday schedules and many other factors. Recent marketing successes of “smart phones” might provide an innovative means of obtaining real time data that could be used to identify optimal paths and stop locations for commuter rail circulator systems. Such advanced technology could allow commuter rail users to provide real-time final destination information that would enable real time optimization of feeder routes. This dissertation focuses on real time optimization of the Commuter Rail Circulator Route Network Design Problem (CRCNDP). The route configuration of the circulator system – where to stop and the route among the stops – is determined on a real-time basis by employing adaptive Tabu Search to timely solve an MIP problem with an objective to minimize total cost incurred to both transit users and transit operators. Numerical experiments are executed to find the threshold for the minimum fraction of travelers that would need to report their destinations via smart phone to guarantee the practical value of optimization based on real-time collected demand against a base case defined as the average performance of all possible routes. The adaptive Tabu Search Algorithm is also applied to three real-size networks abstracted from the Martin Luther King (MLK) station of the new MetroRail system in Austin, Texas. / text

There has been a significant increase in private vehicle ownership in the last decade leading to substantial increase in air pollution, depleting fuel reserves, etc. One of the alternatives known as battery operated electric vehicles (BEVs) has the potential to reduce carbon footprints due to lesser or no emissions and thus the focus on shifting people from gasoline operated vehicles (GVs) to BEVs has increased considerably recently. However, BEVs have a limited ‘range’ and takes considerable time to completely recharge its battery. In addition, charging stations are not as pervasive as gasoline stations. As a result a new fear of getting stranded is observed in BEV drivers, known as range anxiety. Range anxiety has the potential to substantially affect the route choice of a BEV user. It has also been a major cause of lower market shares of BEVs. Range anxiety is a latent feeling which cannot be measured directly. It is not homogenous either and varies among different socio-economic groups. Thus, a better understanding of BEV users’ behavior may shed light on some potential solutions that can then be used to improve their market shares and help in developing new network models which can realistically capture effects of varying EV adoptions. Thus, in this study, we analyze the factors that may impact BEV users’ range anxiety in addition to their route choice behavior using the integrated choice latent variable model (ICLV) proposed by Bhat and Dubey (2014). Our results indicate that an individual’s range anxiety is significantly affected by their age, gender, income, awareness of charging stations, BEV ownership and other category vehicle ownership. Further, it also highlights the importance of including disutility caused by distance while considering network flow models with combined GV and BEV assignment. Finally, a more concentrated effort can be directed towards increasing the awareness of charging station locations in the neighborhood to help reduce the psychological barrier associated with range anxiety. Overcoming this barrier may help increase consumer confidence, resulting in increased BEV adoption and ultimately will lead towards a potentially pollution-free environment. / text