Estimating the marginal cost of railway track renewals using corner solution models

Economic theory advocates marginal cost pricing for efficient utilisation of transport infrastructure. A growing body of literature has emerged on the issue of rail marginal infrastructure wear and tear costs, but the majority of the work is focused on costs for infrastructure maintenance. Railway track renewals are a substantial part of an infrastructure manager’s budget, but in disaggregated statistical analyses they cause problems for traditional regression models since there is a piling up of values of the dependent variable at zero. Previous econometric work has sought to circumvent the problem by aggregation in some way. In this paper we instead apply corner solution models to disaggregate (track-section) data, including the zero observations. We derive track renewal cost elasticities with respect to traffic volumes and in turn marginal renewal costs using Swedish railway renewal data over the period 1999–2009. This paper is the first attempt in the literature to apply corner solution models, and in particular the two-part model, to disaggregate renewal cost data in railways. It is also the first paper that we are aware of to report usage elasticities specifically for renewal costs and therefore adds important new evidence to the previous literature where there is a paucity of studies on renewals and considerable uncertainty over the effects of rail traffic on renewal costs. In the Swedish context, we find that the inclusion of marginal track renewal costs in the track access pricing regime, which currently only reflects marginal maintenance costs, would add substantially to the existing track access charge. EU legislation requires that access charges reflect the ‘costs directly incurred as a result of operating the train service’, which should include a marginal renewal cost component. This change would also increase the cost recovery ratio of the Swedish infrastructure manager, thus meeting a policy objective of the national government.     

Stochastic frontier analysis of excess access to mid-trip battery electric vehicle fast charging

This study aims to explore how factors including charging infrastructure and battery technology associate the way people currently charge their battery electric vehicles, as well as to explore whether good use of battery capacity can be encouraged. Using a stochastic frontier model applied to panel data obtained in a field trial on battery electric vehicle usage in Japan, the remaining charge when mid-trip fast charging begins is treated as a dependent variable. The estimation results obtained using four models, for commercial and private vehicles, respectively, on working and non-working days, show that remaining charge is associated with number of charging stations, familiarity with charging stations, usage of air-conditioning or heater, battery capacity, number of trips, Vehicle Miles of Travel, paid charging. However, the associated factors are not identical for the four models. In general, EVs with high-capacity batteries are initiated at higher remaining charge, and so are the mid-trip fast charging events in the latter period of this trial. The estimation results also show that there are great opportunities to encourage more efficient charging behavior. It appears that the stochastic frontier modeling method is an effective way to model the remaining charge at which fast-charging should be initiated, since it incorporates trip and vehicle characteristics into the estimation process to some extent.     

Bikeshare trip generation in New York City

Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine-grained approach. We estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual). We find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. Our findings are nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non-working days; however, the effect is much stronger on non-working days. Additional nuances can be found in how various land use variables affect bikeshare usage. We use our models, based on 2014 data, to forecast the trips generated at new stations opened in 2015. Results suggest there is large variation in predictive power, partly caused by variation in weather, but also by other factors that cannot be predicted. This leads us to the conclusion that the nuances we find in our inferential analysis are more useful for transportation planners.     

The impact of rail access on condominium prices in Hamburg

Using hedonic price functions, we study the influence of access to public railway stations on the prices of surrounding condominiums in Hamburg, Germany. The study examines the influence of rail infrastructure on residential property prices, not only of individual lines, but for the entire rail network of a metropolitan region. We test the stability of the coefficients for different sets of control variables. The study also estimates public-transit-induced increases in tax revenues due to real estate price increases for a study area outside the United States. We control for spatial dependence and numerous variables correlated with the proximity of railway stations and show that access to the public transit system of the city of Hamburg is to be rated with price increases of up to 4.6%. Such premiums for higher-income neighbourhoods and for subterranean stations tend to be higher. The premiums calculated are significantly lower than average price premiums reported in previous studies, which were mostly based on much fewer variables that rail access might be correlated to.     

