Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models

This study aimed to improve the spatial and temporal transferability of the real-time crash risk prediction models by using the Bayesian updating approach. Data from California’s I-880N freeway in 2002 and 2009 and the I-5N freeway in 2009 were used. The crash risk models for these three datasets are quite different from each other. The model parameters do not remain stable over time or space. The transferability evaluation results show that the crash risk models cannot be directly transferred across time and space. The updating results indicate that the Bayesian updating approach is effective in improving both spatial and temporal transferability even when new data are limited. The predictive performance of the updated model increases with an increase in the sample size of the new data. In addition, when limited new data are available, updating an existing model is better than developing a model using the limited new data.     

Public transport demand estimation by calibrating a trip distribution-mode choice (TDMC) model from passenger counts: A case study in Bandung,Indonesia

A problem always found in developing countries is the lack of information required for short, medium and long term planning purposes due to money and time constraints. This becomes even more valuable for problems which require ‘quick-response’ treatment. A flexible model approach allows monitoring a long term plan in order to check its short term performance at regular intervals using easily-available data. If found necessary, changes to the plan may be evaluated and eventually implemented. For this reason, the approach is deemed appropriate for long term planning and project evaluation even in the case of rapid changes in land-use, socio-economic and population parameters usually occurs in most of developing countries. A key element of the approach is a system to update the forecasting model (in particular its trip distribution and mode choice elements) using low-cost and/or easily-available information. Traffic counts are particularly attractive to be used in developing countries for planning purposes. The estimation of public transport demand, particularly important for planning purposes, is an expensive and time consuming undertaking. The need for a low-cost method to estimate the public transport demand is therefore obvious. The objective of this paper is the development of methods and techniques for modelling the public transport demand using traffic (passenger) count information and other simple zonal-planning data. We will report on a family of aggregate model combined with a family of mode choice logit models which can be calibrated from traffic (passenger) counts and other low-cost data. The model examined was the Gravity (GR) model combined with the Multi-Nominal-Logit (MNL) model. Non-Linear-Least-Squares (NLLS) estimation method was used to calibrate the parameter of the combined model. The combined TDMC model and the calibration method have been implemented into a micro-computer package capable of dealing with the study area consisting of up to 300 zones, 3000 links and 6000 nodes. The approach has been tested using the 1988 Public Transport Data Survey in Bandung (Indonesia). The model was found to provide a reasonably good fit and the calibrated parameter can then be used for forecasting purposes. General conclusion regarding the advantageous and the applicability of the approach to other environments are given.     

Comparison of updating techniques in transferability analysis of work trip mode choice models in developing countries

This study analyzes the performances of updating techniques in transferability of mode choice models in developing countries. A model specification, estimated in Ho Chi Minh City, was transferred to Phnom Penh. Naïve transfer and four updating methods associated with small sized samples were used in the transfer process and were evaluated based on statistical perspective and predictive ability. The study also illustrates the problems faced in model transferability development, due to the lack of available and suitable data in Phnom Penh. This lack is strongly related to different methods and structures applied in collecting the data. Simplified approaches to the difficulties are proposed in the study. The results show that updating ASCs, updating both ASCs and scale parameter, and use of combined transfer estimators all produce significant improvement, both statistically and in predictability, in updating the model. The last two methods have proven to be superior to the first method, owing to the inclusion of transfer bias considerations in the estimations. However, small data samples should not have large transfer bias when using combined transfer estimators. It is also concluded that naïvely transferring a model is not recommended, and Bayesian updating should be avoided when transfer bias exists. Copyright © 2010 John Wiley & Sons, Ltd.     

An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non- working days

The behavior of time allocation to two types of discretionary activities is formulated as a doubly-censored Tobit model. The model is capable of incorporating cases where the entire amount of time available for discretionary activity is allocated to one type of activity and the other type of activity is not engaged at all. The model is applied to examine individuals' allocation of time to in-home and out-of-home discretionary activities on working days and non-working days, using a weekly time-use data set from the Netherlands. Workers' daily activity patterns vary significantly between working days and non-working days, while it can be expected that patterns of time allocation are correlated between working days and non-working days. A set of error components is introduced into the model to represent this correlation, adopting a mass point approach which requires no assumption about the distribution of the error components. The validity of the model is examined statistically.     

