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In this paper, we present a cellular automaton (CA) simulation of a signalized intersection. When there is no exclusive lane for left-turn vehicles, through vehicles and left-turn vehicles have to share one lane. Under such situation usually two-phase signalization is adopted, and the conflicts between the two traffic streams need to be analyzed. We use a refined configuration for the intersection simulation: the geometry of the intersection has been considered and vehicles are assumed to move along 1/4 circle arcs. We focus on the averaged travel times on left lanes and their distributions. The diagrams of intersection approach capacities (IACs) and the corresponding phase diagrams are also presented, which depend on the approach flow rates and the percentage of left-turn vehicles. Besides, we find that the minimum green time could be determined by finding out the critical value for the travel times.

In this paper, we apply car-following model to explore each person's travel time and the system's total travel time on an open road. The analytical and numerical results illustrate that each person's travel time and the system's total cost are directly related to each person's time headway at the origin when the road is long enough and the number of persons is large enough in the traffic system. The above results can help traffic engineers to optimize each person's arrival rate and help readers to understand the relationship between each person's travel time and his arrival rate.

Air transport networks play a significant role in the modern tourism industry, and their operational performance and stability can be measured by reliability analysis. This study, from the perspective of a travel agency, attempts to assess the reliability of a stochastic air transport network (SATN) by considering multiple passenger demands generated by both the starting airport and several connection airports. The SATN is modeled as a stochastic flow network comprised of several nodes and arcs where nodes represent airports, and arcs represent flights connecting two airports. Each flight is associated with a predetermined stochastic capacity following a specific probability distribution, departure time, arrival time, and discount fare. Accordingly, network reliability refers to the probability that the network can successfully transport passengers from their respective source airports to the destination airport within the given travel time and travel cost limit. An algorithm in terms of the concept of lower boundary point is developed to compute network reliability. Furthermore, a simple example and a case study are presented to demonstrate the proposed reliability method and the managerial implications of reliability for travel agencies.

Tsunami waves are considered the most dangerous natural hazard affecting the population of the world living near the coastal belts. With the increasing intensity of economic exploitation of coasts there is also an increase in socio-economic consequences resulting from the hazardous action of tsunami waves generated from submarine seismic activity and other causes. On 26 December 2004, the countries within the vicinity of East Indian Ocean experienced the most devastating tsunami in recorded history. This tsunami was triggered by an earthquake of magnitude 9.0 on the Richter scale at 3.4°N, 95.7°E off the coast of Sumatra in the Indonesian Archipelago at 06:29 hrs IST (00:59 hrs GMT).

As of now (September, 2005), the only Tsunami Warning System (TWS) that is in existence is the one for the Pacific Ocean, which began in the late 1940s. Following the recent disastrous tsunami of 26 December 2004 in the Indian Ocean, the nations around the Indian Ocean rim are now working together to establish a tsunami warning system which should become operational in the near future. One of the most basic information that an Indian Ocean tsunami warning center should have at its disposal, is information on tsunami travel times to various coastal locations surrounding the Indian Ocean rim, as well to several island locations. Devoid of this information, no ETA's (expected times of arrival) can be included in the real-time tsunami warnings.

The importance of ETA for tsunami warning system motivated the computation of arrival times comprising 250 representative coastal locations from 35 countries, showing the feasibility of developing a TWS in a relatively short time-span. Numerical accuracy in computating arrival times for this energetic event has been verified from in situ tide gauge data and satellite track data from Jason-I and Topex/Poseidon in the Indian Ocean and also from coastal stations off South Africa. The expected outcome of this work is to develop a widely distributed tsunami travel time (TTT) atlas which can serve as a valuable information database to reduce warning time in the event of tsunamis in the Indian Ocean and promote awareness among the population dwelling in the littoral belts of the South-Asian countries.

