RDRG can be provided to the driver by two system architectures, namely Autonomous Reliable DynamicRouteGuidance (ARDRG) and Supported Reliable DynamicRouteGuidance (SRDRG). In ARDRG, which has a decentralised system architecture, a route based on default estimates of link travel times, complemented by traffic information about current incidents broadcast by the Traffic Message Channel (TMC), is suggested to the driver. Default travel time data along with the network data is accessed by the guided vehicle from a static database available on an on-board DVD-ROM. The entire route computation takes place in the vehicle unit itself. In contrast, SRDRG has a more centralised system architecture with two-way data exchange between the guided vehicle and a Traffic Information Centre (TIC), increasing the range and accuracy of information made use of in providing guidance. The entire route computation takes place at the TIC and routes are transmitted to the vehicle in parts, for example at decision points. Only the ARDRG system architecture is considered in the work presented here, as this corresponds to most systems installed in vehicles to date.
the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. As such it forms the basis of a considerable amount of machine learning techniques. Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. In particular, such traffic flow forecasting supports the development of proactive traffic control strategies in advanced traffic management systems, real-time routeguidance in advanced traveler information systems and evaluation of these dynamic traffic control.
In case the effect of implementing PGS for a larger network is investigated, it is important to keep in mind that every single PGS device in the model functions independently. In case a re- routing percentage is set to 20% and a traffic flow passes two devices a total of 36% will be re- routed. In reality a PGS is implemented in such a way that drivers pass a PGS multiple times: e.g. first on the outer ring road, then again in the inner city etc. This is however not necessary when using the PGS tool in S-Paramics. In reality the PGS is also used to guide the drivers to- wards a car park. In S-Paramics drivers are instructed to park in another car park by the PGS tool and will chose the shortest route and do not have to be guided as drivers in real life. To reflect the implementation of PGS in a city, it is important that traffic in the model only passes a PGS device once.
The distributed protection architecture discussed in the previous section requires a laboratory-based validation procedure. This is partly catered for by the dynamicmodelling environment which performs three main functions. First of all, the environment feeds the appropriate primary power system information to the protection architecture under testing. It also routes tripping signals from the architecture back to the appropriate power system apparatus in a format suitable for the particular test setup in place (the test setup is discussed in section VI). Secondly, the environment provides a representative communications infrastructure fit for the protection scheme under consideration. This infrastructure also enables internal information exchange between layers to ensure compatibility. The third function is responsible for dynamically simulating different network states with the assistance of the power system information pool. Moreover, a user interface will be developed to allow monitoring the architecture’s behaviour as well as changing its underlying functional parameters if required.
As DynamicRouteGuidancesystems are a rapidly growing market, more sophisticated systems are continuously being developed, including more advanced functions. In recent research work , ,  and , a new DynamicRouteGuidance algorithm was developed, which, besides travel time, also takes travel time reliability into account. By defining reliability as the probability that a link will be uncongested, the algorithm uses a penalty procedure to, where possible, exclude from the route search links classified as unreliable, under the assumption that a link’s state is a binary variable, reliable or unreliable. The level of link congestion is chosen to measure reliability.
To help travelers’ select appropriate route choices to improve the overall capacity of the roads, the efficiency of many of the strategies developed in most of the previous studies was only investigated with homogeneous traffic flows and in symmetric multi-route traffic systems. By contrast, in our simulation scenario, we considered an asymmetric multi-route scene and heterogeneous traffic flow on the roads. We proposed a new strategy called WROFS. Using the NaSch model as the update mechanism for vehicles, we compared the performance of WROFS with two previously proposed strategies (WVDFS and WMVFS) in two asymmetric two-route scenarios, and we obtained simulation results after changing the heterogeneous vehicle number, speed, and flux at each time step, as well as the average flux according to the ratio of dynamic vehicles. The results of these simulations demonstrated that WROFS obtained better overall capacity of the roads than WVDFS and WMVFS. The traffic flux was improved. The oscillations in the flux and velocity were reduced, as well as the extent of congestion.