The Mixed Logit model: The state of practice

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.     

Bayesian predictive travel time methodology for advanced traveller information system

Travellers can benefit from the availability of point‐to‐point driving time estimates on a real time basis for making travel decisions such as route choice at strategic locations (e.g. junctions of major routes). This paper reports a predictive travel time methodology that features a Bayesian approach to fusing and updating information for use in advanced traveller information system. The methodology addresses the issue that data captured in real time on travel conditions becomes obsolete and has archival value only unless it is used as an input to a predictive travel time method for updating the information. The need for fusing real time data with other factors that influence travel time is defined and the concept of predictive travel time is discussed. The methodological framework and its components are advanced and an example application is provided for illustrating the fusion of data captured by infrastructure‐based and mobile technology with model‐based predictions in order to produce expected travel times. Copyright © 2010 John Wiley & Sons, Ltd.     

Transport demand model estimation from traffic counts

Many problems in transport planning and management tasks require an origindestination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or roadside interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the use of low cost and easily available data is particularly attractive.The need of low-cost methods to estimate current and future O-D matrices is even more valuable in developing countries because of the rapid changes in population, economic activity and land use. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of this is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods.The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Three types of demand models have been used: gravity (GR), opportunity (OP) and gravity-opportunity (GO) models. Three estimation methods have been developed to calibrate these models from traffic counts, namely: non-linear-least-squares (NLLS), weighted-non-linear-least-squares (WNLLS) and maximumlikelihood (ML).The 1978 Ripon (urban vehicle movement) survey was used to test these methods. They were found to perform satisfactorily since each calibrated model reproduced the observed O-D matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and the stochastic method due to Burrell, in determining the routes taken through the network.requests for offprints     

Optimal bus fleet management strategy for emissions reduction

Transportation is a major cause for environmental degradation via exhaust emissions. For many transit-oriented metropolitan areas, bus trips often constitute a sizeable mode share. Managing the bus fleet, in particular updating buses to comply with the newer emissions standards, therefore, can have a substantial impact on transportation-induced air quality. This paper presents the approach of remaining life additional benefit–cost (RLABC) analysis for maximising the total net benefit by either early-retiring or retrofitting the current bus fleet within their lifespans. By referring to the net benefits for different bus types estimated by RLABC analysis, the most beneficial management scheme for the current bus fleet can be identified. Optimal bus fleet management (BFM) models based on the RLABC analysis for the operator and the government are developed. Then a government subsidy plan is produced to achieve win–win solutions, which will offer efficient and flexible management schemes. To illustrate the approach, the largest bus company in Hong Kong, which carries more than 23% of the total trips in Hong Kong, is taken as a case study example. Instead of adopting a fixed retirement plan, such as replacing buses at the age of 17 as is currently practised, the proposed method develops an optimal BFM scheme that progressively phases out buses or retrofits them. This study produces promising results to demonstrate the large benefit of this approach for optimal bus fleet management.     

基于港口枢纽处TOD公交站点选址模型的设计

本文针对中小型沿海城市提出的成本模型嵌入到公交站点优化方案中,构建各项成本的表达式,整合并建立以成本最小为目标的公交站点选址模型,对TOD公交站点的选址进行合理规划。此模型可有效运用到在中小型沿海城市中港口枢纽处的公交站点合理设置中,即实现乘客从轮渡到公交的零换乘,又能使港口枢纽对港口枢纽出入口与城市道路交汇处的交通现状影响达到最小,公交的通行能力达到最高。     

Airline crew pairing with fatigue: Modeling and analysis

Crew fatigue is one of the main causes of airline accidents. Regulatory authorities such as the Federal Aviation Agency constantly introduce new fatigue regulations, often in the form of hard constraints on the length of duty and rest periods. The complex nature of travel-related fatigue, however, makes it difficult to account for it indirectly through such constraints. Recent studies show that fatigue depends on human factors such as the homeostatic process and the circadian body clock as well as time-zone differences. In this work, we explicitly account for fatigue in crew pairing optimization through the Three Process Model of Alertness, one of the most comprehensive fatigue models available in the literature. We provide a mathematical model for the crew pairing problem that incorporates fatigue and solve it using a column generation approach. Numerical analysis on two real data sets reveals that the proposed approach is able to reduce the crew fatigue levels substantially with minimal impact on cost. In particular, it is shown that hard constraints on fatigue may still lead to high fatigue levels and that jet-lag and time-zone differences have a major impact. The results of the tests also show that some of the rules and regulations in practice may be omitted if the fatigue is accounted for directly.     

