Transportation Research Part A: General最新文献

Making accurate estimates of bridge replacement costs is essential to assess present and future bridge funding needs. A series of analyses of variance was performed on bridge replacement costs to evaluate the effects of bridge attributes. Replacement cost prediction models were then developed by regression techniques. Bridge attributes which can be easily understood by bridge inspectors and engineers were used as predictor variables. Nonlinear and log-linear models were evaluated for developing cost prediction models. A residual analysis of these models showed that log-linear models were preferred to nonlinear models. Costs of bridges that had been replaced between 1980 and 1985 by the Indiana Department of Transportation (INDOT) were used as a data base. Replacement costs were converted to 1985 price using construction price indices. The final cost prediction models were validated using the costs of selected bridges which were replaced between January and June 1986, by the INDOT. Bridge replacement costs estimated by these models showed a fairly good correlation with the actual contract costs. To estimate current or future costs at a place other than in Indiana, one need to multiply appropriate cost indices.

This paper describes simulation studies that were conducted to assess the performance of a freeway corridor control system. The system combines an advanced traffic management system with a motorist information system that provides route guidance to individual drivers. It has a hierarchical structure: The corridor level control acts in a supervisory capacity dynamically allocating traffic among alternative corridor facilities, including freeways, frontage roads, and signalized arterials. The local level control then selects control parameters for the individual facilities based on the predicted usage at the corridor level. A user specified performance function is optimized in the process. Both recurrent and nonrecurrent congestion scenarios were simulated using the SCOT model as a test bed. It is shown that, in most cases, significant benefits in performance can be obtained when the system operates as designed.

The purpose of this article is to question the presumption that route guidance and information systems necessarily reduce traffic congestion, and to point out the need to consider the general equilibrium effects of information. A simple model of the morning rush hour is adopted in which commuters choose a departure time and one of two routes to work, the capacities of which are stochastic. While expected travel costs are reduced by perfectly informing all drivers about route capacities, this is not necessarily the case if imperfect information is provided. A heuristic explanation is that, absent tolls, congestion is an uninternalized externality. Information can cause drivers to change their departure times in such a way as to exacerbate congestion.

A modelling framework is developed to analyze the effect of in-vehicle real time information strategies on the performance of a congested traffic communing corridor. The framework consists of a special-purpose simulation component and a user decisions component that determines users' responses to the supplied information. The user decisions component is microscopic and determines individual commuters' route switching, at any node of the network, as a function of the supplied information. The traffic simulation component moves vehicles in bundles or macroparticles at the prevailing local speeds, as determined by macroscopic traffic relations. The framework allows the investigation of system performance under alternative behavioral response mechanisms, as well as under different information strategies. Results are presented for simulation experiments in a commuting corridor with a special network structure that simplifies the network computations. The results illustrate the effect of the fraction of users equipped with in-vehicle navigation systems on overall system performance. In addition, alternative assumptions on user response reflecting varying degrees of optimizing behavior are explored. The modelling framework is shown to provide a useful approach for addressing key questions of interest in the design of real time in-vehicle information system.

This paper deals with the applications of artificial intelligence techniques to urban traffic control problems, with the aim of improving the performances of current signal plan selection systems. In particular, an architecture of an intelligent traffic control system is outlined with regards to the different levels of data collection, data analysis and interpretation, decision, and control. The functionalities of hybrid modules introduced are discussed and the artificial intelligence methods used are mentioned. Finally the ongoing research in the field is presented.

The Equilibrium Network Traffic Signal Setting problem is an open research area. It can be approached using global optimization models or iterative procedures. In this paper, after a brief review of the state of the art, the main characteristics of the iterative procedure ENETS are described. In this procedure, traffic signal setting is performed in two successive steps: green timing and scheduling at each junction, and signal coordination on the network. Green timing and scheduling at a single junction is based on a mixed-binary linear program with capacity factor maximization. Signal coordination for the whole network is performed by solving a discrete programming model with total delay minimization. The flow assignment stage refers to the separable user equilibrium model with fixed demand, and uses a feasible direction algorithm, which can also be adopted to cover the cases of elastic demand and/or asymmetric equilibrium. An experimental test of ENETS on a small network and a graphical explanation of the procedure are described and discussed.

Dynamic network models are needed to analyze traffic congestion patterns for new real-time motorist information systems. In previous research, a dynamic network modeling framework incorporating behavioral models of drivers' route and departure time choices and their day-to-day adjustment processes was developed. Network performance in this framework is represented by time dependent arrival and departure rates, link occupancies, and queuing delays. The purpose of this paper is to extend this framework to include explicit models of drivers' information acquisition and integration. The need for these models is motivated by considering the possible beneficial and counter-productive effects that may be caused by enhanced motorist information. Information on network conditions influences the set of routes considered by a driver and also affects the perceived values of the level of service attributes. The paper presents the structure of a dynamic model in which newly acquired information may affect pretrip and en-route travel decisions. To assess the potential magnitudes of the effects that were identified further theoretical and empirical research is needed.

In this article a doubly dynamis assignment model for a general network is presented. It is assumed that users' choices are based on information about travel times and generalized transportation costs occurred in a finite number of previous days and, possibly, in previous periods of the same day. The information may be supplied and managed by an informative system. In this context, path and link flows vary for different subperiods of the same day (within-day dynamics) and for different days (day-to-day dynamics). The proposed model follows a nonequilibrium approach in which both within-day and day-to-day flow fluctuations are modelled as a stochastic process. A model of dynamic network loading for computing within-day variable arc flows from path flows is also presented. The model deals explicitly with queuing at oversaturated intersections and can be formulated as a fixed point problem. A solution scheme for the doubly dynamic assignment model is presented embedding a solution algorithm for the fixed-point problem.

It is well known that a pure privatization policy for the railway sector, as it has been implemented in the US, can solve financial problems, but the price which society has to pay for this policy is that the railway companies have to give up considerable parts of the transport market. As this contradicts the objectives of European transport policy, other ways must be found to increase the market efficiency of the railways without losing the benefits for the environment which stem from substituting car/air travel and road haulage by rail transport. One of these ways is to subdivide the railway system into (at least) two distinct organizations: a network company which is publically owned, controls the investments, and sells network capacity units at prices compatible with public goals, and a set of operation companies which operate on private accounts and maximize profits. The relationships between the government, the network, and the operation divisions may be formulated as principle agents problems. In the paper the decision levels of the publically controlled network cooperation, the operation companies, and the users are defined. The decision problems on each level are described in formal terms and the interfaces between these three levels are pointed out. Finally, some approaches to reorganize the railway system in Europe are described and lessons for the German case, where a new structure for the organization is crucially needed because of the new political situation after the German unification are derived.