Dongjoo Park

FORECASTING FREEWAY LINK TRAVEL TIMES WITH A MULTILAYER FEEDFORWARD NEURAL NETWORK

One of the major requirements of advanced traveler information systems (ATISs) is a mechanism to estimate link travel times. This article examines the use of an artificial neural network (ANN) for predicting freeway link travel times for one through five time periods into the future. Actual freeway link travel times from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system were used as a test bed. It was found that when predicting one or two time periods into the future, the ANN model that only considered previous travel times from the target link gave the best results. However, when predicting three to five time periods into the future, the ANN model that employed travel times from upstream and downstream links in addition to the target link gave superior results. The ANN model also gave the best overall results compared with existing link travel time forecasting techniques.

Forecasting multiple-period freeway link travel times using modular neural networks

With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.

Direct Forecasting of Freeway Corridor Travel Times Using Spectral Basis Neural Networks

With the advent of advanced traveler information systems, the prediction of short-term link and corridor travel times has become increasingly important. The standard method for forecasting corridor travel times is a two-step process in which the link travel times are first forecast and then combined into a corridor travel time. If link travel times are not independent, however, there is the potential for erroneous corridor or route travel time estimates. As an alternative to the two-step approach, a direct or one-step approach for freeway corridor travel time forecasting is proposed that automatically takes into account interrelationships between link travel times. The use of spectral basis neural networks to directly forecast multiple-period freeway corridor travel times is examined first. The model is tested on observed travel times collected as part of the automatic vehicle identification component of the Houston Transtar system. The direct forecasting model is also compared with the two-step model, which uses forecast link travel times as input. It was found that the direct forecasting approach gave better results than any of the other models examined and that link travel time forecasting errors are not additive.

DYNAMIC AND STOCHASTIC SHORTEST PATH IN TRANSPORTATION NETWORKS WITH TWO COMPONENTS OF TRAVEL TIME UNCERTAINTY

Abstract The existing dynamic and stochastic shortest path problem (DSSPP) algorithms assume that the mean and variance of link travel time (or other specific random variable such as cost) are available. When they are used with observed data from previous time periods, this assumption is reasonable. However, when they are applied using forecast data for future time periods, which happens in the context of ATIS, the travel time uncertainty needs to be taken into account. There are two components of travel time uncertainty and these are the individual travel time variance and the mean travel time forecasting error. The objectives of this study are to examine the characteristics of two components of travel time uncertainty, to develop mathematical models for determining the mean and variance of the forecast individual travel time in future time periods in the context of ATIS, and to validate the proposed models. First, this study examines the characteristics of the two components of uncertainty of the individual travel time forecasts for future time periods and then develops mathematical models for estimating the mean and variance of individual route travel time forecasts for future time periods. The proposed models are then implemented and the results are evaluated using the travel time data from a test bed located in Houston, Texas. The results show that the proposed DSSPP algorithms can be applied for both travel time estimation and travel time forecasting.

Dynamic Multi-interval Bus Travel Time Prediction Using Bus Transit Data

The objective of this research is to develop a dynamic model to forecast multi-interval path travel times between bus stops of origin and destination. The research also intends to test the proposed model using real-world data. This research was brought about by the shortcomings of the existing real-time based short-term-prediction models, which have been widely utilised for single interval predictions. The developed model is based on the Nearest Neighbour Non-Parametric Regression using historical and current data collected by the Automatic Vehicle Location technology. In a test with real-world bus data in Seoul, Korea, the proposed multi-interval-prediction model performed effectively in terms of both prediction accuracy and computing time.

IDENTIFYING MULTIPLE AND REASONABLE PATHS IN TRANSPORTATION NETWORKS: A HEURISTIC APPROACH

A fundamental component of many transportation engineering applications is the identification of the route between a given origin and destination. Typically, some type of shortest-path algorithm is used for this task. However, shortest-path algorithms are only applicable when a single criterion, such as minimizing travel time, is used for path selection. When multiple criteria, such as the mean and variance of travel time, are used for path selection, then alternative-path identification methods must be found. The present objective is to develop an algorithm that can identify multiple and reasonable routes in transportation networks so that multiple-criteria decision-making techniques can be used in route selection. First, the definitions of single and multiple routes from a transportation engineering perspective are examined. It is indicated that although the traditional k-shortest-path algorithms can find routes with similar route travel times, the routes may be too similar with respect to the links used and consequently are not appropriate for certain transportation applications. A definition of a reasonable path is developed on the basis of transportation engineering rather than purely mathematical considerations. Two k-reasonable-path algorithms are then illustrated. These algorithms can be used to identify multiple and reasonable routes in transportation networks. Lastly, the two heuristic algorithms were tested on a network from Bryan to College Station, Texas, and the results were compared with the results obtained with a traditional k-shortest-path algorithm. It was found that the reasonable-path algorithms can identify routes that are similar in route travel time but significantly different in terms of the links used.

