A practical capacity model of a single track line

The paper presents a model for determining the practical capacity of a single track line, i.e. the maximum number of trains which can be run along it in a time unit under the condition that each train enters its bottleneck segment with a definite delay.

The input data used in the model are: geometrical characteristics of the bottleneck segment of the line under study, the intensity and structure of demand expressed by a number of trains which are run over the line in a given time unit, the scenario of traffic running over the line under study and the operational tactics of individual train categories processing on the bottleneck segment.

(Two tactics can be applied in the train processing on the line under study; first, the trains of individual categories are given different priorities in the processing, and second, all the trains have the same priority).

The output results of the model are average delays of trains of each category occurring within the train processing performed on the bottleneck segment of the line under study in a given time unit.     

On the use of mathematical programming in the evaluation of transport policies

This paper focuses on the evaluation processes by which decisions regarding transportation alternatives can be assisted. A multidimensional approach usually called multiple criteria decision making is required to represent the complexity of transportation policy and systems.

The multiple criteria decision making techniques can be divided into two groups. The first is based on a ranking scheme approach and the second on a mathematical programming approach.

A multiple objective mathematical programming procedure known as Goal Programming is presented. The authors examined the use of that procedure in real transportation problems.

The results suggest that multiple objective mathematical programming techniques in general do not appear to be appropriate in transportation policy analysis involving mutually exclusive alternatives. Their use can be limited to special cases in the private sector.     

A time‐staged multi‐objective network‐design model

A multi‐objective, time‐staged network‐design problem is formulated. Through transformation, the problem is decomposed into a set of single‐period, single‐objective problems. Lexicographic ordering is instrumental in effecting this transformation; it also allows a backward‐recursion algorithm to be applied using strong pruning criteria. Furthermore, monotonicity properties enable us to solve the problem using the familiar tree‐search algorithms. The solution method has several desirable properties — as shown by an example and a case study of Tripoli Province, Libya. First, the algorithm ensures continuity of project implementation over the multi time‐periods and provides optimality in later computational stages irrespective of the decision at an interim stage. Second, the algorithm tends to provide accessibility to unconnected regions in the study area at low user‐cost without employing weights to the two objective functions of accessibility and user‐cost efficiency. Such a property is deemed advantageous for suggesting transportation investments based purely on purchasing the greatest benefit for each dollar, with political neutrality strictly maintained.     

An algorithm for finding reasonable paths in transit networks

In this paper, reasonable paths in transit networks are defined as possible paths that satisfy the acceptable time criterion and transfer‐walk criterion. A recursive algorithm for finding all of the reasonable paths in a transit network that does not involve a rapid increase in program run‐time with network size is presented. Realistic transit networks in Hong Kong and Guangzhou were selected as case studies of the different phases of the development of a trip planning system. Transport planning practitioners and potential users were invited to test the system to evaluate its performance. The results of the prototype evaluation were satisfactory, and the viability of the system as a useful tool for supporting decision‐making has been confirmed by the positive feedback that was obtained from survey questionnaires.     

Value of travel time: An activity‐based analysis with combined RP and SP data

Under certain assumptions, values of travel time (VOT) can be divided into two parts: one associated with travel (i.e., the value of reducing travel time disutility) and the other associated with activities (the shadow price of time). The empirical results of combining RP and SP data showed that the VOT of travel was greater than that of activity. The results also showed that SP experiments that did not take or only took part of activities into consideration would underestimate VOT.     

Automation of paratransit reservation,routing, and scheduling

Santa Clara County, California experienced a sharp growth in demand‐responsive paratransit ridership for individuals with disabilities, as a result of the passage of the 1990 Americans With Disabilities Act (ADA). This paper describes an automated paratransit system for the ADA‐type paratransit operation implemented in Santa Clara County. It automated paratransit reservation, scheduling, and routing functions. The key components of this system were a digital geographic database (DGD) and an automated trip scheduling system (ATSS). Empirical evidence after one year of operation indicates numerous benefits of this automation. There were significant reductions in the paratransit operating costs and an increase in the percent shared rides. The savings in operating costs far exceeded the annualized capital cost of automation. A user survey indicates that these improvements were achieved without degradation to service quality such as vehicle on‐time performance, invehicle travel times, vehicle response to open return, and ride comfort.     

Hybrid model for prediction of bus arrival times at next station

Effective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time‐of‐day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering‐based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)–based methods. Copyright © 2010 John Wiley & Sons, Ltd.     

The planning of aircraft routes and flight frequencies in an airline network operations

This research is aimed at developing a model that maximizes system profit when determining the aircraft routes and flight frequencies in a network. The model employs network flow techniques to effectively collect or deliver passenger flows from all origins to all destinations using non‐stop and multi‐stop flights in multi‐fleet operations. The model was formulated as a multi‐commodity network flow problem. A Lagrangian‐based algorithm was developed to solve the problem. To test the model in practice, a case study is presented.     

Special issue: Importance of public transport

The transport demand in most major cities around the world can only be met with a high‐quality public transport system. The requirements on bus, rail, underground and tram systems are manifold with reliability and efficiency as the key factors. The service operating hours and the size of the network are often extended in order to serve the needs better. Further, most metropolitan areas are trying to provide more incentives for citizens to leave the car at home and use the local transit systems instead. The reasons are well known. Not only does a public transport system only make economical sense if it is well used, but most urban areas with a high car‐dependency face at least three major problems; safety, congestion, and pollution (noise and air pollution, land separation, etc.). It is generally recognised that to decrease car usage and to increase public transport usage a stick & carrot approach is needed. The London congestion‐charging scheme is an example since all revenues collected by the scheme are put into the improvement of bus and underground services.