Measuring accessibility of urban scales: A trip-based interaction potential model

In the interaction process of city space and citizens’ activity pattens, the regular travel behavior under the cumulative impacts of urban land use is required to be assessed. The aim of this study is to formulate and estimate attraction choice models that provide measurements of accessibility on various scales reflecting the choice of people to travel to facilities or activity places and characterize the interaction between land use patterns and transportation facilities. Based on a dataset inclusive of big data from varied sources, measurement methodologies are proposed encompassing the multidimensional aspect of the accessibility estimation issues. We sketch the characteristics of service facilities and travel impedance in the calibration processes. Logit models and gravity models are applied to simulate the impacts of different scales of trip length on the accessibility scores. Accessibility is aggregated over spatial elements of different scales and trips and herein the spatial accessibility of study zones is estimated as well as the potential of citizens’ travel choices and activity patterns. Such spatial interaction models have potential implication for enhancing our understanding of the cumulative environmental influences on citizen’s travel behavior and vice versa. It can be a substantial part of a more composed proposal of life convenience of residential citizens reflecting the happiness of living in an urban community.     

Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing

Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega-cities is vital, but city size and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Here, we adopt an alternative approach, through the development of a static spatial microsimulation of people’s daily travel behaviour. Using Beijing as a case study, we first derive complete activity-travel records for 1026 residents from an activity diary survey. Then, using the 2000 population census data at the sub-district level, we apply a simulated annealing algorithm to create a synthetic population at fine spatial scale for Beijing and spatially simulate the population’s daily travel, including trip distance and mode choice at the sub-district scale. Finally, we estimate transport CO₂ emission from daily urban travel at the disaggregate level in urban Beijing.     

一种基于GIS的居民出行空间及交通网络评估方法

传统的交通需求4阶段分析模型大多基于各类出行的起讫点调查（OD调查）,建立出行生成、出行分布、出行方式选择和流量分配的4阶段预测模式.由于缺乏关于出行者对道路及其周边相关评价等心理感知方面的考虑,从本质上,这是一种物质性的交通分析模型.基于人本主义思想,借助GIS工具,探讨了人本主义的交通调查和评估方法;最后选择小样本量的出行调查数据为实验,实现了居民微观行为空间、行为过程以及对沿路交通状况及土地利用特征的宏观综合模拟.     

基于贝叶斯网络的城市居民出行方式研究

以西安市城市居民出行方式为研究对象,收集西安市部分区域城市居民出行的调查数据。利用获得的调查数据,综合运用相关性分析方法和K2算法进行贝叶斯网络的结构学习;应用贝叶斯参数估计方法进行贝叶斯网络的参数学习,建立了应用于西安城市居民出行方式分析的贝叶斯网络。应用所建网络分析了是否有私家车、居民性别、居民年龄和出行目的对西安城市居民出行方式的影响。研究结果表明,基于贝叶斯网络建立的西安城市居民出行方式分析模型预测精度较高,具有较高的实用价值。     

A comparative study of machine learning classifiers for modeling travel mode choice

The analysis of travel mode choice is an important task in transportation planning and policy making in order to understand and predict travel demands. While advances in machine learning have led to numerous powerful classifiers, their usefulness for modeling travel mode choice remains largely unexplored. Using extensive Dutch travel diary data from the years 2010 to 2012, enriched with variables on the built and natural environment as well as on weather conditions, this study compares the predictive performance of seven selected machine learning classifiers for travel mode choice analysis and makes recommendations for model selection. In addition, it addresses the importance of different variables and how they relate to different travel modes. The results show that random forest performs significantly better than any other of the investigated classifiers, including the commonly used multinomial logit model. While trip distance is found to be the most important variable, the importance of the other variables varies with classifiers and travel modes. The importance of the meteorological variables is highest for support vector machine, while temperature is particularly important for predicting bicycle and public transport trips. The results suggest that the analysis of variable importance with respect to the different classifiers and travel modes is essential for a better understanding and effective modeling of people’s travel behavior.     

A Hierarchical Gravity Model with Spatial Correlation: Mathematical Formulation and Parameter Estimation

This study presents a hierarchical trip distribution gravity model that can accommodate various spatial correlation structures. It is formulated on the basis of the solution to an equivalent optimization problem, and its parameters are estimated using a sequential maximum likelihood procedure. We conclude that accounting for spatial correlation through a hierarchical structure incorporated into gravity-type trip distribution models significantly increases their explanatory and predictive powers and improves the results they generate for use in transportation system planning processes.     