Solving the bicriteria traffic equilibrium problem with variable demand and nonlinear path costs

In this paper, we present an algorithm for solving the bicriteria traffic equilibrium problem with variable demand and nonlinear path costs. The path cost function considered is comprised of two attributes, travel time and toll, that are combined into a nonlinear generalized cost. Travel demand is determined endogenously according to a travel disutility function. Travelers choose routes with the minimum overall generalized costs. The algorithm involves two components: a bicriteria shortest path routine to implicitly generate the set of non-dominated paths and a projection and contraction method to solve the nonlinear complementarity problem (NCP) describing the traffic equilibrium problem. Numerical experiments are conducted to demonstrate the feasibility of the algorithm to this class of traffic equilibrium problems.

Relation model describing the effects of introducing RFID in the supply chain: evidence from the food and beverage industry in South Korea

The development of information technology has rapidly changed the logistics industry. RFID has become more and more important in the context of supply chain management (SCM), and implementation of RFID in SCM brings with it the potential to manage the information flow and to support communication and collaboration along the supply chain. This study was conducted to build a relation model, which is a structural model, to identify the effects of introducing RFID into the supply chain of the food and beverage industry in Korea. The supply chain of the food and beverage industry was divided into five activities: procurement, production, transport, sale, warehousing, and administration. This study was based on the premise that RFID will be embedded in a transport box or pallet circulated in the SC. The model showed that SC activities have positive relationships through the RFID system, and the introduction of RFID promotes information interchanges between SC activities, which in turn enable the coordination and consolidation of a total SCM. From the results of this study, it is expected that the RFID system does not only enable the SC partners to improve their utilities but also promotes the efficiency of SCM as a whole. This is meaningful considering that there is still a controversy regarding the effects of RFID on SCM.

Estimating trade-off among logistics cost, CO2 and time: A case study of container transportation systems in Korea

One of the basic necessary conditions for successful implementation of policies which encourage the use of intermodal freight transportation systems is to understand “what the desired mode share of the intermodal freight transportation systems is” and also “what the trade-off relationships among various concerns such as logistics cost, time and CO2 emissions are”. The objective of this study is to estimate the trade-off relationships among logistics cost, time and CO2 emissions of the freight transportation systems of Korea. For this, container cargo data, the road network for trucks and the railway network are used as a case study. The relationships are estimated by assigning container cargoes between production zones and consumption zones and by solving linear-programming-based transportation problems. This study clearly shows the trade-off relationships between attributes. The desired levels of modal split of the railway-based intermodal freight transportation system with respect to different aspects are identified. It is considered that the findings of this study would be valuable as anchor points for setting national policy directions on freight transportation system development and determining the level of subsidies for shippers or carriers who shift from trucking to a railway-based intermodal freight transportation system.

An Enhanced Technique for k -Nearest Neighbor Queries with Non-Spatial Selection Predicates

In multimedia databases, k-nearest neighbor queries are popular and frequently contain non-spatial predicates. Among the available techniques for such queries, the incremental nearest neighbor algorithm proposed by Hjaltason and Samet is known as the most useful algorithm [16]. The reason is that if k′ > k neighbors are needed, it can provide the next neighbor for the upper operator without restarting the query from scratch. However, the R-tree in their algorithm has no facility capable of partially pruning tuple candidates that will turn out not to satisfy the remaining predicates, leading their algorithm to inefficiency. In this paper, we propose an RS-tree-based incremental nearest neighbor algorithm complementary to their algorithm. The RS-tree used in our algorithm is a hybrid of the R-tree and the S-tree, as its buddy tree, based on the hierarchical signature file. Experimental results show that our RS-tree enhances the performance of Hjaltason and Samets algorithm.