On 26th December 2004, the countries within the vicinity of East Indian Ocean experienced the most devastating tsunami in recorded history. This tsunami was triggered by an earthquake of magnitude 9.0 on the Richter scale at 3.4°N, 95.7°E off the coast of Sumatra in the Indonesian Archipelago at 06:29 hrs IST (00:59 hrs GMT). One of the most basic information that any tsunami warning center should have at its disposal, is information on Tsunami Travel Times (TTT) to various coastal locations surrounding the Indian Ocean rim, as well as to several island locations. Devoid of this information, no ETA's (expected times of arrival) can be included in the real-time tsunami warnings. The work describes on development of a comprehensive TTT atlas providing ETA's to various coastal destinations in the Indian Ocean rim. This Atlas was first released on the first anniversary of the Indian Ocean Tsunami and was dedicated to the victims. Application of soft computing tools like Artificial Neural Network (ANN) for prediction of ETA can be immensely useful in a real-time mode. The major advantage of using ANN in a real-time tsunami travel time prediction is its high merit in producing ETA at a much faster time and also simultaneously preserving the consistency of prediction. Overall, it can be mentioned that modern technology can prevent or help in minimizing the loss of life and property provided we integrate all essential components in the warning system and put it to the best possible use.

Seismic waves are mechanical vibrations that propagate through the Earth. They cannot propagate through vacuum. There are various types of seismic waves (e.g., body and surface waves); each type can be categorized into two subtypes based on the nature of particle motion during wave propagation. The two body wave types are P wave and S wave. The P wave is also known as primary wave or longitudinal wave. Particle motion or the oscillation of the medium during the propagation of this wave is in the direction of wave propagation; hence, it is similar to a sound wave. During wave propagation, the body experiences compression and dilatation, i.e., change in volume. This wave can propagate through both solid and fluid media and is the fastest of all seismic waves. The S wave is also known as shear wave or secondary wave. The medium oscillates in a direction perpendicular to the direction of propagation of wave front. The body experiences shearing motion that leads to a change in its shape, but no change in volume. As fluids cannot sustain shear, S waves cannot move through them. Hence, an S wave propagates only through solid media. In a given medium, its velocity is lower than that of a P wave; hence, it always arrives after the P wave, and that is why it is also called as secondary wave.

Surface waves develop due to interference of post-critical reflected P and S waves. Their maximum amplitude of vibration decreases exponentially with depth in the Earth. Hence, their propagation effect is maximum near the surface. That is why they are called surface wave. They travel with velocity less than that of S wave and arrive later than body waves. The two types of surface waves are known as Rayleigh wave and Love wave. A Rayleigh wave is generated by the constructive interference of a P wave and the vertical component of an S wave (also called SV wave). In a record of seismic wave called seismogram, normally this is the maximum amplitude at arrival. A Love wave is generated by the constructive interference of upgoing and downgoing components of an SH wave (where particle motion of the S wave is in the horizontal direction). Love waves travel faster than Rayleigh waves. There is another type of wave similar to the surface waves in nature that travels along an interface at deeper locations within the Earth called Stonley wave.

Apart from this, the Earth also experiences free oscillations — standing waves generated due to interference of long-period seismic waves. These waves cause the whole Earth to vibrate. There are two types of free oscillations: spheroidal and toroidal. Spheroidal oscillation is similar to the motion caused by a Rayleigh wave, and Toroidal motion is similar to the motion caused by a Love wave.

This paper is a contribution toward an operational use of large floating car data (FCD) in traffic management. The work focused on a practice-ready traffic surveillance system for Chongqing City of 8 million people. In this system, the real-time GPS FCD of all the 16,000 taxis in the city are analyzed to monitor the traffic performance in the city in different levels, including road segment level, corridor level, sub-area level, and city level. In this paper, the MOE selection, system outcomes and related recommendations on future system improvements are discussed. In this system, travel time ratio has been selected as the key MOE to evaluate traffic performance since it incorporates many factors that affect travel time and it is very intuitive and easy to understand for the public. It is demonstrated that using large-scale of FCD data to monitor traffic performance is a feasible and cost-effective solution to detect and better manage transportation.