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In many developed countries, great effort is being made to encourage bicycle commuting. The main concerns there are bicyclist characteristics and deterrents and incentives to bicycling, so as to mitigate traffic congestion and reduce energy consumption and environmental pollution (2-18). Given the experiences of developed countries during the motorization process, however, it will be difficult to encourage people to switch to bicycle commuting now that they are accustomed to commuting by POVs.As a first step to prevent such a problem in developing countries, this paper tries to evaluate factors that affect the Chinese commuter's decision to use a bicycle. The factors considered in the research included commuter demographic characteristics, bicycling-related perceived benefits, and trip distance. Odds ratio (OR) statistics were used to analyze the effect of each factor on bicycle commuting separately. Two binomial logit (BL) models were developed to quantitatively describe how these factors affected commuter decision making. Specifically, the authors used several latent variables, distilled from a number of indicator variables, as parts of the explanatory variables in one of the BL models. The study also provided some comparisons of results between developed countries and China, and some policy implications for urban transportation planning.The paper is organized as follows. Existing literature related to this topic is presented, followed by an introduction to the current study's methodology. The data collection process and data descriptive analysis are then set out. The OR and BL model estimation results are provided and the findings discussed. The paper ends with concluding remarks and proposes future work.

Establishing the tolerance limits of patient-specific quality assurance (PSQA) processes based on the gamma passing rate (GPR) by using normal statistical process control (SPC) methods involves certain problems. The aim of this study was threefold: (a) to show that the heuristic SPC method can replace the quantile method for establishing tolerance limits in PSQA processes and is more robust, (b) to introduce an iterative procedure of "Identify-Eliminate-Recalculate" for establishing the tolerance limits in PSQA processes with unknown states based on retrospective GPRs, and (c) to recommend a workflow to define tolerance limits based on actual clinical retrospective GPRs. Materials and Methods: A total of 1671 volumetric-modulated arc therapy (VMAT) pretreatment plans were measured on four linear accelerators (linacs) and analyzed by treatment sites using the GPRs under the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Normality testing was performed using the Anderson-Darling (AD) statistic and the optimal distributions of GPRs were determined using the Fitter Python package. The iterative "Identify-Eliminate-Recalculate" procedure was used to identify the PSQA outliers. The tolerance limits of the initial PSQAs, remaining PSQAs after elimination, and in-control PSQAs after correction were calculated using the conventional Shewhart method, two transformation methods, three heuristic methods, and two quantile methods. The tolerance limits of PSQA processes with different states for the respective methods, linacs, and treatment sites were comprehensively compared and analyzed. Results: It was found that 75% of the initial PSQA processes and 63% of the in-control processes were non-normal (AD test, p < 0.05). The optimal distributions of GPRs for the initial and in-control PSQAs varied with different linacs and treatment sites. In the implementation of the "Identify-Eliminate-Recalculate" procedure, the quantile methods could not identify the out-of -control PSQAs effectively due to the influence of outliers. The tolerance limits of the in-control PSQAs, calculated using the quantile of optimal fitting distributions, represented the ground truth. The tolerance limits of the in-control PSQAs and remaining PSQAs after elimination calculated using the heuristic methods were considerably close to the ground truth (the maximum average absolute deviations were 0.50 and 1.03%, respectively). Some transformation failures occurred under both transformation methods. For the in-control PSQAs at 3%/2 mm gamma criteria, the maximum differences in the tolerance limits for four linacs and different treatment sites were 3.10 and 5.02%, respectively.

Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Meanwhile, they are also one of the most fragile parts in railway systems. Points failures cause a large portion of railway incidents. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, minimising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorithm compared with other state-of-the-art methods.

Failure prediction is very important for railway infrastructure. Traditionally, data from various sensors are collected for this task. Value of maintenance logs is often neglected. Maintenance records of equipment usually indicate equipment status. They could be valuable for prediction of equipment faults. In this paper, we propose Fieldregularised Factorization Machines (FrFMs) to predict failures of railway points with maintenance logs. Factorization Machine (FM) and its variants are state-of-the-art algorithms designed for sparse data. They are widely used in click-through rate prediction and recommendation systems. Categorical variables are converted to binary features through one-hot encoding and then fed into these models. However, field information is ignored in this process. We propose Field-regularised Factorization Machines to incorporate such valuable information. Experiments on data set from railway maintenance logs and another public data set show the effectiveness of our methods.

Old town fringe area refers to a surrounding zone of an old town. The fringe undertakes the important function of evacuating traffic volume between suburb districts and central city. Traffic flow and travel behavior of the old town fringe are complex and different from other areas. Thus, recognition of fringe area is of importance for researchers to better understand the unique travel features and propose proper policies for fringe renewal. This study took the city of Nanjing as an example and fused the residents' travel survey data and the point-of-interest data for fringe recognition. The study estimated the travel intensity of each travel analysis zone per day. The mutation point of travel intensity was decided to divide the fringe. The point-of-interest data was used for validating the boundaries of the fringe. The fusion of the two data sets jointly decided the core old town fringe area. The travel behavior characteristics of the fringe area, including the fringe internal trips, crossing fringe trips, and those with only origin or destination in the fringe, were then evaluated and policy suggestions were provided. The findings of the study will benefit the urban space planning and coordinated transportation system development in the fringe area of old towns.

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