A reliable network means a network which can guarantee an acceptable level of service for traffic even if some links of the network are physically damaged (disasters) or a large amount of travel demand is occasionally generated (traffic accident or road work). Network reliability models have been studied for evaluating transport networks in both usual and unusual conditions. The objectives of this paper are (1) to formulate a network flow model for describing flows in degraded conditions and (2) to propose some performance measures of a network when some links are closed to traffic.
A modeling framework was developed to analyze the effect of traffic information strategies on the performance of congested traffic corridors with signal controlled intersections. The framework consists of three components: a traffic network simulation model, a route choice model to determine drivers' responses to given real-time information, and an information model. The traffic network simulation model is based on a periodic simulation approach and describes the dynamics of traffic flow in the network. The vehicles are moved each second according to car-following logic while responding to traffic control devices and movement of nearby vehicles. The driver decision component is microscopic and determines individual drivers' route choices based on available information at any node of the network. The information components calculate travel time information using traffic conditions predicted by traffic network simulation. This framework allows for the investigation of traffic information system performance under variable signal control, as well as under different information strategies. Results are presented for simulation experiments in a simplified actual network. The model gives simulated results close to observed values. The results illustrate the effect of the users equipped with in-vehicle navigation systems on overall system performance. In addition, to optimize system performance under advanced traveler information systems, signal control and informed rate must be evaluated at the same time. This modeling framework provides a useful approach in the design of traffic information systems throughout simulation experiments.
This article focuses on the estimation of a tsunami evacuation destination choice accounting for spatial correlation among alternatives via a Spatially Correlated Logit (SCL). In the context of tsunami evacuation, an allocation parameter is specified not only as a function of adjacency but also of altitude similarity, thus accounting for similar risk levels among alternatives. Using data from a survey conducted on survivors of the Great East Japan Earthquake and ensuing tsunami, empirical findings suggest the existence of considerably high degree of spatial correlation among alternatives, that in the case of tsunami evacuation is better captured by an altitude based allocation parameter.
Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in pre-processing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during pre-processing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities.
