Avishai Ceder

BUS NETWORK DESIGN

This paper describes the bus network design problem, summarizes the different approaches that have been proposed for its solution and proposes a new approach incorporating some of the positive aspects of prior work. The proposed approach is intended to be easier to implement and less demanding in terms of both data requirements and analytical sophistication than previous methods. An algorithm is presented that can be used to design new bus routes taking account of both passenger and operator interests; however, this algorithm focuses on only a single component of the overall bus operations planning process described in this paper.

Creating bus timetables with maximal synchronization

This paper addresses the problem of generating a timetable for a given network of buses so as to maximize their synchronization. It attempts to maximize the number of simultaneous bus arrivals at the connection (transfer) nodes of the network. Transit schedulers, taking into account the satisfaction and convenience of the systems users, appreciate the importance of creating a timetable with maximal synchronization, which enables the transfer of passengers from one route to another with minimum waiting time at the transfer nodes. In this paper, the problem is formulated as a mixed integer linear programming problem, and a heuristic algorithm is developed to solve the problem in polynomial time. The efficiency of this algorithm, compared to optimal solutions, is illustrated through a series of examples.

BUS FREQUENCY DETERMINATION USING PASSENGER COUNT DATA

Abstract The importance of ridership information has led transit properties to increase the amount of manually collected data or alternatively to introduce automated surveillance techniques. Naturally, the bus operators are expected to gain useful information for operations planning by obtaining more accurate passenger counts. This paper describes and analyzes several appropriate data collection approaches for the bus operator in order to set the bus frequencies/headways efficiently. Four different methods are presented to derive the bus frequency: two are based on point check (maximum load) data and two propose the use of ride check (load profile) data. A ride check provides more complete information than a point check, but at a greater cost, and there is a question as to whether the additional information gained justifies the expense. Based on available old profiles, the four methods provide the bus scheduler with adequate guidance in selecting the type of data collection procedure. In addition, the scheduler can evaluate the minimum expected bus runs when the load standard is released and avoid overcrowding (in an average sense) at the same time. Alternative timetables are also investigated in conjunction with minimizing the required bus runs and number of buses for a single route. In this way, the derived headways can be analyzed within an acceptable range while considering the possible changes incurred indirectly to the fleet size. The integration between resource saving and frequency determination procedures allows the schedulers performance to be improved.

Passenger Waiting Strategies for Overlapping Bus Routes

Transit passengers in many urban areas must deal with overlapping bus routes with some routes sharing common stops. Such a passenger can decide either to board an arriving bus or wait for a faster bus---one which will have a shorter in-vehicle travel time. The purpose of this paper is to clarify this route-choice problem using mathematical formulations of passenger waiting times. First an analysis of previous research is carried out based on a field-verified formulation between the variance and the mean of the bus headway and on two headway distribution families. Second, probabilistic mathematical formulations are developed, and an interpretation of the problem is presented. In this second part, overlapping routes are categorized as slow or fast routes, and the proportion of passengers selecting each type of route is derived. The results concerning the route-choice strategies show that an optimal strategy might be to disregard an arriving slow bus. In this case the passenger should wait for a fast bus while disregarding slow buses arriving in the meantime. The results concerning the two route categories show that the intuitive rule in which passengers board the routes in proportion to their frequencies is not a good approximation in all cases, and it can be revised in accordance with this analysis. In addition, this research constructs a framework to consider further aspects of passenger behavior at urban bus stops.

Relationships between road accidents and hourly traffic flow--I : Analyses and interpretation

This research extends the investigation of the relationships between measures of accidents and traffic flow, and considers the hourly flow instead of the average daily traffic (ADT), which has already been reported. The findings of this study serve as a basis for further clarification of the interactions between various levels of traffic flow and road accidents. Eight four-lane road sections were studied during an 8-year period, providing adequate data based on carefully predefined criteria. Power functions are fitted and classified according to: (1) time-sequence analysis for each roadway section; and (2) cross sectional analysis on a one year basis. The results are presented, separately for multi and single vehicle accidents, in a matrix-format. A linear dependency was observed between the power and the logarithm of the multiple constant. This was done in a similar fashion to the previously reported study of the relationships between road accidents and ADT. The results for each type of analysis and type of accident are discussed, and three examples of a practical application are given.