Comparisons of Urban Travel Forecasts Prepared with the Sequential Procedure and a Combined Model

Detailed analyses and comparisons of urban travel forecasts prepared by applying the state-of-practice sequential procedure and the solution of a combined network equilibrium model are presented. The sequential procedure for solving the trip distribution, mode choice and assignment problems with feedback is the current practice in most transportation planning agencies, although its important limitations are well known. The solution of a combined model, in contrast, results from a single mathematical formulation, which ensures a well-converged and consistent result. Using a real network, several methods for solving the sequential procedure with feedback are compared to the solution of the combined model ESTRAUS. The results of these methods are shown to have various levels of instability. The paper concludes with a call for a new paradigm of travel forecasting practice based on an internally consistent model formulation that can be solved to a level of precision suitable for comparing alternative scenarios.     

A General Equilibrium Framework for Integrated Assessment of Transport and Economic Impacts

The use of separate transport and economic models in urban planning provides a limited view of economic impacts, restricts the testing of network design options and lengthens the planning process. Furthermore, the standard methodology for economic appraisal assumes partial economic equilibrium and cannot determine the distribution of impacts from the transport sector to households. Computable general equilibrium (CGE) models can capture general equilibrium effects and measure welfare at the household level, but mostly lack integration with transport models and do not represent all trip generators. This paper develops an integrated traffic assignment and spatial CGE model in nonlinear complementarity form, casted as a framework for economic appraisal of urban transport projects. The CGE submodel generates commuting, shopping and leisure trips as inputs into the transport submodel, which then assigns trips to the network according to user equilibrium. The resulting travel times then feed back into household prices and freight margins. Households and firms fully account for travel times in decisions on where to shop, how much labour to supply and where to source production inputs. Calibration and applications of the model are demonstrated for 14 regions and 2 industries across Sydney using GAMS/PATH on the NEOS server. The welfare of various network improvements is measured using equivalent variations. The model can be calibrated to external strategic transport models, and be extended to simulate additional trip generators and land-use.     

Estimating the temporal interval entropy of neuronal discharge

To better understand the role of timing in the function of the nervous system, we have developed a methodology that allows the entropy of neuronal discharge activity to be estimated from a spike train record when it may be assumed that successive interspike intervals are temporally uncorrelated. The so-called interval entropy obtained by this methodology is based on an implicit enumeration of all possible spike trains that are statistically indistinguishable from a given spike train. The interval entropy is calculated from an analytic distribution whose parameters are obtained by maximum likelihood estimation from the interval probability distribution associated with a given spike train. We show that this approach reveals features of neuronal discharge not seen with two alternative methods of entropy estimation. The methodology allows for validation of the obtained data models by calculation of confidence intervals for the parameters of the analytic distribution and the testing of the significance of the fit between the observed and analytic interval distributions by means of Kolmogorov-Smirnov and Anderson-Darling statistics. The method is demonstrated by analysis of two different data sets: simulated spike trains evoked by either Poissonian or near-synchronous pulsed activation of a model cerebellar Purkinje neuron and spike trains obtained by extracellular recording from spontaneously discharging cultured rat hippocampal neurons.     

Estimation of a reduction in CO2 emissions by shifting commuters’ travel mode from the private car to public transport

A model for forecasting the amount of CO₂ emissions due to urban commuter travel was developed. The model consisted of three submodels: a commuters’ number forecasting model, a fuzzy commute travel mode choice model and a CO₂ emissions estimation. The model was tested using the real data of Osaka, Japan. Using this model, we also forecasted and analysed the efect of policy changes to shift commuters’ travel mode from private car to public transport in order to decrease the amount of CO₂ emissions.     

Intelligent system applications in electronic tourism

Recent changes in tourists’ behavior and the growing importance of Information and Communication Technologies mean that much more attention needs to be given to electronic (e)-tourism. With the Internet becoming the preferred media choice for many travelers to obtain travel information, online travel agencies and their offers are gaining more importance all over the world, also in Turkey. Turkish travel web sites usually provide static information which can be accessed by some kind of search forms, but users need more such as recommendation tools, trip planners which include a Decision Support System inside. For this reason, this paper offers a new approach to the marketing strategies for Turkish travel agencies, but the recommendation tool is also a generic model for all tourism agencies in the world. An intelligent system which works as a recommendation tool for trip planning is created by using Case based reasoning algorithm. The inspiration module of the proposed model recommends users the most available trip alternatives by comparing the older cases with the new client.     

Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media

When travelers plan trips, landmark recommendation systems that consider the trip properties will conveniently aid travelers in determining the locations they will visit. Because interesting locations may vary based on the traveler and the situation, it is important to personalize the landmark recommendations by considering the traveler and the trip. In this paper, we propose an approach that adaptively recommends clusters of landmarks using geo-tagged social media. We first examine the impact of a trip’s spatial and temporal properties on the distribution of popular places through large-scale data analyses. In our approach, we compute the significance of landmarks for travelers based on their trip’s spatial and temporal properties. Next, we generate clusters of landmark recommendations, which have similar themes or are contiguous, using travel trajectory histories. Landmark recommendation performances based on our approach are evaluated against several baseline approaches. Our approach results in increased accuracy and satisfaction compared with the baseline approaches. Through a user study, we also verify that our approach is applicable to lesser-known places and reflects local events as well as seasonal changes.     

Estimation of Bid Functions for Location Choice and Price Modeling with a Latent Variable Approach

A new approach for the estimation of bid-rent functions for residential location choice is proposed. The method is based on the bid-auction approach and considers that the expected maximum bid of the auction is a latent variable that can be related to observed price indicators through a measurement equation. The method has the advantage of allowing for the estimation of the parameters of the bid function that explain the heterogeneous preferences of households for location while simultaneously adjusting the expected maximum bid to reproduce realistic values. The model is applied and validated for a case study on the city of Brussels. Results show that the proposed model outperforms other methods for bid-rent estimation, both in terms of real estate prices and spatial distribution of agents, especially when detailed data describing the real estate goods and their prices is not available.     

出租车数据的城市道路网路段通行时间估计方法

城市路段通行时间估计能够更好地运营和管理城市交通。针对包含起点-终点位置,行程时间和距离信息的GPS行程数据,提出了一种城市道路网短时通行时间的估计模型。首先将城市道路网按照交叉路口分解为多个路段,并基于k-最短路径搜索方法分析司机行进路线。然后针对每一个路段,提出了双车道通行时间多项式关联关系模型,既能提升道路网通行时间精细度,又能避免因训练数据不足导致的路网通行时间过拟合问题。最后以最小化行程期望时间和实际行程时间之间的均方误差为优化目标,拟合道路网通行时间。在纽约出租车数据集上的实验结果表明,所提模型及方法相对于传统单车道估计方法能够更准确地估计城市道路网路段的通行时间。     

Empirical Identification in the Mixed Logit Model: Analysing the Effect of Data Richness

The flexible structure of the mixed logit (ML) model is at the root of the difficulties associated to its estimation. Major problems are parameter identification and the distinction between different substitution patterns. In this paper we focus on the empirical identification problem and investigate the effect of low information richness in the data on the capability of estimating a correct ML model (i.e. with identifiable parameters and free of confounding effects). In particular, we analyse to which extent the empirical identification problem depends on the variability of the data among alternatives, on the degree of heterogeneity of the taste parameters, on the dimension of the sample and on the number of choice tasks for each individual. To test for information richness of the data and its effect on the capability of the ML model to reproduce random heterogeneity in tastes, a collection of datasets was generated varying systematically (a) the standard deviation (SD) of the distribution of travel time differences between the two alternatives, (b) the SD of the random parameter, (c) the number of choice tasks for each individual and (d) the number of individuals in relation to the number of choice tasks. Then, several ML models allowing for random travel time parameters were estimated using different number of draws and results were compared in terms of model goodness of fit and, also, on the capability of reproducing the real parameters used to generate each dataset. Our results suggest that identification problems depend only on the (low) variability of the associated data and disappear as the richness of the data associated to the random parameter increases. However, rich enough data only allows obtaining good statistics but the estimated parameters do not always reproduce the correct values, as the capability of the ML to reproduce random heterogeneity depends on the random parameter distribution (degree of variability and symmetry). Moreover, the capability of the ML to reproduce random heterogeneity increases when more than one choice is available for each individual and the effect of sample size on the empirical identification reduces considerably.     

A study on weighing a semi-labelled sample using spectral and spatial context information

ABSTRACT

Classification of spectrally similar objects is a hard task, mainly when using moderate resolution data. Even though hyperspectral data are a useful source of information, the Hughes phenomenon is highlighted when limited number of training samples are used. For data classification and to mitigate this drawback, the number of training samples needs to be increased in the methodology. In this study, we investigated the estimation of the weights of semi-labelled samples using spectral and spatial context information by relaxation process in a two-steps methodology. The weights of semi-labelled samples in parametric classifier were estimated iteratively in the first step using spectral information only. In the second step, addition of spatial information was done by a relaxation process. This study investigated a more refined approach, improved by the inclusion of spatial context information in the relaxation process. The aim of this work was to mitigate the Hughes phenomenon and improve the separation of similar classes. The proposed methodology was tested using the data (hyperspectral image) from a study area, where the land cover classes are spectrally similar and the accurate separation of these classes was a hard task. Even though several experiments were performed, only a selected number of representative experiments are presented in this work. The results showed that the inclusion of context information can be used for the successful mitigation of the Hughes phenomenon allowing almost twice the number of bands used and increase the classification overall accuracy by up to 8%.     

