道路运输企业质量评价体系研究

文章分析了现有道路运输企业质量评价体系的不足,提出了新的道路评价方法,为将来的道路运输管理和规划提供了一个基础。     

大跨度连续刚构桥施工期截面应力测试与分析

广西布柳河大桥主桥为主跨235m、墩高97米、桥宽仅8.3米的特柔预应力混凝土连续刚构桥。文章结合主桥施工监控实践,介绍大跨高墩连续刚构桥施工期截面应力测试及数据分析的方法,为同类桥型施工提供参考。     

Emergence of resilience as a framework for state Departments of Transportation (DOTs) in the United States

State Departments of Transportation (DOTs) in the United States are responsible for a large portfolio of transportation modes and services, including passenger and freight systems. These responsibilities include operations under routine conditions and during incidents and events that result from various natural and human-caused hazards. During unexpected events, disruptions and reductions in service result in requiring the reallocation and reassignment of personnel, modal, and economic resources. To better prevent and respond to the effects of service disruptions, the concept of resilience has emerged as an important framework, within which, DOTs across the United States are using to plan for the occurrence of threats. In this paper, the key findings of recent reviews of literature and practice related to resilience among state DOTs in the United States are summarized. The review effort focused on a range of risks faced by transportation agencies including climate change, terrorism, cyber-attacks, and aging infrastructure and the ways in which DOTs are confronting them in practice. The topics of this paper range from the fundamental, including definitions of transportation resilience; to the more complex such as examinations of risk, vulnerability and threats; to the most sophisticated topics including administrative-level efforts to conceptualize evolving transportation planning and policies within a resilience framework.     

Examining the influence of multidestination service orientation on transit service productivity: a multivariate analysis

Between 1990 and 2000, U.S. transit agencies added service and increased ridership, but the ridership increase failed to keep pace with the service increase. The result was a decline in service effectiveness (or productivity). This marks the continuation of a long-running and often-studied trend. The scholarly literature attributes this phenomenon, at least in part, to transit agency decisions to decentralize their service rather than focus on serving the traditional CBD market. Many scholars argue that a decentralized service orientation is both ineffective and inefficient because it attracts few riders and requires large per-rider subsidies. This research tests whether a non-traditional, decentralized service orientation, called multidestination service, results in reduced service productivity. Contrary to what the literature suggests, we find that MSAs whose transit agencies pursued a multidestination service orientation did not experience lower productivity. These results indicate that policies that have encouraged the growth of decentralized transit services have not necessarily been detrimental to the industry.

The role of transportation sectors in the Korean national economy: An input-output analysis

The transportation industry has been playing an important role in the economic development of Korea and, thus, has become a critical factor in sustaining the well-being of the Korean people. This paper attempts to analyze the economic impacts of four transportation modes using input-output (I-O) analysis, with specific application to Korea. To this end, we apply the I-O models to the Korean I-O tables generated by the Bank of Korea, paying particular attention to the four transportation sectors in Korea (rail, road, water, and air transportations), considering them as exogenous, and then determining their impacts. Specifically, the production-inducing effects, supply shortage effects, sectoral price effects, forward linkage effects, and backward linkage effects of the four transportation modes are quantitatively derived over the period 2000–2010. For example, the production-inducing effect of a KRW 1.0 production or investment in transportation is larger in the petroleum and transportation equipment sectors than in other sectors. Furthermore, the rail and road transportation sectors have greater supply shortage effects than the other transportation sectors. Finally, the potential uses of the results of this analysis are presented from the perspective of policy instruments, and policy implications are discussed.     

Mining missing train logs from Smart Card data

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gate movement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed S-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

Factors influencing support for local transportation sales tax measures

Sales tax measures passed at the local level and dedicated to transportation projects have become increasingly popular in the United States. While revenues from fuel taxes stagnate, growth of local transportation sales taxes (LTSTs), most approved in local elections, has led to a gradual shift of the financial base for transportation projects away from user fees and toward broader-based taxes. In this study, the relationship between voter support and the social, political, and geographic characteristics of the voters is explored. Using precinct-level voting data and census demographic data for three local transportation sales tax elections in Sonoma County, in the San Francisco Metropolitan area of California, regression models were constructed to analyze this relationship. In addition, the relationship between the outcomes of the three measures was explored to better understand which transportation projects might have garnered more support for the successful measure. It was found that the closer voters lived to the transportation projects to be funded, the greater their support. Higher incomes were also positively related to support, controlling for other variables. Political leanings were found to affect support, with the direction of the effect dependent upon the project list in each measure’s expenditure plan. Finally, it appears that the latest measure, which passed successfully, benefited greatly from its multi-modal expenditure plan.     

Identifying preferences for public transport investments under a constrained budget

As urban areas face increasing demands for new transport infrastructure to promote a sustainable future with an increasing reality of constrained government budgets, the debate on whether we should focus on rail or bus-based investments continues unabated in many jurisdictions. Associated with the debate is an emotional (or ideological) bias by communities in favour of one mode, especially rail, which carries much sway at the political level as if there is no budget constraint. This paper presents a stated choice experiment to investigate this context as two unlabelled options described by 20 potential drivers of community preferences for improved public transport, where each choice scenario is conditioned on an estimated construction cost and a total annual transport infrastructure budget for the relevant geographical jurisdiction. This is followed by a labelling of each alternative to reveal whether the option is bus rapid transit (BRT) or light rail (LRT) and to establish whether this additional information influences preference revision. Data is collected in all eight capital cities of Australia in mid 2014. Mixed logit models with heteroscedastic conditioning in terms of the cost of the project infrastructure and whether the alternative is labelled BRT or LRT, provide new evidence on the nature and extent of community modal bias in a budget-constrained choice setting. The conclusions are twofold. On the one hand, if a fully compensatory choice rule is assumed (as is common in all previous modal comparison studies), LRT is predominantly preferred over BRT despite budgetary constraints, similarities in quality of service attributes and the opportunity to choose a greater network coverage for a given construction cost. However, when we allow for attribute non-attendance (a semi-compensatory choice rule), the modal bias is no longer a significant driver of preferences.     

Factors affecting public transportation usage rate: Geographically weighted regression

As the number of private vehicles grows worldwide, so does air pollution and traffic congestion, which typically constrain economic development. To achieve transportation sustainability and continued economic development, the dependency on private vehicles must be decreased by increasing public transportation usage. However, without knowing the key factors that affect public transportation usage, developing strategies that effectively improve public transportation usage is impossible. Therefore, this study respectively applies global and local regression models to identify the key factors of usage rates for 348 regions (township or districts) in Taiwan. The global regression model, the Tobit regression model (TRM), is used to estimate one set of parameters that are associated with explanatory variables and explain regional differences in usage rates, while the local regression model, geographically weighted regression (GWR), estimates parameters differently depending on spatial correlations among neighbouring regions. By referencing related studies, 32 potential explanatory variables in four categories, social-economic, land use, public transportation, and private transportation, are chosen. Model performance is compared in terms of mean absolute percentage error (MAPE) and spatial autocorrelation coefficient (Moran’ I). Estimation results show that the GWR model has better prediction accuracy and better accommodation of spatial autocorrelation. Seven variables are significantly tested, and most have parameters that differ across regions in Taiwan. Based on these findings, strategies are proposed that improve public transportation usage.