Area-wide urban traffic control: A Bee Colony Optimization approach

The paper describes a new method of optimizing traffic signal settings. The area-wide urban traffic control system developed in the paper is based on the Bee Colony Optimization (BCO) technique. The BCO method is based on the principles of the collective intelligence applied by the honeybees during the nectar collecting process. The optimal (or near-optimal) values of cycle length, offsets, and splits are discovered by minimizing the total travel time of all network users travelling through signalized intersections. The set of numerical experiments is performed on well-known traffic benchmark network. The results obtained by the BCO approach are compared with the results found by Simulated Annealing (SA). It has been shown that the suggested BCO approach outperformed the SA.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

Pre-timed control for an under-saturated and over-saturated isolated intersection: a Bee Colony Optimization approach

In this paper we study the problem of determining the optimum cycle and phase lengths for isolated signalized intersections. Calculation of the optimal cycle and green phase lengths is based on the minimization of the average control delay experienced by all vehicles that arrive at the intersection within a given time period. We consider under-saturated as well as over-saturated conditions at isolated intersections. The defined traffic signal timing problem, that belongs to the class of combinatorial optimization problems, is solved using the Bee Colony Optimization (BCO) metaheuristic approach. The BCO is a biologically inspired method that explores collective intelligence applied by honey bees during the nectar collecting process. The numerical experiments performed on some examples show that the proposed approach is competitive with other methods. The obtained results show that the proposed approach is capable of generating high-quality solutions within negligible processing times.     

Disruption management in public transit: the bee colony optimization approach

Disruptions in carrying out planned bus schedules occur daily in many public transit companies. Disturbances are often so large that it is necessary to perform re-planning of planned bus and crew activities. Dispatchers in charge of traffic operations must frequently find an answer to the following question in a very short period of time: How should available buses be distributed among bus routes in order to minimize total passengers' waiting time on the network? We propose a model for assigning buses to scheduled routes when there is a shortage of buses. The proposed model is based on the bee colony optimization (BCO) technique. It is a biologically inspired method that explores collective intelligence applied by honey bees during the nectar collecting process. It has been shown that this developed BCO approach can generate high-quality solutions within negligible processing times.     

Implications of the cost of public funds in public transit subsidization and regulation

This paper identifies some implications of the cost of public funds (CF) in public transit subsidization and regulation. Regulation is considered because a monopolistic operator is assumed. A social welfare maximization model is proposed, subject to individual rationality and vehicle capacity constraints. Optimality conditions are provided and a key formula is derived about CF’s role in balancing the need to cover the fixed operation cost through fares on the operator’s side and the effort to maintain the user surplus on the passengers’ side. Major findings from this model’s formulation include: (1) CF determines the extent to which the passengers’ surplus is compromised in order to cover the fixed part of the operating cost, and (2) subsidy is unjustified when CF exceeds the critical shadow price of the financial constraint. Analytical relations are illustrated through numerical examples.     

Spatial variations in attitudes toward expanded public transit service

Although recent budgetary considerations by the Federal govenment do not portend well for urban public transit, some transit systems are considering expansion into less densely-settled areas further from the Central Business District. Of some concern to planners has been their belief that suburban and rural dwellers may be much less inclined than urban dwellers to support expansion of transit service. This paper presents an analysis of a random-digit dialing/mail-out, mail-back survey conducted in Washtenaw County, Michigan which was designed specifically to examine differences in attitudes between urban and rural residents. Six mutually-exclusive spatial strata were established based upon population density. This paper tests for expected spatial differences in socioeconomic and demographic variables and then examines spatial variations in attitudes toward public transportation. The major conclusion is that the expected spatial variations in attitudes about transit service provision between the spatial strata do not arise. Most of the significant differences found are with respect to questions which relate to where transit is provided. Residents in rural (urban) areas support more strongly the provision of services to rural (urban) areas. Many residents, however, will support transit service that may not benefit them directly.     

Subsidy and public transit performance: A factor analytic approach

The need to measure and evaluate transit system performance has led to the development of numerous performance indicators. However, depending upon the indicator, we oftentimes reach different conclusions regarding transit system performance. The research reported in this paper uses factor analytic methods to generate a set of underlying attributes (factors) that capture the performance of public transit systems in Indiana. Similar to what is reported in the literature, this study finds three attributes that best describe transit system performance: efficiency, effectiveness, and overall performance. Based upon systemsÕ factor scores, the study finds that systems scoring highly on one attribute generally perform well on the remaining attributes. Further, there is an inverse relationship between system performance and subsidies, a finding that supports performance based subsidy allocations.     

