A case for measuring individuals’ access to private-vehicle travel as a matter of degrees: lessons from focus groups with Mexican immigrants in California

In auto-oriented communities, access to an automobile is essential for good mobility, but not everyone owns a car or is able to drive. Little is known about how individuals in these circumstances might still use vehicles for transportation. To provide insight on the nature of vehicle use by those with potentially limited vehicle access, we present qualitative findings from focus groups with recent Mexican immigrants living in California, half of whom owned no cars. Our results demonstrate varying degrees of participants’ access to vehicle travel not always corresponding to auto ownership, with extensive sharing of cars, borrowing of cars, and getting rides. We describe the different dimensions of vehicle access that participants experienced and identify specific factors that seemed to influence their access levels. We discuss the implications of our findings for transportation policy and future research.

Social influences on household location, mobility and activity choice in integrated micro-simulation models

Agent-based approaches to simulating long-term location and mobility decisions and short-term activity and travel decisions of households and individuals are receiving increasing attention in land-use and transportation interaction (LUTI) models to predict land-use changes and travel behaviour in mutual interaction. Social interactions between households and between individuals potentially have an influence on a wide range of the long-term and short-term choices involved in these systems. In this paper we identify the areas in which social interactions play a role and address the question how these influences can be modelled in the context of agent-based LUTI models. We distinguish impacts on activity participation (joint activity participation, support-and-help activities) and impacts on decision making (information exchange, social adaptation of preferences and aspirations) as the two main areas of social influence. A prototype of a LUTI model is proposed that accounts for impacts of the social network on longer-term mobility decision making through information exchange and social adaptation of preferences and aspirations. The model is demonstrated in a numerical simulation.     

The social context of informal commuting: Slugs, strangers and structuration

Despite considerable interest in the role of social interactions and social context on transportation, there have been very few attempts to explore specific cases of social interaction influencing transportation systems. This paper explores the social practice of slugging, an informal system of carpooling in the Washington, DC area. Slugging emerged in response to the establishment of Virginia’s High Occupancy Vehicle (HOV) lanes in the early 1970s, as single drivers picked up riders alongside the road (slugs) in order to meet the requirements for driving in the less congested HOV lanes. Drawing on the work of sociologist Anthony Giddens, as well as the sociological insights of Georg Simmel and Stanley Milgram, we suggest that the practice of slugging highlights the processes of institutionalization and structuration. This paper details how the region’s mass transportation policies and urban culture have combined to result in an institutionalized practice with particular norms and logics of behavior. We conclude that looking at specific cases where social context has affected transportation, like slugging, could provide useful insights on the impact of social context on transportation policies and systems.     

Achieving environmental sustainability beyond technological improvements: Potential role of high-speed rail in the United States of America

In order to reduce energy use and cut emissions that contribute to climate change, countries need to radically reinvent their fossil-fuel intensive transportation systems. As a major consumer of energy and contributor to greenhouse gas (GHG) emissions, the U.S. transportation sector faces extraordinary challenges in the twenty-first century. Transportation in the U.S. depends heavily on fossil-fuel dependent cars and planes to the near exclusion of more energy-efficient electric trains. In order to address this concern, some policy makers refer to “technological optimism” which seeks no systemic change but instead focuses on employing technology to reduce the energy demand and environmental impact of the status quo. On the other hand, some researchers suggest a systematic paradigm shift away from cars and planes to intermodal systems that improve the sustainability of the system as a whole. High-speed rail (HSR) is arguably such an investment that can further this shift and help to achieve a more diversified and balanced transportation system. In this respect, by largely examining the role of the U.S. cars and planes “culture” in the economy, this paper elaborates on how building a HSR system may help U.S. advance towards environmental sustainability in transportation, make a break from the status quo, and create a more balanced, multimodal transportation system that will improve the quality and efficiency of travel.     

The role of household members in transporting adults with disabilities in the United States

Because of certain requirements under US federal law, many studies have been published in recent years on the role of fixed-route transit and paratransit in meeting the travel needs of persons with disabilities. Although persons with disabilities are several times more likely to take rides from household members than to take public transit, little research has been conducted to explore the circumstances under which such rides are given or taken. To address this gap in literature, this study examines the role of household members in transporting persons with disabilities in contemporary America. It explores how the circumstances for the ride takers may change in the future, identifies future challenges in providing mobility to persons with disabilities, and examines ways to meet those challenges. Using nationwide data from the 2009 National Household Travel Survey, the study compares the rides taken by persons with disabilities from household members with trips made by other travel modes, the persons who take rides with those who do not take rides, and the drivers who provide rides with those who do not provide rides. Probit models are used for the comparisons. Implications of the findings are discussed in light of potential demographic changes in the future, especially the growth of single-person households and the consequent loss of household support for transportation. Due to similarities in circumstances in other developed countries, an international context to the study is also provided.     