Examining determinants of rail ridership: a case study of the Orlando SunRail system

The current study contributes to the literature on transit ridership by considering daily boarding and alighting data from a recently launched commuter rail system in Orlando, Florida – SunRail. The analysis is conducted based on daily boarding and alighting data for 10 months for the year 2015. With the availability of repeated observations for every station, the potential impact of common unobserved factors affecting ridership variables are considered. The current study develops an estimation framework, for boarding and alighting separately, that accounts for these unobserved effects at multiple levels – station, station-week and station-day. In addition, the study examines the impact of various observed exogenous factors such as station level, transportation infrastructure, transit infrastructure, land use, built environment, sociodemographic and weather variables on ridership. The model system developed will allow us to predict ridership for existing stations in the future as well as potential ridership for future expansion sites.     

Estimating bike-share trips using station level data

Bicycle sharing systems (BSS) have increased in number rapidly since 2007. The potential benefits of BSS, mainly sustainability, health and equity, have encouraged their adoption through support and promotion by mayors in Europe and North America alike. In most cases municipal governments desire their BSS to be successful and, with few exceptions, state them as being so. New technological improvements have dramatically simplified the use and enforcement of bicycle return, resulting in the widespread adoption of BSS. Unfortunately little evaluation of the effectiveness of differently distributed and managed BSS has taken place. Comparing BSS systems quantitatively is challenging due to the limited data made available. The metrics of success presented by municipalities are often too general or incomparable to others making relative evaluations of BSS success arduous. This paper presents multiple methodologies allowing the estimation of the number of daily trips, the most significant measure of BSS usage, based on data that is commonly available, the number of bicycles available at a station over time. Results provide model coefficients as well as trip count estimates for select cities. Of four spatial and temporal aggregate models the day level aggregation is found to be most effective for estimation. In addition to trip estimation this work provides a rigorous formalization of station level data and the ability to distinguish spatio-temporal rebalancing quantities as well as new characteristics of BSS station use.     

An integrated model for discrete and continuous decisions with application to vehicle ownership,type and usage choices

This paper proposes an integrated econometric framework for discrete and continuous choice dimensions. The model system is applied to the problem of household vehicle ownership, type and usage. A multinomial probit is used to estimate household vehicle ownership, a multinomial logit is used to estimate the vehicle type (class and vintage) choices, and a regression is used to estimate the vehicle usage decisions. Correlation between the discrete (number of vehicles) and the continuous (total annual miles traveled) parts is captured with a full variance–covariance matrix of the unobserved factors. The model system is estimated using Simulated Log-Likelihood methods on data extracted from the 2009 US National Household Travel Survey and a secondary dataset on vehicle characteristics. Model estimates are applied to evaluate changes in vehicle holding and miles driven, in response to the evolution of social societies, living environment and transportation policies.     

Impacts of Transport Infrastructure on Productivity and Economic Growth: Recent Advances and Research Challenges

ABSTRACT

The paper provides an update of the survey focusing on estimating the contribution of transport infrastructure to productivity and economic growth. The central questions addressed are possible reasons behind the conflicting results reported in the literature on the elasticity of economic output with respect to transport infrastructure investment. After providing a systematic review of recent empirical studies on the effects of transport infrastructure on productivity and economic growth, the paper notes that controversial results can be attributed to ten causes (grouped into three categories for distinguishing): (1) related to different contexts: research period, geographical scales, and country's capability in enabling economic development; (2) related to different phenomena that are being measured: different economic sectors, different types of transport infrastructure, and different quality levels of transport infrastructure; and (3) related to distinct ways of measuring a similar phenomenon: measures used to describe the dependent variable and explanatory variable, functional specification, and estimation method of the econometric model. Strong network externalities of transport infrastructure may result in nonlinearity of the relationship between transport infrastructure and economic growth. Moreover, the absence of spatial concerns in infrastructure's impacts is another important source of inconclusive results. Finally, building on recent literature, the paper has discussed policy implications and identified several research avenues for further research.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

Adding temporal information to direct-demand models: Hourly estimation of bicycle and pedestrian traffic in Blacksburg,VA