USER AND OPERATOR PERSPECTIVES IN TRANSIT NETWORK DESIGN

The basic construction of the objective functions of the transit network design problem is described, and a new approach taking account of both passenger and operator interests is proposed. The approach presented combines the philosophy of the mathematical programming approaches with decision-making techniques in order to allow the user to select from a number of alternatives. The nature of the overall formulation is nonlinear and mixed-integer programming.

Optimal response to oil spill: The strategic decision case

In this paper we develop a model for the problem of a locating appropriate levels and types of cleanup capability to respond to oil spills and b allocating such capability among points of high oil spill potential. The model takes into account frequency of spill occurrence, variability of spill volumes, different cleanup technologies, equipment efficiency and operability, fixed costs to open a facility, equipment acquisition, transportation and operating costs, and costs of damage as functions of spill volume and level of response. The model can also accept policy stipulations on response times. We present an illustrative application of the model in the New England region and discuss its possible uses within existing and alternative policy environments.

Transportation projects selection process using fuzzy sets theory

Government transportation agencies are faced with the problem of efficiently selecting a subset of transportation projects for implementation. This selection process is based on multiple objectives which are often measured in incommensurable units. Usually, the problem is treated by neglecting or biasing the qualitative characteristics of the various projects. Moreover, the usual selection methods cannot deal effectively with the decision makers’ preferences or vagueness. Fuzzy sets theory is able to cope with inexact information, and therefore is believed to be an appropriate tool for use in the projects’ selection process. This work presents an efficient technique for the selection of transportation projects using fuzzy sets theory. The selection procedure is a multiple objectives process, and projects are rated both on a quantitative and qualitative basis, using linguistic variables. In order to describe appropriately a given transportation policy, both fuzzy weighted average and noncompensatory fuzzy decision rules are used in the proposed approach. In addition, this work contains a case study of a selection process of interurban road projects in Israel. The results of the proposed method, obtained by a fuzzy expert system, are compared with the results obtained by an ordinary crisp process.

Transit Route Design Using Scheduling and Multiobjective Programming Techniques

This research provides a new and efficient approach to solve the Transit Route Design (TRD ) problem. This is considered the most complex and cumbersome problem across network route allocation problems. The wide range of the TRD characteristics creates difficulties to formulize the problem uniquely. At the same time, the TRD complexity type creates combinatorial problems, and its formulation cannot be solved via known mathematical programming approaches and packages. The suggested model deals with both its complexity and its practical issues. This is the first time that three transit operational components are being considered simultaneously: route design; timetabling (frequencies); and vehicle scheduling. The approach used has an impact on three components involved: the operator, the user, and the considered authority. The objectives of these three components do not always coincide. From the operator viewpoint, the system should minimize its expenses while, from the user perspective, the system should maximize its level-of-service. This trade-off situation creates this work’s optimization framework. The formulation of this work contains two objective functions — each to be minimized. However, it is impossible to treat both functions simultaneously, and hence, multi-objective programming is being used. This multi-objective programming technique was not used, to our knowledge, for solving the TOD problem. In fact, due to the problem complexity, the ordinary mathematical programming methods cannot be used in this technique, and therefore, a new approach is provided. This new procedure is heuristic in nature and divided into two phases: (a) generation of finite sets of alternative efficient non-inferior solutions, and (b) evaluation and selection of the various solutions using multi-objective preference techniques for discrete variables (“compromise programming” procedure). This approach enables to solve the complex TRD problem. It combines mathematical programming with decision-making methods, using search and enumeration processes while performing the optimization. Thus, it is possible to encounter relatively large-scale problems (networks) with the possibility to interact with the solution method during intermediate steps.

A procedure to adjust transit trip departure times through minimizing the maximum headway

Abstract Transit frequency is usually determined at the heavier load route segment, whereas at other segments the operation may be inefficient due to situations characterized by empty seats. The transit schedulers attempt to overcome this problem by deleting manually some departure times to allow for the initiation of certain trips beyond the beginning of the route and/or termination—before the end of the route. The outcome of this process is the reduction of the number of vehicles required to carry on the timetable. This work describes an automated procedure for the scheduler to adjust the number of departures at each route timepoint to that required from a passenger load standpoint. Given a complete timetable, the procedure reduces the number of departures with the objective to minimize the maximum headway to be obtained, where the headway is associated with passenger wait time. This minimax headway problem is solved through: 1. (i) representation of the problem on a directed network with a special pattern; 2. (ii) applying a modified shortest-path algorithm on the network to determine the minimax headway; and 3. (iii) applying an algorithm to ensure that the exact number of required departures will be included in the optimal solution. Finally, some computational results and an example are demonstrated.