Economies of scale in bus transit: A review of concepts and evidence

The issue of returns of scale in bus transit continues to be a subject of debate among transportation analysts. From a public policy perspective, returns to scale are relevant to many policy areas such as transit service pricing, cost allocation, productivity and organization of the industry. Empirical studies conducted during the past decade have generated conflicting results. Constant, decreasing and increasing returns to scale have all been reported.This paper identifies the sources of these conflicting results: confusion regarding the concept of scale economies, variable definition, assumptions regarding the shape of the cost function, and certain characteristics of the data base. The paper also discusses the theoretical concept of scale economies, and an interpretation of the concept for bus transit is presented. It is concluded that recent studies which have utilized generalized cost functions more accurately represent the economic structure of bus transit, and provide a stronger basis for transit policy analysis.     

Accelerated Service Life Testing of Automotive Vehicles on a Test Course

The accelerated service life testing of automotive vehicles for durability to road roughness induced dynamic loads is often accomplished in the laboratory using road roughness simulation facilities [1-5]. However, such tests can also be accomplished by a carefully designed field operation on a test course [6], where both the speed of the vehicle and the roughness of die test course become variables that control the degree of the test acceleration. Field tests are generally harder to control than laboratory tests, but offer a greater degree of realism since the vehicle is fully operational during the test exactly as it will be in service. This paper formulates the criteria for accelerated service life tests on a test course, evaluates the assumptions that must be enforced to obtain valid results, and explores the sensitivity of the results to the critical test parameters, namely, the vehicle speed and the road roughness severity of the test course relative to the service environment.     

Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.

     

State-of-the art of freight forecast modeling: lessons learned and the road ahead

In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.     

A human factors systems approach to examining oil spills in tankbarge operations: analysis and reappraisal of management policies

An important problem in the US maritime industry is the large number of oil spills that occur as a result of oil transfer operations involving tankbarges. Recently, the US Coast Guard initiated a new programme entitled Prevention Through People that represents a concerted effort towards preventing human errors leading to casualities and oil spills. The purpose of the study reported in this paper was to explore tankbarge oil transfer operations from the standpoint of regulatory, management, and operational perspectives in order to determine the likelihood that this new initiative will indeed result in a better understanding of the causal basis for human error in these operations and, more generally, in the maritime industry. The results of this novel study are discussed and summarized in terms of four broad classes of recommendations.     

Relationship between proximity to transit and ridership for journey-to-work trips in Chicago

The use of privately owned vehicles (POVs) contributes significantly to US energy consumption (EC) and greenhouse gas emissions (GHGe). Strategies for reducing POV use include shifting trips to other modes, particularly public transit. Choices to use transit are based on characteristics of travelers, their trips, and the quality of competing transportation services. Here we focus on the proximity of rail stations to trip origins/destinations as a factor affecting mode choice for work trips. Using household travel survey data from Chicago, we evaluate the profile of journey-to-work (JTW) trips, assessing mode share and potential for more travelers to use rail. For work trips having the origin/destination as close as 1 mile from rail transit stations, POVs were still the dominant travel mode, capturing as much as 61%, followed by rail use at 14%. This high degree of POV use coupled with the proportion of JTW trips within close proximity to rail stations indicated that at least some of these trips may be candidates for shifting from POV to rail. For example, shifting all work trips with both the origin/destination within 1 mile of commuter rail stations would potentially reduce the energy associated with all work-related POV driving trips by a maximum of 24%. Based on the analysis of trips having the origin and destination closest to train stations, a complete shift in mode from POV to train could exceed CO₂ reduction goals targeted in the Chicago Climate Action Plan. This could occur with current settlement patterns and the use of existing infrastructure. However, changes in traveler behavior and possibly rail operation would be necessary, making policy to motivate this change essential.     

