Development and calibration of a long-distance passenger traffic assignment model

This paper studies the assignment of long-distance passenger traffic on a highway corridor network. First, we propose a traditional model for the long-distance traffic assignment considering interactions with local commuter traffic. It addresses the effect of local networks on highway corridors. An iterative algorithm is developed to solve for the exact solution. Then, to address the potential computational issues that arise therein, a decomposition method is proposed by introducing a new concept of corridor elasticity. An assignment procedure for long-distance passenger traffic is developed accordingly. Numerical tests show that the proposed decomposition method makes significant improvements in computational performance at a small loss of optimality. This decomposition method well approximates the exact assignment from the traditional formulation, especially when the highway corridors are near-saturation. The proposed decomposition method appears practical for application.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

基于家庭属性差异的大学生出行方式选择行为研究

为剖析家庭属性差异对大学生出行方式选择行为的影响，基于非集计理论，构建家庭属性差异的大学生出行选择多元Logit 模型. 根据四川省2 571 份大学生出行行为调查问卷，运用SPSS 软件标定模型参数，获取影响大学生出行选择的主要家庭属性因素，并进行敏感性分析. 结果表明：家庭平均年收入、经济净流对大学生出行方式选择有显著的影响；以航空运输为参考，家庭平均年收入、经济净流对公路运输方式选择的影响大于铁路运输；“祖辈替孙辈购买机票”的折扣票务形式可提高大学生选择航空出行的概率.     

The potential mitigation of CO2 emissions via modal substitution of high-speed rail for short-haul air travel from a life cycle perspective – An Australian case study

The objective of this study is to provide a strategic evaluation of the mitigation of CO₂ emissions via modal substitution of high-speed rail for short-haul air travel on the Sydney–Melbourne, Australia city-pair from a life cycle perspective. It has been demonstrated that when considering CO₂ emissions from vehicle operations, the modal shift from air to high-speed rail on this city-pair has the potential to provide a means of CO₂ mitigation. However, uncertainty exists with regard to the level of mitigation potential when considering the whole-of-life performance of the systems. Given the significant difference in the infrastructure requirements between the air mode and the high-speed rail mode, this study quantifies the life cycle CO₂ load attributable to each system and examines the effect on CO₂ mitigation potential. The study concluded that while the inclusion of the linehaul infrastructure did increase the CO₂ load associated with high-speed rail mode, it did not equate to or exceed the CO₂ load per trip as experienced by the air mode. The avoided annual life cycle CO₂ emission in the target year 2056 was 0.37 Mt representing an 18% reduction when compared to the air mode only on the city pair. In fact, the scenario comparison indicated that the substitution of high-speed rail for short-haul air travel on the city pair resulted in CO₂ emissions avoidance throughout the longitudinal period.     

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.     

The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey

The main focus of travel behaviour research has been explaining differences in behaviour between individuals (interpersonal variability) with less emphasis given to the variability of behaviour within individuals (intrapersonal variability). The subject of this paper is the variability of transport modes used by individuals in their weekly travel. Our review shows that previous studies have not allowed the full use of different modes in weekly travel to be taken into account, have used categorical variables as simple indicators of modal variability and have only considered a limited set of explanatory indicators in seeking to explain modal variability. In our analysis we use National Travel Survey data for Great Britain. We analyse modal variability with continuous measures of modal variability (Herfindahl–Hirschman Index, the difference in mode share between the primary and secondary mode, the total number of modes used). Taking inspiration from Hägerstrand (1970), we conceive that modal variability is determined by different types of spatial mobility constraints and find that reduced modal variability is predicted for having mobility difficulties, being aged over 60, being non-white, working full-time, living in smaller settlement, lower household income, having regular access to a car, having no public transport pass/season ticket and not owning a bicycle. The findings can support a change in perspective in transport policy from encouraging people to replace the use of one mode with another to encouraging people to make a change to their relative use of different transport modes.     

Does crowding affect the path choice of metro passengers?

This paper investigates crowding effect on the path choice of metro passengers. We show people reroute not only to avoid the delay from crowding but also to evade crowding itself. More specifically, a logit model fits best when it uses the transit delay from crowding as well as the passenger load of a connection in addition to the conventional explanatory variables. Also, we demonstrate that crowding decreases the overall welfare of metro passengers. The model is tested on the real path choice data acquired by the recent algorithm by Hong et al. (2015) known to detect the real path choice from Smart Card data in more than 90% of the cases.     

基于信令数据的出行强度影响因素分析模型

为探究城市交通出行强度影响因素及不同因素的影响程度，本文从土地利用与交通基础设施建设两方面出发，分析包括土地利用混合指数，职住混合率熵指数，公共交通站点 500 m覆盖率，路网可达性等17个指标与出行强度的相关关系；基于相关系数和拟合优度分析，提取7个与出行强度强相关指标，基于所识别指标构建北京市中心城区出行强度多元回归模型.模型结果表明，职住混合率熵指数对出行强度的影响最为显著，公共交通站点覆盖率对出行强度的影响比道路网密度和可达性更为明显.此外，给出单一土地利用/交通基础设施指标对出行强度拟合结果的离群特征分析方法，用于评估不同区域基础设施供给与交通出行需求之间的平衡关系.     

Policy packages for modal shift and CO2 reduction in Lille,France

This paper proposes different policy scenarios to cut CO₂ emissions caused by the urban mobility of passengers. More precisely, we compare the effects of the ‘direct tool’ of carbon tax, to a combination of ‘indirect tools’ – not originally aimed at reducing CO₂ (i.e. congestion charging, parking charges and a reduction in public transport travel time) in terms of CO₂ impacts through a change in the modal split. In our model, modal choices depend on individual characteristics, trip features (including the effects of policy tools), and land use at origin and destination zones. Personal “CO₂ emissions budgets” resulting from the trips observed in the metropolitan area of Lille (France) in 2006 are calculated and compared to the situation related to the different policy scenarios. We find that an increase of 50% in parking charges combined with a cordon toll of €1.20 and a 10% travel time decrease in public transport services (made after recycling toll-revenues) is the winning scenario. The combined effects of all the policy scenarios are superior to their separate effects.