Modelling household travel energy consumption and CO2 emissions based on the spatial form of neighborhoods and streets: A case study of Jinan,China

This paper presents a direct modelling approach to evaluate household travel energy consumption and CO₂ emissions based on the spatial form of neighborhoods and streets. It integrates one multinomial logit model and four double hurdle models to predict travel outcomes: vehicle ownership portfolio choice, car travel distance, bus travel distance, motorcycle travel distance and ebike travel distance. The energy consumption and CO₂ emissions are then estimated using these travel outcomes. A full application of the modelling approach is demonstrated through a pilot project in Jinan, China. A holdout validation is also performed to address the over-fitting problem of models calibrated from the training set data. Results show that the proposed approach is operational and appropriate for sketch planning applications to promote clean energy and low carbon city planning in urbanizing China.     

A Copula based observation network design approach

In this paper, a method for environmental observation network design using the framework of spatial modeling with copulas is proposed. The methodology is developed to enlarge or redesign an existing monitoring network by taking the configuration which would increase the expected gain defined in a utility function. The utility function takes the estimation uncertainty, critical threshold value and gain-loss of a certain decision into account. In this approach, the studied spatial variable is considered as a random field in where variations in time is neglected and the variable of interest is static in nature. The uniqueness of this approach lies in the fact that the uncertainty estimation at the unsampled location is based on the full conditional distribution calculated as conditional copula in this study. Unlike the traditional Kriging variance which is a function of mere measurements density and spatial configuration of data points, the conditional copula account for the influence from data values. This is important specially if we are interested in purpose oriented network design (pond) as for example the detection of noncompliance with water quality standards, the detection of higher quantiles in the marginal probability distributions at ungauged locations, the presence or absence of a geophysical variable as soil contaminants, hydrocarbons, golds and so on. An application of the methodology to the groundwater quality parameters in the South-West region of Germany shows its potential.     

基于出行需求的城市车辆共享模式演化分析

车辆共享从资源分配的角度提高了汽车资源利用率.为了激励车辆共享,有关出行需求和出行者体验的研究势在必行.通过DBSCAN算法测量了城市内的交通需求聚集度,验证了车辆共享的可行性.确定了影响用户效用的关键因素,提出了基于Logit模型的数学模型以描述出行者体验和汽车资源利用率,预测出行者选择.同时,利用真实数据和调查结果,使测量和模型更加准确、真实.通过仿真实验,观测并分析了交通模式的演化过程及结果,发现城市出行需求量和交通需求聚集度是影响交通模式演化的主要因素.出行需求量需达到一定数值,演化才能达到稳定状态.城市出行交通需求聚集度越高,车辆共享的参与者则越多,其所获效用也越高.当出行需求量大于290,且交通需求聚集度大于0.9时,所有出行者都将选择车辆共享.最后,基于神州专车的出行数据,实验并观测了北京市交通模式的演化,发现若要在不加入经济因素或政策干预的情况下实施车辆共享,其交通模式将无法达到稳定状态.     

A hybrid variable neighborhood search and simulated annealing algorithm to estimate the three parameters of the Weibull distribution

The Weibull distribution plays an important role in failure distribution modeling of reliability research. While there are three parameters in the general form of this distribution, for simplicity, one of its parameters is usually omitted and as a result, the others are estimated easily. However, due to its more flexibility, when the general form of the Weibull distribution is of interest, the estimation procedure is not an easy task anymore. For example, in the maximum likelihood estimation method, the likelihood function that is formed for a three-parameter Weibull distribution is very hard to maximize. In this paper, a new hybrid methodology based on a variable neighborhood search and a simulated annealing approach is proposed to maximize the likelihood function of a three-parameter Weibull distribution. The performance of the proposed methodology in terms of both the estimation accuracy and the required CPU time is then evaluated and compared to the ones of an existing current method through a wide range of numerical examples in which a sensitivity analysis is performed on the sample size. The results of the comparison study show that while the proposed method provides accurate estimates as well as those of the existing method, it requires significantly less CPU time.