The applicability of non-cooperative game theory in transport analysis

Various models that incorporate concepts from Non-Cooperative Game Theory (NCGT) are described in the transport literature. Game Theory provides powerful tools for analysing transport systems, but these tools have some drawbacks that should be recognised. In the current paper we review games that describe transport problems and discuss them within a uniform context. Although the paper does not introduce new tools, it presents insights concerning the relations between transport models and games. We divide existing games into groups and show that some common features characterise multiple games. We distinguish between games that make a conceptual contribution and games that are suitable for application. Compact or symmetric game structures make remarkable observations but often do not support actual decision-making. Less aesthetic formats, most of which are Stackelberg games between authorities and travellers, are stronger as instruments that assist in determining real-life policies; these formulations can be treated by practitioners as mathematical programs with equilibrium constraints and not as games. Yaron Hollander is currently conducting economic research of bus reliability at the Institute for Transport Studies at the University of Leeds. He previously worked for the Technion—Israel Institute of Technology; for the Israel Institute for Transportation Planning and Research; and for the public transport department at Ayalon Highways Co. Joseph N. Prashker is a professor at the Faculty of Civil and Environmental Engineering at the Technion—Israel Institute of Technology. Till recently he served as head of the Transportation Research Institute at the Technion. His interests are behavioural demand models, network analysis, and Game Theory applications in transportation.     

Empirical analysis of transportation investment and economic development at state, county and municipality levels

Numerous studies have found positive correlation between transportation infrastructure investment and economic development. Basically these studies use a conventional production function model augmented by a public capital input, mainly highways, rail and other transportation facilities. While the range of the measured economic growth effects varies widely among studies, the positive elasticity between transportation investment and economic development is now commonly accepted. Still a major puzzling issue is that the magnitude of the measured effect seems to decline significantly as the econometric model is further refined, mainly with regard to space and time lags. That is, the use of national or state data produces elasticity results, which are much larger than when using county or municipality data. Similarly, when we introduce into the econometric model a lag between the times when the transportation investments are made and when the economic benefits transpire, the measured elasticities decline with the size of the lag. Thus, the main objective of this paper is to investigate these issues analytically and empirically and provide a plausible explanation. We do so by using alternative econometric models, applying them to a database, which is composed of longitudinal state, county and municipality observations from 1990 to 2000. The key result is that transportation investments produce strong spillover effects relative to space and time. Unless these factors are properly accounted for many reported empirical results are likely to be overly biased, with important policy implications.     

Integrating geographic information systems into transit ridership forecast models

Researchers have produced sophisticated modal split and transit demand models, including forecasts that are sensitive to the level of service. However, little effort has been made to integrate these models into corridor studies and route alignment analyses since (a) re-routing is itself an extremely complex modeling task, and (b) the results of the demand models are presented in tabular form with no facility to visualize spatial patterns and relationships that, if recognized, would aid in the routing tasks. GIS tools can be used, together with the demand models, to identify both clusters of city blocks that house families with certain socioeconomic characteristics and potential trip destinations conducive to transit use. In other words, GIS tools can be used to better measure some of the factors that are needed by transit demand models. The results of these models can be displayed graphically, enabling analysts to target places needing improved service, evaluate route re-alignment alternatives, and operate more efficient and effective bus lines. This paper examines how a particular class of model used by transit agencies for estimating ridership can be integrated with GIS tools in order to facilitate such analyses. It also explores the effects of visualization of routes, demographics, and employment data on the process of designing route alignments with better targeting of high transit ridership areas. This paper is part of a research project sponsored by the Region One University Transportation Center, at MIT.     

The transaction cost approach to modeling the demand for automobiles

The purchase of an automobile involves significant transaction costs in addition to the purchase price. Therefore, the assumptions implied by static car holding models are invalid. This paper describes a dynamic approach to the modeling of level-of-ownership and auto-type choice, based on a transaction choice model which utilizes information on past car ownership. The cost or disutility of a transaction depends on the attributes of the household and the purchased car, as well as on past car-ownership characteristics. A set of assumptions underlying the incorporation of transaction costs in the model is presented. The paper discusses the econometric implications of omitting the dynamic attributes (i.e., past ownership characteristics). A disaggregate model was estimated, using a choice-based sample consisting of a random sample of households enriched with a sample of households which transacted in the car market during the study period. This sampling method combined with a random choice of a subset of the car alternatives provides for a cost-effective method to estimate a transaction model.