The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data

Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people’s interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users’ interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines.     

Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars

Recently, policy makers’ expectations about the role of electric cars in reducing emissions have risen substantially. In parallel, academic research on purchase intentions has dramatically increased. Originally, most studies have focused on utility attributes and price. More recently, several hybrid choice models have been estimated to include the impact of attitudes on choice probabilities. In addition, a few studies have caught the attention to social influence. In contributing to this line of research, this paper reports the results of an expanded hybrid choice, which simultaneously estimated all these different effects in a single integrated model of purchase intention. Results indicate that the model performs well. Costs considerations contribute most to the utility of electric cars. Social influence is less important, but there is also evidence that people tend to take it into consideration when there are positive public opinions about electric cars and the market share becomes almost half of friends of their social network. The intention to purchase an electric car is also influenced by attitudes about environmental concerns and technology acceptance.     

我国综合交通系统的社会贡献评价研究——以2000-2017年数据为例

本文首先介绍了研究背景,提出研究目的。通过梳理国内外相关文献,提出现有研究尚未涉及综合交通系统社会贡献方面。在解析综合交通社会贡献机理基础上,根据我国2000—2017年社会系统和综合交通数据确定我国社会系统发展指数S和交通系统发展指数T的组成指标,在每种交通方式内采用因子分析法确定各指标权重,结合各指标数值得到S和T数值。应用格兰杰检验法证明T是S的原因,由S关于T的回归函数可得我国综合交通社会贡献评价函数,处理后得到最佳状态数值,发现实际数值与之相比仍有较大提升空间。最后,提出相关政策建议。     

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.     

Exploring transportation equity: Development and application of a transportation justice framework

Recent efforts to emphasize social equity in transportation are emerging as local, regional and national governments have set initiatives to identify, existing and potential, disproportionate impacts to low-income and minority populations, also referred to as transportation justice (TJ). Currently, there are suggested methods for identifying transportation justice areas; however, there is no streamlined method instituted across transportation agencies. Each jurisdiction identifies transportation justice (or environmental justice) areas based on their own methodology, typically based on either average regional thresholds, graduated thresholds, or a more unique in-house index methodology. This research explores and evaluates existing methods and develops a rigorous and comprehensive method called the Transportation Justice Threshold Index Framework (TJTIF) using Geographic Information Systems (GIS), as well as factors based on demographics, socio-economics, and transportation/land use. The framework is applied to a case study region in Pennsylvania reflective of the Marcellus Shale impact area, highlighting Sullivan County, PA. The methodology and the case study application serve as an example for how transportation agencies throughout the country can promote social sustainability and enhance transportation equity.     

Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.     

A latent class accelerated hazard model of social activity duration

Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.     

基于熵权TOPSIS法的绿色交通发展分析——以舟山为例

绿色交通是一种低污染且有利于城市环境多元化的城市交通运输系统。这可以解决我国因私人汽车增加等原因所带来的交通拥堵、汽车排放尾气增加、资源短缺、城市能耗严重、生态环境破坏和空气污染等问题。且该运输系统该运输系统在解决以上问题的同时,可以降低建设维护成本和促进社会公平。目前,我国绿色交通发展在持续进步,本文以舟山为例,从基础指标、交通工具和道路建设三个方面选取数据,并将熵权法和TOPSIS法结合对2016年至2018年舟山绿色交通的发展状况进行分析,结果表明,舟山2017年绿色交通发展状况最优,其次是2018年。     

From willingness to action: Do push-pull-mooring factors matter for shifting to green transportation?