(ii): Park et al. (2007a) developed heuristic algorithms to identify a manageable subset of near-optimal routes (multi-path) for the online routing of vehicles. Park et al. (2007b) examined an adaptive routeguidance system with the use of learning algorithms to predict which route should be selected. Furthermore, a comprehensive overview of different heuristic shortest path algorithms has been conducted by Fuet et al. (2006). Some researchers also use meta-heuristic algorithms for finding near optimal solutions of the shortest path problem. Pahlavani et al. (2006) examined the problem of Multi Criteria routeguidance in urban networks, and offered a new approach based on genetic algorithms and geographic information systems. Pahlavani and Delavar (2014) in their study analysed some approaches of neuro-fuzzy through conducting preferences of the drivers to evaluate and design multi criteria route planning in urban transportation networks. Gu et al. (2010) presented a community-based approach of ant colony optimization for solving the shortest paths problem in dynamicrouteguidance. The results of simulation experiments showed the effectiveness of the proposed algorithm. Yoshikawa and Otani (2010) also proposed a new algorithm that combines tabu search and ant colony optimization. Masoomi et al. (2011) used multi- objective ant colony algorithms, which change in contrast to the structure of the one objective algorithm, for developing a user-based routing algorithm. The researchers tried to implement one of the ITS properties that focus on user wishes. (iii): Other approaches to solving complex problems, such as routeguidancesystems using the multi agent system approach to improve the management and coordination of traffic networks [Martiet et al. 2009]. Chen and Cheng (2010)
Urban road traffic congestion has been a global issue for many years due to rapid urbanization. Residents in cities are suffering from the most annoying side-product of urbanization every day. According to data from IBM , there are more than one billion cars running on all the roads around the world, and the number will dou- ble by 2020. Traffic congestion not only causes mental stress in drivers, but also leads to more severe pollu- tion, higher gasoline consumption and huge economic loss [2,3]. There are three levels of solutions to urban road traffic congestion: reducing road traffic demand, shifting road traffic to other travel mode, and spatially distributing traffic to maximize the usage of traffic network capacity. Since it is not always possible to reduce the number of trips or persuade drivers to change their travel mode, dis- tributing traffic through routeguidance is considered a most feasible, effective and economic solution to urban
I would like to thank my professors (Eric & Walter) and coaches (Edwin & Dirk) for their guidance and help to finish my work successfully. Further I want to thank my colleagues at Omnitrans. They made each day enjoyable and above all inspired me to work on transport modelling. John, I am grateful for your remarks along the way that helped me to improve this thesis. Then I would like to thank my family, especially my parents, for always supporting me and for giving me the opportunity to realise my objectives. Last, but definitely not least I want to thank my friends, without them those 6 years of study would have been less interesting. Special gratitude goes to Maarten, Erik, Everhard and Ties for supporting me with this research.
Hawas, Napeñas and Hamdouch  developed two algorithms for inter-vehicular communication (IVC)-based routeguidance in a traffic network. Although the per- formance of such IVC-based algorithms is quite rea- sonable as compared to the centralized systems, there are still many challenges such as the rapid topology changes, the frequent fragmentations and the small effective net- work diameter. Because of the high relative speed of vehicles, the IVC network experiences very rapid changes in topology. Also, due to the low deployment of vehicles having IVC, the IVC network is subject to frequent fragmentation. Finally, because of the poor connectivity, the effective network diameter is usually small. These aspects impose restrictions if deployed via IVC tech- nologies. For instance, one should compromise the extra effectiveness of having wider ranges of communication against the possible degradation in performance due to poor communication.
Over the last two decades, applications of multibody dynam- ics have expanded over the fields of robotics, automobile, aerospace, bio-mechanics, and many others. With continuous development in the above mentioned fields, many complex multibody systems have evolved whose dynamics play a piv- otal role in their behaviour. Hence, computer-aided dynamic analysis of multibody systems has been a prime motive to the engineers, as high speed computing facilities are readily available. In order to perform computer-aided dynamic anal- ysis, the actual system is represented with its dynamic model which has the information of its link parameters, joint vari- ables and constraints. The dynamic model is nothing but the equations of motion of the multibody system at hand derived from the physical laws of motions. For a system with fewer links, it is easier to obtain explicit expressions for the equa- tions of motion. However, finding equations of motion for complex systems with many links is not an easy task. Some- times even with 4 or 5 links, say, a 4-bar mechanism, it is
(1977), Shah, D.O. and Walker, R.D. (1978), Wade, W.H. et al., (1978), Schechter, R.S. and Wade, W.H. (1980/1981)]. Moreover, this scale can also be used for comparison and evaluation of different surfactant systems and the introduction of the equivalent alkane carbon number (EACN) concept [Sandvik, E.L. et. al., (1976), Knaggs, E. (1976), Smith, G.D. (1979)].
