Regulatory reform and freight mode choice

Reform of trucking Vehicle Weights and Dimensions (VWD) regulations in Canada is now underway. The effect these reforms will have on the freight transportation industry are only recently the subject of research. This paper is part of this new research effort, aimed at understanding how regulatory reform in the trucking sector will affect the existing competitive relationship between trucks and the railways. The paper presents the results of study and research into modelling the relationship between mode choice and the service characteristics that are affected by VWD reform.Using several periods of data, a series of econometric models are developed which help to elucidate the relative relationships between the mode service characteristics for both of the principal interprovincial freight modes. A technique is developed and presented to model railway user costs in order to overcome the complex and often unrepresentable pricing activities of Canadian railways.The strength of the developed econometric models is presented, including their significance and statistical reliability. This is further reinforced by the similarities exhibited by all the models in the series. The selected model is applied to predict market service responses required of the railway industry in order to compete with the trucking sector now adapting to the new regulations.The impact of the newly implemented vehicle weight and dimension reform on the rail transportation industry is analyzed and railway industry improvements aimed at maintaining its market share are presented. The results predicted by the model show that railway user cost reductions should be moderate, and likely less than the level of inflation over the period of time when trucking converts its fleet to take advantage of the new regulations.Abbreviations AASHTO American Association of State Highway and Transportation Officials - CCMTA Canadian Conference of Motor Transport Administrators - EPI End products, inedible commodity classification - GVW Gross Vehicle Weight - NA 1988 VWD National Agreement - RTAC Roads and Transportation Association of Canada - VWD Vehicle Weight(s) and Dimension(s) - WA 1988 VWD Western Agreement     

交通信息服务条件下的出行选择分析

首先 ,探讨了出行者的出行心理、行为以及信息之间的相互关系 ,在此基础上 ,简要分析了出行者的出行选择。其次 ,考虑在交通信息服务条件下 ,由于出行者本身以及信息方面的原因 ,出行选择可能出现过激反应、集聚反应等问题。最后 ,针对上述问题 ,提出应该加以重视的或有待进一步研究的几个问题。     

Introduction: Habitual travel choice

In this introduction to the special issue on habitual travel choice, we provide a brief account of the role of habit in travel behaviour, discuss more generally what habitual choice is, and briefly review the issues addressed in the solicited papers. These issues include how habitual travel behaviour should be measured, how to model the learning process that makes travel choice habitual, and how to break and replace car-use habits.     

平地机安装自动找平系统的使用与校对

平地机被广泛应用于各种路面工程的平整及其它工程的改良施工中,介绍了平地机自动找平系统的组成、作用、控制,着重对系统的三大作用、校对时间作了说明.     

紊流混摩区摩阻因数近似公式分析

根据对90号无铅汽油和0号柴油进行摩阻实验所取得的实测数据，对紊流混摩区的近似公式进行了误差的计算和比较，并分析了误差产生的主要原因。计算和分析结果表明：科尔布鲁克公式是紊流混摩区的较准确的计算公式，近似公式只适用于粗略的计算和数学分析。     

高速公路可变收费标准模型研究

在介绍可变收费的理论基础上,分析影响高速公路可变收费标准制定的主要因素,并讨论不同时段收费费率同交通量的关系。在设定的可变收费方案下,建立了出行者选择的二元logit模型,根据调研样本,采用最小二乘法对模型参数标定并对设定的可变收费方案进行评价,结果证明可变收费能使交通流在各时段合理分布,从而提高了高速公路的服务水平。     

Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.     

Continuum theory for pedestrian traffic flow: Local route choice modelling and its implications

This contribution puts forward a novel multi-class continuum model that captures some of the key dynamic features of pedestrian flows. It considers route choice behaviour on both the strategic (pre-trip) and tactical (en-route) level. To achieve this, we put forward a class-specific equilibrium direction relation of the pedestrians, which is governed by two parts: one part describing the global route choice, which is pre-determined based on the expectations of the pedestrians, and one part describing the local route choice, which is a density-gradient dependent term that reflects local adaptations based on prevailing flow conditions.Including the local route choice term in the multi-class model causes first of all dispersion of the flow: pedestrians will move away from high density areas in order to reduce their overall walking costs. Second of all, for the crossing flow and bi-directional flow cases, local route choice causes well known self-organised patterns to emerge (i.e. diagonal stripes and bi-directional lanes). We study under which demand conditions self-organisation occurs and fails, as well as what the impact is of the choices of the different model parameters. In particular, the differences in the weights reflecting the impact of the own and the other classes appear to have a very strong impact on the self-organisation process.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.