Motivating individuals to choose green transportation is becoming increasingly important. Based on push-pull-mooring framework, this study aims to explore how push, pull and mooring factors foster individual’s willingness to shift to green transportation. This study also analyzes the role of information provision in narrowing the gap between shifting willingness and behavior. The findings revealed that push factors, including perceived environmental threats and perceived inconvenience, drive individual’s mode-shift away from private cars, whereas the pull factors, including green transport policies and campaigns, and green transport system attract individual’s mode-shift to green transportation. Moreover, the mooring factor, namely inertia, not only negatively affects individual’s shifting willingness but also negatively moderates the effects of push and pull factors on individual’s shifting willingness. In addition, shifting willingness positively affects the shifting behavior and the information provision positively moderates the relationship among them. Such findings are vital to achieve the realization of China’s low-carbon goals.     

Shared ride services in North America: definitions,impacts, and the future of pooling

ABSTRACT

Shared ride services allow riders to share a ride to a common destination. They include ridesharing (carpooling and vanpooling); ridesplitting (a pooled version of ridesourcing/transportation network companies); taxi sharing; and microtransit. In recent years, growth of Internet-enabled wireless technologies, global satellite systems, and cloud computing - coupled with data sharing – are causing people to increase their use of mobile applications to share a ride. Some shared ride services, such as carpooling and vanpooling, can provide transportation, infrastructure, environmental, and social benefits. This paper reviews common shared ride service models, definitions, and summarises existing North American impact studies. Additionally, we explore the convergence of shared mobility; electrification; and automation, including the potential impacts of shared automated vehicle (SAV) systems. While SAV impacts remain uncertain, many practitioners and academic research predict higher efficiency, affordability, and lower greenhouse gas emissions. The impacts of SAVs will likely depend on the number of personally owned automated vehicles; types of sharing (concurrent or sequential); and the future modal split among public transit, shared fleets, and pooled rides. We conclude the paper with recommendations for local governments and public agencies to help in managing the transition to highly automated vehicles and encouraging higher occupancy modes.     

衡水市冬季公交免费乘坐政策实施效果评估

优先发展公共交通,倡导绿色出行方式,可以缓解交通拥堵,减少交通污染。河北省衡水市在国内率先实施了冬季公交免费乘坐政策,政策出台的初衷是为了改善衡水市空气质量,经综合评估后,该政策在提高公交乘客满意度、增强公共交通吸引力和缓解交通拥堵等方面取得了明显成效,为国内三、四线城市的公共交通优先发展提供了有益的经验借鉴。     

Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory

In this paper, we explore the diurnal dynamics of joint activity participation in a small city in Pennsylvania, USA, using behavioral data and an inventory of business establishments. We account for the variation caused by the collective impact of social, temporal and spatial choices of individuals to produce predicted space–time visualizations of activity participation. The focus is on how social contexts of an activity impact the temporal and spatial decisions regarding the activity locations and how this impact varies depending on activity types. A comparison across activity types and social interaction types is made among spatial patterns during a day. The CentreSIM dataset, which is a household-based activity diary survey collected in Centre County (Pennsylvania, USA) in 2003, provides very detailed social interaction information enabling the analysis of social, spatial and temporal aspects of activity participation. In this paper we use this information to develop a spatio-temporal interpolation method and demonstration based on kriging. In this way, we extract the dynamic social taxonomy of places from the behavioral information in the dataset and suggest how urban and transportation models can be informed from the dynamics of places by observing “what is taking place” (activities being pursued in the context of this paper) combined with “what exists” (business establishments) or “what is available” (businesses that are open). The method here can also be used to improve the design of urban environments (e.g., filling gaps in desired activity locations), manage specific places (e.g., extending the opening and closing times of businesses), study transportation policies that are sensitive to time of day (e.g., pricing of parking to discourage crowding and traffic congestion), and modeling of spatio-temporal decisions of social activities in travel demand models (e.g., to guide the development of model specification and representation of the space in which behavioral models are applied).     

Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.     

Disentangling the role of cars and transit in employment and labor earnings

We examine the relationship between transportation access on the one hand and individuals’ employment and labor earnings on the other. We improve on existing studies by bringing a large national panel data set to bear on this question, attempting to disentangle the mechanisms by which individuals improve their economic standing and, finally, comparing the economic benefits to the direct costs of car ownership. To do this, we use nine waves from the Panel Study of Income Dynamics from 1999 to 2015. We find that access to a car is a strong predictor of future economic benefit for individuals, and that at very high levels of transit access, carless individuals can also fare equally well. Access to an automobile is strongly associated with employment, job retention, and earning more money over time. Though having a car is associated with economic benefits, owning and operating a car is expensive; yet, our findings suggest that the benefits may outweigh the costs for most people living outside neighborhoods with truly excellent transit service.