It is noted in passing that the existence of at least one (though artificial) network satisfying Boyce's last conjecture above may be established by reference to Braess's paradox (Braess', 1968, and discussed in Sheffi, 1985). In terms of the four link example given by Sheffi, Braess's paradox concerns the construction of an additional (fifth) link in the network; comparing the user equilibrium flow pattern before and after building the link, it is seen that all drivers have a greater travel time in the `after' situation. In fact, the user equilibrium pattern for the four link network corresponds to a system optimal pattern for the five link case, with the additional link assigned zero flow. Thus, for the five link network, all drivers would have shorter travel times under a SO assignment than under a UE assignment. In one respect this could be regarded as an illustration of an ideal routeguidance system: if it were supposed that without guidance in the system, all drivers follow a UE and (with 100% take-up) guidance were used to route according to an SO pattern, then in this example every individual would benefit due to the co-operation between drivers which is possible through such an information system. In another respect, this could be seen as an indication of possible advantages of SO guidance over UE guidance.
While point-to-point links have become very common , Multi-Terminal DC (MTDC) systems are the next step en- visaged , . They will result in more complex HVDC network topologies. An illustrative example is sketched in Fig. 1 where AC grids are shown with gray boxes, and DC with white. The system includes two point-to-point HVDC links. One of them allows power exchange (and possibly frequency support) between the asynchronous AC systems #1 and #2. The other link connects a wind park to AC system #2. The overlay MTDC grid #1 serves as a backbone reinforcing AC system #1 by offering an alternative path for power transfers between remote locations inside that system. The MTDC grid #2 collects the energy produced by two off-shore wind parks but also allows power transfers between the AC systems #1 and #2. The generators within each wind park are connected through AC cables which make up a separate AC grid. The latter can be modeled in detail or replaced by a single, isolated AC bus if the generators are lumped into one equivalent.
attributes are potentially effective in the route-choice of cyclists, though some are of greater importance. Attributes can depend on many aspects, including a region, aim of cycling, age of cyclist, etc. As an example in the icy conditions of Sweden’s winters, the safety of a route, taking into account, the least junction with other modes, or the least passing on ice, is a major priority for cyclists. Regarding these facts, there is the conclusion that, a certain network would favour certain attributes generally, with these attributes being the key ones, and the attributes which are preferred by a certain group are the more specific attributes. There is a limitation when dealing with attributes, and that is the process of integrating these attributes into a model. Apart from that, there is the issue of data availability, which for some attributes can never be precise, or logical, to implement into a numerical model. This would indicate that from all attributes potentially available, only a few will be integrated into the model. Regarding the facts mentioned above, there is some diversity in the attributes which have been considered in available models for routing cyclists. The attributes presented in present literature vary but are mostly:
Sangheetaa Sukumran et al. proposed a solution to tries to identify a route, its route request will be forwarded by the neighbouring nodes only if it reputation value is higher than the threshold value i.e. this node must be in the white list. Thus a node needs to maintain a good reputation value in order to enjoy network services. A misbehaving node which is isolated has no chance of re-joining the network until the entire network is reformed. This will decrease the efficiency and effectiveness of the network, low reputation value node is not allowed to participate in a network until network is reformed. We provided a solution that uses reputation with cache clearance process that not only improve the efficiency and reduce network overhead but also permit every node to participate into the route selection process for communication. 
Route Discovery – Node “A” trying to send data packet to “D” to initiate the route discovery “A” transmit a ROUTE REQUEST message as a single local broadcast packet, which is received by approximately all nodes within transmission range of “A”. Each ROUTE REQUEST message identifies the initiator and target of the Route discovery and also contains a unique request ID, determined by the initiator of the REQUEST. Each ROUTE REQUEST also contains a record listing, the address of each intermediate node through which this particular copy of the ROUTE REQUEST message has been forwarded. This route record is initialized to an empty list by the initiator of the route discovery.
The present day demands of manufacturing industry require swift reaction, the ability to take advantage of new opportunities and rapid development. This is particularly true of the car manufacturing sector. End producers are seeking the best, the quickest, the cheapest producers of subassemblies. The car industry estimates that reducing the lead-time to market of a new car model by one day saves 2 million US dollars. This kind of thinking results in the introduction of automated production lines and the construction of information systems connecting all phases of car production. Systems play a vital role in linking initial ideas with development, testing, production planning and production and to the logistics, connecting producers of subassemblies with the final producers of the cars according to the demands of buyers and sales departments.