Modeling Car Ownership in Turkey Using Fuzzy Regression

Abstract

This article develops a model which can be used to determine car ownership in Turkey. Because of the lack of disaggregated data, the model is based on aggregated data. As owning a car is mainly affected by economic, social and demographic factors, the car ownership model has a multi-variable form. In order to explain the effects of these factors on car ownership in Turkey, a fuzzy multiple-regression model is used. The major reason for applying fuzzy regression is to overcome the intercorrelation problem associated with the independent variables. In this study, the urbanization rate, average family size, gross national product per capita, average car cost, gasoline price and total length of roads are selected as independent variables. The results show that, by applying a multi-variable approach to possibilistic regression, the model provides not only a crisp output but also an output range for car ownership in Turkey between 1970 and 2000.     

蒸汽锅炉连续排污热力回收工程实践与分析

工业蒸汽锅炉连续排污系统在使用过程中,不但排出炉水表层污水,而且附带着大量的热能一同排出,浪费能源。文中以2台8 t/h蒸汽锅炉为例,通过对其连续排污余热计算,提出一种投资少、利用率高的余热回收方案。该方案采用加装板式换热器的方法,利用连续排污废水余热加热锅炉给水,在提高了锅炉给水温度的同时,回收了连排污水附带的大量余热。通过该方案在企业中的实际应用,分析整套系统的节能、节水效果,可回收热量537 600 kJ/h.     

A technique for the investigation of scheme benefits in large-scale transport networks

Existing methods of evaluating large-scale transport networks involve the use of mathematical models of traffic flow which are generally both large and complex. However, the time and cost involved in the use of these models normally restricts their use for the detailed forecasting of traffic flows and costs to the assessment of a relatively small number of alternative patterns of overall investment. In order to evaluate the individual projects and groups of projects which go to make up an overall investment plan, it is, therefore, usually necessary to make simplifying assumptions about the influence of any one project on the overall traffic pattern, so as to isolate it from the influence of neighbouring projects. These assumptions generally result in the loss of a certain amount of the detail normally available from a standard model, and the task of assessing the relative value of different projects and the amount of interaction between them is made more difficult.This paper describes a new technique, designed to permit the evaluation of individual projects whilst still retaining the level of detail available from a full-scale mathematical model. The aim has been to produce a cheap and easy-to-use technique, capable of producing substantially the same results as a standard model. The technique uses newly developed computer algorithms which short-cut the full-scale model by forecasting the changes in an existing travel pattern resulting from the influence of a particular project. Initial tests suggest that approaching the problem in this way can save up to 70% of the computing time and costs involved in the use of a standard model for the evaluation of individual projects.The technique as described here is envisaged as a tool for aiding strategic investment decisions. It can, however, provide data for more detailed investigations, and could, with modifications, carry out these investigations on smaller problems than those for which it was originally designed.Crown copyright reserved, 1973     

Modeling the impacts of mandatory and discretionary lane-changing maneuvers

In this paper, a novel mesoscopic multilane model is proposed to enable simultaneous simulation of mandatory and discretionary lane-changing behaviors to realistically capture multilane traffic dynamics. The model considers lane specific fundamental diagrams to simulate dynamic heterogeneous lane flow distributions on expressways. Moreover, different priority levels are identified according to different lane-changing motivations and the corresponding levels of urgency. Then, an algorithm is proposed to estimate the dynamic mandatory and discretionary lane-changing demands. Finally, the lane flow propagation is defined by the reaction law of the demand–supply functions, which can be regarded as an extension of the Incremental-Transfer and/or Priority Incremental-Transfer principles. The proposed mesoscopic multilane cell transmission model is calibrated and validated on a complex weaving section of the State Route 241 freeway in Orange County, California, showing both the positive and negative impact of lane changing maneuvers, e.g., balancing effect and capacity drop, respectively. Moreover, the empirical study verifies that the model requires no additional data other than the cell transmission model does. Thus, the proposed model can be deployed as a simple simulation tool for accessing dynamic mesoscopic multilane traffic state from data available to most management centers, and also the potential application in predicting the impact of traffic incident or lane control strategy.     