Integrating vehicle positioning data in quality control programs in public transit: a European perspective

Abstract

The purpose of this paper was to show how vehicle positioning data collected through global positioning systems (GPS) or similar applications can be used in quality control programs of public transit operators to better assess the quality and performance of transportation services, and improve them. The paper describes the concept of the integration between quality control programs and vehicle monitoring systems, presents a case study where the concept has been successfully implemented, and discusses the benefits from the adoption of such an approach. The implementation of the concept is characterized by efficiency, accuracy, reliability, and optimization: efficiency in terms of data flow; accuracy and reliability in terms of quality and performance indicator values; and optimization in terms of optimum use of the available information technology infrastructure. The paper places particular emphasis on aspects relating to the interface between urban transportation services, vehicle positioning technology, and policy-making.     

Estimating the influence of common disruptions on urban rail transit networks

With the continuous expansion of urban rapid transit networks, disruptive incidents—such as station closures, train delays, and mechanical problems—have become more common, causing such problems as threats to passenger safety, delays in service, and so on. More importantly, these disruptions often have ripple effects that spread to other stations and lines. In order to provide better management and plan for emergencies, it has become important to identify such disruptions and evaluate their influence on travel times and delays. This paper proposes a novel approach to achieve these goals. It employs the tap-in and tap-out data on the distribution of passengers from smart cards collected by automated fare collection (AFC) facilities as well as past disruptions within networks. Three characteristic types of abnormal passenger flow are divided and analyzed, comprising (1) “missed” passengers who have left the system, (2) passengers who took detours, and (3) passengers who were delayed but continued their journeys. In addition, the suggested computing method, serving to estimate total delay times, was used to manage these disruptions. Finally, a real-world case study based on the Beijing metro network with the real tap-in and tap-out passenger data is presented.     

A test of inter-modal performance measures for transit investment decisions

Choices among alternative transit capital investments are often complex and politically controversial. There is renewed interest in the use of performance indicators to assist in making rational and defensible choices for the investment of public funds. To improve the evaluation of rail and bus performance and provide more useful information for transit investment decision-makers, it is important to use performance indicators that fairly and efficiently compare different transit modes. This paper proposes a set of inter-modal performance indicators in which service input, service output, and service consumption are measured by total cost, revenue capacity miles/hours, and unlinked passenger trips/miles respectively based on economic principles and evaluation objectives. The proposed improvements involve the inclusion of capital as well as operating costs in such comparisons, and the recognition of the widely varying capacities of transit vehicles for seated and standing passengers. Two California cases, the Los Angeles – Long Beach Corridor and the Market/Judah Corridor in San Francisco, are used for testing their usefulness in the evaluation of the efficiency and effectiveness of rail and bus services. The results show substantial differences between performance indicators in current use and those proposed in this study. The enhanced inter-modal performance indicators are more appropriate for comparing the efficiency and effectiveness of different modes or a combination of transit modes at the corridor and system levels where most major investment decisions are made. This revised version was published online in June 2006 with corrections to the Cover Date.     

Estimation of origin‐destination matrices for a multimodal public transit network

This paper presents a procedure for the estimation of origin‐destination (O‐D) matrices for a multimodal public transit network. The system consists of a number of favored public transit modes that are obtained from a modal split process in a traditional four‐step transportation model. The demand of each favored mode is assigned to the multimodal network, which is comprised of a set of connected links of different public transit modes. An entropy maximization procedure is proposed to simultaneously estimate the O‐D demand matrices of all favored modes, which are consistent with target data sets such as the boarding counts and line segment flows that are observed directly in the network. A case study of the Hong Kong multimodal transit network is used to demonstrate the effectiveness of the proposed methodology.     

An analysis of public bus transit performance in Indian cities

Maintaining and enhancing public transit service in Indian cities is important, to meet rapidly growing mass mobility needs, and curb personal motor vehicle activity and its impacts at low cost. Indian cities rely predominantly on buses for public transport, and are likely to continue to do so for years. However, the public bus transit service is inadequate, and unaffordable for the urban poor. The paper explores the factors that contribute to and affect efforts to improve this situation, based on an analysis of the financial and operational performance of the public bus transit service in the four metropolitan centres and four secondary cities during the 1990s. Overall, there were persistent losses, owing to increasing input costs and declining productivity. The losses occurred despite rapidly increasing fares, and ridership declined. The situation, and the ability to address it, is worse in the secondary cities than the metropolitan centres. We suggest a disaggregated approach based on the needs and motivations of different groups in relation to public transit, along with improved operating conditions and policies to internalize costs of personal motor vehicle use, to address the challenge of providing financially viable and affordable public bus transit service.     

Efficiency of urban public transit: A meta analysis

The aim of this paper is twofold. First, to provide a statistical overview of the literature on public transit efficiency performance. Second, to statistically explain the variation in efficiency findings reported in the literature. To this end, first some key concepts of efficiency analysis will be introduced, while next the different frontier methodologies that are used in the literature will be discussed. The empirical part of this paper consists of a statistical summary of the literature as well as meta-regression analyses for different samples of the literature in order to identify key determinants of technical efficiency (TE) of public transit operators. For a broad sample of observations, we found significant and consistent effects of the type of database, region and output measurement method. For the sample of non-parametric studies we found that the type of frontier assumptions also have an impact on the efficiency ratio. Further results show that there is no statistical difference in TE ratios between parametric and non-parametric studies. Finally, we found a positive univariate relationship between the number of inputs in the estimated specification and the efficiency ratio.     