Cycling and walking are environmentally-friendly transport modes, providing alternatives to automobility. However, exposure to hazards (e.g., crashes) may influence the choice to walk or cycle for risk-averse populations, minimizing non-motorized travel as an alternative to driving. Most models to estimate non-motorized traffic volumes (and subsequently hazard exposure) are based on specific time periods (e.g., peak-hour) or long-term averages (e.g., Annual Average Daily Traffic), which do not allow for estimating hazard exposure by time of day. We calculated Annual Average Hourly Traffic estimates of bicycles and pedestrians from a comprehensive traffic monitoring campaign in a small university town (Blacksburg, VA) to develop hourly direct-demand models that account for both spatial (e.g., land use, transportation) and temporal (i.e., time of day) factors. We developed two types of models: (1) hour-specific models (i.e., one model for each hour of the day) and (2) a single spatiotemporal model that directly incorporates temporal variables. Our model results were reasonable (adj-R² for the hour-specific [spatiotemporal] bicycle model: ∼0.47 [0.49]; pedestrian model: ∼0.69 [0.72]). We found correlation among non-motorized traffic, land use (e.g., population density), and transportation (e.g., on-street facility) variables. Temporal variables had a similar magnitude of correlation as the spatial variables. We produced spatial estimates that vary by time of day to illustrate spatiotemporal traffic patterns for the entire network. Our temporally-resolved models could be used to assess exposure to hazards (e.g. air pollution, crashes) or locate safety-related infrastructure (e.g., striping, lights) based on targeted time periods (e.g., peak-hour, nighttime) that temporally averaged estimates cannot.     

A combined destination and route choice model for a bicycle sharing system

This paper studies the supply variables that influence the destination and route choices of users of a bicycle sharing system in the Chilean city of Santiago. A combined trip demand logit model is developed whose explanatory variables represent attributes relating to the topology of the possible routes and other characteristics such as the presence of bikeways, bus service and controlled intersections. The data for the explanatory variables and system users were collected through field surveys of the routes and interviews conducted at the system stations. The results of the model show that proximity to stops on the Santiago Metro and the existence of bikeways are the main factors influencing destination and route choices. Also indicated by the model estimates are gender differences, a preference for tree-lined routes and an avoidance of routes with bus services. Finally, the outcomes reveal considerable potential for the integration of bicycle sharing systems with Metro transit.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.     

A spatial autoregressive panel model to analyze road network spillovers on production

The production function approach is used to introduce the effect of public infrastructure on economic growth focusing on its spillover effects. We improve the existing literature both from a conceptual and methodological perspective. As regressors we incorporate variables related to the new concepts of internal and imported transport infrastructure capital stocks, which are actually used in commercial flows, calculated by network analysis performed in GIS. The internally used capital stock represents own infrastructure that benefits accessing markets within the region itself, while the imported capital stock captures the spillover effect associated to the use of the infrastructure situated in neighboring regions. From a methodological perspective, we introduce spatial interdependence into these models, applying the most recent spatial econometric techniques based on instrumental variables estimation in spatial autoregressive panel models in comparison with Maximum Likelihood estimation methods. We illustrate the methodology with Spanish provincial panel data for the period 1980–2007. Results support the hypothesis that the imported capital has a positive spillover effect on production.     

Accessibility and transport infrastructure improvement assessment: The role of borders and multilateral resistance

The market potential indicator is a commonly used tool in transport planning for evaluating the potential economic effects derived from improvements in transport infrastructures. The general assumption is that exports from a given region will rise with increased accessibility, thus benefiting economic activities. However, the specification of the market potential model is typically very simple and ignores both the impact of competing rivals and the role of international borders, which leads to unrealistic results. Spatial interaction models on bilateral trade have already proved that international trade is affected by multilateral resistance, borders, adjacency, language or currency. Nevertheless, apart from some recent analyses that simply calibrate the distance decay parameter from trade datasets, these variables have hardly been integrated into research on market potential. This paper sets out to demonstrate that more realistic results are obtained by calibrating the distance-decay parameter and introducing the impact of competing rivals and border effects into the market potential formulation. The proposed model is then applied to the assessment of the accessibility impacts of new road transport infrastructure in the European Union between 2001 and 2012, which shows that the greatest improvements in accessibility were experienced by peripheral countries with high road infrastructure investment.     