Using nonparametrics to specify a model to measure the value of travel time

Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time–cost trading experiment. The analysis favours a model with random WTP as the only source of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory.     

High‐speed rail cost recovery time based on an integer optimization model

With increasing gasoline prices, electric high‐speed rail (HSR) systems represent one means to mitigate overexposure to volatile prices. However, additional research is needed related to funding this infrastructure. In this paper, we develop a new integer optimization model to address this problem and use a hypothetical case study to demonstrate the approach. The objective of the approach is to minimize the time period in which the cost of HSR construction and operation can be recovered. This is an iterative process based on an integer optimization model, whose objective function is to determine the optimum recovery time (ORT), by setting the HSR ticket price and frequency. Embedded in the optimization model is a multinomial logit model for calculating the demand for HSR as a function of these decision variables, thus capturing the effects of level of service on market share. In particular, the optimization model accounts for the role of different types of subsidies toward HSR construction (one‐time subsidies at construction, annual subsidies, and subsidies depending on frequency). This method can also help determine whether an HSR system should be built or how much subsidy should be provided given a fixed expected cost recovery time. By integrating the logit model into the objective function evaluation, the effects of ticket price and service frequency on service demand can be directly captured. Copyright © 2014 John Wiley & Sons, Ltd.     

基于微观仿真模型的立交方案研究

采用微观交通仿真模型进行立交方案运行分析,可得到行程时间、速度、延误、排队长度等定量指标,为从交通运行角度研究立交方案提供了一种方法。文章阐述了采用微观交通仿真模型的立交方案分析流程,并结合南宁市某立交方案仿真分析实例,论证了采用该仿真模型进行立交方案交通运行分析的可行性。     

Optimal deployment of autonomous vehicle lanes with endogenous market penetration

This paper develops a mathematical approach to optimize a time-dependent deployment plan of autonomous vehicle (AV) lanes on a transportation network with heterogeneous traffic stream consisting of both conventional vehicles (CVs) and AVs, so as to minimize the social cost and promote the adoption of AVs. Specifically, AV lanes are exclusive lanes that can only be utilized by AVs, and the deployment plan specifies when, where, and how many AV lanes to be deployed. We first present a multi-class network equilibrium model to describe the flow distributions of both CVs and AVs, given the presence of AV lanes in the network. Considering that the net benefit (e.g., reduced travel cost) derived from the deployment of AV lanes will further promote the AV adoption, we proceed to apply a diffusion model to forecast the evolution of AV market penetration. With the equilibrium model and diffusion model, a time-dependent deployment model is then formulated, which can be solved by an efficient solution algorithm. Lastly, numerical examples based on the south Florida network are presented to demonstrate the proposed models.     

Improving recovery-to-optimality robustness through efficiency-balanced design of timetable structure

To improve the service quality of the railway system (e.g., punctuality and travel times) and to enhance the robust timetabling methods further, this paper proposes an integrated two-stage approach to consider the recovery-to-optimality robustness into the optimized timetable design without predefined structure information (defined as flexible structure) such as initial departure times, overtaking stations, train order and buffer time. The first-stage timetabling model performs an iterative adjustment of all departure and arrival times to generate an optimal timetable with balanced efficiency and recovery-to-optimality robustness. The second-stage dispatching model evaluates the recovery-to-optimality robustness by simulating how each timetable generated from the first-stage could recover under a set of restricted scenarios of disturbances using the proposed dispatching algorithm. The concept of recovery-to-optimality is examined carefully for each timetable by selecting a set of optimally refined dispatching schedules with minimum recovery cost under each scenario of disturbance. The robustness evaluation process enables an updating of the timetable by using the generated dispatching schedules. Case studies were conducted in a railway corridor as a special case of a simple railway network to verify the effectiveness of the proposed approach. The results show that the proposed approach can effectively attain a good trade-off between the timetable efficiency and obtainable robustness for practical applications.