The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations

In this paper, the crowding effect in a transit vehicle is modeled in a time-expanded network that considers the daily variation in passenger flows. The study models the daily variation of in-vehicle crowding in a real large-scale transit system. A transit assignment for this real network is modeled and implemented by constructing a crowding cost function that follows the valuation of crowding and by using the reliable shortest path finding method. The direct application of the crowding model to a real network for the Utah Transit Authority indicates that crowd modeling with multi-user classes could influence public transportation system planning and affect the revenues of transit agencies. Moreover, the addition of the disutility factor, crowding, does not always appear to cause an increase in disutility for transit users.     

Locating inspection facilities in traffic networks: an artificial intelligence approach

Abstract

In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? and (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.     

Evolution of public transit modes in a commuter corridor

This paper explores how the selection of public transit modes can be optimized over a planning horizon. This conceptual analysis sacrifices geographic detail in order to better highlight the relations among important factors. First, a set of static models is proposed to identify which type of service, e.g., bus only, rail only, or bus and rail, is the most cost-effective in terms of the average trip cost for given demand. After analyzing essential factors in a long-term planning process, e.g., economies of scale in rail extension and future cost discounting, a dynamic model incorporating such considerations is formulated to optimize the decision over a planning horizon. While analytical solutions can be obtained for some decision variables, the final model is solved with a graphical method by exploring the tradeoffs between the initial and recurring costs. Major findings from this study include: (a) there exists a minimum economic length for a rail line, which can be determined numerically; (b) economies of scale favor large extensions and excess supplied capacity; (c) the rail-only service is largely dominated by the feeder-trunk service, even in the long run.     

Strategic management of organizational turnarounds in the transit industry

The traditional wisdom that there are increasing returns to scale among bus transit systems has been shaken with recent research findings. The implication from the literature is that unless many transit systems restructure along new organizational lines the financial and service provision difficulties will continue. Very few public transit systems have attempted to strategically manage change and turnaround organizationally.The objectives of this research were to identify the salient factors in organizational turnarounds and to determine whether these factors were evident within transit organizations that have attempted to manage change strategically. The author reviewed the corporate turnaround literature and conducted four case studies of strategic planning/management within the transit industry.All four cases exhibited organizational declines or perceived declines as imminent. They initiated turnarounds through reorganizations and efforts at strategic management. The reorganizations that occurred at all four cases were relatively minor, involving some changes in function. Only minor changes in management occurred and commitment to strategic management varied. Strategic objectives were not quantifiable. All of the cases could have improved their communication below the middle-management levels. The measures of performance in general did not relate a specific strategy and program to a particular turnaround effect. Through management commitment and some minor organizational restructuring two cases achieved some degree of turnaround. Total commitment to strategic management, organizational change, adequate communication, and accurate performance measures are keys to definitive turnarounds.     

Use characteristics and demographics of rural transit riders: a case study in Tennessee

Intercity bus (ICB), deviated fixed route transit (DFRT) and demand responsive transit (DRT) are three major modes of rural public transportation. This paper focuses on the characteristics and motivations of DFRT and DRT riders, compared to non-riders, in Tennessee. A rural DFRT rider survey, a rural DRT rider survey and a rural (non-rider) resident survey were performed. It is found that DFRT and DRT riders have similar demographics to ICB riders. The most common trip purpose for DFRT and DRT passengers is medical care, which is different from ICB trips. Ninety percent of the riders have difficulty finding alternative transportation modes, suggesting they are captive riders, not choice riders. Regression results indicate that people choosing transit modes tend to have lower personal and household income, own fewer cars, to not be homeowners, and be of non-white race. Rural residents who receive more education are more likely to be open-minded to use rural transit.     

Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals

This paper focuses on developing mathematical optimization models for the train timetabling problem with respect to dynamic travel demand and capacity constraints. The train scheduling models presented in this paper aim to minimize passenger waiting times at public transit terminals. Linear and non-linear formulations of the problem are presented. The non-linear formulation is then improved through introducing service frequency variables. Heuristic rules are suggested and embedded in the improved non-linear formulation to reduce the computational time effort needed to find the upper bound. The effectiveness of the proposed train timetabling models is illustrated through the application to an underground urban rail line in the city of Tehran. The results demonstrate the effectiveness of the proposed demand-oriented train timetabling models, in terms of decreasing passenger waiting times. Compared to the baseline and regular timetables, total waiting time is reduced by 6.36% and 10.55% respectively, through the proposed mathematical optimization models.