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Intelligent Transportation Systems (intelligent + transportation_system)
Selected AbstractsObserving freeway ramp merging phenomena in congested trafficJOURNAL OF ADVANCED TRANSPORTATION, Issue 2 2007Majid Sarvi This work conducts a comprehensive investigation of traffic behavior and characteristics during freeway ramp merging under congested traffic conditions. On the Tokyo Metropolitan Expressway, traffic congestion frequently occurs at merging bottleneck sections, especially during heavy traffic demand. The Tokyo Metropolitan Expressway public corporation, generally applies different empirical strategies to increase the flow rate and decrease the accident rate at the merging sections. However, these strategies do not rely either on any behavioral characteristics of the merging traffic or on the geometric design of the merging segments. There have been only a few research publications concerned with traffic behavior and characteristics in these situations. Therefore, a three-year study is undertaken to investigate traffic behavior and characteristics during the merging process under congested situations. Extensive traffic data capturing a wide range of traffic and geometric information were collected using detectors, videotaping, and surveys at eight interchanges in Tokyo Metropolitan Expressway. Maximum discharged flow rate from the head of the queue at merging sections in conjunction with traffic and geometric characteristics were analyzed. In addition, lane changing maneuver with respect to the freeway and ramp traffic behaviors were examined. It is believed that this study provides a thorough understanding of the freeway ramp merging dynamics. In addition, it forms a comprehensive database for the development and implementation of congestion management techniques at merging sections utilizing Intelligent Transportation System. [source] Intelligent transportation systems , Is it a compatible tool for developing countries?JOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2006S. Akhtar First page of article [source] Estimating time dependent O-D trip tables during peak periodsJOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2000Srinivas S. Pulugurtha Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [source] Case,Based Reasoning for Assessing Intelligent Transportation Systems BenefitsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2003Adel Sadek Existing transportation planning modeling tools have critical limitations with respect to assessing the benefits of intelligent transportation systems (ITS) deployment. In this article, we present a novel framework for developing modeling tools for quantifying ITS deployments benefits. This approach is based on using case,based reasoning (CBR), an artificial intelligence paradigm, to capture and organize the insights gained from running a dynamic traffic assignment (DTA) model. To demonstrate the feasibility of the approach, the study develops a prototype system for evaluating the benefits of diverting traffic away from incident locations using variable message signs. A real,world network from the Hartford area in Connecticut is used in developing the system. The performance of the prototype is evaluated by comparing its predictions to those obtained using a detailed DTA model. The prototype system is shown to yield solutions comparable to those obtained from the DTA model, thus demonstrating the feasibility of the approach. [source] Parallel Algorithms for Dynamic Shortest Path ProblemsINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 3 2002Ismail Chabini The development of intelligent transportation systems (ITS) and the resulting need for the solution of a variety of dynamic traffic network models and management problems require faster-than-real-time computation of shortest path problems in dynamic networks. Recently, a sequential algorithm was developed to compute shortest paths in discrete time dynamic networks from all nodes and all departure times to one destination node. The algorithm is known as algorithm DOT and has an optimal worst-case running-time complexity. This implies that no algorithm with a better worst-case computational complexity can be discovered. Consequently, in order to derive algorithms to solve all-to-one shortest path problems in dynamic networks, one would need to explore avenues other than the design of sequential solution algorithms only. The use of commercially-available high-performance computing platforms to develop parallel implementations of sequential algorithms is an example of such avenue. This paper reports on the design, implementation, and computational testing of parallel dynamic shortest path algorithms. We develop two shared-memory and two message-passing dynamic shortest path algorithm implementations, which are derived from algorithm DOT using the following parallelization strategies: decomposition by destination and decomposition by transportation network topology. The algorithms are coded using two types of parallel computing environments: a message-passing environment based on the parallel virtual machine (PVM) library and a multi-threading environment based on the SUN Microsystems Multi-Threads (MT) library. We also develop a time-based parallel version of algorithm DOT for the case of minimum time paths in FIFO networks, and a theoretical parallelization of algorithm DOT on an ,ideal' theoretical parallel machine. Performances of the implementations are analyzed and evaluated using large transportation networks, and two types of parallel computing platforms: a distributed network of Unix workstations and a SUN shared-memory machine containing eight processors. Satisfactory speed-ups in the running time of sequential algorithms are achieved, in particular for shared-memory machines. Numerical results indicate that shared-memory computers constitute the most appropriate type of parallel computing platforms for the computation of dynamic shortest paths for real-time ITS applications. [source] Short-term prediction of motorway travel time using ANPR and loop dataJOURNAL OF FORECASTING, Issue 6 2008Yanying LiArticle first published online: 28 MAY 200 Abstract Travel time is a good operational measure of the effectiveness of transportation systems. The ability to accurately predict motorway and arterial travel times is a critical component for many intelligent transportation systems (ITS) applications. Advanced traffic data collection systems using inductive loop detectors and video cameras have been installed, particularly for motorway networks. An inductive loop can provide traffic flow at its location. Video cameras with image-processing software, e.g. Automatic Number Plate Recognition (ANPR) software, are able to provide travel time of a road section. This research developed a dynamic linear model (DLM) model to forecast short-term travel time using both loop and ANPR data. The DLM approach was tested on three motorway sections in southern England. Overall, the model produced good prediction results, albeit large prediction errors occurred at congested traffic conditions due to the dynamic nature of traffic. This result indicated advantages of use of the both data sources. Copyright © 2008 John Wiley & Sons, Ltd. [source] | |||||||||||||