Dynamic model of activity-type choice and scheduling

This paper presents a model for the choice of activity-type and timing, incorporating the dynamics of scheduling, estimated on a six-week travel diary. The main focus of the study is the inclusion of past history of activity involvement and its influence on current activity choice. The econometric formulation adopted, explicitly accounts both for correlation across alternatives and for state dependency. The results indicate that behavioral variables are superior to socio-economic variables and that consideration of the correlation pattern over alternatives clearly improves the fit of the model. This is a first but significant contribution to changing the current static demand models into dynamic activity based ones. The availability of other multi-week travel surveys and the progress made recently on advanced econometric techniques should encourage the transferability of this study to different regions or model scale.     

Who cycles more? Determining cycling frequency through a segmentation approach in Montreal,Canada

The decision to cycle frequently in an urban setting is a complex process and is affected by a variety of factors. This study analyzed the various factors influencing cycling frequency among 1707 cyclists from Montreal, Canada using an ordinal logistic regression. A segmentation of cyclists is used in a series of ordinal logistic models to better understand the different impacts of variables on the frequency of cycling among each group of cyclists for commute and for utilitarian purposes. Our models show a variation in the impacts of each dependent variable on frequency of cycling across the various segments of cyclists. Mainly making cyclists feel safe not only on bicycle specific infrastructure but also on regular streets, emphasizing the low cost, convenience and improving the opinion on cycling in the population are effective interventions to increase bicycle usage. Also, it was shown that women were less likely to cycle to work than men, but more likely to cycle for other utilitarian trips, pointing at the presence of specific barriers to commuting for woman. Although the findings from this study are specific to Montreal, they can be of interest to transportation planners and engineers working toward increasing cycling frequency in other regions.     

Forecasting Freight Flows

Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment. This article models long-term dynamic physical trade flows and estimates a dynamic panel data model for foreign trade for the EU15 and two countries from the EFTA (European Free Trade Association) 1967–2002. The analysis suggests that a dynamic three-way-effects gravity equation is the best-fitted econometric model. The analysis uses a structural relationship to explain the structure of the exchange of the goods—a relationship that can be used in the year of forecast. This article also provides a new methodology for converting monetary aggregates into quantity aggregates. The resulting commodity growth rates constitute a valuable input to freight models for forecasting future capacity problems.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model

The focus of the current research was to evaluate how the individual’s social characteristics and urban infrastructure impacts the usage of Private Motorized Modes (PMM). Based on individual and urban characteristics a multilevel analysis was conducted on the possibility of commuting trip by private motorized modes on the rush time of 78 cities around the world. Also the selected cities were classified through a principal component analysis, and based on the classification the impact of and urban variables on the possibility of commuting trips made by private motorized modes (PCTP) was verified. Results showed a diverse range of variables related to the usage of PMM, as well as the urban structure and railway lengths being an important variable in travel behavior.     

Estimation of the determinants of bicycle mode share for the journey to work using census data

A model is presented that relates the proportion of bicycle journeys to work for English and Welsh electoral wards to relevant socio-economic, transport and physical variables. A number of previous studies have exploited existing disaggregate data sets. This study uses UK 2001 census data, is based on a logistic regression model and provides complementary evidence based on aggregate data for the determinants of cycle choice. It suggests a saturation level for bicycle use of 43%. Smaller proportions cycle in wards with more females and higher car ownership. The physical condition of the highway, rainfall and temperature each have an effect on the proportion that cycles to work, but the most significant physical variable is hilliness. The proportion of bicycle route that is off-road is shown to be significant, although it displays a low elasticity (+0.049) and this contrasts with more significant changes usually forecast by models constructed from stated preference based data. Forecasting shows the trend in car ownership has a significant effect on cycle use and offsets the positive effect of the provision of off-road routes for cycle traffic but only in districts that are moderately hilly or hilly. The provision of infrastructure alone appears insufficient to engender higher levels of cycling.