Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art

ABSTRACT

The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.     

大数据背景下重庆市综合交通模型维护升级

利用传统交通调查数据开展交通模型维护升级工作存在抽样率低、样本量小、人工成本高、精度低、实施难度大等多种现实问题.在大数据技术不断更新完善的背景下,有必要探索新的模型维护升级技术和方法.首先,阐述重庆市交通大数据的发展历程、数据类型、数据采集与数据格式.其次,在重庆市综合交通模型的框架结构中,分别利用大数据资源探索公共汽车线网的构建、各等级道路流量延误函数标定、常住人口和就业岗位分析、出行分布特征分析和公共交通出行需求分析等相关技术方法.最后,以重庆市综合交通模型的应用实践成果验证模型的可靠性和准确性.     

基于粒子群算法的道路交叉口信号配时优化模型——以昆明市为例

在现有交通资源下,利用交通信号的动态调控缓解交通拥堵是一种行之有效的方式。首先探讨了道路交叉口信号控制的空间和时间优化思路,在时间优化方面提出基于粒子群算法的信号配时优化模型。以昆明市学府路为例,在分析大量交通流数据的基础上,根据三相交通流理论,对交通状态进行划分并提出有针对性的控制策略。将信号配时优化模型应用于学府路3个相邻的关键交叉口。交通仿真和方案试运行结果显示,优化前后同步流状态下交叉口延误平均降低21.0%,车辆排队长度平均降低12.4%;堵塞状态下交叉口延误平均降低32.0%,车辆排队长度平均降低24.9%。这一结果表明该模型在道路交叉口信号配时优化中具有合理性和有效性。     

Reframing Engagement Methods for Climate Change Adaptation

Climate change poses known and unknown risks for coastal communities and also challenges for university faculty and local government staff who communicate about cli- mate sciences. Conceived as a way to move beyond traditional models of science communication, this project involved public and private decision makers in specific at-risk communities in Oregon (U.S. Pacific coast) and Maine (Atlantic coast). Both state projects sought to move behavior toward decisive action that results in coastal communities that are more resilient to climate variability at all scales. To promote engagement between project staffs and publics, a dialogic model of communication was advanced, beginning with interviews and focus groups that in turn shaped further engagement through workshops and targeted video products. This means of communication led to a deeper understanding of participants’ knowledge, beliefs, perceptions, values, and barriers to action related to climate change and its effects. Coinciding with this, project participant evaluations in both Oregon and Maine indicate that the workshops and videos were successful at informing them on this complex issue; and in both states, project participation led to action outcomes. We believe that applied elsewhere our multifaceted and adaptive approach will garner similar results, provided sufficient dedicated staffing and attention to methods.     

LUTI operational models review based on the proposition of an a priori ALUTI conceptual model

ABSTRACT

This paper focuses on understanding to what extent the components of LUTI models and their mutual interactions are conceptually represented by eight operational LUTI models. This is important for the understanding of LUTI models’ mechanisms, firstly because it may reduce communication barriers between planning communities, secondly because it may help us understand the models’ applicability, and thirdly it may highlight the models’ shortcomings and point for future research. We present a discussion about what subsystems should be considered for LUTI modelling, from which we derived an “a priori” conceptual ALUTI model (incorporating Activities, besides Land Use and Transport). By comparing the rationale behind each model with this conceptual model, we establish the basis for our review, focussing on whether these models incorporate the ALUTI components, its inner workings and the relationships between these components. Results indicate three main limitations of the reviewed models. First, models not always adequately include all the components of the a priori ALUTI model. Second, the ALUTI subsystems’ internal functions are not explicitly modelled in several of the models reviewed, making it difficult to evaluate how planning decisions affect the subsystem. Third, only few models recognise all mutual interactions, especially in respect to the Activity subsystem.     

Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.     

Forecasting Travel Demand with Alternatively Structured Models of Trip Frequency

Abstract

This paper develops alternatively structured trip frequency/generation models, and investigates their forecast performance. The first model presented is the simple linear model with a discussion of its theoretical shortcomings. Models that address, in a progressive fashion, the underlying shortcomings of the linear model are then presented. These models are namely the truncated normal model, the Poisson model, the negative binomial model, and an ordered logit model. The modeling unit employed in the study is the individual. The models are assessed by how closely they are able to replicate trips produced by each individual in the dataset, and by each traffic zone. This assessment of performance in prediction is conducted on an estimation dataset collected in the Toronto Region in 1986, and on an independent dataset collected in the same geographic region, 10 years later, in 1996. The results show that, notwithstanding the simplicity of the simple linear model and its lack of an explicit underlying travel behavioral theory, it predicts travel in the base and forecast years with less error compared to any of the more complex models.     

基于萤火虫优化的BP神经网络算法研究

为了改进神经网络结构和参数的设置方法,在萤火虫算法和BP神经网络的基础上,提出了一种萤火虫算法优化BP神经网络的算法.该算法利用萤火虫算法得到更优的网络初始权值和阈值,弥补BP神经网络连接权值和阈值选择上的缺陷.将该算法应用到Duffing系统产生的混沌时间序列进行算法的有效性验证,并与BP神经网络进行比较,仿真结果表明该算法具有更高的预测准确性,从而证明该算法在该预测领城的可行性和有效性.     

五体船波浪中运动与增阻的主片体协同优化研究

以波浪中的运动和增阻为目标,进行了五体船主体型线、片体主尺度和布置的协同优化。解决了型线自动变换、参数化自动建模及计算网格合并等关键技术,实现了五体船全船的自动建模。利用ISIGHT进行建模与水动力计算的集成,建立了主片体协同多目标优化系统。运用遗传算法进行多目标优化,结果表明优化后五体船运动和增阻都有所改善,并对非规则波中的情况进行了一定的探讨。